rdbnomics/0000755000176200001440000000000013745355632012247 5ustar liggesusersrdbnomics/NAMESPACE0000644000176200001440000000047313745341126013463 0ustar liggesusers# Generated by roxygen2: do not edit by hand export(dbnomics) export(rdb) export(rdb_by_api_link) export(rdb_datasets) export(rdb_dimensions) export(rdb_last_updates) export(rdb_providers) export(rdb_rename_xts) export(rdb_series) export(rdb_to_xts) import(curl) import(data.table) import(jsonlite) rdbnomics/README.md0000644000176200001440000000474313732216355013530 0ustar liggesusers# rdbnomics [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/rdbnomics)](https://cran.r-project.org/package=rdbnomics) [![](http://cranlogs.r-pkg.org/badges/grand-total/rdbnomics)](https://cran.r-project.org/package=rdbnomics) [![pipeline status](https://git.nomics.world/dbnomics/rdbnomics/badges/master/pipeline.svg)](https://git.nomics.world/dbnomics/rdbnomics/commits/master) [![status](https://tinyverse.netlify.com/badge/rdbnomics)](https://CRAN.R-project.org/package=rdbnomics) ## DBnomics R client This package provides you access to DBnomics data series. DBnomics is an open-source project with the goal of aggregating the world's economic data in one location, free of charge to the public. DBnomics covers hundreds of millions of series from international and national institutions (Eurostat, World Bank, IMF, ECB, ...). To use this package, you have to provide the codes of the provider, dataset and series you want. You can retrieve them directly on the website. You have access to the API through this link and the documentation is here. DBnomics is hosted on its own gitlab platform. However, in order to install the package more easily, we created a mirror of this package on github. To install `rdbnomics` from CRAN: ```r install.packages("rdbnomics") library(rdbnomics) ``` To install `rdbnomics` from github: ```r remotes::install_github("dbnomics/rdbnomics", build_vignettes = TRUE, force = TRUE) library(rdbnomics) ``` After installation, a vignette is available to the user with: * many examples, * how to configure a proxy, * how to use default R internet connection, * transform time series with filters, * ... Just follow the link rdbnomics or run: ```r vignette("rdbnomics") ``` A lot of examples are available in the functions' documentations: ```r ?rdb ?rdb_datasets ?rdb_dimensions ?rdb_series ?rdb_providers ?rdb_last_updates ?rdb_to_xts ?rdb_rename_xts ``` ## P.S. Visit https://db.nomics.world/ ! rdbnomics/man/0000755000176200001440000000000013744116260013011 5ustar liggesusersrdbnomics/man/rdb_to_xts.Rd0000644000176200001440000000232013732202726015444 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rdb_to_xts.R \name{rdb_to_xts} \alias{rdb_to_xts} \title{Transform the data.table object into a xts object} \usage{ rdb_to_xts( x, needed_columns = c("period", "series_code", "series_name", "value"), series_columns = c("series_code", "series_name") ) } \arguments{ \item{x}{\code{data.table}. The \code{data.table} returned by the \code{rdb} function.} \item{needed_columns}{Vector of character strings (default \code{c("period", "series_code", "series_name", "value")}). Vector of column names which are needed to transform the \code{data.table} into a \code{xts} object.} \item{series_columns}{Vector of character strings (default \code{c("series_code", "series_name")}). Vector of series column names.} } \value{ A \code{xts} object. } \description{ For some analysis, it is more convenient to have a \code{xts} object instead of a \code{data.table} object. } \examples{ \dontrun{ library(xts) library(data.table) library(rdbnomics) df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") rdb_to_xts(df) } } \seealso{ \code{\link{rdb}}, \code{\link{rdb_rename_xts}} } \author{ Sebastien Galais } rdbnomics/man/rdbnomics.Rd0000644000176200001440000000036213732202726015261 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rdbnomics.R \docType{package} \name{rdbnomics} \alias{rdbnomics} \title{Package rdbnomics} \description{ DBnomics R client (). } rdbnomics/man/rdb_dimensions.Rd0000644000176200001440000000646413744120107016304 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rdb_dimensions.R \name{rdb_dimensions} \alias{rdb_dimensions} \title{Download list of dimensions for datasets of DBnomics providers.} \usage{ rdb_dimensions( provider_code = NULL, dataset_code = NULL, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), simplify = FALSE, ... ) } \arguments{ \item{provider_code}{Character string (default \code{NULL}). DBnomics code of one or multiple providers. If \code{NULL}, the providers are firstly dowloaded with the function \code{\link{rdb_providers}} and then the datasets are requested.} \item{dataset_code}{Character string (default \code{NULL}). DBnomics code of one or multiple datasets of a provider. If \code{NULL}, the datasets codes are dowloaded with the function \code{\link{rdb_datasets}} and then the dimensions are requested.} \item{use_readLines}{Logical (default \code{FALSE}). If \code{TRUE}, then the data are requested and read with the base function \code{readLines} i.e. through the default R internet connection. This can be used to get round the error \code{Could not resolve host: api.db.nomics.world}.} \item{curl_config}{Named list (default \code{NULL}). If not \code{NULL}, it is used to configure a proxy connection. This configuration is passed to the function \code{curl_fetch_memory} of the package \pkg{curl}. A temporary \code{curl_handle} object is created internally with arguments equal to the provided list in \code{curl_config}.\cr For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. For available curl options see \code{\link[curl]{curl_options}}, \code{names(curl_options())} and \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}.} \item{simplify}{Logical (default \code{FALSE}). If \code{TRUE}, when the dimensions are requested for only one provider and one dataset then a named list of \code{data.table}s is returned, not a nested named list of \code{data.table}s.} \item{...}{Additionals arguments.} } \value{ A nested named list of \code{data.table}s or a named list of \code{data.table}s. } \description{ \code{rdb_dimensions} downloads the list of dimensions (if they exist) for available datasets of a selection of providers from \href{https://db.nomics.world/}{DBnomics}. } \details{ By default, the function returns a nested named list of \code{data.table}s containing the dimensions of datasets for providers from \href{https://db.nomics.world/}{DBnomics}. } \examples{ \dontrun{ rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10") rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE) rdb_dimensions(provider_code = "IMF") # /!\ It is very long ! options(rdbnomics.progress_bar_dimensions = TRUE) rdb_dimensions() options(rdbnomics.progress_bar_dimensions = FALSE) rdb_dimensions( provider_code = "IMF", dataset_code = "WEO:2019-10", use_readLines = TRUE ) rdb_dimensions( provider_code = "IMF", dataset_code = "WEO:2019-10", curl_config = list(proxy = "", proxyport = ) ) } } \seealso{ \code{\link{rdb_providers}}, \code{\link{rdb_last_updates}}, \code{\link{rdb_datasets}}, \code{\link{rdb_series}} } \author{ Sebastien Galais } rdbnomics/man/rdb_by_api_link.Rd0000644000176200001440000001230413744120107016402 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rdb_by_api_link.R \name{rdb_by_api_link} \alias{rdb_by_api_link} \title{Download DBnomics data using API link (deprecated).} \usage{ rdb_by_api_link( api_link, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), filters = getOption("rdbnomics.filters") ) } \arguments{ \item{api_link}{Character string. DBnomics API link of the search.} \item{use_readLines}{Logical (default \code{FALSE}). If \code{TRUE}, then the data are requested and read with the base function \code{readLines} i.e. through the default R internet connection. This can be used to get round the error \code{Could not resolve host: api.db.nomics.world}.} \item{curl_config}{Named list (default \code{NULL}). If not \code{NULL}, it is used to configure a proxy connection. This configuration is passed to the function \code{curl_fetch_memory} of the package \pkg{curl}. A temporary \code{curl_handle} object is created internally with arguments equal to the provided list in \code{curl_config}.\cr For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. For available curl options see \code{\link[curl]{curl_options}}, \code{names(curl_options())} and \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}.} \item{filters}{List (default \code{NULL}). This argument must be a named list for one filter because the function \code{toJSON} of the package \pkg{jsonlite} is used before sending the request to the server. For multiple filters, you have to provide a list of valid filters (see examples).\cr A valid filter is a named list with an element \code{code} which is a character string, and an element \code{parameters} which is a named list with elements \code{frequency} and \code{method} or a NULL.} } \value{ A \code{data.table}. } \description{ \code{rdb_by_api_link} downloads data series from \href{https://db.nomics.world/}{DBnomics}. } \details{ This function gives you access to hundreds of millions data series from \href{https://api.db.nomics.world/}{DBnomics API} (documentation about the API can be found \href{https://api.db.nomics.world/v22/apidocs}{here}). The API link is given on the \href{https://db.nomics.world/}{DBnomics website}. } \examples{ \dontrun{ # Fetch two series from different datasets of different providers : df1 <- rdb_by_api_link( paste0( "https://api.db.nomics.world/v22/", "series?observations=1&series_ids=AMECO/ZUTN/EA19.1.0.0.0.ZUTN,IMF/CPI/A.AT.PCPIT_IX" ) ) # Fetch one series from the dataset 'Doing Business' of WB provider : df2 <- rdb_by_api_link( paste0( "https://api.db.nomics.world/v22/series/WB/DB?dimensions=\%7B\%22", "indicator\%22\%3A\%5B\%22IC.REG.PROC.FE.NO\%22\%5D\%7D&q=Doing\%20Business", "&observations=1&format=json&align_periods=1&offset=0&facets=0" ) ) ## Use a specific proxy to fetch the data # Fetch one series from the dataset 'Doing Business' of WB provider : h <- list( proxy = "", proxyport = , proxyusername = "", proxypassword = "" ) options(rdbnomics.curl_config = h) df2 <- rdb_by_api_link( paste0( "https://api.db.nomics.world/v22/series/WB/DB?dimensions=\%7B\%22", "indicator\%22\%3A\%5B\%22IC.REG.PROC.FE.NO\%22\%5D\%7D&q=Doing\%20Business", "&observations=1&format=json&align_periods=1&offset=0&facets=0" ) ) # or to use once df2 <- rdb_by_api_link( paste0( "https://api.db.nomics.world/v22/series/WB/DB?dimensions=\%7B\%22", "indicator\%22\%3A\%5B\%22IC.REG.PROC.FE.NO\%22\%5D\%7D&q=Doing\%20Business", "&observations=1&format=json&align_periods=1&offset=0&facets=0" ), curl_config = h ) ## Use R default connection to avoid a proxy failure (in some cases) # Fetch one series from the dataset 'Doing Business' of WB provider : options(rdbnomics.use_readLines = TRUE) df2 <- rdb_by_api_link( paste0( "https://api.db.nomics.world/v22/series/WB/DB?dimensions=\%7B\%22", "indicator\%22\%3A\%5B\%22IC.REG.PROC.FE.NO\%22\%5D\%7D&q=Doing\%20Business", "&observations=1&format=json&align_periods=1&offset=0&facets=0" ) ) # or to use once df2 <- rdb_by_api_link( paste0( "https://api.db.nomics.world/v22/series/WB/DB?dimensions=\%7B\%22", "indicator\%22\%3A\%5B\%22IC.REG.PROC.FE.NO\%22\%5D\%7D&q=Doing\%20Business", "&observations=1&format=json&align_periods=1&offset=0&facets=0" ), use_readLines = TRUE ) ## Apply filter(s) to the series # One filter df3 <- rdb_by_api_link( "https://api.db.nomics.world/v22/series/IMF/WEO:2019-10/ABW.BCA?observations=1", filters = list( code = "interpolate", parameters = list(frequency = "daily", method = "spline") ) ) # Two filters df3 <- rdb_by_api_link( "https://api.db.nomics.world/v22/series/IMF/WEO:2019-10/ABW.BCA?observations=1", filters = list( list( code = "interpolate", parameters = list(frequency = "quarterly", method = "spline") ), list( code = "aggregate", parameters = list(frequency = "annual", method = "average") ) ) ) } } \seealso{ \code{\link{rdb}} } \author{ Sebastien Galais } rdbnomics/man/rdb.Rd0000644000176200001440000002164413744122031014047 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rdb.R \name{rdb} \alias{rdb} \title{Download DBnomics data.} \usage{ rdb( provider_code = NULL, dataset_code = NULL, ids = NULL, dimensions = NULL, mask = NULL, query = NULL, api_link = NULL, filters = getOption("rdbnomics.filters"), use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), verbose = getOption("rdbnomics.verbose_warning"), ... ) } \arguments{ \item{provider_code}{Character string (default \code{NULL}). DBnomics code of the provider.} \item{dataset_code}{Character string (default \code{NULL}). DBnomics code of the dataset.} \item{ids}{Character string (default \code{NULL}). DBnomics code of one or several series.} \item{dimensions}{List or character string (single quoted) (default \code{NULL}). DBnomics code of one or several dimensions in the specified provider and dataset. If it is a named list, then the function \code{toJSON} (from the package \pkg{jsonlite}) is applied to generate the json object.} \item{mask}{Character string (default \code{NULL}). DBnomics code of one or several masks in the specified provider and dataset.} \item{query}{Character string (default \code{NULL}). A query to filter/select series from a provider's dataset.} \item{api_link}{Character string. DBnomics API link of the search. It should starts with \code{http://} or \code{https://}.} \item{filters}{List (default \code{NULL}). This argument must be a named list for one filter because the function \code{toJSON} of the package \pkg{jsonlite} is used before sending the request to the server. For multiple filters, you have to provide a list of valid filters (see examples).\cr A valid filter is a named list with an element \code{code} which is a character string, and an element \code{parameters} which is a named list with elements \code{frequency} and \code{method} or a NULL.} \item{use_readLines}{Logical (default \code{FALSE}). If \code{TRUE}, then the data are requested and read with the base function \code{readLines} i.e. through the default R internet connection. This can be used to get round the error \code{Could not resolve host: api.db.nomics.world}.} \item{curl_config}{Named list (default \code{NULL}). If not \code{NULL}, it is used to configure a proxy connection. This configuration is passed to the function \code{curl_fetch_memory} of the package \pkg{curl}. A temporary \code{curl_handle} object is created internally with arguments equal to the provided list in \code{curl_config}.\cr For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. For available curl options see \code{\link[curl]{curl_options}}, \code{names(curl_options())} and \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}.} \item{verbose}{Logical (default \code{FALSE}). Show warnings of the function.} \item{...}{Arguments to be passed to the internal function \code{.rdb}.} } \value{ A \code{data.table}. } \description{ \code{rdb} downloads data series from \href{https://db.nomics.world/}{DBnomics} using shortcuts like \code{ids}, \code{dimensions}, \code{mask}, \code{query} or using an \code{api_link}. } \details{ This function gives you access to hundreds of millions data series from \href{https://api.db.nomics.world/}{DBnomics API} (documentation about the API can be found \href{https://api.db.nomics.world/v22/apidocs}{here}). The code of each series is given on the \href{https://db.nomics.world/}{DBnomics website}. \cr\cr In the event that only the argument \code{ids} is provided (and those in the ellipsis \code{...}), the argument name can be dropped. The character string vector is directly passed to \code{ids}. \cr If only the argument \code{api_link} is provided (and those in the ellipsis \code{...}), then the argument name can be dropped. The character string vector is directly passed to \code{api_link}. \cr In the same way, if only \code{provider_code}, \code{dataset_code} and \code{mask} are provided then the arguments names can be dropped. The last character string is automatically passed to \code{mask}. } \examples{ \dontrun{ ## By ids # Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider: df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") # or when no argument names are given (provider_code -> ids) df1 <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN") # Fetch two series from dataset 'Unemployment rate' (ZUTN) of AMECO provider: df2 <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN")) # Fetch two series from different datasets of different providers: df3 <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "IMF/BOP/A.FR.BCA_BP6_EUR")) ## By dimensions # Fetch one value of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider: df1 <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea12")) # or df1 <- rdb("AMECO", "ZUTN", dimensions = '{"geo":["ea12"]}') # Fetch two values of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider: df2 <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea12", "dnk"))) # or df2 <- rdb("AMECO", "ZUTN", dimensions = '{"geo":["ea12","dnk"]}') # Fetch several values of several dimensions from dataset 'Doing business' (DB) of World Bank: dim <- list( country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS") ) df3 <- rdb("WB", "DB", dimensions = dim) # or dim <- paste0( '{"country":["DZ","PE"],', '"indicator":["ENF.CONT.COEN.COST.ZS","IC.REG.COST.PC.FE.ZS"]}' ) df3 <- rdb("WB", "DB", dimensions = dim) ## By mask # Fetch one series from dataset 'Balance of Payments' (BOP) of IMF: df1 <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR") # or when no argument names are given except provider_code and dataset_code (ids -> mask) df1 <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR") # Fetch two series from dataset 'Balance of Payments' (BOP) of IMF: df2 <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") # Fetch all series along one dimension from dataset 'Balance of Payments' (BOP) of IMF: df3 <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR") # Fetch series along multiple dimensions from dataset 'Balance of Payments' (BOP) of IMF: df4 <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR") ## By query # Fetch one series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) from IMF : df1 <- rdb("IMF", "WEO:2019-10", query = "France current account balance percent") # Fetch series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) from IMF : df2 <- rdb("IMF", "WEO:2019-10", query = "current account balance percent") ## By api_link # Fetch two series from different datasets of different providers : df1 <- rdb( api_link = paste0( "https://api.db.nomics.world/v22/", "series?observations=1&series_ids=AMECO/ZUTN/EA19.1.0.0.0.ZUTN,IMF/CPI/A.AT.PCPIT_IX" ) ) # Fetch one series from the dataset 'Doing Business' of WB provider : df2 <- rdb( api_link = paste0( "https://api.db.nomics.world/v22/series/WB/DB?dimensions=\%7B\%22", "indicator\%22\%3A\%5B\%22IC.REG.PROC.FE.NO\%22\%5D\%7D&q=Doing\%20Business", "&observations=1&format=json&align_periods=1&offset=0&facets=0" ) ) # or when no argument names are given (provider_code -> api_link) df1 <- rdb( paste0( "https://api.db.nomics.world/v22/", "series?observations=1&series_ids=AMECO/ZUTN/EA19.1.0.0.0.ZUTN,IMF/CPI/A.AT.PCPIT_IX" ) ) ## Use a specific proxy to fetch the data # Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider : h <- list( proxy = "", proxyport = , proxyusername = "", proxypassword = "" ) options(rdbnomics.curl_config = h) df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") # or to use once options(rdbnomics.curl_config = NULL) df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h) ## Use R default connection to avoid a proxy failure (in some cases) # Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider : options(rdbnomics.use_readLines = TRUE) df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") # or to use once df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE) ## Apply filter(s) to the series # One filter df1 <- rdb( ids = c("IMF/WEO:2019-10/ABW.BCA.us_dollars", "IMF/WEO:2019-10/ABW.BCA_NGDPD.pcent_gdp"), filters = list( code = "interpolate", parameters = list(frequency = "daily", method = "spline") ) ) # Two filters df1 <- rdb( ids = c("IMF/WEO:2019-10/ABW.BCA.us_dollars", "IMF/WEO:2019-10/ABW.BCA_NGDPD.pcent_gdp"), filters = list( list( code = "interpolate", parameters = list(frequency = "quarterly", method = "spline") ), list( code = "aggregate", parameters = list(frequency = "annual", method = "average") ) ) ) } } \author{ Sebastien Galais } rdbnomics/man/figures/0000755000176200001440000000000013744116260014455 5ustar liggesusersrdbnomics/man/figures/logo.png0000644000176200001440000007544013371134545016137 0ustar liggesusersPNG  IHDRsRGBgAMA a pHYs&?zIDATx^kՙ?/&fb3q]TA\;(A@䦀"((" (>\џcTbjRIa̔5J%T*Ij|_uڬ{g_ߪOW^g=W*zlLSs[G?3Rt̔w:ɜ^ |?DU;RZRI8cesb+3&@J`\Xλʜ6]eZL3SA칀IB:'yl*1!iZ&݈W|Zr?UZʔ ZMIjt̻|e! 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EC8R92tlҸrƕԖ!X!4tlBtl]N BHбBu!4tlBplGr^w1!бBHRc˴zˌ+-+Ràc#ƅBHCAF!hJQbaBP%Ʊ]ٿ+ńBBH#aбBy~?ulGs)aбBH"a(ǶڒJ$)aбBH"aWR[c#b0ƱKC,B!wg]>0-k'!kO4csw jBj =Iw%Ԭc;je.%:.Kvl39B g796g+]e>.3ٿ;?==K,|B!0p p,r?grѸ0>37_kX1BmXUď(?[xKh<8]J.V!ty`?)VKGr8~#N0 %f;]zqJ7〻r;gv*sƻT^;D*O.5y{_RD9+/.XB3x5GX?wšbN~eNtw\@b$J2wv*?D[K[ĉU 7q5GAgt]<]cfpEQͯ>qRAGGjs/IpPx.1LU2Wb3 1T&~δz iEQJ{f!8^z/6+>c)EQT͙\Cړ.k9do-:)iڽ{+z7m3{rEQJ/ܜw(وvF"̬#s5v=?/{2{aEQTX*)8.8,{5&-, fЂ^).9]jEQT(텛\K} UБ٫s$LKR}?rN(ne.vn1Y~`u ]i7n!]ď6JgxMӠ(酛smίRv]yf-ELWYVb0}fGgwĝuGkv%'~P4:Q|* 7%F\W_)}3{{ѝp9/Keg& 3ULQռ[RZϺ'gnn5Z0nrf{TJ(J0S[y[kkbf8#{uev&#ĂéKe ֛ΉEQ W;٘`~LC5cqdvE8C{pûG4&~Qd(JRIif"-pk~Eg&M]NpJ^a(ٌͭ9ew1yP'*?,xUf'~`)qL1EKQEu\Wss 7pûz+mN|q!EQUJsfwf| #sRi?(@S Kc-܌Y!'o(jDai'A", proxyport = )) } } \seealso{ \code{\link{rdb_providers}}, \code{\link{rdb_datasets}}, \code{\link{rdb_dimensions}} } \author{ Sebastien Galais } rdbnomics/man/rdb_series.Rd0000644000176200001440000001040013744120107015407 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rdb_series.R \name{rdb_series} \alias{rdb_series} \title{Download list of series for datasets of DBnomics providers.} \usage{ rdb_series( provider_code = NULL, dataset_code = NULL, dimensions = NULL, query = NULL, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), simplify = FALSE, verbose = FALSE, ... ) } \arguments{ \item{provider_code}{Character string (default \code{NULL}). DBnomics code of one or multiple providers. If \code{NULL}, the providers are firstly dowloaded with the function \code{\link{rdb_providers}} and then the datasets are requested.} \item{dataset_code}{Character string (default \code{NULL}). DBnomics code of one or multiple datasets of a provider. If \code{NULL}, the datasets codes are dowloaded with the function \code{\link{rdb_datasets}} and then the series are requested.} \item{dimensions}{List or character string (single quoted) (default \code{NULL}). DBnomics code of one or several dimensions in the specified provider and dataset. If it is a named list, then the function \code{toJSON} (from the package \pkg{jsonlite}) is applied to generate the json object.} \item{query}{Character string (default \code{NULL}). A query to filter/select series from a provider's dataset.} \item{use_readLines}{Logical (default \code{FALSE}). If \code{TRUE}, then the data are requested and read with the base function \code{readLines} i.e. through the default R internet connection. This can be used to get round the error \code{Could not resolve host: api.db.nomics.world}.} \item{curl_config}{Named list (default \code{NULL}). If not \code{NULL}, it is used to configure a proxy connection. This configuration is passed to the function \code{curl_fetch_memory} of the package \pkg{curl}. A temporary \code{curl_handle} object is created internally with arguments equal to the provided list in \code{curl_config}.\cr For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. For available curl options see \code{\link[curl]{curl_options}}, \code{names(curl_options())} and \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}.} \item{simplify}{Logical (default \code{FALSE}). If \code{TRUE}, when the series are requested for only one provider and one dataset then a \code{data.table} is returned, not a nested named list of \code{data.table}s.} \item{verbose}{Logical (default \code{FALSE}). Show number of series per datasets and providers.} \item{...}{Additionals arguments.} } \value{ A nested named list of \code{data.table}s or a \code{data.table}. } \description{ \code{rdb_series} downloads the list of series for available datasets of a selection of providers from \href{https://db.nomics.world/}{DBnomics}. \cr /!\ We warn the user that this function can be (very) long to execute. We remind that DBnomics requests data from 63 providers to retrieve 21675 datasets for a total of approximately 720 millions series. } \details{ By default, the function returns a nested named list of \code{data.table}s containing the series of datasets for providers from \href{https://db.nomics.world/}{DBnomics}. } \examples{ \dontrun{ rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10") ## With dimensions rdb_series("IMF", "WEO:2019-10", dimensions = list(`weo-country` = "AGO")) rdb_series("IMF", "WEO:2019-10", dimensions = list(`weo-subject` = "NGDP_RPCH"), simplify = TRUE) ## With query rdb_series("IMF", "WEO:2019-10", query = "ARE") rdb_series("IMF", c("WEO:2019-10", "WEOAGG:2019-10"), query = "NGDP_RPCH") rdb_series(provider_code = "IMF", verbose = TRUE) options(rdbnomics.progress_bar_series = TRUE) rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10") options(rdbnomics.progress_bar_series = FALSE) rdb_series( provider_code = "IMF", dataset_code = "WEO:2019-10", use_readLines = TRUE ) rdb_series( provider_code = "IMF", dataset_code = "WEO:2019-10", curl_config = list(proxy = "", proxyport = ) ) } } \seealso{ \code{\link{rdb_providers}}, \code{\link{rdb_last_updates}}, \code{\link{rdb_datasets}}, \code{\link{rdb_dimensions}} } \author{ Sebastien Galais } rdbnomics/man/rdb_datasets.Rd0000644000176200001440000000541313732202726015742 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rdb_datasets.R \name{rdb_datasets} \alias{rdb_datasets} \title{Download list of datasets for DBnomics providers.} \usage{ rdb_datasets( provider_code = NULL, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), simplify = FALSE, ... ) } \arguments{ \item{provider_code}{Character string (default \code{NULL}). DBnomics code of one or multiple providers. If \code{NULL}, the providers are firstly dowloaded with the function \code{\link{rdb_providers}} and then the available datasets are requested.} \item{use_readLines}{Logical (default \code{FALSE}). If \code{TRUE}, then the data are requested and read with the base function \code{readLines} i.e. through the default R internet connection. This can be used to get round the error \code{Could not resolve host: api.db.nomics.world}.} \item{curl_config}{Named list (default \code{NULL}). If not \code{NULL}, it is used to configure a proxy connection. This configuration is passed to the function \code{curl_fetch_memory} of the package \pkg{curl}. A temporary \code{curl_handle} object is created internally with arguments equal to the provided list in \code{curl_config}.\cr For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. For available curl options see \code{\link[curl]{curl_options}}, \code{names(curl_options())} and \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}.} \item{simplify}{Logical (default \code{FALSE}). If \code{TRUE}, when the datasets are requested for only one provider then a \code{data.table} is returned, not a list of \code{data.table}s.} \item{...}{Additionals arguments.} } \value{ A named list of \code{data.table}s or a \code{data.table}. } \description{ \code{rdb_datasets} downloads the list of available datasets for a selection of providers (or all of them) from \href{https://db.nomics.world/}{DBnomics}. } \details{ By default, the function returns a named list of \code{data.table}s containing the datasets of the providers from \href{https://db.nomics.world/}{DBnomics}. } \examples{ \dontrun{ rdb_datasets(provider_code = "IMF") rdb_datasets(provider_code = "IMF", simplify = TRUE) rdb_datasets(provider_code = c("IMF", "BDF")) options(rdbnomics.progress_bar_datasets = TRUE) rdb_datasets() options(rdbnomics.progress_bar_datasets = FALSE) rdb_datasets(provider_code = "IMF", use_readLines = TRUE) rdb_datasets( provider_code = "IMF", curl_config = list(proxy = "", proxyport = ) ) } } \seealso{ \code{\link{rdb_providers}}, \code{\link{rdb_last_updates}}, \code{\link{rdb_dimensions}}, \code{\link{rdb_series}} } \author{ Sebastien Galais } rdbnomics/man/rdb_providers.Rd0000644000176200001440000000406513732202726016151 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rdb_providers.R \name{rdb_providers} \alias{rdb_providers} \title{Download list of DBnomics providers.} \usage{ rdb_providers( code = FALSE, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config") ) } \arguments{ \item{code}{Logical (default \code{FALSE}). If \code{TRUE}, then only the providers are returned in a vector.} \item{use_readLines}{Logical (default \code{FALSE}). If \code{TRUE}, then the data are requested and read with the base function \code{readLines} i.e. through the default R internet connection. This can be used to get round the error \code{Could not resolve host: api.db.nomics.world}.} \item{curl_config}{Named list (default \code{NULL}). If not \code{NULL}, it is used to configure a proxy connection. This configuration is passed to the function \code{curl_fetch_memory} of the package \pkg{curl}. A temporary \code{curl_handle} object is created internally with arguments equal to the provided list in \code{curl_config}.\cr For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. For available curl options see \code{\link[curl]{curl_options}}, \code{names(curl_options())} and \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}.} } \value{ A \code{data.table} or a vector. } \description{ \code{rdb_providers} downloads the list of providers from \href{https://db.nomics.world/}{DBnomics}. } \details{ By default, the function returns a \code{data.table} containing the list of providers from \href{https://db.nomics.world/}{DBnomics} with additional informations such as the region, the website, etc. } \examples{ \dontrun{ rdb_providers() rdb_providers(code = TRUE) rdb_providers(use_readLines = TRUE) rdb_providers(curl_config = list(proxy = "", proxyport = )) } } \seealso{ \code{\link{rdb_last_updates}}, \code{\link{rdb_datasets}}, \code{\link{rdb_dimensions}}, \code{\link{rdb_series}} } \author{ Sebastien Galais } rdbnomics/man/rdb_rename_xts.Rd0000644000176200001440000000201513731750353016275 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rdb_rename_xts.R \name{rdb_rename_xts} \alias{rdb_rename_xts} \title{Rename the xts object columns} \usage{ rdb_rename_xts(x, fun = NULL, ...) } \arguments{ \item{x}{\code{xts} object. The \code{xts} object returned by the function \code{rdb_to_xts}.} \item{fun}{function (default NULL). The function to apply to the column names.} \item{...}{Arguments for the function \code{fun}.} } \value{ A \code{xts} object. } \description{ In the \code{xts} object returned by the function \code{rdb_to_xts}, the series codes are used as column names. If you prefer the series names (or apply a function to them), the function \code{rdb_rename_xts} is here for that. } \examples{ \dontrun{ library(xts) library(data.table) library(rdbnomics) df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") df <- rdb_to_xts(df) rdb_rename_xts(df) } } \seealso{ \code{\link{rdb}}, \code{\link{rdb_to_xts}} } \author{ Sebastien Galais } rdbnomics/man/dbnomics.Rd0000644000176200001440000000174013744120107015073 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dbnomics.R \name{dbnomics} \alias{dbnomics} \title{DBnomics ggplot2 theme} \usage{ dbnomics(color_palette = "Set1", ...) } \arguments{ \item{color_palette}{Character string (default \code{"Set1"}) to change the default color palette. If you want to use the default palette, set it to \code{NULL}.} \item{...}{Arguments to be passed to the function \code{ggplot2::theme}.} } \description{ \code{dbnomics} is a simple ggplot2 theme for drawing nicer graphics. We do not recommend to use it. It has been included in the package to avoid errors when reproducing the vignette examples. } \examples{ \dontrun{ library(magrittr) library(ggplot2) rdb("IMF", "WEO:2019-10", query = "France current account balance percent") \%>\% ggplot(aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() } } \author{ Sebastien Galais } rdbnomics/DESCRIPTION0000644000176200001440000000166713745355632013767 0ustar liggesusersPackage: rdbnomics Type: Package Title: Download DBnomics Data Version: 0.6.4 Authors@R: c(person("Sebastien", "Galais", role = c("cre", "ctb"), email = "s915.stem@gmail.com"), person("Thomas", "Brand", role = c("aut"), email = "thomas.brand@cepremap.org")) Description: R access to hundreds of millions data series from DBnomics API (). Depends: R (>= 3.1.0) License: AGPL-3 URL: https://git.nomics.world/dbnomics/rdbnomics BugReports: https://git.nomics.world/dbnomics/rdbnomics/-/issues Encoding: UTF-8 LazyData: true Imports: curl, data.table, jsonlite Suggests: knitr, rmarkdown, tinytest VignetteBuilder: knitr RoxygenNote: 7.1.1 NeedsCompilation: no Packaged: 2020-10-25 18:23:42 UTC; sebas Author: Sebastien Galais [cre, ctb], Thomas Brand [aut] Maintainer: Sebastien Galais Repository: CRAN Date/Publication: 2020-10-25 20:10:02 UTC rdbnomics/build/0000755000176200001440000000000013745341256013343 5ustar liggesusersrdbnomics/build/vignette.rds0000644000176200001440000000032013745341256015675 0ustar liggesusersb```b`a`f`b2 1#PH$f&妠I 8As2SJ!tgr# t0X X%gwI-HK î?"5lP5,n90{C2K7(1 棸(\^P7@btr$$ Yxrdbnomics/tests/0000755000176200001440000000000013744116260013400 5ustar liggesusersrdbnomics/tests/tinytest.R0000644000176200001440000000026613653300263015407 0ustar liggesusersif (requireNamespace("tinytest", quietly = TRUE)) { home <- length(unclass(utils::packageVersion("rdbnomics"))[[1]]) == 4 tinytest::test_package("rdbnomics", at_home = home) }rdbnomics/vignettes/0000755000176200001440000000000013745341256014254 5ustar liggesusersrdbnomics/vignettes/dbnomics002.png0000644000176200001440000026432513414003374017003 0ustar liggesusersPNG  IHDRMƜsRGBgAMA a pHYs%%IR$IDATx^wUgy/gfr}}sJ4YoКcoǹV d>:,Q_tCx'+~"K'LӝjM8zQs<3(#3uǭ6LΗЂާTc*ss'ۘ)9_=+LeE^xKCeSK=/\0Ykk}c9 f^y7-Z>sRL+˚3~3r9Q^4q nbYT>Y<;nΝ-}8mxW5y v2e #|6ܩcGˣޔGˇ_CՌKG=d7 װ;-r(,)UiO,Oɞ ٹOO.LI,=oiٜZU@"{_ ?߽PG~AY0mѧG'䓢?/ACЩSsg>=F[$ǧE?{cd/}('jMӆϻegG^rͰ'؞>-w>6BT[uqwOeytQsknﶩZG<{1QY\#;g1ܢ /::;E[1Csc-Ic_>x{l/~`ΰFzݸɣV-%{YCǥyU+3cgOFHixgqYi7?3cX3oܴ@n6fcXeސڟjs7N6hŠwh/3H> fzcnw[c>9K,aӬ7 qGAGLc\!`sbOyyy#Ӛɹfۙ5eL^1FwIwslDs<&eÌ+!3dĹ1ᅲs{fv-YoP[~1n|S3fH1sF?$.Yn.Qs;~Sf>3b"ԗNOwwcIco~Jm,2\;v6Sn4d:6!{L7/K[UrۇߥHF7:V;Gٺݞ~-xhOO2VgͼU:/>x\ ns=D-{p3ӟNs[94k~im:={Iż4{H;x.C~:jd;cF<|özlF<1jӴ,1qY:,5aqŭ3fHsΝuy2sz'F`dG<[$g!}{;njR eG:#ITҫwmNUL媉+iţS&8Cߺۣʝ>?O"7!{ԵVMbGQJ2;AO϶9dxde`OvUzz?uVݽcLj-hz)֙wG9kƌ0/>ovyCE?!.6#|uCo\oѐ!#[tQ_YUι|)`=%#qzUn 9= 3b&LQofdI(9#W\OZ{Λ! 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Explore all the economic data from different providers (national and international statistical institutes, central banks, etc.), for free, following the link [db.nomics.world](https://db.nomics.world) (*N.B.: in the examples, data have already been retrieved on april 6th 2020*). [![](dbnomics001.png)](https://db.nomics.world) # Fetch time series by `ids` First, let's assume that we know which series we want to download. A series identifier (`ids`) is defined by three values, formatted like this: `provider_code`/`dataset_code`/`series_code`. ## Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider ```{r, echo = FALSE} library <- function(...) { suppressWarnings( suppressPackageStartupMessages(base::library(..., quietly = TRUE)) ) } ``` ```{r} library(data.table) library(rdbnomics) ``` ```{r, echo = FALSE} reorder_cols <- function(x) { data.table::setDT(x) cols <- c( "provider_code", "dataset_code", "dataset_name", "series_code", "series_name", "original_period", "period", "original_value", "value", "@frequency" ) if ("unit" %in% colnames(x)) { cols <- c(cols, "unit", "Unit") } if ("geo" %in% colnames(x)) { cols <- c(cols, "geo", "Country") } if ("freq" %in% colnames(x)) { cols <- c(cols, "freq", "Frequency") } cols_add <- setdiff(colnames(x), cols) cols <- c(cols, cols_add) cols <- cols[cols %in% colnames(x)] cols <- match(cols, colnames(x)) x[, .SD, .SDcols = cols] } knitr::opts_chunk$set(dev.args = list(bg = "transparent")) display_table <- function(DT) { DT <- head(DT) DT <- as.data.table( lapply(DT, function(x) { if (is.character(x)) { ifelse( nchar(x) > 16, paste0(substr(x, 1, 16), "..."), x ) } else { x } }) ) DT[] } ``` ```{r, eval = FALSE} df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") df <- df[!is.na(value)] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df001 data.table::setDT(df) ``` In such `data.table`, you will always find at least ten columns: - `provider_code` - `dataset_code` - `dataset_name` - `series_code` - `series_name` - `original_period` (character string) - `period` (date of the first day of `original_period`) - `original_value` (character string) - `value` - `@frequency` (harmonized frequency generated by DBnomics) The other columns depend on the provider and on the dataset. They always come in pairs (for the code and the name). In the data.frame `df`, you have: - `unit` (code) and `Unit` (name) - `geo` (code) and `Country` (name) - `freq` (code) and `Frequency` (name) ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` In the event that you only use the argument `ids`, you can drop it and run: ```{r, eval = FALSE} df <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ``` ## Fetch two series from dataset 'Unemployment rate' (ZUTN) of AMECO provider ```{r, eval = FALSE} df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN")) df <- df[!is.na(value)] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df002 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.7, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` ## Fetch two series from different datasets of different providers ```{r, eval = FALSE} df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "Eurostat/une_rt_q/Q.SA.Y15-24.PC_ACT.T.EA19")) df <- df[!is.na(value)] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df003 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics(legend.text = element_text(size = 7)) i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) legend_text[2] <- sapply( legend_text[2], function(y) { paste0( paste0( strsplit(y, "active ")[[1]], collapse = "active\n" ), "\n" ) } ) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` # Fetch time series by `mask` The code mask notation is a very concise way to select one or many time series at once. ## Fetch one series from dataset 'Balance of Payments' (BOP) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR") df <- df[!is.na(value)] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df004 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value), max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` In the event that you only use the arguments `provider_code`, `dataset_code` and `mask`, you can drop the name `mask` and run: ```{r, eval = FALSE} df <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR") ``` ## Fetch two series from dataset 'Balance of Payments' (BOP) of IMF You just have to add a `+` between two different values of a dimension. ```{r, eval = FALSE} df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") df <- df[!is.na(value)] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df005 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 2*10^4, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "s") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` ## Fetch all series along one dimension from dataset 'Balance of Payments' (BOP) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR") df <- df[!is.na(value)] df <- df[order(-period, REF_AREA)] df <- head(df, 100) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df006 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ## Fetch series along multiple dimensions from dataset 'Balance of Payments' (BOP) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR") df <- df[!is.na(value)] df <- df[order(period), head(.SD, 50), by = INDICATOR] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df007 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` # Fetch time series by `dimensions` Searching by `dimensions` is a less concise way to select time series than using the code `mask`, but it works with all the different providers. You have a "*Description of series code*" at the bottom of each dataset page on the [DBnomics website](https://db.nomics.world). ## Fetch one value of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider ```{r, eval = FALSE} df <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea19")) df <- df[!is.na(value))] # or # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}') # df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df008 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` ## Fetch two values of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider ```{r, eval = FALSE} df <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea19", "dnk"))) df <- df[!is.na(value))] # or # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}') # df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df009 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` ## Fetch several values of several dimensions from dataset 'Doing business' (DB) of World Bank ```{r, eval = FALSE} df <- rdb("WB", "DB", dimensions = list(country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"))) df <- df[!is.na(value))] # or # df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}') # df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df010 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(series_name, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 7, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` # Fetch time series with a `query` The query is a Google-like search that will filter/select time series from a provider's dataset. ## Fetch one series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "WEO:2019-10", query = "France current account balance percent") df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df014 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` ## Fetch series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "WEO:2019-10", query = "current account balance percent") df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df015 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = `WEO Country`)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # ggtitle("Current account balance (% GDP)") + # dbnomics(legend.direction = "horizontal") i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value), max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` # Fetch time series found on the web site When you don't know the codes of the dimensions, provider, dataset or series, you can: - go to the page of a dataset on [DBnomics website](https://db.nomics.world), for example [Doing Business](https://db.nomics.world/WB/DB), - select some dimensions by using the input widgets of the left column, ![](dbnomics002.png) - click on "*Copy API link*" in the menu of the "*Download*" button, ![](dbnomics003.png) - use the `rdb(api_link = ...)` function such as below. ```{r, eval = FALSE} df <- rdb(api_link = "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0") df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df011 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(period, series_name)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "s") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "s") points(x3, y3, col = cols[3], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` In the event that you only use the argument `api_link`, you can drop the name and run: ```{r, eval = FALSE} df <- rdb("https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0") ``` # Fetch time series from the cart On the cart page of the [DBnomics website](https://db.nomics.world), click on "*Copy API link*" and copy-paste it as an argument of the `rdb(api_link = ...)` function. Please note that when you update your cart, you have to copy this link again, because the link itself contains the ids of the series in the cart.

![](dbnomics005.png)
```{r, eval = FALSE} df <- rdb(api_link = "https://api.db.nomics.world/v22/series?observations=1&series_ids=BOE/6008/RPMTDDC,BOE/6231/RPMTBVE") df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df012 data.table::setDT(df) ``` ```{r, echo = FALSE} df[ , series_name := sapply( series_name, function(y) { paste0( paste0( strsplit(y, "institutions' ")[[1]], collapse = "institutions'\n" ), "\n" ) } ) ] ``` ```{r, echo = FALSE} df <- df[order(period, series_name)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 4*10^3, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` # Fetch the available datasets of a provider When fetching series from [DBnomics](https://db.nomics.world), you need to give a provider and a dataset before specifying correct dimensions. With the function `rdb_datasets`, you can download the list of the available datasets for a provider. For example, to fetch the **IMF** datasets, you have to use: ```{r, eval = FALSE} rdb_datasets(provider_code = "IMF") ``` The result is a named list (its name is **IMF**) with one element which is a `data.table`: ```{r, eval = TRUE, echo = FALSE} str(rdbnomics:::rdbnomics_df016) ``` With the same function, if you want to fetch the available datasets for multiple providers, you can give a vector of providers and get a named list. ```{r, eval = FALSE} rdb_datasets(provider_code = c("IMF", "BDF")) ``` ```{r, eval = TRUE, echo = FALSE} str(rdbnomics:::rdbnomics_df018) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df018 DT <- sapply(DT, function(y) { paste0(": ", nrow(y)) }) DT <- paste0("Number of datasets for ", names(DT), " ", unname(DT)) cat(DT, sep = "\n") ``` In the event that you only request the datasets for one provider, if you define `simplify = TRUE`, then the result will be a `data.table` not a named list. ```{r, eval = FALSE} rdb_datasets(provider_code = "IMF", simplify = TRUE) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df017 data.table::setDT(DT) display_table(DT) ``` The extent of datasets gathered by [DBnomics](https://db.nomics.world) can be appreciate by using the function with the argument `provider_code` set to `NULL`: ```{r, eval = FALSE} options(rdbnomics.progress_bar_datasets = TRUE) rdb_datasets() options(rdbnomics.progress_bar_datasets = FALSE) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df019 DT <- data.table(Provider = names(DT), `Number of datasets` = sapply(DT, nrow)) DT <- DT[order(Provider)] display_table(DT) ``` # Fetch the possible dimensions of available datasets of a provider When fetching series from [DBnomics](https://db.nomics.world), it can be interesting and especially useful to specify dimensions for a particular dataset to download only the series you want to analyse. With the function `rdb_dimensions`, you can download these dimensions and their meanings. For example, for the dataset **WEO:2019-10** of the **IMF**, you may use: ```{r, eval = FALSE} rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10") ``` The result is a nested named list (its names are **IMF**, **WEO:2019-10** and the dimensions names) with a `data.table` at the end of each branch: ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10` DT <- paste0("Number of dimensions for IMF/WEO:2019-10 : ", length(DT)) cat(DT, sep = "\n") ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[1]] display_table(DT) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[2]] display_table(DT) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[3]] display_table(DT) ``` In the event that you only request the dimensions for one dataset for one provider, if you define `simplify = TRUE`, then the result will be a named list `data.table` not a nested named list. ```{r, eval = FALSE} rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE) ``` ```{r, eval = TRUE, echo = FALSE} str(rdbnomics:::rdbnomics_df021) ``` You can measure the vast extent of datasets gathered by [DBnomics](https://db.nomics.world) by downloading all the possible dimensions. To do this, you have to set the arguments `provider_code` and `dataset_code` to `NULL`. ⚠ It's relatively long to run and heavy to show so we display the first 100. ```{r, eval = FALSE} options(rdbnomics.progress_bar_datasets = TRUE) rdb_dimensions() options(rdbnomics.progress_bar_datasets = FALSE) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df022 DT <- DT[order(Provider, Dataset)] DT <- head(DT, 100) display_table(DT) # rdbnomics_df022 %>% # sapply(function(u) { # sapply( # u, # function(x) { # sapply( # x, # function(y) { # nrow(y) # }, # simplify = FALSE # ) %>% # { # data.table(Dimension = names(.), `Number of codes` = unname(.)) # } # }, # simplify = FALSE # ) %>% # rbindlist(idcol = "Dataset") # }, # simplify = FALSE # ) %>% # rbindlist(idcol = "Provider") ``` # Fetch the series codes and names of available datasets of a provider You can download the list of series, and especially their codes, of a dataset's provider by using the function `rdb_series`. The result is a nested named list with a `data.table` at the end of each branch. If you define `simplify = TRUE`, then the result will be a `data.table` not a nested named list. For example, for the **IMF** provider and the dataset **WEO:2019-10**, the command is (only first 100): ```{r, eval = FALSE} rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df023 DT <- head(DT, 100) display_table(DT) ``` Like the function `rdb()`, you can add features to `rdb_series()`. You can ask for the series with specific `dimensions`: ```{r, eval = FALSE} rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", dimensions = list(`weo-subject` = "NGDP_RPCH"), simplify = TRUE) ``` or with a `query`: ```{r, eval = FALSE} rdb_series(provider_code = "IMF", dataset_code = c("WEO:2019-10", "WEOAGG:2019-10"), query = "NGDP_RPCH") ``` ⚠ We ask the user to use this function parsimoniously because there are a huge amount of series per dataset. Please only fetch for one dataset if you need it or visit the website [https://db.nomics.world](https://db.nomics.world). For example, for the **IMF** provider, the number of series is (only first 5): ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df024 DT <- DT[order(-`Number of series`)] DT <- head(DT, 5) display_table(DT) ``` # Proxy configuration or connection error `Could not resolve host` When using the function `rdb`, you may come across the following error: ```{r, eval = FALSE} Error in open.connection(con, "rb") : Could not resolve host: api.db.nomics.world ``` To get round this situation, you have two options: 1. configure **curl** to use a specific and authorized proxy. 2. use the default R internet connection i.e. the Internet Explorer proxy defined in *internet2.dll*. ## Configure **curl** to use a specific and authorized proxy In **rdbnomics**, by default the function `curl_fetch_memory` (of the package **curl**) is used to fetch the data. If a specific proxy must be used, it is possible to define it permanently with the package option `rdbnomics.curl_config` or on the fly through the argument `curl_config`. Because the object is a named list, its elements are passed to the connection (the `curl_handle` object created internally with `new_handle()`) with `handle_setopt()` before using `curl_fetch_memory`. To see the available parameters, run `names(curl_options())` in *R* or visit the website https://curl.haxx.se/libcurl/c/curl_easy_setopt.html. Once they are chosen, you define the curl object as follows: ```{r, eval = FALSE} h <- list( proxy = "", proxyport = , proxyusername = "", proxypassword = "" ) ``` ### Set the connection up for a session The curl connection can be set up for a session by modifying the following package option: ```{r, eval = FALSE} options(rdbnomics.curl_config = h) ``` When fetching the data, the following command is executed: ```{r, eval = FALSE} hndl <- curl::new_handle() curl::handle_setopt(hndl, .list = getOption("rdbnomics.curl_config")) curl::curl_fetch_memory(url = <...>, handle = hndl) ``` After configuration, just use the standard functions of **rdbnomics** e.g.: ```{r, eval = FALSE} df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ``` This option of the package can be disabled with: ```{r, eval = FALSE} options(rdbnomics.curl = NULL) ``` ### Use the connection only for a function call If a complete configuration is not needed but just an "on the fly" execution, then use the argument `curl_config` of the function `rdb`: ```{r, eval = FALSE} df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h) ``` ## Use the default R internet connection To retrieve the data with the default R internet connection, **rdbnomics** will use the base function `readLines`. ### Set the connection up for a session To activate this feature for a session, you need to enable an option of the package: ```{r, eval = FALSE} options(rdbnomics.use_readLines = TRUE) ``` And then use the standard function as follows: ```{r, eval = FALSE} df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ``` This configuration can be disabled with: ```{r, eval = FALSE} options(rdbnomics.use_readLines = FALSE) ``` ### Use the connection only for a function call If you just want to do it once, you may use the argument `use_readLines` of the function `rdb`: ```{r, eval = FALSE} df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE) ``` # Transform time series with filters The **rdbnomics** package can interact with the *Time Series Editor* of DBnomics to transform time series by applying filters to them. Available filters are listed on the filters page [https://editor.nomics.world/filters](https://editor.nomics.world/filters). Here is an example of how to proceed to interpolate two annual time series with a monthly frequency, using a spline interpolation: ```{r, eval = FALSE} filters <- list( code = "interpolate", parameters = list(frequency = "monthly", method = "spline") ) ``` The request is then: ```{r, eval = FALSE} df <- rdb( ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"), filters = filters ) ``` If you want to apply more than one filter, the `filters` argument will be a list of valid filters: ```{r, eval = FALSE} filters <- list( list( code = "interpolate", parameters = list(frequency = "monthly", method = "spline") ), list( code = "aggregate", parameters = list(frequency = "bi-annual", method = "end_of_period") ) ) df <- rdb( ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"), filters = filters ) ``` The `data.table` columns change a little bit when filters are used. There are two new columns: - `period_middle_day`: the middle day of `original_period` (can be useful when you compare graphically interpolated series and original ones). - `filtered` (boolean): `TRUE` if the series is filtered, `FALSE` otherwise. The content of two columns are modified: - `series_code`: same as before for original series, but the suffix `_filtered` is added for filtered series. - `series_name`: same as before for original series, but the suffix ` (filtered)` is added for filtered series. ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df013 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(filtered, series_name, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df[!is.na(value)], aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() df <- df[!is.na(value)] i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 4, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` rdbnomics/vignettes/dbnomics004.png0000644000176200001440000021266513414003034016776 0ustar liggesusersPNG  IHDR~ sRGBgAMA a pHYs%%IR$IDATx^wW{f=sf/3c=x0`I&IY EAd02 alB q  ƀMϮջ[RIꖾY,T]aWuҮw/VCD=kD}6Kmn=[КuuƀCՋYOkVRݗ $[>ڸs0XwiNwVZ?-5;xHNuy"'K3#zӶ>~>hcj_Ƶ/}*X?tM9yUC, =wM\uMcKu_Ͱ!]'sZAYkgc5Lۯi/V33?(^-xVFկms\ޣvRnf Q]4]`GvfKbJ5)>4/'+#z,&e#J6Y+oٲh630ͻt ]_k`EM}~b[֣=&zpavC^Ƶ[_~m!0Y'X#cO^iQ_|b[EٵG\|Es+]s[Wk׫IAѳ<Dg} VJl0N/zeѳqt0G:Hm\g]ob ƍ5hDsjZG,1==cYgdm,A:C~" : pBڣvuĿ?5׆գoZh)zV@:zn r 3K=ˇ6QlP{>Htܨ&}K N>e+Ѯ$@EuRlRO|kz|+I[f ûY10h}^ Ra +fb"3jգ]/]u1YX%_|qڢokdr } uz*-j>i-}Ѫ;_oU7n׿iw=[L!}U'W2"^Ye`Ub;$=[Af6y߬s߽ޤTm]4 \?a~eiV=-|g]ߘ=idlާ룷ӫwFu1fqJ`0SZV^DKō(gP`mՕEf>ynqGoEJ?Dk3 q؈8,%3ee@oTHu:ݟ=m-H)sn}?`|OVa@ß|{M6]Qa[i "%z^n.c}|CGr$/k6K|8ġ%0MAoT dgBEn8#k%D 3DgE :=(B сDgD TZ%};&Wx~y)U5U~_z#26d8tDA|an^Oy* . f(,J_LQZ=Dlc^Ě:{b{投hޣ?SDL;L̮}~ .CGKG1}XD~O3\iVrdd5 Kޘ]iev086F7cl:+MjGJ\$|.:\t*G/5n7ez;ė-3W8 o^^J3DHuuڵ{K[+-^#;~h+sU5MYn :wlٿZݜqݟ79xHĜ?c9n]-V^!?p萱Ȑme^n.cCBXiݮ}0ҼT8q08`acmB)F[%Ȏ:sb 7B*z.%e.CgYaF7ՙ[܄bͲs/7m'ʘxJ0ݱlclk_Yw 3^-zM+?D¬yr?c?0W&7ZuSeK?eYIbY_-}JkGbO)s3;^oiuk[y=R[֮0zFl8NPfw$E)ELӍa)ӕZLOw_u=UM۳ÊD:ۼOL ƲZ)%תߐùƬ]WtR})úZbOs<0z,N7s)/QKV1څSakcbW؊y,j߁:ؙbw$Aδg*T|siJ{GaecvovÓ9KIbRͪNsu ?可V;;~{SAsN?gE*ZZii~ZqS4M(Ui)8`N~ͷiW%W8q"h-]Θ\k:*͂E'gQ>l--X#GOЃ *GPʫlt[Ū$LQZ*qWT ̣yz WD =;yHbъFkK48 [!fejf/YnW?zNJ)jm!1u MرE;v}8#ak:{;S]7y=r?]F~!E+,*((H_}kr J3G{9qn vc#ak`@pO<ߠN# ˆE[c DZmcbفXE "S [v촏8p|=r+ V<\{M} yRKBp#ǽl;=n2ak쯙, JLtq]2\%]&DώA q^v}r9竫!*؏^$zQBEn[:`XaƷO0s/OY' >ؕnp JR6oUx0։]cWak7tQbyVWyeoH~=;v{Z}ZCGrSf +zvy 3"DZyBDZ}KX%`Ѽc>JgV˭ֹOXepGb+}%v؉J_}F'5M݆.]TPzѳ`aMۥ,K?{ԘuV3툞@!z:nH ˓h >\D{:wlJ`@M6yT.2_b?c^[?nn?%7bGҋlĪϘ.{C*{DpSFkKc]@ z:FZoLJҰ[CGK憨cO9yA )3ǥ))"ѳVpeFVû+mdLt42qjf:{T9ZԮ={v4v$:aGiqS+ӥ2_=/[Z)kcקƲ}u87EJb7J5z~Ma=n@ѻ>Z<[v4f"45@!i`Mؽ5ѳV0XF&*k}bV׮?/:Dtb+y`mo=V;qęJWdE`u>e_L.Z 5ۘ m۬(pIMP$gJ!2pI bÅxo2%(JZv@-:ojmXi:Q}JֹIb1HѴ5fr"tO"'zJiYՙ[Lӱۤi|S =EXQPѳ践uy6fm \#o6Qb&XyHF9c*q4vQ!*69qb6nR^ Pƈ*je<}y=[b{c(s3p4ѴXQs)/emޞr"~= 4ooe)G}sW☟{MY*"BU) P4|, WͲY(xظ)gVo="A,Ҽf0n~$LqN,XM, CJO=kUz/}/N+Gl2v(꜈" [){*7/OxN,r7xb&bTPPp$qS}Qzlewv,Bv2C 3cD='wM |A-?7[>˅kWg6WNА}^ׯӕ銹[jsXn_e// XVe1]˗l\gUaSv{e=wl}hpTJ1)O2l_k sfAZoX'v\uNϷh_n.o=g,b֑:Rre'rٓ/97Jo _xM# {zlG7_7텗.^7~cWHZE 'gAU=Ϯg1}.a,=b#z( fdl/=2zsk=+|g6nNɽe/}{GZ_=uk5=xh9i׵fNX`a-:1"=MYO~2;mi ݹ>_bDϷ~yxrր+o殐_OK2tz{ ?h_?ˬ{b?=7|m;Z~)gQO矔d)yrַ/Z׃N sb_\rAqtr9mΖ0W "i38v\3e#zz px KTqw_S=D>V@ޑmfA7sƁL=ɝgTlD% 7Nmȣe|l}S5f҇e竕骩W~X-G+[|=~{uCXa/'#u?J~WR>-ezmIY$c==.2n9Oe-wsܼ}7/zM7XV{; 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7cyYP[{,v;b{ONWrwJR.T:A?ZlRMJ0Nv{L=T*4/biʰOm:{e72|>* )N`~Mw] _=:۴wCYņwVUypH'LEeX'|g"EGURKlS5Cn-ێ8!,(ǕVvDUGF)+Dg}~ /_I̚!Z߈hq{}y]"YϚ\Ձe7T:"*Tsۚ|q&-YGKYzJZmjmZ w3h1'^*u!*wtBtu[?s/ =wOQZ1Q3$U 5c1*7 ޑٴgѢ^eK\)8O+IresS-76;jeׂ_ лEGa FNV#.NpW埈W3n >,w*7샭}eWK.sW,Q{t :3e;@TãP,%\"iiJ׻>!G0{JW ~MK=XI).g}5U·BgѴGlS&[sXlP|-76FU0~\YE?Vq %O{v-I^ :;U҇;Hߗ-*_qUA\nYldlE@E~YXdSʖs-ls*<ݹ%ΝYKa*Y[aHّ]Pi[C-ˎ-]'GI}fQŲw-?fۉu~++!|s+o vŷ_IqڟZRc2W鱋d]MeN?S1_ +[%7k@k%/5WuImL>\w ;P-IPn, $gUaH޸7띎?YQfg,*T/?x>VP};f@dP+[{/]'r>JgY߉_XMe]*4P]6G%7heotJoko2N3)Kuww'IE׊.,SVlR j/xM%|9!8lY3V=}vJn$zաFzr dNiެjLreN:e(JZdj'c*/5t7k곯<%Ӥrr[_M7Eگo;}6>QOwJo0RGD 8T?価 ."9w+Ҧ fkIQa3BUVvihv8 !؍ε?GgDt({#UWOrӫ{\ vT{Ǜ~z'Vh^FғT,8<*֔BtR-r %wꍖgJjsPםl>䜣(4U۴ώ}\ɪtem9䥸odθ:I4Dh1H|Ѭ2g>oM3\9\>OCȧ#Zȧ[S곚mpn^ {:}T ܋ٰnrsMbv-Wvt6xn,șu-V}]dT ۅuBK&({{ZydN9h/3-}܂I`fqb?j~A(B8+Eۉ'˝)Qv#8@厢8LW[AYD.7btB'z ).|n"TjaI$(2}W۹VIQ%CZ$% #' ggplot(aes(x = period, y = value, color = series_name)) + #' geom_line(size = 1.2) + #' geom_point(size = 2) + #' dbnomics() #' } #' @author Sebastien Galais #' @export dbnomics <- function(color_palette = "Set1", ...) { code <- paste( " ggplot2_ok <- try(utils::packageVersion('ggplot2'), silent = TRUE)", " if (inherits(ggplot2_ok, 'try-error')) {", " stop(", " 'Please install the package ggplot2 to use dbnomics().',", " call. = FALSE", " )", " }", " result <- list(", " ggplot2::scale_x_date(expand = c(0, 0)),", " ggplot2::scale_y_continuous(", " labels = function(x) { format(x, big.mark = ' ') }", " ),", " ggplot2::xlab(''),", " ggplot2::ylab(''),", " ggplot2::theme_bw(),", " ggplot2::theme(", " legend.position = \"bottom\", legend.direction = 'vertical',", " legend.background = ggplot2::element_rect(", " fill = 'transparent', colour = NA", " ),", " legend.key = ggplot2::element_blank(),", " panel.background = ggplot2::element_rect(", " fill = 'transparent', colour = NA", " ),", " plot.background = ggplot2::element_rect(", " fill = 'transparent', colour = NA", " ),", " legend.title = ggplot2::element_blank()", " ),", " ggplot2::theme(...),", " ggplot2::annotate(", " geom = 'text', label = 'DBnomics ', ", " x = structure(Inf, class = 'Date'), y = -Inf,", " hjust = 1.1, vjust = -0.4, col = 'grey', ", " fontface = 'italic'", " )", " )", " if (!is.null(color_palette)) {", " check_argument(color_palette, 'character')", " result <- c(", " result,", " list(ggplot2::scale_color_brewer(palette = color_palette))", " )", " }", " result", sep = "\n" ) eval(parse(text = code)) } rdbnomics/R/rdb_rename_xts.R0000644000176200001440000000277513731206636015574 0ustar liggesusers#' Rename the xts object columns #' #' In the \code{xts} object returned by the function \code{rdb_to_xts}, the #' series codes are used as column names. If you prefer the series names #' (or apply a function to them), the function \code{rdb_rename_xts} is here for #' that. #' #' @param x \code{xts} object. The \code{xts} object returned by the function #' \code{rdb_to_xts}. #' @param fun function (default NULL). The function to apply to the column names. #' @param ... Arguments for the function \code{fun}. #' @return A \code{xts} object. #' @examples #' \dontrun{ #' library(xts) #' library(data.table) #' library(rdbnomics) #' #' df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") #' df <- rdb_to_xts(df) #' rdb_rename_xts(df) #' } #' @seealso \code{\link{rdb}}, \code{\link{rdb_to_xts}} #' @author Sebastien Galais #' @export rdb_rename_xts <- function(x, fun = NULL, ...) { code <- paste( " xts_ok <- try(utils::packageVersion('xts'), silent = TRUE)", " if (inherits(xts_ok, 'try-error')) {", " stop(", " 'Please install the package xts to use rdb_to_xts().',", " call. = FALSE", " )", " }", " nm <- xts::xtsAttributes(x)$codename", " cols <- nm$series_name[match(names(x), nm$series_code)]", " if (is.null(fun)) {", " names(x) <- cols", " } else {", " names(x) <- sapply(X = cols, FUN = fun, ..., USE.NAMES = FALSE)", " }", " x", sep = "\n" ) eval(parse(text = code)) } rdbnomics/R/sysdata.rda0000644000176200001440000214544013744124636014617 0ustar liggesusersBZh91AY&SY,yol` L V=9bwN}cϻ׾^ڒB =T݀[.x>`e2N}u_-=WF$!>ξ}.r_};םǝ'Jt w/<}Ԑo{[6)t ꯹}rPr }7wrP [@}FNu]'Qs2Xz>QmuG9,cUL>,/N氤n5pnxׇݗJ}cC@Vv-Y^p $_wrZwtݹt:1ק{Oky} Uw\t =htܤt&u݅v{Pr5h[Wښ=p;pYkhث@Po7y=tm @a4 Mihi@d&FF2=1242hOB 44jdb2hTjSejmCd=MALh 4HԈ2&T4MC F&hhz@ h4h hhhOU$I0MFQJd)F=@P4 4 RD4O@"z4L&i3Bfh5&ԏFj=2M=!oR=0(d=M'h&(jQD& 3@SģG44M4hhh2KՉ當>g$"R$gXBIєB)l BDbP"BQ DX"TieԨXDJ%,Ғ+RF*ؠҥ-ҌY~c^nhu=צg k\wc;VM~uA {80dBFtcL}O30$6!\FFUAAAslPꏜ֎A>hs 2!1EPu}-G CXA'A'Ar9|O8sKTϽ?5toKe㵇e3ПaaD9.3nУ=z;:nq>jAww>*t Ynt0ItÒ E9C=B_o8Wa!jp,\a{ǞsBW;81N=8 A;Bo'7ƘNgo oHqN;rA$%r?v5K 0s^ e/ V$-+F5^67McGyA/{/c/Uy"?*j"UW_q} ((oN\"7%b@fQjh{aպ } v)ܚW*NT~&A61Aϫ_eҪ |QK*[2%x$_O{pQ^n=?ݿ$%X؏CL l"*~I}3`̩45 -0&KVTX} W3ER`iµ5FbJ2`jObSfR+rI~~Ƙ>5l eqkt "IqLuX*V҆a7\$! 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get_data get_data <- function(x, userl, curl_args, headers = NULL, opt = NULL, run = 0) { if (run > 0) { sys_sleep <- getOption("rdbnomics.sleep_run") check_argument(sys_sleep, c("integer", "numeric")) Sys.sleep(sys_sleep) } tryCatch({ if (userl) { # Only readLines if (as.numeric(R.Version()$major) == 3) { if (as.numeric(R.Version()$minor) < 2) { try( suppressMessages(suppressWarnings(utils::setInternet2(TRUE))), silent = TRUE ) } } verb_warn_rl <- getOption("rdbnomics.verbose_warning_readLines") check_argument(verb_warn_rl, "logical") if (verb_warn_rl) { response <- try(readLines(x), silent = TRUE) } else { response <- try(suppressWarnings(readLines(x)), silent = TRUE) } if (inherits(response, "try-error")) { stop("BAD REQUEST", call. = FALSE) } else { jsonlite::fromJSON(response) } } else { # With curl if (!is.null(curl_args)) { if (inherits(curl_args, "curl_handle")) { curl_args <- list(handle = curl_args) } if (!inherits(curl_args, "list")) { stop( paste0( "Argument 'curl_config' or option 'rdbnomics.curl_config' can ", "only be of class 'curl_handle' or 'list'." ), call. = FALSE ) } if (inherits(curl_args, "list")) { if (is.null(names(curl_args))) { stop("The list 'curl_config' must be named.", call. = FALSE) } if (length(curl_args) <= 0) { stop("The list 'curl_config' is empty.", call. = FALSE) } nm <- names(curl_args) nm <- no_empty_char(nm) if (length(curl_args) != length(nm)) { stop("All elements of 'curl_config' must be named.", call. = FALSE) } } } try(curl::handle_reset(tmp_curl), silent = TRUE) if (!list_has_curl_handle(curl_args)) { tmp_curl <- list(handle = curl::new_handle()) if (!is.null(curl_args)) { curl::handle_setopt(tmp_curl$handle, .list = curl_args) } } else { tmp_curl <- curl_args } if (!is.null(headers) & !is.null(opt)) { curl::handle_setheaders(tmp_curl$handle, .list = headers) curl::handle_setopt(tmp_curl$handle, .list = opt) } response <- do.call(curl::curl_fetch_memory, c(list(url = x), tmp_curl)) check_x <- curl::parse_headers(response$headers) check_x <- utils::head(check_x, 1) http_ok <- getOption("rdbnomics.http_ok") check_argument(http_ok, "character") if (grepl(http_ok, toupper(check_x))) { response <- rawToChar(response$content) if (!is.null(jsonlite::fromJSON(response)$errors)) { cat("\n") for (ie in 1:nrow(jsonlite::fromJSON(response)$errors)) { cat( jsonlite::fromJSON(response)$errors[ie,]$message, " : ", jsonlite::fromJSON(response)$errors[ie,]$provider_code, "/", jsonlite::fromJSON(response)$errors[ie,]$dataset_code, "/", jsonlite::fromJSON(response)$errors[ie,]$series_code, "\n" ) } } jsonnum <- try( jsonlite::fromJSON(response)$series$num_found, silent = TRUE ) if (!inherits(jsonnum, "try-error")) { if (!is.null(jsonnum)) { if (jsonlite::fromJSON(response)$series$num_found <= 0) { run <- 100 stop( "Error when fetching the data.", call. = FALSE ) } } } jsonlite::fromJSON(response) } else { errormessage <- try( paste0( "\n", check_x, "\n", jsonlite::fromJSON(rawToChar(response$content))$error ), silent = TRUE ) if (inherits(errormessage, "try-error")) { errormessage <- paste0("\n", check_x) } stop(errormessage, call. = FALSE) } } }, error = function(e) { try_run <- getOption("rdbnomics.try_run") check_argument(try_run, c("integer", "numeric")) myerror <- try( grepl("'curl_config'", e$message) | grepl("BAD[[:blank:]]+REQUEST", toupper(e$message)), silent = TRUE ) if (!inherits(myerror, "try-error")) { if (myerror) { try_run <- -1L } } if (run < try_run) { get_data(x, userl, curl_args, headers = headers, opt = opt, run = run + 1) } else { stop(e) } }) } #------------------------------------------------------------------------------- # deploy deploy <- function(DT, columns = NULL, reference_column = "value") { if (!data.table::is.data.table(DT)) { stop("DT is not a data.table.", call. = FALSE) } if (nrow(DT) <= 0) { return(DT) } if (ncol(DT) <= 0) { return(DT) } has_list <- sapply(1:ncol(DT), function(x) { inherits(DT[[x]], "list") }, USE.NAMES = FALSE) has_list <- sum(has_list, na.rm = TRUE) has_list <- (has_list > 0) if (has_list) { DT[, dotI := .I] DT <- split(DT, DT$dotI) DT <- lapply(DT, function(y) { y <- as.list(y) # Reference length to_list_length <- length(y[[reference_column]][[1]]) # Transform lists into vectors if (is.null(columns)) { for (iv in names(y)) { v <- y[[iv]] if (inherits(v, "list")) { v <- unlist(v) if (length(v) == 1 & to_list_length != 1) { # New col y[[iv]] <- paste0(trim(v), ",") } else if ( (length(v) == to_list_length + 1) | (length(v) == 2 & to_list_length != 2) ) { # New col y[[iv]] <- paste0(trim(v[1]), ",", utils::tail(v, -1)) } else if (length(v) != to_list_length) { y[[iv]] <- paste(unique(v), collapse = ",") } else { y[[iv]] <- v } } } } else { for (i in columns) { y[[i]] <- unlist(y[[i]]) } } data.table::as.data.table(y) }) DT <- data.table::rbindlist(DT, use.names = TRUE, fill = TRUE) DT[, dotI := NULL] } DT[] } #------------------------------------------------------------------------------- # list_has_dataframe list_has_dataframe <- function(x) { check_for_dataframe <- lapply(x, function(y) { y <- sapply(y, inherits, what = "data.frame", simplify = FALSE) unlist(y) }) check_for_dataframe <- unlist(check_for_dataframe) check_for_dataframe <- sum(check_for_dataframe, na.rm = TRUE) (check_for_dataframe > 0) } #------------------------------------------------------------------------------- # no_empty_char no_empty_char <- function(x) { if (inherits(x, "character")) { x <- x[x != "" & !is.na(x)] } x } #------------------------------------------------------------------------------- # dataframe_to_columns dataframe_to_columns <- function(x) { has_dataframe <- sapply(x, function(y) { if (inherits(y, "data.frame")) { return(NULL) } "" }, simplify = FALSE) has_dataframe <- Filter(is.null, has_dataframe) if (length(has_dataframe) <= 0) { return(x) } for (i in names(has_dataframe)) { # cols <- colnames(x[[i]]) x <- cbind(x, x[[i]]) # names(x)[(ncol(x) - length(cols) + 1):ncol(x)] <- paste( # i, cols, # sep = "_" # ) x[[i]] <- NULL } x } #------------------------------------------------------------------------------- # get_version get_version <- function(x) { if ("python_project_version" %in% names(x$`_meta`)) { api_version <- numeric_version(x$`_meta`$python_project_version) } else if ("version" %in% names(x$`_meta`)) { api_version <- numeric_version(x$`_meta`$version) } else { stop("Can't find the version.", call. = FALSE) } api_version <- unlist(api_version) api_version <- api_version[api_version != 0] api_version <- utils::head(api_version, 1) authorized_version(api_version) api_version } #------------------------------------------------------------------------------- # authorized_version authorized_version <- function(x) { versions <- getOption("rdbnomics.authorized_api_version") check_argument(versions, c("integer", "numeric"), len = FALSE) name <- deparse(substitute(versions)) if (is.null(versions)) { stop(paste0(name, " cannot be NULL."), call. = FALSE) } if (!inherits(versions, c("numeric", "integer"))) { stop( paste0(name, " must be of class 'integer' or 'numeric'."), call. = FALSE ) } if (length(versions) <= 0) { stop(paste0(name, " must be of length greater than 0."), call. = FALSE) } if (x %notin% versions) { stop( paste0( "Only versions ", paste0(versions, collapse = ", "), " are supported." ), call. = FALSE ) } invisible() } #------------------------------------------------------------------------------- # trim trim <- function(x) { gsub("^[[:blank:]]+|[[:blank:]]+$", "", x) } #------------------------------------------------------------------------------- # date_format date_format <- function(x) { x <- no_empty_char(x) if (length(x) <= 0) { return(FALSE) } sum(grepl("^[0-9]{4}-[0-9]{2}-[0-9]{2}$", trim(x)), na.rm = TRUE) == length(x) } #------------------------------------------------------------------------------- # timestamp_format timestamp_format <- function(x, y) { x <- no_empty_char(x) if (length(x) <= 0) { return(FALSE) } sum(grepl(y, trim(x)), na.rm = TRUE) == length(x) } #------------------------------------------------------------------------------- # check_argument check_argument <- function(x, type, len = TRUE, n = 1, not_null = TRUE) { name <- deparse(substitute(x)) if (not_null) { if (is.null(x)) { stop(paste0(name, " cannot be NULL."), call. = FALSE) } } if (!inherits(x, type)) { stop( paste0( name, " must be of class '", paste0(type, collapse = "', '"), "'." ), call. = FALSE ) } if (len) { if (length(x) != n) { stop(paste0(name, " must be of length ", n, "."), call. = FALSE) } } invisible() } #------------------------------------------------------------------------------- # to_json_if_list to_json_if_list <- function(x) { if (inherits(x, "list")) { if (is.null(names(x))) { stop("The list 'dimensions' must be named.", call. = FALSE) } if (length(x) <= 0) { stop("The list 'dimensions' is empty.", call. = FALSE) } nm <- names(x) nm <- no_empty_char(nm) if (length(x) != length(nm)) { stop("All elements of 'dimensions' must be named.", call. = FALSE) } return(jsonlite::toJSON(x)) } x } #------------------------------------------------------------------------------- # transform_date_timestamp transform_date_timestamp <- function(DT) { timezone <- getOption("rdbnomics.timestamp_tz") check_argument(timezone, "character") from_timestamp <- c( # "^[0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}:[0-9]{2}:[0-9]{2}Z$", "^[0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}:[0-9]{2}:[0-9]{2}\\.?[0-9]*Z$", "^[0-9]{4}-[0-9]{2}-[0-9]{2}[[:blank:]]+[0-9]{2}:[0-9]{2}:[0-9]{2}$" ) to_timestamp <- c( # "%Y-%m-%dT%H:%M:%SZ", "%Y-%m-%dT%H:%M:%OSZ", "%Y-%m-%d %H:%M:%S" ) cols <- copy(colnames(DT)) cols <- cols[ (tolower(cols) != "observations_attributes") & !grepl("name$", tolower(cols)) ] if (length(cols) > 0) { DT[ , (cols) := lapply(.SD, function(x) { if (inherits(x, "character")) { if (date_format(x)) { return(suppressWarnings(as.Date(x))) } for (i in seq_along(from_timestamp)) { if (timestamp_format(x, from_timestamp[i])) { return( suppressWarnings( as.POSIXct(x, tz = timezone, format = to_timestamp[i]) ) ) } } } x }), .SDcols = cols ] } invisible(DT) } #------------------------------------------------------------------------------- # avoid_partial_argument avoid_partial_argument <- function(x) { x <- as.list(x) x <- names(x) x <- no_empty_char(x) if (is.null(x)) { return(invisible()) } if (length(x) <= 0) { return(invisible()) } args_ok <- c(names(formals(rdb)), names(formals(rdb_by_api_link))) args_ok <- args_ok[args_ok %notin% c("...", "api_link")] if (length(setdiff(x, args_ok)) > 0) { stop("Please avoid partial argument matching.", call. = FALSE) } invisible() } #------------------------------------------------------------------------------- # correct_argument correct_argument <- function() { args_ok <- c(names(formals(rdb)), names(formals(rdb_by_api_link))) args_ok[args_ok %notin% c("...", "api_link")] } #------------------------------------------------------------------------------- # call_ok call_ok <- function(x) { x <- as.list(x) x <- names(x) modif_arg <- FALSE if (is.null(x)) { modif_arg <- TRUE } if (!is.null(x)) { x <- no_empty_char(x) if (length(x) <= 0) { modif_arg <- TRUE } else { x <- pmatch(x, correct_argument(), duplicates.ok = TRUE) if (sum(is.na(x)) <= 0) { modif_arg <- TRUE } } } modif_arg } #------------------------------------------------------------------------------- # remove_columns remove_columns <- function(DT, x, expr = FALSE) { if (expr) { cols <- grep(x, colnames(DT), value = TRUE) } else { cols <- intersect(x, colnames(DT)) } if (length(cols) > 0) { DT[, (cols) := NULL] } invisible(DT) } #------------------------------------------------------------------------------- # reduce_to_one reduce_to_one <- function(DT) { x <- DT[ , lapply(.SD, function(y) { length(unique(y)) }) ] x <- as.list(x) x <- Filter(function(y){ y > 1 }, x) x <- names(x) DT[, (x) := NULL] invisible(DT) } #------------------------------------------------------------------------------- # filter_type filter_type <- function(x) { test <- tryCatch({ res <- "ko" n <- length(x) y <- sapply(x, filter_ok) y <- sum(y, na.rm = TRUE) if (y == n) { res <- "list" } res }, error = function(e) { "ko" }) if (test == "ko") { test <- tryCatch({ res <- filter_ok(x) if (res) { res <- "notlist" } else { res <- "ko" } res }, error = function(e) { "ko" }) } test } #------------------------------------------------------------------------------- # filter_ok filter_ok <- function(x) { tryCatch({ res <- FALSE nm1 <- names(x) nm2 <- names(x$parameters) if (identical(nm1, c("code", "parameters"))) { if (is.null(nm2)) { res <- TRUE } if (identical(nm2, c("frequency", "method"))) { res <- TRUE } } res }, error = function(e) { FALSE }) } #------------------------------------------------------------------------------- # get_geo_colname get_geo_colname <- function(x) { y <- try( { expr <- "dimensions_label[s]*\\." elt <- grep(expr, names(unlist(x)), value = TRUE) if (is.null(x$dataset$code)) { codes <- gsub(".*/|\\..*", "", elt) } else { codes <- x$dataset$code if (length(codes) == 1) { codes <- rep(codes, length(elt)) } } lapply(seq_along(elt), function(i) { z <- elt[i] ref_elt <- gsub(".*\\.", "", z) elt_ <- gsub('\\.', '"]][["', z) elt_ <- paste0('[["', elt_, '"]]') res <- c(codes[i], ref_elt, eval(parse(text = paste0("x", elt_)))) if (length(res) > 0) { if (res[2] == res[3]) { if (res[3] == capital_first(res[3])) { res[3] <- toupper(res[3]) } else { res[3] <- capital_first(res[3]) } } res } else { res } }) }, silent = TRUE ) if (inherits(y, "try-error")) { return(NULL) } y } #------------------------------------------------------------------------------- # get_geo_names get_geo_names <- function(x, colname) { y <- try( { codes <- sapply(colname, `[[`, 1) nm <- sapply(colname, `[[`, 2) DTs <- lapply(seq_along(codes), function(i) { expr <- paste0( "(", paste0(codes[i], collapse = "|"), ")*", "\\.dimensions_value[s]*_label[s]*\\.(", paste0(nm[i], collapse = "|"), "){1}\\." ) elt <- grep(expr, names(unlist(x)), value = TRUE) for (y in nm) { elt <- gsub(paste0(y, '\\..*'), y, elt) } elt <- unique(elt) if (length(elt) > 0) { elt_ <- gsub('\\.', '"]][["', elt) elt_ <- paste0('[["', elt_, '"]]') y <- eval(parse(text = paste0("x", elt_))) suppressWarnings( setnames( data.table(X1 = codes[i], X2 = names(y), X3 = unname(unlist(y))), c("dataset_code", colname[[i]][2:3]) )[] ) } else { expr <- paste0( "(", paste0(codes[i], collapse = "|"), ")*", "\\.dimensions_value[s]*_label[s]*\\.(", paste0(nm[i], collapse = "|"), "){1}[0-9]*" ) elt <- grep(expr, names(unlist(x)), value = TRUE) for (y in nm) { elt <- gsub(paste0(y, '\\..*'), y, elt) } elt <- gsub("[0-9]*$", "", elt) elt <- unique(elt) if (length(elt) > 0) { elt_ <- gsub('\\.', '"]][["', elt) elt_ <- paste0('[["', elt_, '"]]') y <- eval(parse(text = paste0("x", elt_))) suppressWarnings( setnames( data.table(X1 = codes[i], X2 = y[, 1], X3 = y[, 2]), c("dataset_code", colname[[i]][2:3]) )[] ) } else { NULL } } }) DTs <- Filter(Negate(is.null), DTs) if (length(DTs) <= 0) { NULL } else { DTs } }, silent = TRUE ) if (inherits(y, "try-error")) { return(NULL) } y } #------------------------------------------------------------------------------- # list_has_curl_handle list_has_curl_handle <- function(x) { if (is.null(x)) { return(FALSE) } if (!inherits(x, "list")) { return(FALSE) } y <- sapply(x, inherits, what = "curl_handle") y <- sum(y, na.rm = TRUE) y <- y > 0 if (y) { TRUE } else { FALSE } } #------------------------------------------------------------------------------- # unpack unpack <- function(DT) { if (is.null(DT)) { return(NULL) } if (is.list(DT) & !data.table::is.data.table(DT)) { if (length(DT) <= 0) { return(NULL) } } if (nrow(DT) <= 0) { return(NULL) } data.table::setDT(DT) DT[, k := .I] DT <- split(DT, by = "k") lapply(DT, function(DT_) { if (!("children" %in% colnames(DT_))) { DT_[, k := NULL] DT_[] } else { if (is.null(DT_$children[[1]])) { DT_[, k := NULL] DT_[, .SD, .SDcols = setdiff(colnames(DT_), "children")] } else { lapply(DT_$children, unpack) } } }) } #------------------------------------------------------------------------------- # rbindlist_recursive rbindlist_recursive <- function(l) { if (is.null(l)) { return(NULL) } if (data.table::is.data.table(l)) { return(l) } if ( sum(sapply(l, is.data.table), na.rm = TRUE) != length(l) ) { l <- lapply(l, rbindlist_recursive) } data.table::rbindlist(l, use.names = TRUE, fill = TRUE) } #------------------------------------------------------------------------------- # check_datasets check_datasets <- function(l, run = 1) { if (is.null(l)) { return(l) } if (run == 0) { return(l) } for (i1 in names(l)) { if (is.null(l[[i1]])) { l[[i1]] <- NULL } else { if (length(l[[i1]]) <= 0) { l[[i1]] <- NULL } else { if (nrow(l[[i1]]) <= 0) { l[[i1]] <- NULL } } } } check_datasets(l, run = run - 1) } #------------------------------------------------------------------------------- # check_dimensions check_dimensions <- function(l, run = 1) { if (is.null(l)) { return(l) } if (run == 0) { return(l) } for (i1 in names(l)) { if (length(l[[i1]]) <= 0) { l[[i1]] <- NULL } else { for (i2 in names(l[[i1]])) { if (is.null(l[[i1]][[i2]])) { l[[i1]][[i2]] <- NULL } else { if (length(l[[i1]][[i2]]) <= 0) { l[[i1]][[i2]] <- NULL } } } } } check_dimensions(l, run = run - 1) } #------------------------------------------------------------------------------- # capital_first capital_first <- function(x) { if (is.null(x)) { return(x) } if (length(x) <= 0) { return(x) } paste0( toupper(substr(x, 1, 1)), tolower(substr(x, 2, nchar(x))) ) } #------------------------------------------------------------------------------- # new_title new_title <- function(x) { if (is.null(x)) { return("unknown") } if (length(x) <= 0) { return("unknown") } if (x != capital_first(x)) { return(capital_first(x)) } toupper(x) } #------------------------------------------------------------------------------- # get_dimensions_values get_dimensions_values <- function(DT, ...) { tryCatch({ tmpDT <- DT[, .SD, .SDcols = c("provider_code", "dataset_code")] tmpDT <- unique(tmpDT) tmpDT[, k := .I] tmpDT <- lapply(tmpDT$k, function(x) { tmpx <- rdb_dimensions( provider_code = tmpDT[k == x]$provider_code, dataset_code = tmpDT[k == x]$dataset_code, ... ) tmpx <- tmpx[[1]][[1]] lapply(tmpx, function(y) { DT2 <- data.table::copy(y) DT2[, dataset_code := tmpDT[k == x]$dataset_code] data.table::setcolorder( DT2, c("dataset_code", setdiff(colnames(DT2), "dataset_code")) ) DT2 }) }) tmpDT <- unlist(tmpDT, recursive = FALSE) unname(tmpDT) }, error = function(e) { NULL }) } #------------------------------------------------------------------------------- # get_dimensions_names get_dimensions_names <- function(DT = NULL, dimensions_values = NULL, ...) { tryCatch({ if (is.null(dimensions_values)) { tmpDT <- get_dimensions_values(DT, ...) } else { tmpDT <- dimensions_values } lapply(tmpDT, function(x) { c( unique(x$dataset_code), utils::tail(colnames(x), -1) ) }) }, error = function(e) { NULL }) } #------------------------------------------------------------------------------- # JUST A TRY, DO NOT USE ! # get_geo_colname2 get_geo_colname2 <- function(x, y) { u <- try( { nm <- unlist(x) nm <- names(nm) nm <- grep("\\.dimensions_value[s]*_label[s]\\.", nm, value = TRUE) nm <- gsub("\\.dimensions_value[s]*_label[s]", "", nm) nm <- gsub("dataset[s]*\\.*", "", nm) nm <- gsub("\\.[^.]*$", "", nm) nm <- unique(nm) nm <- lapply(nm, function(u) { pc <- gsub("/.*", "", u) dc <- gsub(paste0(pc, "/"), "", u) dc <- gsub("\\..*", "", dc) code <- gsub(paste0(pc, "/", dc, "\\."), "", u) c(pc, dc, code, new_title(code)) }) nm <- lapply(nm, `[`, 2:4) if (!is.null(y)) { for (i in seq_along(nm)) { for (j in seq_along(y)) { if (nm[[i]][1] == y[[j]][1] & nm[[i]][2] == y[[j]][2]) { nm[[i]][3] <- y[[j]][3] } } } } nm }, silent = TRUE ) if (inherits(u, "try-error")) { return(y) } u } #------------------------------------------------------------------------------- # ellipsis_default ellipsis_default <- function(name, x, default) { if (length(x) <= 0) { return(default) } tmp <- x[[name]] if (is.null(tmp)) { return(default) } tmp } #------------------------------------------------------------------------------- # additional_info additional_info <- function(x) { # Additional informations to translate geo, freq, ... if (!getOption("rdbnomics.translate_codes")) { additional_geo_column <- additional_geo_mapping <- NULL } else { additional_geo_column <- get_geo_colname(x) additional_geo_mapping <- get_geo_names(x, additional_geo_column) # Check coherence if (is.null(additional_geo_column) | is.null(additional_geo_mapping)) { additional_geo_column <- additional_geo_mapping <- NULL } if (!is.null(additional_geo_column) & !is.null(additional_geo_mapping)) { if (length(additional_geo_column) != length(additional_geo_mapping)) { if ( length(additional_geo_column) == 0 | length(additional_geo_mapping) == 0 ) { additional_geo_column <- additional_geo_mapping <- NULL } else { check_agc <- sapply(additional_geo_column, paste0, collapse = "|") additional_geo_column <- stats::setNames(additional_geo_column, check_agc) check_agm <- sapply(additional_geo_mapping, function(u) { u1 <- u$dataset_code[1] u2 <- colnames(u)[2:3] u2 <- paste0(u2, collapse = "|") paste0(u1, "|", u2) }) additional_geo_mapping <- stats::setNames(additional_geo_mapping, check_agm) keep <- intersect(check_agc, check_agm) if (length(keep) == 0) { additional_geo_column <- additional_geo_mapping <- NULL } else { additional_geo_column <- additional_geo_column[sort(keep)] additional_geo_mapping <- additional_geo_mapping[sort(keep)] } } } } } list(additional_geo_column, additional_geo_mapping) } rdbnomics/R/zzz.R0000644000176200001440000000346113725464754013440 0ustar liggesusers.onAttach <- function(libname, pkgname) { packageStartupMessage("Visit .") } .onLoad <- function(libname, pkgname) { # To ensure backward compatibility pkg <- getNamespace(pkgname) assign( "strrep", function (x, times) { x <- as.character(x) if (length(x) == 0L) { return(x) } unlist(.mapply(function(x, times) { if (is.na(x) || is.na(times)) { return(NA_character_) } if (times <= 0L) { return("") } paste0(replicate(times, x), collapse = "") }, list(x = x, times = times), MoreArgs = list()), use.names = FALSE) }, envir = pkg ) # Package options opts <- list( rdbnomics.use_readLines = FALSE, rdbnomics.sleep_run = 1L, rdbnomics.try_run = 2L, rdbnomics.verbose_warning = TRUE, rdbnomics.api_base_url = "https://api.db.nomics.world", rdbnomics.editor_base_url = "https://editor.nomics.world", rdbnomics.api_version = 22, rdbnomics.editor_version = 1, rdbnomics.authorized_api_version = c(21, 22), rdbnomics.verbose_warning_readLines = FALSE, rdbnomics.timestamp_tz = "GMT", rdbnomics.http_ok = "200([[:blank:]]+OK)?[[:blank:]]*$", rdbnomics.curl_config = NULL, rdbnomics.rdb_no_arg = TRUE, rdbnomics.metadata = TRUE, rdbnomics.filters = NULL, rdbnomics.progress_bar = TRUE, rdbnomics.translate_codes = TRUE, rdbnomics.progress_bar_datasets = FALSE, rdbnomics.progress_bar_dimensions = FALSE, rdbnomics.progress_bar_series = FALSE ) opts <- append( opts, list(rdbnomics = sort(gsub("rdbnomics\\.", "", names(opts)))), 0 ) op <- options() op.rdbnomics <- opts toset <- !(names(op.rdbnomics) %in% names(op)) if (any(toset)) options(op.rdbnomics[toset]) invisible() } rdbnomics/R/rdb_by_api_link.R0000644000176200001440000001302213744116537015676 0ustar liggesusers#' Download DBnomics data using API link (deprecated). #' #' \code{rdb_by_api_link} downloads data series from #' \href{https://db.nomics.world/}{DBnomics}. #' #' This function gives you access to hundreds of millions data series from #' \href{https://api.db.nomics.world/}{DBnomics API} (documentation about #' the API can be found \href{https://api.db.nomics.world/v22/apidocs}{here}). #' The API link is given on the #' \href{https://db.nomics.world/}{DBnomics website}. #' #' @param api_link Character string. DBnomics API link of the search. #' @param use_readLines Logical (default \code{FALSE}). If \code{TRUE}, then #' the data are requested and read with the base function \code{readLines} i.e. #' through the default R internet connection. This can be used to get round the #' error \code{Could not resolve host: api.db.nomics.world}. #' @param curl_config Named list (default \code{NULL}). If not #' \code{NULL}, it is used to configure a proxy connection. This #' configuration is passed to the function \code{curl_fetch_memory} of the package #' \pkg{curl}. A temporary \code{curl_handle} object is created internally #' with arguments equal to the provided list in \code{curl_config}.\cr #' For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. #' For available curl options see \code{\link[curl]{curl_options}}, #' \code{names(curl_options())} and #' \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}. #' @param filters List (default \code{NULL}). This argument must be a named #' list for one filter because the function \code{toJSON} of the package \pkg{jsonlite} #' is used before sending the request to the server. For multiple filters, #' you have to provide a list of valid filters (see examples).\cr #' A valid filter is a named list with an element \code{code} which is a character string, #' and an element \code{parameters} which is a named list with elements \code{frequency} #' and \code{method} or a NULL. #' @return A \code{data.table}. #' @examples #' \dontrun{ #' # Fetch two series from different datasets of different providers : #' df1 <- rdb_by_api_link( #' paste0( #' "https://api.db.nomics.world/v22/", #' "series?observations=1&series_ids=AMECO/ZUTN/EA19.1.0.0.0.ZUTN,IMF/CPI/A.AT.PCPIT_IX" #' ) #' ) #' #' # Fetch one series from the dataset 'Doing Business' of WB provider : #' df2 <- rdb_by_api_link( #' paste0( #' "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22", #' "indicator%22%3A%5B%22IC.REG.PROC.FE.NO%22%5D%7D&q=Doing%20Business", #' "&observations=1&format=json&align_periods=1&offset=0&facets=0" #' ) #' ) #' #' #' ## Use a specific proxy to fetch the data #' # Fetch one series from the dataset 'Doing Business' of WB provider : #' h <- list( #' proxy = "", #' proxyport = , #' proxyusername = "", #' proxypassword = "" #' ) #' options(rdbnomics.curl_config = h) #' df2 <- rdb_by_api_link( #' paste0( #' "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22", #' "indicator%22%3A%5B%22IC.REG.PROC.FE.NO%22%5D%7D&q=Doing%20Business", #' "&observations=1&format=json&align_periods=1&offset=0&facets=0" #' ) #' ) #' # or to use once #' df2 <- rdb_by_api_link( #' paste0( #' "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22", #' "indicator%22%3A%5B%22IC.REG.PROC.FE.NO%22%5D%7D&q=Doing%20Business", #' "&observations=1&format=json&align_periods=1&offset=0&facets=0" #' ), #' curl_config = h #' ) #' #' #' ## Use R default connection to avoid a proxy failure (in some cases) #' # Fetch one series from the dataset 'Doing Business' of WB provider : #' options(rdbnomics.use_readLines = TRUE) #' df2 <- rdb_by_api_link( #' paste0( #' "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22", #' "indicator%22%3A%5B%22IC.REG.PROC.FE.NO%22%5D%7D&q=Doing%20Business", #' "&observations=1&format=json&align_periods=1&offset=0&facets=0" #' ) #' ) #' # or to use once #' df2 <- rdb_by_api_link( #' paste0( #' "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22", #' "indicator%22%3A%5B%22IC.REG.PROC.FE.NO%22%5D%7D&q=Doing%20Business", #' "&observations=1&format=json&align_periods=1&offset=0&facets=0" #' ), #' use_readLines = TRUE #' ) #' #' #' ## Apply filter(s) to the series #' # One filter #' df3 <- rdb_by_api_link( #' "https://api.db.nomics.world/v22/series/IMF/WEO:2019-10/ABW.BCA?observations=1", #' filters = list( #' code = "interpolate", #' parameters = list(frequency = "daily", method = "spline") #' ) #' ) #' #' # Two filters #' df3 <- rdb_by_api_link( #' "https://api.db.nomics.world/v22/series/IMF/WEO:2019-10/ABW.BCA?observations=1", #' filters = list( #' list( #' code = "interpolate", #' parameters = list(frequency = "quarterly", method = "spline") #' ), #' list( #' code = "aggregate", #' parameters = list(frequency = "annual", method = "average") #' ) #' ) #' ) #' } #' @seealso \code{\link{rdb}} #' @author Sebastien Galais #' @export rdb_by_api_link <- function( api_link, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), filters = getOption("rdbnomics.filters") ) { .Deprecated("rdb(api_link = ...)", old = "rdb_by_api_link(api_link = ...)") .rdb( api_link = api_link, use_readLines = use_readLines, curl_config = curl_config, filters = filters ) } rdbnomics/R/rdb.R0000644000176200001440000004315413744121571013341 0ustar liggesusers#' Download DBnomics data. #' #' \code{rdb} downloads data series from #' \href{https://db.nomics.world/}{DBnomics} using shortcuts like \code{ids}, #' \code{dimensions}, \code{mask}, \code{query} or using an \code{api_link}. #' #' This function gives you access to hundreds of millions data series from #' \href{https://api.db.nomics.world/}{DBnomics API} (documentation about #' the API can be found \href{https://api.db.nomics.world/v22/apidocs}{here}). #' The code of each series is given on the #' \href{https://db.nomics.world/}{DBnomics website}. \cr\cr #' In the event that only the argument \code{ids} is provided (and those in the #' ellipsis \code{...}), the argument name can be dropped. The character string #' vector is directly passed to \code{ids}. \cr #' If only the argument \code{api_link} is provided (and those in the #' ellipsis \code{...}), then the argument name can be dropped. The character string #' vector is directly passed to \code{api_link}. \cr #' In the same way, if only \code{provider_code}, \code{dataset_code} and #' \code{mask} are provided then the arguments names can be dropped. The #' last character string is automatically passed to \code{mask}. #' #' #' @param provider_code Character string (default \code{NULL}). DBnomics code #' of the provider. #' @param dataset_code Character string (default \code{NULL}). DBnomics code #' of the dataset. #' @param ids Character string (default \code{NULL}). DBnomics code of one or #' several series. #' @param dimensions List or character string (single quoted) (default \code{NULL}). #' DBnomics code of one or several dimensions in the specified provider and dataset. #' If it is a named list, then the function \code{toJSON} (from the #' package \pkg{jsonlite}) is applied to generate the json object. #' @param mask Character string (default \code{NULL}). DBnomics code of one or #' several masks in the specified provider and dataset. #' @param query Character string (default \code{NULL}). A query to #' filter/select series from a provider's dataset. #' @param api_link Character string. DBnomics API link of the search. It should #' starts with \code{http://} or \code{https://}. #' @param filters List (default \code{NULL}). This argument must be a named #' list for one filter because the function \code{toJSON} of the package \pkg{jsonlite} #' is used before sending the request to the server. For multiple filters, #' you have to provide a list of valid filters (see examples).\cr #' A valid filter is a named list with an element \code{code} which is a character string, #' and an element \code{parameters} which is a named list with elements \code{frequency} #' and \code{method} or a NULL. #' @param use_readLines Logical (default \code{FALSE}). If \code{TRUE}, then #' the data are requested and read with the base function \code{readLines} i.e. #' through the default R internet connection. This can be used to get round the #' error \code{Could not resolve host: api.db.nomics.world}. #' @param curl_config Named list (default \code{NULL}). If not #' \code{NULL}, it is used to configure a proxy connection. This #' configuration is passed to the function \code{curl_fetch_memory} of the package #' \pkg{curl}. A temporary \code{curl_handle} object is created internally #' with arguments equal to the provided list in \code{curl_config}.\cr #' For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. #' For available curl options see \code{\link[curl]{curl_options}}, #' \code{names(curl_options())} and #' \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}. #' @param verbose Logical (default \code{FALSE}). Show warnings of the function. #' @param ... Arguments to be passed to the internal function \code{.rdb}. #' @return A \code{data.table}. #' @examples #' \dontrun{ #' ## By ids #' # Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider: #' df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") #' # or when no argument names are given (provider_code -> ids) #' df1 <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN") #' #' # Fetch two series from dataset 'Unemployment rate' (ZUTN) of AMECO provider: #' df2 <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN")) #' #' # Fetch two series from different datasets of different providers: #' df3 <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "IMF/BOP/A.FR.BCA_BP6_EUR")) #' #' #' ## By dimensions #' # Fetch one value of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider: #' df1 <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea12")) #' # or #' df1 <- rdb("AMECO", "ZUTN", dimensions = '{"geo":["ea12"]}') #' #' # Fetch two values of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider: #' df2 <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea12", "dnk"))) #' # or #' df2 <- rdb("AMECO", "ZUTN", dimensions = '{"geo":["ea12","dnk"]}') #' #' # Fetch several values of several dimensions from dataset 'Doing business' (DB) of World Bank: #' dim <- list( #' country = c("DZ", "PE"), #' indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS") #' ) #' df3 <- rdb("WB", "DB", dimensions = dim) #' # or #' dim <- paste0( #' '{"country":["DZ","PE"],', #' '"indicator":["ENF.CONT.COEN.COST.ZS","IC.REG.COST.PC.FE.ZS"]}' #' ) #' df3 <- rdb("WB", "DB", dimensions = dim) #' #' #' ## By mask #' # Fetch one series from dataset 'Balance of Payments' (BOP) of IMF: #' df1 <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR") #' # or when no argument names are given except provider_code and dataset_code (ids -> mask) #' df1 <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR") #' #' # Fetch two series from dataset 'Balance of Payments' (BOP) of IMF: #' df2 <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") #' #' # Fetch all series along one dimension from dataset 'Balance of Payments' (BOP) of IMF: #' df3 <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR") #' #' # Fetch series along multiple dimensions from dataset 'Balance of Payments' (BOP) of IMF: #' df4 <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR") #' #' #' ## By query #' # Fetch one series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) from IMF : #' df1 <- rdb("IMF", "WEO:2019-10", query = "France current account balance percent") #' # Fetch series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) from IMF : #' df2 <- rdb("IMF", "WEO:2019-10", query = "current account balance percent") #' #' #' ## By api_link #' # Fetch two series from different datasets of different providers : #' df1 <- rdb( #' api_link = paste0( #' "https://api.db.nomics.world/v22/", #' "series?observations=1&series_ids=AMECO/ZUTN/EA19.1.0.0.0.ZUTN,IMF/CPI/A.AT.PCPIT_IX" #' ) #' ) #' #' # Fetch one series from the dataset 'Doing Business' of WB provider : #' df2 <- rdb( #' api_link = paste0( #' "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22", #' "indicator%22%3A%5B%22IC.REG.PROC.FE.NO%22%5D%7D&q=Doing%20Business", #' "&observations=1&format=json&align_periods=1&offset=0&facets=0" #' ) #' ) #' # or when no argument names are given (provider_code -> api_link) #' df1 <- rdb( #' paste0( #' "https://api.db.nomics.world/v22/", #' "series?observations=1&series_ids=AMECO/ZUTN/EA19.1.0.0.0.ZUTN,IMF/CPI/A.AT.PCPIT_IX" #' ) #' ) #' #' #' ## Use a specific proxy to fetch the data #' # Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider : #' h <- list( #' proxy = "", #' proxyport = , #' proxyusername = "", #' proxypassword = "" #' ) #' options(rdbnomics.curl_config = h) #' df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") #' # or to use once #' options(rdbnomics.curl_config = NULL) #' df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h) #' #' #' ## Use R default connection to avoid a proxy failure (in some cases) #' # Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider : #' options(rdbnomics.use_readLines = TRUE) #' df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") #' # or to use once #' df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE) #' #' #' ## Apply filter(s) to the series #' # One filter #' df1 <- rdb( #' ids = c("IMF/WEO:2019-10/ABW.BCA.us_dollars", "IMF/WEO:2019-10/ABW.BCA_NGDPD.pcent_gdp"), #' filters = list( #' code = "interpolate", #' parameters = list(frequency = "daily", method = "spline") #' ) #' ) #' #' # Two filters #' df1 <- rdb( #' ids = c("IMF/WEO:2019-10/ABW.BCA.us_dollars", "IMF/WEO:2019-10/ABW.BCA_NGDPD.pcent_gdp"), #' filters = list( #' list( #' code = "interpolate", #' parameters = list(frequency = "quarterly", method = "spline") #' ), #' list( #' code = "aggregate", #' parameters = list(frequency = "annual", method = "average") #' ) #' ) #' ) #' } #' @author Sebastien Galais #' @export rdb <- function( provider_code = NULL, dataset_code = NULL, ids = NULL, dimensions = NULL, mask = NULL, query = NULL, api_link = NULL, filters = getOption("rdbnomics.filters"), use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), verbose = getOption("rdbnomics.verbose_warning"), ... ) { # Checking 'verbose' check_argument(verbose, "logical") # Setting API url api_base_url <- getOption("rdbnomics.api_base_url") check_argument(api_base_url, "character") # Setting API version api_version <- getOption("rdbnomics.api_version") check_argument(api_version, c("numeric", "integer")) authorized_version(api_version) # Setting API metadata metadata <- getOption("rdbnomics.metadata") check_argument(metadata, "logical") # Building API base url api_base_url <- paste0(api_base_url, "/v", api_version, "/series") # Checking arguments provider_code_null <- is.null(provider_code) provider_code_not_null <- !provider_code_null dataset_code_null <- is.null(dataset_code) dataset_code_not_null <- !dataset_code_null dimensions_null <- is.null(dimensions) dimensions_not_null <- !dimensions_null mask_null <- is.null(mask) mask_not_null <- !mask_null ids_null <- is.null(ids) ids_not_null <- !ids_null query_null <- is.null(query) query_not_null <- !query_null api_link_null <- is.null(api_link) api_link_not_null <- !api_link_null # provider_code is considered as api_link in some cases if ( provider_code_not_null & dataset_code_null & dimensions_null & mask_null & ids_null & query_null & api_link_null & getOption("rdbnomics.rdb_no_arg") ) { is_http <- grepl("^http(s)*://", tolower(provider_code)) if (sum(is_http, na.rm = TRUE) > 0) { fcall <- sys.call() modif_arg <- call_ok(fcall) if (modif_arg) { api_link <- provider_code provider_code <- NULL provider_code_null <- TRUE provider_code_not_null <- !provider_code_null api_link_null <- FALSE api_link_not_null <- !api_link_null } } } # By api_link i.e. .rdb(api_link = api_link) if (api_link_not_null) { check_argument(api_link, "character", not_null = FALSE) if (api_version == 22) { return( .rdb( api_link = api_link, filters = filters, use_readLines = use_readLines, curl_config = curl_config, ... ) ) } else { stop( paste0("Don't know what to do for API version ", api_version, "."), call. = FALSE ) } } # provider_code is considered as ids in some cases if ( provider_code_not_null & dataset_code_null & dimensions_null & mask_null & ids_null & query_null & api_link_null & getOption("rdbnomics.rdb_no_arg") ) { fcall <- sys.call() modif_arg <- call_ok(fcall) if (modif_arg) { ids <- provider_code provider_code <- NULL provider_code_null <- TRUE provider_code_not_null <- !provider_code_null ids_null <- FALSE ids_not_null <- !ids_null } } # ids is considered as mask in some cases if ( provider_code_not_null & dataset_code_not_null & dimensions_null & mask_null & ids_not_null & query_null & api_link_null & getOption("rdbnomics.rdb_no_arg") ) { fcall <- sys.call() modif_arg <- call_ok(fcall) if (modif_arg) { mask <- ids ids <- NULL mask_null <- FALSE mask_not_null <- !mask_null ids_null <- TRUE ids_not_null <- !ids_null } } # By dimensions if (dimensions_not_null) { if (provider_code_null | dataset_code_null) { stop( paste0( "When you filter with 'dimensions', you must specifiy ", "'provider_code' and 'dataset_code' as arguments of the function." ), call. = FALSE ) } dimensions <- to_json_if_list(dimensions) check_argument(dimensions, c("character", "json"), not_null = FALSE) check_argument(provider_code, "character", not_null = FALSE) check_argument(dataset_code, "character", not_null = FALSE) if (api_version == 21) { link <- paste0( api_base_url, "?provider_code=", provider_code, "&dataset_code=", dataset_code, "&dimensions=", dimensions ) } else if (api_version == 22) { link <- paste0( api_base_url, "/", provider_code, "/", dataset_code, ifelse(metadata, "?", paste0("?metadata=", as.numeric(metadata), "&")), "observations=1&dimensions=", dimensions ) } else { stop( paste0("Don't know what to do for API version ", api_version, "."), call. = FALSE ) } return( .rdb( api_link = link, filters = filters, use_readLines = use_readLines, curl_config = curl_config, ... ) ) } # By mask if (mask_not_null) { if (provider_code_null | dataset_code_null) { stop( paste0( "When you filter with 'mask', you must specifiy 'provider_code' ", "and 'dataset_code' as arguments of the function." ), call. = FALSE ) } check_argument(mask, "character", not_null = FALSE) check_argument(provider_code, "character", not_null = FALSE) check_argument(dataset_code, "character", not_null = FALSE) if (api_version == 21) { link <- paste0( api_base_url, "?provider_code=", provider_code, "&dataset_code=", dataset_code, "&series_code_mask=", mask ) } else if (api_version == 22) { link <- paste0( api_base_url, "/", provider_code, "/", dataset_code, "/", mask, ifelse(metadata, "?", paste0("?metadata=", as.numeric(metadata), "&")), "observations=1" ) } else { stop( paste0("Don't know what to do for API version ", api_version, "."), call. = FALSE ) } return( .rdb( api_link = link, filters = filters, use_readLines = use_readLines, curl_config = curl_config, ... ) ) } # By ids if (ids_not_null) { if (provider_code_not_null | dataset_code_not_null) { if (verbose) { warning( paste0( "When you filter with 'ids', ", "'provider_code' and 'dataset_code' are not considered." ) ) } } if (!is.character(ids)) { stop("'ids' must be of class 'character'.", call. = FALSE) } if (length(ids) <= 0) { stop("'ids' is empty.", call. = FALSE) } if (api_version == 21) { link <- paste0( api_base_url, "?series_ids=", paste(ids, collapse = ",") ) } else if (api_version == 22) { link <- paste0( api_base_url, ifelse(metadata, "?", paste0("?metadata=", as.numeric(metadata), "&")), "observations=1&series_ids=", paste(ids, collapse = ",") ) } else { stop( paste0("Don't know what to do for API version ", api_version, "."), call. = FALSE ) } return( .rdb( api_link = link, filters = filters, use_readLines = use_readLines, curl_config = curl_config, ... ) ) } # By query if (query_not_null) { if (provider_code_null | dataset_code_null) { stop( paste0( "When you filter with a 'query', you must specifiy 'provider_code' ", "and 'dataset_code' as arguments of the function." ), call. = FALSE ) } check_argument(query, "character", not_null = FALSE) if (verbose) { if (query == "") { warning( paste0( "Your 'query' is empty, the entire dataset ", provider_code, "/", dataset_code, " will be downloaded. It can be long !" ) ) } } if (api_version == 22) { link <- paste0( api_base_url, "/", provider_code, "/", dataset_code, "?q=", utils::URLencode(query), ifelse(metadata, "&", paste0("&metadata=", as.numeric(metadata), "&")), "observations=1" ) } else { stop( paste0("Don't know what to do for API version ", api_version, "."), call. = FALSE ) } return( .rdb( api_link = link, filters = filters, use_readLines = use_readLines, curl_config = curl_config, ... ) ) } stop( "Please provide correct 'provider_code', 'dataset_code', 'dimensions', ", "'mask', 'ids', 'query', 'api_link' or 'filters'.", call. = FALSE ) } rdbnomics/R/rdb_datasets.R0000644000176200001440000001212113653242346015222 0ustar liggesusers#' Download list of datasets for DBnomics providers. #' #' \code{rdb_datasets} downloads the list of available datasets for a selection #' of providers (or all of them) from \href{https://db.nomics.world/}{DBnomics}. #' #' By default, the function returns a named list of \code{data.table}s #' containing the datasets of the providers from #' \href{https://db.nomics.world/}{DBnomics}. #' #' @param provider_code Character string (default \code{NULL}). DBnomics code #' of one or multiple providers. If \code{NULL}, the providers are firstly #' dowloaded with the function \code{\link{rdb_providers}} and then the #' available datasets are requested. #' @param use_readLines Logical (default \code{FALSE}). If \code{TRUE}, then #' the data are requested and read with the base function \code{readLines} i.e. #' through the default R internet connection. This can be used to get round the #' error \code{Could not resolve host: api.db.nomics.world}. #' @param curl_config Named list (default \code{NULL}). If not #' \code{NULL}, it is used to configure a proxy connection. This #' configuration is passed to the function \code{curl_fetch_memory} of the package #' \pkg{curl}. A temporary \code{curl_handle} object is created internally #' with arguments equal to the provided list in \code{curl_config}.\cr #' For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. #' For available curl options see \code{\link[curl]{curl_options}}, #' \code{names(curl_options())} and #' \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}. #' @param simplify Logical (default \code{FALSE}). If \code{TRUE}, when the #' datasets are requested for only one provider then a \code{data.table} #' is returned, not a list of \code{data.table}s. #' @param ... Additionals arguments. #' @return A named list of \code{data.table}s or a \code{data.table}. #' @examples #' \dontrun{ #' rdb_datasets(provider_code = "IMF") #' #' rdb_datasets(provider_code = "IMF", simplify = TRUE) #' #' rdb_datasets(provider_code = c("IMF", "BDF")) #' #' options(rdbnomics.progress_bar_datasets = TRUE) #' rdb_datasets() #' options(rdbnomics.progress_bar_datasets = FALSE) #' #' #' rdb_datasets(provider_code = "IMF", use_readLines = TRUE) #' #' rdb_datasets( #' provider_code = "IMF", #' curl_config = list(proxy = "", proxyport = ) #' ) #' } #' @seealso \code{\link{rdb_providers}}, \code{\link{rdb_last_updates}}, #' \code{\link{rdb_dimensions}}, \code{\link{rdb_series}} #' @author Sebastien Galais #' @export rdb_datasets <- function( provider_code = NULL, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), simplify = FALSE, ... ) { # TO CHECK THE DATASETS # parse_web <- sapply(provider_code, function(x) { # y <- readLines(paste0("https://db.nomics.world/", x)) # y <- y[grepl(paste0("", proxyport = ) #' ) #' } #' @seealso \code{\link{rdb_providers}}, \code{\link{rdb_last_updates}}, #' \code{\link{rdb_datasets}}, \code{\link{rdb_dimensions}} #' @author Sebastien Galais #' @export rdb_series <- function( provider_code = NULL, dataset_code = NULL, dimensions = NULL, query = NULL, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), simplify = FALSE, verbose = FALSE, ... ) { # Additionals arguments progress_bar <- ellipsis_default("progress_bar", list(...), TRUE) check_argument(progress_bar, "logical") only_first_two <- ellipsis_default("only_first_two", list(...), FALSE) check_argument(only_first_two, "logical") only_number_series <- ellipsis_default("only_number_series", list(...), FALSE) check_argument(only_number_series, "logical") # All providers if (is.null(provider_code)) { provider_code <- rdb_providers( code = TRUE, use_readLines = use_readLines, curl_config = curl_config ) } check_argument(provider_code, "character", len = FALSE) if (is.null(dataset_code)) { dataset_code <- rdb_datasets( provider_code = provider_code, use_readLines = use_readLines, curl_config = curl_config, simplify = FALSE, progress_bar = FALSE ) dataset_code <- sapply(dataset_code, `[[`, "code", simplify = FALSE) } else { check_argument(dataset_code, "character", len = FALSE) dataset_code <- list(dataset_code) dataset_code <- stats::setNames(dataset_code, provider_code) } # Checking arguments query_null <- is.null(query) query_not_null <- !query_null if (query_not_null) { check_argument(query, "character", not_null = FALSE) } dimensions_null <- is.null(dimensions) dimensions_not_null <- !dimensions_null if (dimensions_not_null) { dimensions <- to_json_if_list(dimensions) check_argument(dimensions, c("character", "json"), not_null = FALSE) } check_argument(use_readLines, "logical") check_argument(simplify, "logical") # Setting API url api_base_url <- getOption("rdbnomics.api_base_url") check_argument(api_base_url, "character") # Setting API version api_version <- getOption("rdbnomics.api_version") check_argument(api_version, c("numeric", "integer")) authorized_version(api_version) # Fetching all datasets series <- sapply(provider_code, function(pc) { if (getOption("rdbnomics.progress_bar_series") & progress_bar) { pb <- utils::txtProgressBar( min = 0, max = length(dataset_code[[pc]]), style = 3 ) } tmp_ser <- sapply(seq_along(dataset_code[[pc]]), function(i) { tryCatch({ dc <- dataset_code[[pc]][i] api_link <- paste0( api_base_url, "/v", api_version, "/series/", pc, "/", dc ) if (query_not_null) { api_link <- paste0(api_link, "?q=", utils::URLencode(query)) } if (dimensions_not_null) { api_link <- paste0( api_link, ifelse(grepl("\\?", api_link), "&", "?"), "dimensions=", dimensions ) } DBlist <- get_data(api_link, use_readLines, curl_config) limit <- DBlist$series$limit num_found <- DBlist$series$num_found if (only_number_series) { return(data.table(Number_of_series = num_found)) } if (verbose) { if (getOption("rdbnomics.progress_bar_series") & progress_bar) { cat("\n") } cat( paste0( "The dataset '", dc, "' from provider '", pc, "' contains ", num_found, " series." ), "\n" ) } DBdata <- list( data.table::data.table( series_code = DBlist$series$docs$series_code, series_name = DBlist$series$docs$series_name ) ) if (num_found > limit) { DBdata0 <- DBdata rm(DBdata) sequence <- seq(1, floor(num_found / limit), 1) if (getOption("rdbnomics.progress_bar_series") & progress_bar) { pb_offset <- utils::txtProgressBar( min = 0, max = max(sequence), style = 3 ) } # Modifying link if (grepl("offset=", api_link)) { api_link <- gsub("\\&offset=[0-9]+", "", api_link) api_link <- gsub("\\?offset=[0-9]+", "", api_link) } sep <- ifelse(grepl("\\?", api_link), "&", "?") if (only_first_two) { sequence <- utils::head(sequence, 1) } DBdata <- lapply(sequence, function(j) { # Modifying link tmp_api_link <- paste0( api_link, sep, "offset=", format(j * limit, scientific = FALSE) ) # Fetching data DBlist <- get_data(tmp_api_link, use_readLines, curl_config) if (getOption("rdbnomics.progress_bar_series") & progress_bar) { utils::setTxtProgressBar(pb_offset, j) } # Extracting data data.table::data.table( series_code = DBlist$series$docs$series_code, series_name = DBlist$series$docs$series_name ) }) DBdata <- append(DBdata, DBdata0, 0) rm(DBdata0) if (getOption("rdbnomics.progress_bar_series") & progress_bar) { close(pb_offset) } } DBdata <- rbindlist(DBdata, use.names = TRUE, fill = TRUE) if (getOption("rdbnomics.progress_bar_series") & progress_bar) { utils::setTxtProgressBar(pb, i) } DBdata }, error = function(e) { NULL }) }, simplify = FALSE) if (getOption("rdbnomics.progress_bar_series") & progress_bar) { close(pb) } tmp_ser <- stats::setNames(tmp_ser, dataset_code[[pc]]) Filter(Negate(is.null), tmp_ser) }, simplify = FALSE) series <- Filter(Negate(is.null), series) # We remove the empty lists, the empty data.tables, etc. series <- check_dimensions(series, 2) if (length(series) <= 0) { warning( "Error when fetching the series.", call. = FALSE ) return(NULL) } if (simplify) { len <- sapply(series, length) if (length(series) == 1 & len[1] == 1) { return(series[[1]][[1]]) } } series } rdbnomics/R/rdbnomics.R0000644000176200001440000000101213675744670014553 0ustar liggesusers#' Package rdbnomics #' #' DBnomics R client (). #' #' @docType package #' @name rdbnomics #' #' @import curl jsonlite data.table #' NULL if (getRversion() >= "2.15.1") { vars <- c( ".", ":=", "value", "dotI", "period", "period_start_day", "series_code", "filtered", "original_period", "series_name", "original_value", "period_middle_day", "code", "name", "A", "B", "C", "k", "V1", "V2", "dataset_code", "EQUAL" ) utils::globalVariables(unique(vars)) }rdbnomics/R/dot_rdb.R0000644000176200001440000002502613676473051014214 0ustar liggesusers.rdb <- function( api_link, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), filters = getOption("rdbnomics.filters") ) { # Checking 'api_link' if (is.null(api_link)) { return(NULL) } check_argument(api_link, "character") # Checking 'use_readLines' check_argument(use_readLines, "logical") # Checking 'filters' if (!is.null(filters)) { check_filter <- filter_type(filters) if (check_filter == "ko") { stop( paste0( "If only one filter is applied then 'filters' must be a named list ", "with two elements : 'code' and 'parameters'." ), "\n", paste0( "'code' is a character string and 'parameters' is an empty list ", "or a named list ('frequency' and 'method')." ), "\n", "For more informations, visit .", "\n", paste0( "If multiple filters are applied then 'filters' must be a unnamed ", "list of valid filters." ), call. = FALSE ) } if (use_readLines) { warning( "When applying filters, the curl functions must be used.", "\n", "As a consequence, 'use_readLines' is set to FALSE.", call. = FALSE ) use_readLines <- FALSE } } # Fetching data DBlist <- get_data(api_link, use_readLines, curl_config) # Getting API version api_version <- get_version(DBlist) if (api_version == 22) { data_elt <- "docs" } else { stop( paste0("Don't know what to do for API version ", api_version, "."), call. = FALSE ) } # If data is empty, return NULL if (is.data.frame(DBlist$series[[data_elt]])) { if (nrow(DBlist$series[[data_elt]]) <= 0) { return(NULL) } } if (inherits(DBlist$series[[data_elt]], "list")) { if (length(DBlist$series[[data_elt]]) <= 0) { return(NULL) } } # Checking if the limit has been reached num_found <- DBlist$series$num_found limit <- DBlist$series$limit # Additional informations to translate geo, freq, ... adds <- additional_info(DBlist) additional_geo_column <- adds[[1]] additional_geo_mapping <- adds[[2]] rm(adds) # Extracting data DBdata <- list(DBlist$series[[data_elt]]) rm(DBlist) if (num_found > limit) { DBdata0 <- DBdata rm(DBdata) sequence <- seq(1, floor(num_found / limit), 1) # Modifying link if (grepl("offset=", api_link)) { api_link <- gsub("\\&offset=[0-9]+", "", api_link) api_link <- gsub("\\?offset=[0-9]+", "", api_link) } sep <- ifelse(grepl("\\?", api_link), "&", "?") DBdata <- lapply(sequence, function(i) { # Modifying link tmp_api_link <- paste0( api_link, sep, "offset=", format(i * limit, scientific = FALSE) ) # Fetching data DBlist <- get_data(tmp_api_link, use_readLines, curl_config) # Extracting data DBlist$series[[data_elt]] }) DBdata <- append(DBdata, DBdata0, 0) rm(DBdata0) } # Transform data.frames inside DBdata if (list_has_dataframe(DBdata)) { DBdata <- lapply(DBdata, dataframe_to_columns) } # Transforming into data.table DBdata <- lapply(DBdata, data.table::setDT) # Gathering data DBdata <- data.table::rbindlist(DBdata, use.names = TRUE, fill = TRUE) # Expanding list columns DBdata <- deploy(DBdata) # To reproduce the Python module, we copy the 'value' element DBdata[ , original_value := format( value, scientific = FALSE, decimal.mark = ".", big.mark = "", trim = TRUE, drop0trailing = TRUE ) ] # Transforming date format and timestamp format transform_date_timestamp(DBdata) # Modifying column names tryCatch({ data.table::setnames(DBdata, "period", "original_period") }, error = function(e) { stop( paste0( "The retrieved dataset doesn't have a column named 'period', it's not ", "normal please check ." ), call. = FALSE ) }) DBdata[, original_period := as.character(original_period)] tryCatch({ data.table::setnames(DBdata, "period_start_day", "period") }, error = function(e) { stop( paste0( "The retrieved dataset doesn't have a column named ", "'period_start_day', it's not normal please check ." ), call. = FALSE ) }) # DBnomics editor if (!is.null(filters)) { if (check_filter == "notlist") { filters <- list(filters) } # Filters are applied by 'series_code' codes <- unique(DBdata$series_code) multicodes <- length(codes) > 1 # A progress bar is displayed if there are more than one code if (multicodes & getOption("rdbnomics.progress_bar")) { pb <- utils::txtProgressBar(min = 0, max = length(codes), style = 3) } DBlist <- lapply(seq_along(codes), function(i) { x <- codes[i] tmpdata <- DBdata[series_code == x] # 'series' for the POST request series <- list( frequency = unique(tmpdata$`@frequency`), period_start_day = tmpdata$period, value = tmpdata$value ) # POST request header headers <- list( "Content-Type" = "application/json", "Accept" = "application/json" ) # POST elements opt <- list( customrequest = "POST", postfields = as.character( jsonlite::toJSON( list(filters = filters, series = list(series)), auto_unbox = TRUE ) ) ) # Editor url editor_link <- paste0( getOption("rdbnomics.editor_base_url"), "/api/v", getOption("rdbnomics.editor_version"), "/apply" ) request <- get_data(editor_link, FALSE, curl_config, headers, opt) request <- dataframe_to_columns(request$filter_results$series) data.table::setDT(request) request <- deploy(request) request[ , original_value := format( value, scientific = FALSE, decimal.mark = ".", big.mark = "", trim = TRUE, drop0trailing = TRUE ) ] transform_date_timestamp(request) # Some columns from the original dataset will be replaced by the # filtered dataset tmpdata <- remove_columns( tmpdata, c( "@frequency", "original_period", "period", "value", "original_value", "indexed_at", "series_code" ) ) try( { cols_to_remove <- sapply(additional_geo_column, `[[`, 2) tmpdata <- remove_columns(tmpdata, cols_to_remove) rm(cols_to_remove) }, silent = TRUE ) tmpdata <- remove_columns(tmpdata, "^observation", expr = TRUE) # The aim is to keep only unique informations tmpdata <- unique(tmpdata) if (nrow(tmpdata) > 1) { reduce_to_one(tmpdata) tmpdata <- unique(tmpdata) } if (multicodes & getOption("rdbnomics.progress_bar")) { utils::setTxtProgressBar(pb, i) } # Entire dataset with replaced columns cbind(tmpdata, request) }) if (multicodes & getOption("rdbnomics.progress_bar")) { close(pb) } DBlist <- stats::setNames(DBlist, codes) DBlist <- data.table::rbindlist( DBlist, use.names = TRUE, fill = TRUE, idcol = "series_code" ) # Add filtered suffix DBlist[, series_code := paste0(series_code, "_filtered")] DBlist[, series_name := paste(series_name, "(filtered)")] # We rename the column 'frequency' try( setnames( DBlist, "frequency", grep("^[@]*frequency$", colnames(DBdata), value = TRUE) ), silent = TRUE ) tryCatch({ data.table::setnames(DBlist, "period", "original_period") }, error = function(e) { stop( paste0( "The retrieved dataset doesn't have a column named 'period', it's not ", "normal please check ." ), call. = FALSE ) }) DBlist[, original_period := as.character(original_period)] # In case of different classes, the class of the column 'original_period' # is set to 'character' if ( inherits(DBdata$original_period, "character") & !inherits(DBlist$original_period, "character") ) { DBlist[, original_period := as.character(original_period)] } if ( !inherits(DBdata$original_period, "character") & inherits(DBlist$original_period, "character") ) { DBdata[, original_period := as.character(original_period)] } tryCatch({ data.table::setnames(DBlist, "period_start_day", "period") }, error = function(e) { stop( paste0( "The retrieved dataset doesn't have a column named ", "'period_start_day', it's not normal please check ." ), call. = FALSE ) }) # Add boolean to distinct be filtered and non-filtered series DBdata[, filtered := FALSE] DBlist[, filtered := TRUE] # For R 3.1 diff_cols <- setdiff(colnames(DBlist), colnames(DBdata)) if (length(diff_cols) > 0) { DBdata[, period_middle_day := as.Date(NA)] } DBdata <- list(DBdata, DBlist) DBdata <- data.table::rbindlist(DBdata, use.names = TRUE, fill = TRUE) } # Additional informations translations if (!is.null(additional_geo_column) & !is.null(additional_geo_mapping)) { for (i in seq_along(additional_geo_mapping)) { if (additional_geo_column[[i]][2] %in% colnames(DBdata)) { addcol <- additional_geo_column[[i]][3] suffix <- "" if (addcol %in% colnames(DBdata)) { suffix <- "_add" newcol <- paste0(addcol, suffix) setnames(additional_geo_mapping[[i]], addcol, newcol) } DBdata <- merge( DBdata, additional_geo_mapping[[i]], by = c("dataset_code", additional_geo_column[[i]][2]), all.x = TRUE, all.y = FALSE, sort = FALSE, allow.cartesian = FALSE ) if (suffix != "") { DBdata[, (addcol) := ifelse(is.na(get(newcol)), get(addcol), get(newcol))] DBdata[, (newcol) := NULL] } } } } # We reorder the columns by their names setcolorder(DBdata, sort(colnames(DBdata))) DBdata[] } rdbnomics/R/rdb_providers.R0000644000176200001440000000545713645157632015451 0ustar liggesusers#' Download list of DBnomics providers. #' #' \code{rdb_providers} downloads the list of providers from #' \href{https://db.nomics.world/}{DBnomics}. #' #' By default, the function returns a \code{data.table} #' containing the list of providers from #' \href{https://db.nomics.world/}{DBnomics} with additional informations such as #' the region, the website, etc. #' #' @param code Logical (default \code{FALSE}). If \code{TRUE}, then only the #' providers are returned in a vector. #' @param use_readLines Logical (default \code{FALSE}). If \code{TRUE}, then #' the data are requested and read with the base function \code{readLines} i.e. #' through the default R internet connection. This can be used to get round the #' error \code{Could not resolve host: api.db.nomics.world}. #' @param curl_config Named list (default \code{NULL}). If not #' \code{NULL}, it is used to configure a proxy connection. This #' configuration is passed to the function \code{curl_fetch_memory} of the package #' \pkg{curl}. A temporary \code{curl_handle} object is created internally #' with arguments equal to the provided list in \code{curl_config}.\cr #' For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. #' For available curl options see \code{\link[curl]{curl_options}}, #' \code{names(curl_options())} and #' \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}. #' @return A \code{data.table} or a vector. #' @examples #' \dontrun{ #' rdb_providers() #' #' rdb_providers(code = TRUE) #' #' rdb_providers(use_readLines = TRUE) #' #' rdb_providers(curl_config = list(proxy = "", proxyport = )) #' } #' @seealso \code{\link{rdb_last_updates}}, \code{\link{rdb_datasets}}, #' \code{\link{rdb_dimensions}}, \code{\link{rdb_series}} #' @author Sebastien Galais #' @export rdb_providers <- function( code = FALSE, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config") ) { # Checking arguments check_argument(code, "logical") check_argument(use_readLines, "logical") # Setting API url api_base_url <- getOption("rdbnomics.api_base_url") check_argument(api_base_url, "character") # Setting API version api_version <- getOption("rdbnomics.api_version") check_argument(api_version, c("numeric", "integer")) authorized_version(api_version) providers <- paste0(api_base_url, "/v", api_version, "/providers") providers <- get_data(providers, use_readLines, curl_config) providers <- providers$providers$docs data.table::setDT(providers) if (code) { providers <- providers$code providers <- no_empty_char(providers) providers <- sort(providers) } else { transform_date_timestamp(providers) providers <- providers[order(code)] } providers[] } rdbnomics/R/rdb_last_updates.R0000644000176200001440000000637413653315111016106 0ustar liggesusers#' Download informations about the last DBnomics updates. #' #' \code{rdb_last_updates} downloads informations about the last updates from #' \href{https://db.nomics.world/}{DBnomics}. #' #' By default, the function returns a \code{data.table} #' containing the last 100 updates from #' \href{https://db.nomics.world/}{DBnomics} with additional informations. #' #' @param all Logical (default \code{FALSE}). If \code{TRUE}, then the full #' dataset of the last updates is retrieved. #' @param use_readLines Logical (default \code{FALSE}). If \code{TRUE}, then #' the data are requested and read with the base function \code{readLines} i.e. #' through the default R internet connection. This can be used to get round the #' error \code{Could not resolve host: api.db.nomics.world}. #' @param curl_config Named list (default \code{NULL}). If not #' \code{NULL}, it is used to configure a proxy connection. This #' configuration is passed to the function \code{curl_fetch_memory} of the package #' \pkg{curl}. A temporary \code{curl_handle} object is created internally #' with arguments equal to the provided list in \code{curl_config}.\cr #' For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. #' For available curl options see \code{\link[curl]{curl_options}}, #' \code{names(curl_options())} and #' \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}. #' @return A \code{data.table}. #' @examples #' \dontrun{ #' rdb_last_updates() #' #' rdb_last_updates(all = TRUE) #' #' rdb_last_updates(use_readLines = TRUE) #' #' rdb_last_updates(curl_config = list(proxy = "", proxyport = )) #' } #' @seealso \code{\link{rdb_providers}}, \code{\link{rdb_datasets}}, #' \code{\link{rdb_dimensions}} #' @author Sebastien Galais #' @export rdb_last_updates <- function( all = FALSE, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config") ) { # Checking arguments check_argument(all, "logical") check_argument(use_readLines, "logical") # Setting API url api_base_url <- getOption("rdbnomics.api_base_url") check_argument(api_base_url, "character") # Setting API version api_version <- getOption("rdbnomics.api_version") check_argument(api_version, c("numeric", "integer")) authorized_version(api_version) updates <- paste0(api_base_url, "/v", api_version, "/last-updates") updates <- get_data(updates, use_readLines, curl_config) if (api_version == 22){ n <- updates$datasets$num_found lim <- updates$datasets$limit updates <- updates$datasets$docs data.table::setDT(updates) if (all) { sequence <- seq(0, floor(n / lim) * lim, lim) updates <- lapply(sequence, function(x) { link <- paste0( api_base_url, "/v", api_version, "/last-updates?datasets.offset=", x ) dataset <- get_data(link, use_readLines, curl_config) data.table::setDT(dataset$datasets$docs) }) updates <- data.table::rbindlist(updates, use.names = TRUE, fill = TRUE) } } else { stop( paste0("Don't know what to do for API version ", api_version, "."), call. = FALSE ) } transform_date_timestamp(updates) updates[] } rdbnomics/R/rdb_to_xts.R0000644000176200001440000000632513731206701014733 0ustar liggesusers#' Transform the data.table object into a xts object #' #' For some analysis, it is more convenient to have a \code{xts} object #' instead of a \code{data.table} object. #' #' @param x \code{data.table}. The \code{data.table} returned by the \code{rdb} #' function. #' @param needed_columns Vector of character strings (default #' \code{c("period", "series_code", "series_name", "value")}). Vector of column #' names which are needed to transform the \code{data.table} into a \code{xts} #' object. #' @param series_columns Vector of character strings (default #' \code{c("series_code", "series_name")}). Vector of series column #' names. #' @return A \code{xts} object. #' @examples #' \dontrun{ #' library(xts) #' library(data.table) #' library(rdbnomics) #' #' df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") #' rdb_to_xts(df) #' } #' @seealso \code{\link{rdb}}, \code{\link{rdb_rename_xts}} #' @author Sebastien Galais #' @export rdb_to_xts <- function( x, needed_columns = c("period", "series_code", "series_name", "value"), series_columns = c("series_code", "series_name") ) { code <- paste( " xts_ok <- try(utils::packageVersion('xts'), silent = TRUE)", " if (inherits(xts_ok, 'try-error')) {", " stop(", " 'Please install the package xts to use rdb_to_xts().',", " call. = FALSE", " )", " }", " if (is.null(x)) {", " return(NULL)", " }", " all_cols <- length(setdiff(needed_columns, colnames(x))) != 0", " if (all_cols) {", " stop(", " paste0(", " 'To export as a xts object, some columns are missing. Needed columns ',", " 'are \u0022', paste0(needed_columns, collapse = '\u0022, \u0022'),", " '\u0022'", " ),", " call. = FALSE", " )", " }", " x <- x[, .SD, .SDcols = needed_columns]", " data.table::setcolorder(x, needed_columns)", " attr_names <- NULL", " if (!is.null(series_columns)) {", " attr_names <- unique(x[, .SD, .SDcols = series_columns])", " }", " if (nrow(x) > 0) {", " x <- data.table::dcast.data.table(", " x, period ~ series_code,", " value.var = 'value'", " )", " } else {", " orig <- Sys.Date() - as.numeric(Sys.Date())", " x <- data.table(", " period = as.Date(numeric(), origin = orig),", " no_code = numeric()", " )", " }", " if (!inherits(x[[1]], 'Date')) {", " stop(", " paste0(", " 'The first needed column ', needed_columns[1], ' is not of class ',", " 'Date which is a problem for data.table::as.xts.data.table().'", " ),", " '\n',", " paste0(", " 'Please check with packageVersion(\u0022rdbnomics\u0022) that your version is ',", " 'greater or equal to 0.5.2. Otherwise update rdbnomics or contact ',", " 's915.stem@gmail.com.'", " ),", " call. = FALSE", " )", " }", " x <- data.table::as.xts.data.table(x)", " xts::xtsAttributes(x) <- list(codename = attr_names)", " x", sep = "\n" ) eval(parse(text = code)) } rdbnomics/R/rdb_dimensions.R0000644000176200001440000002043713744117032015565 0ustar liggesusers#' Download list of dimensions for datasets of DBnomics providers. #' #' \code{rdb_dimensions} downloads the list of dimensions (if they exist) for #' available datasets of a selection of providers from #' \href{https://db.nomics.world/}{DBnomics}. #' #' By default, the function returns a nested named list of \code{data.table}s #' containing the dimensions of datasets for providers from #' \href{https://db.nomics.world/}{DBnomics}. #' #' @param provider_code Character string (default \code{NULL}). DBnomics code #' of one or multiple providers. If \code{NULL}, the providers are firstly #' dowloaded with the function \code{\link{rdb_providers}} and then the #' datasets are requested. #' @param dataset_code Character string (default \code{NULL}). DBnomics code #' of one or multiple datasets of a provider. If \code{NULL}, the datasets #' codes are dowloaded with the function \code{\link{rdb_datasets}} and then #' the dimensions are requested. #' @param use_readLines Logical (default \code{FALSE}). If \code{TRUE}, then #' the data are requested and read with the base function \code{readLines} i.e. #' through the default R internet connection. This can be used to get round the #' error \code{Could not resolve host: api.db.nomics.world}. #' @param curl_config Named list (default \code{NULL}). If not #' \code{NULL}, it is used to configure a proxy connection. This #' configuration is passed to the function \code{curl_fetch_memory} of the package #' \pkg{curl}. A temporary \code{curl_handle} object is created internally #' with arguments equal to the provided list in \code{curl_config}.\cr #' For \code{curl_fetch_memory} arguments see \code{\link[curl]{curl_fetch}}. #' For available curl options see \code{\link[curl]{curl_options}}, #' \code{names(curl_options())} and #' \href{https://curl.haxx.se/libcurl/c/curl_easy_setopt.html}{libcurl}. #' @param simplify Logical (default \code{FALSE}). If \code{TRUE}, when the #' dimensions are requested for only one provider and one dataset then a #' named list of \code{data.table}s is returned, not a nested named list of #' \code{data.table}s. #' @param ... Additionals arguments. #' @return A nested named list of \code{data.table}s or a named list of #' \code{data.table}s. #' @examples #' \dontrun{ #' rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10") #' #' rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE) #' #' rdb_dimensions(provider_code = "IMF") #' #' # /!\ It is very long ! #' options(rdbnomics.progress_bar_dimensions = TRUE) #' rdb_dimensions() #' options(rdbnomics.progress_bar_dimensions = FALSE) #' #' rdb_dimensions( #' provider_code = "IMF", dataset_code = "WEO:2019-10", #' use_readLines = TRUE #' ) #' #' rdb_dimensions( #' provider_code = "IMF", dataset_code = "WEO:2019-10", #' curl_config = list(proxy = "", proxyport = ) #' ) #' } #' @seealso \code{\link{rdb_providers}}, \code{\link{rdb_last_updates}}, #' \code{\link{rdb_datasets}}, \code{\link{rdb_series}} #' @author Sebastien Galais #' @export rdb_dimensions <- function( provider_code = NULL, dataset_code = NULL, use_readLines = getOption("rdbnomics.use_readLines"), curl_config = getOption("rdbnomics.curl_config"), simplify = FALSE, ... ) { # Additionals arguments progress_bar <- ellipsis_default("progress_bar", list(...), TRUE) check_argument(progress_bar, "logical") # All providers if (is.null(provider_code) & !is.null(dataset_code)) { stop( "If you give datasets codes, please give also a provider code.", call. = FALSE ) } if (is.null(provider_code)) { provider_code <- rdb_providers( code = TRUE, use_readLines = use_readLines, curl_config = curl_config ) } check_argument(provider_code, "character", len = FALSE) if (is.null(dataset_code)) { dataset_code <- rdb_datasets( provider_code = provider_code, use_readLines = use_readLines, curl_config = curl_config, simplify = FALSE, progress_bar = FALSE ) dataset_code <- sapply(dataset_code, `[[`, "code", simplify = FALSE) } else { check_argument(dataset_code, "character", len = FALSE) dataset_code <- list(dataset_code) dataset_code <- stats::setNames(dataset_code, provider_code) } # Checking arguments check_argument(use_readLines, "logical") check_argument(simplify, "logical") # Setting API url api_base_url <- getOption("rdbnomics.api_base_url") check_argument(api_base_url, "character") # Setting API version api_version <- getOption("rdbnomics.api_version") check_argument(api_version, c("numeric", "integer")) authorized_version(api_version) # Fetching all datasets dimensions <- sapply(provider_code, function(pc) { if (getOption("rdbnomics.progress_bar_dimensions") & progress_bar) { pb <- utils::txtProgressBar( min = 0, max = length(dataset_code[[pc]]), style = 3 ) } tmp_dim <- sapply(seq_along(dataset_code[[pc]]), function(i) { tryCatch({ dc <- dataset_code[[pc]][i] tmp <- paste0(api_base_url, "/v", api_version, "/datasets/", pc, "/", dc) tmp <- get_data(tmp, use_readLines, curl_config) tmp1 <- tmp$datasets$docs$dimensions_labels if (is.null(tmp1)) { tmp1 <- try( tmp$datasets[[paste0(pc, "/", dc)]]$dimensions_labels, silent = TRUE ) if (inherits(tmp1, "try-error")) { tmp1 <- NULL } } if (is.null(tmp1)) { # Sometimes "dimensions_labels" is missing tmp1 <- data.table::data.table(A = character(), B = character()) } else { tmp1 <- as.list(tmp1) tmp1 <- data.table::data.table(A = unlist(tmp1), B = names(tmp1)) tmp1 <- unique(tmp1) # Normally column B is in capital letters if (nrow(tmp1) > 0) { tmp1[, EQUAL := as.numeric(A == B)] tmp1[ EQUAL == 1, A := ifelse(A == capital_first(A), toupper(A), capital_first(A)) ] tmp1[, EQUAL := NULL] } } tmp2 <- tmp$datasets$docs$dimensions_values_labels if (is.null(tmp2)) { tmp2 <- try( tmp$datasets[[paste0(pc, "/", dc)]]$dimensions_values_labels, silent = TRUE ) if (inherits(tmp2, "try-error")) { tmp2 <- NULL } } tmp3 <- sapply(names(tmp2), function(nm) { z <- tmp2[[nm]] if ( is.list(z) & !is.data.frame(z) & !data.table::is.data.table(z) ) { # "z" is actually a list with a matrix z <- z[[1]] z <- as.data.table(z) } else { if (is.matrix(z)) { # "z" is actually a matrix z <- as.data.table(z) } else { z <- as.data.table(z) z <- as.list(z) z <- data.table::data.table(V1 = names(z), V2 = unlist(z)) } } data.table::setnames(z, "V1", nm) new_name <- tmp1[B == nm]$A data.table::setnames( z, "V2", ifelse(length(new_name) <= 0, new_title(nm), new_name) ) z }, simplify = FALSE) if (getOption("rdbnomics.progress_bar_dimensions") & progress_bar) { utils::setTxtProgressBar(pb, i) } tmp3 }, error = function(e) { NULL }) }, simplify = FALSE) if (getOption("rdbnomics.progress_bar_dimensions") & progress_bar) { close(pb) } tmp_dim <- stats::setNames(tmp_dim, dataset_code[[pc]]) Filter(Negate(is.null), tmp_dim) }, simplify = FALSE) dimensions <- Filter(Negate(is.null), dimensions) # We remove the empty lists, the empty data.tables, etc. dimensions <- check_dimensions(dimensions, 2) if (length(dimensions) <= 0) { warning( "Error when fetching the dimensions.", call. = FALSE ) return(NULL) } if (simplify) { len <- sapply(dimensions, length) if (length(dimensions) == 1 & len[1] == 1) { return(dimensions[[1]][[1]]) } } dimensions } rdbnomics/NEWS.md0000644000176200001440000000755013745340751013350 0ustar liggesusers# rdbnomics 0.6.4 * Update series name WEO to WEO:2019-10 and WEOAGG to WEOAGG:2019-10. For all WEO releases (and WEO by group of countries), each release is considered as a different dataset, and therefore carries a different code. For example WEO:2008-10 for the October 2008 release. The WEO dataset no longer exists on DBnomics. To get the latest release automatically, you can use the WEO:latest code. # rdbnomics 0.6.3 * New badge in README. * Correction of .gitlab-ci.yml with pkg-config. * Correction of `rdb()` examples in the doc and README. * Simplification of README. * New fonctions `rdb_to_xts()` and `rdb_rename_xts()` that were in README. # rdbnomics 0.6.2 * Remove **ggplot2**, **DT** and **dplyr** vignette dependencies. * Internal function `get_data` better handles errors. # rdbnomics 0.6.1 * New arguments `dimensions` and `query` for the function `rdb_series()`. # rdbnomics 0.6.0 * New function `rdb_datasets()` to request the available datasets of the providers (@fmgithub2017, #3 github). * New function `rdb_dimensions()` to request the list of the dimensions of the available datasets of the providers (@fmgithub2017, #3 github). * New function `rdb_series()` to request the list of the series of the available datasets of the providers (@fmgithub2017, #3 github). * Add a clearer error message to the function to_xts() in README (Martin Feldkircher, 20200326 email). * Update vignette for `rdb_datasets()`, `rdb_dimensions()` and `rdb_series()`. # rdbnomics 0.5.2 * Correction of a bug in the internal function `deploy`. The cases `to_list_length = 1` and `to_list_length = 2` are now well handled (@julia.schmidt, 20200205 forum.db.nomics.world). # rdbnomics 0.5.1 * New argument 'query' for function `rdb()`. * New argument 'api_link' for function `rdb()` to replace `rdb_by_api_link()`. * New internal function `.rdb()` because `rdb_by_api_link()` is deprecated. * New function `dbnomics()` which is used in the vignette (@blu2ego, #2 github). * Small class correction for R 3.1. # rdbnomics 0.5.0 * New filters tool from . * If the retrieved dataset contains columns with codes (like ISO codes, geographic codes, ...), then correspondences are performed to translate these codes if possible. * Internal function `get_data` better handles errors. The message is clearer. # rdbnomics 0.4.7 * Use of `metadata` from the API to download less data. # rdbnomics 0.4.6 * Internal function `deploy` better handles recursive lists. # rdbnomics 0.4.5 * The vignette "rdbnomics-tutorial" is now named "rdbnomics". * `rdb()` and `rdb_...()` functions now have an argument to configure curl. It is also an option of the package. The vignette "rdbnomics" is modified consequently. * `rdb()` and `rdb_by_api_link()` functions return an error if the columns `period` and `period_start_day` don't exist. * `rdb()` function passes argument `provider_code` to `ids` if only `provider_code` is provided. It is considered that the user wants to use `ids` without naming the arguments. * `rdb()` function passes argument `ids` to `mask` if only `provider_code`, `dataset_code` and `ids` are provided. It is considered that the user wants to use `mask` without naming the argument. # rdbnomics 0.4.4 * Change of the API version from 21 to 22. * The vignette "rdbnomics-tutorial" is modified to be backward compatible with R Markdown v1 (i.e. without pandoc or rmarkdown). * The vignette "rdbnomics-tutorial" has an appendix. * New `rdb_providers()` returns the available providers. * New `rdb_last_updates()` shows the last updates. * In the functions, dates and timestamps are transformed using `as.Date()` and `as.POSIXct()`. * `dimensions` in `rdb()` can be a list (@Iwo, #1 gitlab). # rdbnomics 0.4.3 * First release on CRAN. rdbnomics/MD50000644000176200001440000000451613745355632012565 0ustar liggesusers56c87f69bf5410f5f10bc6b924d6cf4b *DESCRIPTION 0b41d0e8e6b1fa6c0a333aec3c7d0cee *NAMESPACE 40b42b62e4afaa7f3235940b917c054e *NEWS.md 71c90cfd6b2e818d84492898e0f13a45 *R/dbnomics.R be4e2cd0a77e4d9a316d81748fbf446c *R/dot_rdb.R 3498edbfbb423971465c9a0146edeea4 *R/rdb.R 3e7c0b752a2ab66ebb0c4a19207ccc3e *R/rdb_by_api_link.R 9b5506d2ec62413cdfa7652ddb317af7 *R/rdb_datasets.R b6dadeb0b7fe5751be9c1ae750049040 *R/rdb_dimensions.R 6a4bfa3a8cd10c6235716ed5a21ff948 *R/rdb_last_updates.R 577b434204b7b4aed0c29d8875db71f7 *R/rdb_providers.R c8c1ba5de9d605156b4482563d862f22 *R/rdb_rename_xts.R 8d7d0833ab3ad688b60c2b594e4b2321 *R/rdb_series.R 543cdf71fa881defff2e605487db303a *R/rdb_to_xts.R b9e21bfbebc9c434fc11b4efd76dcb92 *R/rdbnomics.R 5bdc9f2e27d35abee451c55f74696d7a *R/sysdata.rda f620900f06aeea716f4fc711ad2575b8 *R/utils.R eae90e9ddb9bb6ba4a66e155dc02a397 *R/zzz.R ff5b4e0b3219ab7e212be92eed8ed490 *README.md 480acb4edd961cb7baab2605cfbb9d15 *build/vignette.rds 3b9005a8f66396e26cca616683aab7d4 *inst/doc/rdbnomics.R 6d3c7f7d8e76e6f116249cbe20a69b54 *inst/doc/rdbnomics.Rmd 5bac65d3cf701a94f146011c344d07ab *inst/doc/rdbnomics.html 34a640426f9d425aad82906ab3ade4bd *inst/tinytest/test_rdb.R 1eec3ec0eff6f9764775467a4e807b52 *inst/tinytest/test_rdb_last_updates.R e3371ab224089888c068cae30fcc83ee *inst/tinytest/test_rdb_providers.R c68e4f7d16e4a4927350dd2fc65d4dff *man/dbnomics.Rd 54193b888f996476a11b1e5968e1ae04 *man/figures/logo.png 03ec71a072099056be6882932f02e5cd *man/rdb.Rd fbe87d1b77a6dbd0f35a07c91dd30d90 *man/rdb_by_api_link.Rd 59bf6541f14a5d65720a49fa22045121 *man/rdb_datasets.Rd ce3526330f28ec140a4503d0798556d9 *man/rdb_dimensions.Rd 77cf44517033844e5061cb7641888a93 *man/rdb_last_updates.Rd ce2d301a7bae475d5eb6d372fece4f0c *man/rdb_providers.Rd a12e937f853ad5e149a24788fc42993d *man/rdb_rename_xts.Rd 8b70b9d0303ae2fefded69cf1a2260fc *man/rdb_series.Rd a87201cb5224838ba36186adf6faa007 *man/rdb_to_xts.Rd 26dc62716a40a38c7fdb4be94087baa0 *man/rdbnomics.Rd 3035f1e6b9fd0922c48b9c9d44794fc8 *tests/tinytest.R e98953a1eb427a74175b91efdad549e1 *vignettes/dbnomics001.png 0c459913b70e2638e3667fece3af2c8a *vignettes/dbnomics002.png de513c0de5c2be3212c475914c98615d *vignettes/dbnomics003.png 34946e93869403ca124b2ed6c5dab526 *vignettes/dbnomics004.png fab6a1c58f5207ef75d20afbf2f987a1 *vignettes/dbnomics005.png 6d3c7f7d8e76e6f116249cbe20a69b54 *vignettes/rdbnomics.Rmd rdbnomics/inst/0000755000176200001440000000000013745341256013221 5ustar liggesusersrdbnomics/inst/doc/0000755000176200001440000000000013745341256013766 5ustar liggesusersrdbnomics/inst/doc/rdbnomics.html0000644000176200001440000736367213745341255016663 0ustar liggesusers DBnomics R client

1 DBnomics: the world’s economic database

Explore all the economic data from different providers (national and international statistical institutes, central banks, etc.), for free, following the link db.nomics.world
(N.B.: in the examples, data have already been retrieved on april 6th 2020).

2 Fetch time series by ids

First, let’s assume that we know which series we want to download. A series identifier (ids) is defined by three values, formatted like this: provider_code/dataset_code/series_code.

2.1 Fetch one series from dataset ‘Unemployment rate’ (ZUTN) of AMECO provider

library(data.table)
library(rdbnomics)
df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
df <- df[!is.na(value)]

In such data.table, you will always find at least ten columns:

  • provider_code
  • dataset_code
  • dataset_name
  • series_code
  • series_name
  • original_period (character string)
  • period (date of the first day of original_period)
  • original_value (character string)
  • value
  • @frequency (harmonized frequency generated by DBnomics)

The other columns depend on the provider and on the dataset. They always come in pairs (for the code and the name). In the data.frame df, you have:

  • unit (code) and Unit (name)
  • geo (code) and Country (name)
  • freq (code) and Frequency (name)
##    provider_code dataset_code        dataset_name         series_code
## 1:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 2:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 3:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 4:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 5:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 6:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
##            series_name original_period     period original_value value
## 1: Annually – (Perc...            1998 1998-01-01           10.4  10.4
## 2: Annually – (Perc...            1999 1999-01-01            9.7   9.7
## 3: Annually – (Perc...            2000 2000-01-01            8.9   8.9
## 4: Annually – (Perc...            2001 2001-01-01            8.3   8.3
## 5: Annually – (Perc...            2002 2002-01-01            8.6   8.6
## 6: Annually – (Perc...            2003 2003-01-01            9.1   9.1
##    @frequency                unit                Unit  geo   Country freq
## 1:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
## 2:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
## 3:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
## 4:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
## 5:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
## 6:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
##    Frequency          indexed_at
## 1:  Annually 2019-11-08 16:33:50
## 2:  Annually 2019-11-08 16:33:50
## 3:  Annually 2019-11-08 16:33:50
## 4:  Annually 2019-11-08 16:33:50
## 5:  Annually 2019-11-08 16:33:50
## 6:  Annually 2019-11-08 16:33:50

In the event that you only use the argument ids, you can drop it and run:

df <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN")

2.2 Fetch two series from dataset ‘Unemployment rate’ (ZUTN) of AMECO provider

df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"))
df <- df[!is.na(value)]
##    provider_code dataset_code        dataset_name      series_code
## 1:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 2:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 3:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 4:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 5:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 6:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
##            series_name original_period     period original_value value
## 1: Annually – (Perc...            1960 1960-01-01              2   2.0
## 2: Annually – (Perc...            1961 1961-01-01            1.6   1.6
## 3: Annually – (Perc...            1962 1962-01-01            1.4   1.4
## 4: Annually – (Perc...            1963 1963-01-01              2   2.0
## 5: Annually – (Perc...            1964 1964-01-01            1.2   1.2
## 6: Annually – (Perc...            1965 1965-01-01            0.9   0.9
##    @frequency                unit                Unit geo Country freq
## 1:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 2:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 3:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 4:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 5:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 6:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
##    Frequency          indexed_at
## 1:  Annually 2019-11-08 16:33:50
## 2:  Annually 2019-11-08 16:33:50
## 3:  Annually 2019-11-08 16:33:50
## 4:  Annually 2019-11-08 16:33:50
## 5:  Annually 2019-11-08 16:33:50
## 6:  Annually 2019-11-08 16:33:50

2.3 Fetch two series from different datasets of different providers

df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "Eurostat/une_rt_q/Q.SA.Y15-24.PC_ACT.T.EA19"))
df <- df[!is.na(value)]
##    provider_code dataset_code        dataset_name         series_code
## 1:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 2:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 3:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 4:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 5:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 6:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
##            series_name original_period     period original_value value
## 1: Annually – (Perc...            1997 1997-01-01           11.5  11.5
## 2: Annually – (Perc...            1998 1998-01-01             11  11.0
## 3: Annually – (Perc...            1999 1999-01-01           10.2  10.2
## 4: Annually – (Perc...            2000 2000-01-01            9.2   9.2
## 5: Annually – (Perc...            2001 2001-01-01            8.4   8.4
## 6: Annually – (Perc...            2002 2002-01-01            8.6   8.6
##    @frequency                unit Unit  geo Country freq Frequency age
## 1:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
## 2:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
## 3:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
## 4:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
## 5:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
## 6:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
##    Age class FREQ Geopolitical entity (reporting)          indexed_at
## 1:        NA   NA                              NA 2020-05-07 08:46:14
## 2:        NA   NA                              NA 2020-05-07 08:46:14
## 3:        NA   NA                              NA 2020-05-07 08:46:14
## 4:        NA   NA                              NA 2020-05-07 08:46:14
## 5:        NA   NA                              NA 2020-05-07 08:46:14
## 6:        NA   NA                              NA 2020-05-07 08:46:14
##    observations_attributes s_adj Seasonal adjustment sex Sex Unit of measure
## 1:                            NA                  NA  NA  NA              NA
## 2:                            NA                  NA  NA  NA              NA
## 3:                            NA                  NA  NA  NA              NA
## 4:                            NA                  NA  NA  NA              NA
## 5:                            NA                  NA  NA  NA              NA
## 6:                            NA                  NA  NA  NA              NA

3 Fetch time series by mask

The code mask notation is a very concise way to select one or many time series at once.

3.1 Fetch one series from dataset ‘Balance of Payments’ (BOP) of IMF

df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR")
df <- df[!is.na(value)]
##    provider_code dataset_code        dataset_name      series_code
## 1:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
## 2:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
## 3:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
## 4:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
## 5:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
## 6:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
##            series_name original_period     period original_value     value
## 1: Annual – France ...            1999 1999-01-01      47672.847 47672.847
## 2: Annual – France ...            2000 2000-01-01      17550.669 17550.669
## 3: Annual – France ...            2001 2001-01-01      23278.392 23278.392
## 4: Annual – France ...            2002 2002-01-01      18718.406 18718.406
## 5: Annual – France ...            2003 2003-01-01      14195.493 14195.493
## 6: Annual – France ...            2004 2004-01-01       7520.754  7520.754
##    @frequency FREQ Frequency          indexed_at           Indicator
## 1:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 2:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 3:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 4:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 5:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 6:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
##      INDICATOR observations_attributes REF_AREA Reference Area
## 1: BCA_BP6_EUR          OFFICIAL_BPM,K       FR         France
## 2: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
## 3: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
## 4: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
## 5: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
## 6: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France

In the event that you only use the arguments provider_code, dataset_code and mask, you can drop the name mask and run:

df <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR")

3.2 Fetch two series from dataset ‘Balance of Payments’ (BOP) of IMF

You just have to add a + between two different values of a dimension.

df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR")
df <- df[!is.na(value)]
##    provider_code dataset_code        dataset_name      series_code
## 1:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
## 2:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
## 3:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
## 4:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
## 5:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
## 6:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
##            series_name original_period     period original_value  value
## 1: Annual – Spain –...            1999 1999-01-01         -19295 -19295
## 2: Annual – Spain –...            2000 2000-01-01         -27921 -27921
## 3: Annual – Spain –...            2001 2001-01-01         -30672 -30672
## 4: Annual – Spain –...            2002 2002-01-01         -27959 -27959
## 5: Annual – Spain –...            2003 2003-01-01         -31153 -31153
## 6: Annual – Spain –...            2004 2004-01-01         -47116 -47116
##    @frequency FREQ Frequency          indexed_at           Indicator
## 1:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 2:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 3:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 4:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 5:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 6:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
##      INDICATOR observations_attributes REF_AREA Reference Area
## 1: BCA_BP6_EUR          OFFICIAL_BPM,K       ES          Spain
## 2: BCA_BP6_EUR           OFFICIAL_BPM,       ES          Spain
## 3: BCA_BP6_EUR           OFFICIAL_BPM,       ES          Spain
## 4: BCA_BP6_EUR           OFFICIAL_BPM,       ES          Spain
## 5: BCA_BP6_EUR           OFFICIAL_BPM,       ES          Spain
## 6: BCA_BP6_EUR           OFFICIAL_BPM,       ES          Spain

3.3 Fetch all series along one dimension from dataset ‘Balance of Payments’ (BOP) of IMF

df <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR")
df <- df[!is.na(value)]
df <- df[order(-period, REF_AREA)]
df <- head(df, 100)
##    provider_code dataset_code        dataset_name      series_code
## 1:           IMF          BOP Balance of Payme... A.DE.BCA_BP6_EUR
## 2:           IMF          BOP Balance of Payme... A.EE.BCA_BP6_EUR
## 3:           IMF          BOP Balance of Payme... A.LU.BCA_BP6_EUR
## 4:           IMF          BOP Balance of Payme... A.LV.BCA_BP6_EUR
## 5:           IMF          BOP Balance of Payme... A.PT.BCA_BP6_EUR
## 6:           IMF          BOP Balance of Payme... A.RS.BCA_BP6_EUR
##            series_name original_period     period original_value      value
## 1: Annual – Germany...            2019 2019-01-01         245532 245532.000
## 2: Annual – Estonia...            2019 2019-01-01        615.256    615.256
## 3: Annual – Luxembo...            2019 2019-01-01     2846.74554   2846.746
## 4: Annual – Latvia ...            2019 2019-01-01           -163   -163.000
## 5: Annual – Portuga...            2019 2019-01-01           -181   -181.000
## 6: Annual – Serbia ...            2019 2019-01-01    -3159.86552  -3159.866
##    @frequency FREQ Frequency          indexed_at           Indicator
## 1:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 2:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 3:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 4:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 5:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 6:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
##      INDICATOR observations_attributes REF_AREA Reference Area
## 1: BCA_BP6_EUR           OFFICIAL_BPM,       DE        Germany
## 2: BCA_BP6_EUR           OFFICIAL_BPM,       EE        Estonia
## 3: BCA_BP6_EUR           OFFICIAL_BPM,       LU     Luxembourg
## 4: BCA_BP6_EUR           OFFICIAL_BPM,       LV         Latvia
## 5: BCA_BP6_EUR           OFFICIAL_BPM,       PT       Portugal
## 6: BCA_BP6_EUR           OFFICIAL_BPM,       RS         Serbia

3.4 Fetch series along multiple dimensions from dataset ‘Balance of Payments’ (BOP) of IMF

df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR")
df <- df[!is.na(value)]
df <- df[order(period), head(.SD, 50), by = INDICATOR]
##    provider_code dataset_code        dataset_name      series_code
## 1:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
## 2:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
## 3:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
## 4:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
## 5:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
## 6:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
##            series_name original_period     period original_value     value
## 1: Annual – France ...            1999 1999-01-01      47672.847 47672.847
## 2: Annual – France ...            2000 2000-01-01      17550.669 17550.669
## 3: Annual – France ...            2001 2001-01-01      23278.392 23278.392
## 4: Annual – France ...            2002 2002-01-01      18718.406 18718.406
## 5: Annual – France ...            2003 2003-01-01      14195.493 14195.493
## 6: Annual – France ...            2004 2004-01-01       7520.754  7520.754
##    @frequency FREQ Frequency          indexed_at           Indicator
## 1:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 2:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 3:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 4:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 5:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
## 6:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
##      INDICATOR observations_attributes REF_AREA Reference Area
## 1: BCA_BP6_EUR          OFFICIAL_BPM,K       FR         France
## 2: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
## 3: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
## 4: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
## 5: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
## 6: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France

4 Fetch time series by dimensions

Searching by dimensions is a less concise way to select time series than using the code mask, but it works with all the different providers. You have a “Description of series code” at the bottom of each dataset page on the DBnomics website.

4.1 Fetch one value of one dimension from dataset ‘Unemployment rate’ (ZUTN) of AMECO provider

df <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea19"))
df <- df[!is.na(value))]
# or
# df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}')
# df <- df[!is.na(value))]
##    provider_code dataset_code        dataset_name         series_code
## 1:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 2:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 3:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 4:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 5:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
## 6:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
##            series_name original_period     period original_value value
## 1: Annually – (Perc...            1998 1998-01-01           10.4  10.4
## 2: Annually – (Perc...            1999 1999-01-01            9.7   9.7
## 3: Annually – (Perc...            2000 2000-01-01            8.9   8.9
## 4: Annually – (Perc...            2001 2001-01-01            8.3   8.3
## 5: Annually – (Perc...            2002 2002-01-01            8.6   8.6
## 6: Annually – (Perc...            2003 2003-01-01            9.1   9.1
##    @frequency                unit                Unit  geo   Country freq
## 1:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
## 2:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
## 3:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
## 4:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
## 5:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
## 6:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
##    Frequency          indexed_at
## 1:  Annually 2019-11-08 16:33:50
## 2:  Annually 2019-11-08 16:33:50
## 3:  Annually 2019-11-08 16:33:50
## 4:  Annually 2019-11-08 16:33:50
## 5:  Annually 2019-11-08 16:33:50
## 6:  Annually 2019-11-08 16:33:50

4.2 Fetch two values of one dimension from dataset ‘Unemployment rate’ (ZUTN) of AMECO provider

df <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea19", "dnk")))
df <- df[!is.na(value))]
# or
# df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}')
# df <- df[!is.na(value))]
##    provider_code dataset_code        dataset_name      series_code
## 1:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 2:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 3:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 4:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 5:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 6:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
##            series_name original_period     period original_value value
## 1: Annually – (Perc...            1960 1960-01-01              2   2.0
## 2: Annually – (Perc...            1961 1961-01-01            1.6   1.6
## 3: Annually – (Perc...            1962 1962-01-01            1.4   1.4
## 4: Annually – (Perc...            1963 1963-01-01              2   2.0
## 5: Annually – (Perc...            1964 1964-01-01            1.2   1.2
## 6: Annually – (Perc...            1965 1965-01-01            0.9   0.9
##    @frequency                unit                Unit geo Country freq
## 1:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 2:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 3:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 4:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 5:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 6:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
##    Frequency          indexed_at
## 1:  Annually 2019-11-08 16:33:50
## 2:  Annually 2019-11-08 16:33:50
## 3:  Annually 2019-11-08 16:33:50
## 4:  Annually 2019-11-08 16:33:50
## 5:  Annually 2019-11-08 16:33:50
## 6:  Annually 2019-11-08 16:33:50

4.3 Fetch several values of several dimensions from dataset ‘Doing business’ (DB) of World Bank

df <- rdb("WB", "DB", dimensions = list(country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS")))
df <- df[!is.na(value))]
# or
# df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}')
# df <- df[!is.na(value))]
##    provider_code dataset_code   dataset_name         series_code
## 1:            WB           DB Doing Business IC.REG.COST.PC.F...
## 2:            WB           DB Doing Business IC.REG.COST.PC.F...
## 3:            WB           DB Doing Business IC.REG.COST.PC.F...
## 4:            WB           DB Doing Business IC.REG.COST.PC.F...
## 5:            WB           DB Doing Business IC.REG.COST.PC.F...
## 6:            WB           DB Doing Business IC.REG.COST.PC.F...
##            series_name original_period     period original_value value
## 1: Cost of business...            2003 2003-01-01           16.3  16.3
## 2: Cost of business...            2004 2004-01-01           14.6  14.6
## 3: Cost of business...            2005 2005-01-01           12.4  12.4
## 4: Cost of business...            2006 2006-01-01           13.4  13.4
## 5: Cost of business...            2007 2007-01-01           13.2  13.2
## 6: Cost of business...            2008 2008-01-01           10.8  10.8
##    @frequency country Country          indexed_at           indicator
## 1:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
## 2:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
## 3:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
## 4:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
## 5:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
## 6:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
##              Indicator observations_attributes
## 1: Cost of business...              ATTRIBUTE,
## 2: Cost of business...              ATTRIBUTE,
## 3: Cost of business...              ATTRIBUTE,
## 4: Cost of business...              ATTRIBUTE,
## 5: Cost of business...              ATTRIBUTE,
## 6: Cost of business...              ATTRIBUTE,

5 Fetch time series with a query

The query is a Google-like search that will filter/select time series from a provider’s dataset.

5.1 Fetch one series from dataset ‘WEO by countries (2019-10 release)’ (WEO:2019-10) of IMF

df <- rdb("IMF", "WEO:2019-10", query = "France current account balance percent")
df <- df[!is.na(value))]
##    provider_code dataset_code        dataset_name         series_code
## 1:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
## 2:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
## 3:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
## 4:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
## 5:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
## 6:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
##            series_name original_period     period original_value  value
## 1: France – Current...            1980 1980-01-01         -0.595 -0.595
## 2: France – Current...            1981 1981-01-01         -0.771 -0.771
## 3: France – Current...            1982 1982-01-01         -2.062 -2.062
## 4: France – Current...            1983 1983-01-01         -0.838 -0.838
## 5: France – Current...            1984 1984-01-01         -0.142 -0.142
## 6: France – Current...            1985 1985-01-01         -0.063 -0.063
##    @frequency      unit           Unit          indexed_at weo-country
## 1:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
## 2:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
## 3:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
## 4:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
## 5:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
## 6:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
##    weo-subject WEO Country         WEO Subject
## 1:   BCA_NGDPD      France Current account ...
## 2:   BCA_NGDPD      France Current account ...
## 3:   BCA_NGDPD      France Current account ...
## 4:   BCA_NGDPD      France Current account ...
## 5:   BCA_NGDPD      France Current account ...
## 6:   BCA_NGDPD      France Current account ...

5.2 Fetch series from dataset ‘WEO by countries (2019-10 release)’ (WEO:2019-10) of IMF

df <- rdb("IMF", "WEO:2019-10", query = "current account balance percent")
df <- df[!is.na(value))]
##    provider_code dataset_code        dataset_name         series_code
## 1:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
## 2:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
## 3:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
## 4:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
## 5:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
## 6:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
##            series_name original_period     period original_value  value
## 1: Germany – Curren...            1980 1980-01-01         -1.782 -1.782
## 2: Germany – Curren...            1981 1981-01-01         -0.684 -0.684
## 3: Germany – Curren...            1982 1982-01-01          0.866  0.866
## 4: Germany – Curren...            1983 1983-01-01          0.666  0.666
## 5: Germany – Curren...            1984 1984-01-01          1.423  1.423
## 6: Germany – Curren...            1985 1985-01-01          2.662  2.662
##    @frequency      unit           Unit          indexed_at weo-country
## 1:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
## 2:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
## 3:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
## 4:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
## 5:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
## 6:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
##    weo-subject WEO Country         WEO Subject
## 1:   BCA_NGDPD     Germany Current account ...
## 2:   BCA_NGDPD     Germany Current account ...
## 3:   BCA_NGDPD     Germany Current account ...
## 4:   BCA_NGDPD     Germany Current account ...
## 5:   BCA_NGDPD     Germany Current account ...
## 6:   BCA_NGDPD     Germany Current account ...

6 Fetch time series found on the web site

When you don’t know the codes of the dimensions, provider, dataset or series, you can:

  • go to the page of a dataset on DBnomics website, for example Doing Business,

  • select some dimensions by using the input widgets of the left column,

  • click on “Copy API link” in the menu of the “Download” button,

  • use the rdb(api_link = ...) function such as below.

df <- rdb(api_link = "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0")
df <- df[!is.na(value))]
##    provider_code dataset_code   dataset_name         series_code
## 1:            WB           DB Doing Business IC.REG.PROC.FE.N...
## 2:            WB           DB Doing Business IC.REG.PROC.FE.N...
## 3:            WB           DB Doing Business IC.REG.PROC.FE.N...
## 4:            WB           DB Doing Business IC.REG.PROC.FE.N...
## 5:            WB           DB Doing Business IC.REG.PROC.FE.N...
## 6:            WB           DB Doing Business IC.REG.PROC.FE.N...
##            series_name original_period     period original_value value
## 1: Starting a busin...            2004 2004-01-01              8     8
## 2: Starting a busin...            2004 2004-01-01             10    10
## 3: Starting a busin...            2004 2004-01-01             10    10
## 4: Starting a busin...            2005 2005-01-01              5     5
## 5: Starting a busin...            2005 2005-01-01             10    10
## 6: Starting a busin...            2005 2005-01-01             10    10
##    @frequency country Country          indexed_at           indicator
## 1:     annual      FR  France 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
## 2:     annual      IT   Italy 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
## 3:     annual      ES   Spain 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
## 4:     annual      FR  France 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
## 5:     annual      IT   Italy 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
## 6:     annual      ES   Spain 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
##              Indicator observations_attributes
## 1: Starting a busin...              ATTRIBUTE,
## 2: Starting a busin...              ATTRIBUTE,
## 3: Starting a busin...              ATTRIBUTE,
## 4: Starting a busin...              ATTRIBUTE,
## 5: Starting a busin...              ATTRIBUTE,
## 6: Starting a busin...              ATTRIBUTE,

In the event that you only use the argument api_link, you can drop the name and run:

df <- rdb("https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0")

7 Fetch time series from the cart

On the cart page of the DBnomics website, click on “Copy API link” and copy-paste it as an argument of the rdb(api_link = ...) function. Please note that when you update your cart, you have to copy this link again, because the link itself contains the ids of the series in the cart. 
df <- rdb(api_link = "https://api.db.nomics.world/v22/series?observations=1&series_ids=BOE/6008/RPMTDDC,BOE/6231/RPMTBVE")
df <- df[!is.na(value))]
##    provider_code dataset_code        dataset_name series_code
## 1:           BOE         6008 Industrial analy...     RPMTDDC
## 2:           BOE         6231 Industrial analy...     RPMTBVE
## 3:           BOE         6008 Industrial analy...     RPMTDDC
## 4:           BOE         6231 Industrial analy...     RPMTBVE
## 5:           BOE         6008 Industrial analy...     RPMTDDC
## 6:           BOE         6231 Industrial analy...     RPMTBVE
##            series_name original_period     period original_value value
## 1: Monthly amounts ...         2009-07 2009-07-01          10150 10150
## 2: Monthly amounts ...         2009-07 2009-07-01          11066 11066
## 3: Monthly amounts ...         2009-08 2009-08-01          10400 10400
## 4: Monthly amounts ...         2009-08 2009-08-01          11260 11260
## 5: Monthly amounts ...         2009-09 2009-09-01          11595 11595
## 6: Monthly amounts ...         2009-09 2009-09-01          11208 11208
##    @frequency freq    Freq          indexed_at Instrument Currency
## 1:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
## 2:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
## 3:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
## 4:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
## 5:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
## 6:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
##    INSTRUMENT_CURRENCY         Instruments INSTRUMENTS           Of Sector
## 1:                   £ Deposits and rep...       LDPRP Education provid...
## 2:                   £     Lending - total      ALENDG Education provid...
## 3:                   £ Deposits and rep...       LDPRP Education provid...
## 4:                   £     Lending - total      ALENDG Education provid...
## 5:                   £ Deposits and rep...       LDPRP Education provid...
## 6:                   £     Lending - total      ALENDG Education provid...
##    OF_SECTOR Output In OUTPUT_IN Seasonal Adjustment SEASONAL_ADJUSTMENT
## 1:        ED  Sterling         £ Not seasonally a...                   U
## 2:        ED  Sterling         £ Not seasonally a...                   U
## 3:        ED  Sterling         £ Not seasonally a...                   U
## 4:        ED  Sterling         £ Not seasonally a...                   U
## 5:        ED  Sterling         £ Not seasonally a...                   U
## 6:        ED  Sterling         £ Not seasonally a...                   U
##                 Sector SECTOR                Type TYPE
## 1: UK resident mone...   BKBS Amounts outstand...    L
## 2: UK resident mone...   BKBS Amounts outstand...    L
## 3: UK resident mone...   BKBS Amounts outstand...    L
## 4: UK resident mone...   BKBS Amounts outstand...    L
## 5: UK resident mone...   BKBS Amounts outstand...    L
## 6: UK resident mone...   BKBS Amounts outstand...    L

8 Fetch the available datasets of a provider

When fetching series from DBnomics, you need to give a provider and a dataset before specifying correct dimensions. With the function rdb_datasets, you can download the list of the available datasets for a provider.
For example, to fetch the IMF datasets, you have to use:

rdb_datasets(provider_code = "IMF")

The result is a named list (its name is IMF) with one element which is a data.table:

## List of 1
##  $ IMF:Classes 'data.table' and 'data.frame':    17 obs. of  2 variables:
##   ..$ code: chr [1:17] "BOP" "BOPAGG" "COMMP" "COMMPP" ...
##   ..$ name: chr [1:17] "Balance of Payments (BOP)" "Balance of Payments (BOP), World and Regional Aggregates" "Primary Commodity Prices" "Primary Commodity Prices Projections" ...
##   ..- attr(*, ".internal.selfref")=<externalptr>

With the same function, if you want to fetch the available datasets for multiple providers, you can give a vector of providers and get a named list.

rdb_datasets(provider_code = c("IMF", "BDF"))
## List of 2
##  $ IMF:Classes 'data.table' and 'data.frame':    17 obs. of  2 variables:
##   ..$ code: chr [1:17] "BOP" "BOPAGG" "COMMP" "COMMPP" ...
##   ..$ name: chr [1:17] "Balance of Payments (BOP)" "Balance of Payments (BOP), World and Regional Aggregates" "Primary Commodity Prices" "Primary Commodity Prices Projections" ...
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##  $ BDF:Classes 'data.table' and 'data.frame':    45 obs. of  2 variables:
##   ..$ code: chr [1:45] "AME" "BLS" "BPM6" "BSI" ...
##   ..$ name: chr [1:45] "Macro Economy - Euro Area" "Bank Lending Survey" "Balance of payments- 6th manual" "Monetary Statistics - Euro Area" ...
##   ..- attr(*, ".internal.selfref")=<externalptr>
## Number of datasets for IMF : 17
## Number of datasets for BDF : 45

In the event that you only request the datasets for one provider, if you define simplify = TRUE, then the result will be a data.table not a named list.

rdb_datasets(provider_code = "IMF", simplify = TRUE)
##      code                name
## 1:    BOP Balance of Payme...
## 2: BOPAGG Balance of Payme...
## 3:  COMMP Primary Commodit...
## 4: COMMPP Primary Commodit...
## 5:    CPI Consumer Price I...
## 6:   CPIS Coordinated Port...

The extent of datasets gathered by DBnomics can be appreciate by using the function with the argument provider_code set to NULL:

options(rdbnomics.progress_bar_datasets = TRUE)
rdb_datasets()
options(rdbnomics.progress_bar_datasets = FALSE)
##    Provider Number of datasets
## 1:     AFDB                  1
## 2:    AMECO                481
## 3:      BCB                 85
## 4:    BCEAO                 28
## 5:      BDF                 45
## 6:      BEA                555

9 Fetch the possible dimensions of available datasets of a provider

When fetching series from DBnomics, it can be interesting and especially useful to specify dimensions for a particular dataset to download only the series you want to analyse. With the function rdb_dimensions, you can download these dimensions and their meanings.
For example, for the dataset WEO:2019-10 of the IMF, you may use:

rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10")

The result is a nested named list (its names are IMF, WEO:2019-10 and the dimensions names) with a data.table at the end of each branch:

## Number of dimensions for IMF/WEO:2019-10 : 3
##                   unit                Unit
## 1:                 idx               Index
## 2: national_currenc... National currenc...
## 3: national_currenc... National currenc...
## 4:               pcent             Percent
## 5:        pcent_change      Percent change
## 6:           pcent_gdp      Percent of GDP
##    weo-country         WEO Country
## 1:         ABW               Aruba
## 2:         AFG         Afghanistan
## 3:         AGO              Angola
## 4:         ALB             Albania
## 5:         ARE United Arab Emir...
## 6:         ARG           Argentina
##    weo-subject         WEO Subject
## 1:         BCA Current account ...
## 2:   BCA_NGDPD Current account ...
## 3:     FLIBOR6 Six-month London...
## 4:         GGR General governme...
## 5:    GGR_NGDP General governme...
## 6:        GGSB General governme...

In the event that you only request the dimensions for one dataset for one provider, if you define simplify = TRUE, then the result will be a named list data.table not a nested named list.

rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE)
## List of 3
##  $ unit       :Classes 'data.table' and 'data.frame':    12 obs. of  2 variables:
##   ..$ unit: chr [1:12] "idx" "national_currency" "national_currency_per_current_international_dollar" "pcent" ...
##   ..$ Unit: chr [1:12] "Index" "National currency" "National currency per current international dollar" "Percent" ...
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##  $ weo-country:Classes 'data.table' and 'data.frame':    194 obs. of  2 variables:
##   ..$ weo-country: chr [1:194] "ABW" "AFG" "AGO" "ALB" ...
##   ..$ WEO Country: chr [1:194] "Aruba" "Afghanistan" "Angola" "Albania" ...
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##  $ weo-subject:Classes 'data.table' and 'data.frame':    45 obs. of  2 variables:
##   ..$ weo-subject: chr [1:45] "BCA" "BCA_NGDPD" "FLIBOR6" "GGR" ...
##   ..$ WEO Subject: chr [1:45] "Current account balance" "Current account balance" "Six-month London interbank offered rate (LIBOR)" "General government revenue" ...
##   ..- attr(*, ".internal.selfref")=<externalptr>

You can measure the vast extent of datasets gathered by DBnomics by downloading all the possible dimensions. To do this, you have to set the arguments provider_code and dataset_code to NULL.
⚠ It’s relatively long to run and heavy to show so we display the first 100.

options(rdbnomics.progress_bar_datasets = TRUE)
rdb_dimensions()
options(rdbnomics.progress_bar_datasets = FALSE)
##    Provider Dataset Dimension Number of codes
## 1:     AFDB bbkawjf   country             109
## 2:     AFDB bbkawjf frequency               1
## 3:     AFDB bbkawjf indicator            1279
## 4:     AFDB bbkawjf     scale               1
## 5:     AFDB bbkawjf     units             169
## 6:    AMECO    AAGE      freq               1

10 Fetch the series codes and names of available datasets of a provider

You can download the list of series, and especially their codes, of a dataset’s provider by using the function rdb_series. The result is a nested named list with a data.table at the end of each branch. If you define simplify = TRUE, then the result will be a data.table not a nested named list.
For example, for the IMF provider and the dataset WEO:2019-10, the command is (only first 100):

rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE)
##            series_code         series_name
## 1: ABW.BCA.us_dolla... Aruba – Current ...
## 2: ABW.BCA_NGDPD.pc... Aruba – Current ...
## 3: ABW.FLIBOR6.pcen... Aruba – Six-mont...
## 4: ABW.GGR.national... Aruba – General ...
## 5: ABW.GGR_NGDP.pce... Aruba – General ...
## 6: ABW.GGSB.nationa... Aruba – General ...

Like the function rdb(), you can add features to rdb_series(). You can ask for the series with specific dimensions:

rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", dimensions = list(`weo-subject` = "NGDP_RPCH"), simplify = TRUE)

or with a query:

rdb_series(provider_code = "IMF", dataset_code = c("WEO:2019-10", "WEOAGG:2019-10"), query = "NGDP_RPCH")

⚠ We ask the user to use this function parsimoniously because there are a huge amount of series per dataset. Please only fetch for one dataset if you need it or visit the website https://db.nomics.world.
For example, for the IMF provider, the number of series is (only first 5):

##    Dataset Number of series
## 1:    CPIS          2903710
## 2:    CDIS           772274
## 3:  GFSIBS           681984
## 4:     BOP           535322
## 5:     DOT           459980

11 Proxy configuration or connection error Could not resolve host

When using the function rdb, you may come across the following error:

Error in open.connection(con, "rb") :
  Could not resolve host: api.db.nomics.world

To get round this situation, you have two options:

  1. configure curl to use a specific and authorized proxy.

  2. use the default R internet connection i.e. the Internet Explorer proxy defined in internet2.dll.

11.1 Configure curl to use a specific and authorized proxy

In rdbnomics, by default the function curl_fetch_memory (of the package curl) is used to fetch the data. If a specific proxy must be used, it is possible to define it permanently with the package option rdbnomics.curl_config or on the fly through the argument curl_config. Because the object is a named list, its elements are passed to the connection (the curl_handle object created internally with new_handle()) with handle_setopt() before using curl_fetch_memory.

To see the available parameters, run names(curl_options()) in R or visit the website https://curl.haxx.se/libcurl/c/curl_easy_setopt.html. Once they are chosen, you define the curl object as follows:

h <- list(
  proxy = "<proxy>",
  proxyport = <port>,
  proxyusername = "<username>",
  proxypassword = "<password>"
)

11.1.1 Set the connection up for a session

The curl connection can be set up for a session by modifying the following package option:

options(rdbnomics.curl_config = h)

When fetching the data, the following command is executed:

hndl <- curl::new_handle()
curl::handle_setopt(hndl, .list = getOption("rdbnomics.curl_config"))
curl::curl_fetch_memory(url = <...>, handle = hndl)

After configuration, just use the standard functions of rdbnomics e.g.:

df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")

This option of the package can be disabled with:

options(rdbnomics.curl = NULL)

11.1.2 Use the connection only for a function call

If a complete configuration is not needed but just an “on the fly” execution, then use the argument curl_config of the function rdb:

df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h)

11.2 Use the default R internet connection

To retrieve the data with the default R internet connection, rdbnomics will use the base function readLines.

11.2.1 Set the connection up for a session

To activate this feature for a session, you need to enable an option of the package:

options(rdbnomics.use_readLines = TRUE)

And then use the standard function as follows:

df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")

This configuration can be disabled with:

options(rdbnomics.use_readLines = FALSE)

11.2.2 Use the connection only for a function call

If you just want to do it once, you may use the argument use_readLines of the function rdb:

df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE)

12 Transform time series with filters

The rdbnomics package can interact with the Time Series Editor of DBnomics to transform time series by applying filters to them.
Available filters are listed on the filters page https://editor.nomics.world/filters.

Here is an example of how to proceed to interpolate two annual time series with a monthly frequency, using a spline interpolation:

filters <- list(
  code = "interpolate",
  parameters = list(frequency = "monthly", method = "spline")
)

The request is then:

df <- rdb(
  ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"),
  filters = filters
)

If you want to apply more than one filter, the filters argument will be a list of valid filters:

filters <- list(
  list(
    code = "interpolate",
    parameters = list(frequency = "monthly", method = "spline")
  ),
  list(
    code = "aggregate",
    parameters = list(frequency = "bi-annual", method = "end_of_period")
  )
)

df <- rdb(
  ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"),
  filters = filters
)

The data.table columns change a little bit when filters are used. There are two new columns:

  • period_middle_day: the middle day of original_period (can be useful when you compare graphically interpolated series and original ones).
  • filtered (boolean): TRUE if the series is filtered, FALSE otherwise.

The content of two columns are modified:

  • series_code: same as before for original series, but the suffix _filtered is added for filtered series.
  • series_name: same as before for original series, but the suffix (filtered) is added for filtered series.
##    provider_code dataset_code        dataset_name      series_code
## 1:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 2:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 3:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 4:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 5:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
## 6:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
##            series_name original_period     period original_value value
## 1: Annually – (Perc...            1960 1960-01-01              2   2.0
## 2: Annually – (Perc...            1961 1961-01-01            1.6   1.6
## 3: Annually – (Perc...            1962 1962-01-01            1.4   1.4
## 4: Annually – (Perc...            1963 1963-01-01              2   2.0
## 5: Annually – (Perc...            1964 1964-01-01            1.2   1.2
## 6: Annually – (Perc...            1965 1965-01-01            0.9   0.9
##    @frequency                unit                Unit geo Country freq
## 1:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 2:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 3:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 4:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 5:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
## 6:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
##    Frequency filtered          indexed_at period_middle_day
## 1:  Annually    FALSE 2019-11-08 16:33:50              <NA>
## 2:  Annually    FALSE 2019-11-08 16:33:50              <NA>
## 3:  Annually    FALSE 2019-11-08 16:33:50              <NA>
## 4:  Annually    FALSE 2019-11-08 16:33:50              <NA>
## 5:  Annually    FALSE 2019-11-08 16:33:50              <NA>
## 6:  Annually    FALSE 2019-11-08 16:33:50              <NA>

  1. Banque de France, https://github.com/s915↩︎

  2. CEPREMAP↩︎

rdbnomics/inst/doc/rdbnomics.R0000644000176200001440000010027213745341255016072 0ustar liggesusers## ---- echo = FALSE------------------------------------------------------------ library <- function(...) { suppressWarnings( suppressPackageStartupMessages(base::library(..., quietly = TRUE)) ) } ## ----------------------------------------------------------------------------- library(data.table) library(rdbnomics) ## ---- echo = FALSE------------------------------------------------------------ reorder_cols <- function(x) { data.table::setDT(x) cols <- c( "provider_code", "dataset_code", "dataset_name", "series_code", "series_name", "original_period", "period", "original_value", "value", "@frequency" ) if ("unit" %in% colnames(x)) { cols <- c(cols, "unit", "Unit") } if ("geo" %in% colnames(x)) { cols <- c(cols, "geo", "Country") } if ("freq" %in% colnames(x)) { cols <- c(cols, "freq", "Frequency") } cols_add <- setdiff(colnames(x), cols) cols <- c(cols, cols_add) cols <- cols[cols %in% colnames(x)] cols <- match(cols, colnames(x)) x[, .SD, .SDcols = cols] } knitr::opts_chunk$set(dev.args = list(bg = "transparent")) display_table <- function(DT) { DT <- head(DT) DT <- as.data.table( lapply(DT, function(x) { if (is.character(x)) { ifelse( nchar(x) > 16, paste0(substr(x, 1, 16), "..."), x ) } else { x } }) ) DT[] } ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") # df <- df[!is.na(value)] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df001 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN")) # df <- df[!is.na(value)] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df002 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.7, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "Eurostat/une_rt_q/Q.SA.Y15-24.PC_ACT.T.EA19")) # df <- df[!is.na(value)] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df003 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics(legend.text = element_text(size = 7)) i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) legend_text[2] <- sapply( legend_text[2], function(y) { paste0( paste0( strsplit(y, "active ")[[1]], collapse = "active\n" ), "\n" ) } ) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR") # df <- df[!is.na(value)] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df004 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value), max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR") ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") # df <- df[!is.na(value)] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df005 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 2*10^4, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "s") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR") # df <- df[!is.na(value)] # df <- df[order(-period, REF_AREA)] # df <- head(df, 100) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df006 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- reorder_cols(df) display_table(df) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR") # df <- df[!is.na(value)] # df <- df[order(period), head(.SD, 50), by = INDICATOR] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df007 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- reorder_cols(df) display_table(df) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea19")) # df <- df[!is.na(value))] # # or # # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}') # # df <- df[!is.na(value))] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df008 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea19", "dnk"))) # df <- df[!is.na(value))] # # or # # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}') # # df <- df[!is.na(value))] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df009 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("WB", "DB", dimensions = list(country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"))) # df <- df[!is.na(value))] # # or # # df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}') # # df <- df[!is.na(value))] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df010 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- df[order(series_name, period)] df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 7, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "WEO:2019-10", query = "France current account balance percent") # df <- df[!is.na(value))] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df014 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "WEO:2019-10", query = "current account balance percent") # df <- df[!is.na(value))] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df015 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = `WEO Country`)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # ggtitle("Current account balance (% GDP)") + # dbnomics(legend.direction = "horizontal") i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value), max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb(api_link = "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0") # df <- df[!is.na(value))] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df011 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- df[order(period, series_name)] df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "s") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "s") points(x3, y3, col = cols[3], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0") ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb(api_link = "https://api.db.nomics.world/v22/series?observations=1&series_ids=BOE/6008/RPMTDDC,BOE/6231/RPMTBVE") # df <- df[!is.na(value))] ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df012 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df[ , series_name := sapply( series_name, function(y) { paste0( paste0( strsplit(y, "institutions' ")[[1]], collapse = "institutions'\n" ), "\n" ) } ) ] ## ---- echo = FALSE------------------------------------------------------------ df <- df[order(period, series_name)] df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 4*10^3, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ## ---- eval = FALSE------------------------------------------------------------ # rdb_datasets(provider_code = "IMF") ## ---- eval = TRUE, echo = FALSE----------------------------------------------- str(rdbnomics:::rdbnomics_df016) ## ---- eval = FALSE------------------------------------------------------------ # rdb_datasets(provider_code = c("IMF", "BDF")) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- str(rdbnomics:::rdbnomics_df018) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- DT <- rdbnomics:::rdbnomics_df018 DT <- sapply(DT, function(y) { paste0(": ", nrow(y)) }) DT <- paste0("Number of datasets for ", names(DT), " ", unname(DT)) cat(DT, sep = "\n") ## ---- eval = FALSE------------------------------------------------------------ # rdb_datasets(provider_code = "IMF", simplify = TRUE) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- DT <- rdbnomics:::rdbnomics_df017 data.table::setDT(DT) display_table(DT) ## ---- eval = FALSE------------------------------------------------------------ # options(rdbnomics.progress_bar_datasets = TRUE) # rdb_datasets() # options(rdbnomics.progress_bar_datasets = FALSE) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- DT <- rdbnomics:::rdbnomics_df019 DT <- data.table(Provider = names(DT), `Number of datasets` = sapply(DT, nrow)) DT <- DT[order(Provider)] display_table(DT) ## ---- eval = FALSE------------------------------------------------------------ # rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10") ## ---- eval = TRUE, echo = FALSE----------------------------------------------- DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10` DT <- paste0("Number of dimensions for IMF/WEO:2019-10 : ", length(DT)) cat(DT, sep = "\n") ## ---- eval = TRUE, echo = FALSE----------------------------------------------- DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[1]] display_table(DT) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[2]] display_table(DT) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[3]] display_table(DT) ## ---- eval = FALSE------------------------------------------------------------ # rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- str(rdbnomics:::rdbnomics_df021) ## ---- eval = FALSE------------------------------------------------------------ # options(rdbnomics.progress_bar_datasets = TRUE) # rdb_dimensions() # options(rdbnomics.progress_bar_datasets = FALSE) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- DT <- rdbnomics:::rdbnomics_df022 DT <- DT[order(Provider, Dataset)] DT <- head(DT, 100) display_table(DT) # rdbnomics_df022 %>% # sapply(function(u) { # sapply( # u, # function(x) { # sapply( # x, # function(y) { # nrow(y) # }, # simplify = FALSE # ) %>% # { # data.table(Dimension = names(.), `Number of codes` = unname(.)) # } # }, # simplify = FALSE # ) %>% # rbindlist(idcol = "Dataset") # }, # simplify = FALSE # ) %>% # rbindlist(idcol = "Provider") ## ---- eval = FALSE------------------------------------------------------------ # rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- DT <- rdbnomics:::rdbnomics_df023 DT <- head(DT, 100) display_table(DT) ## ---- eval = FALSE------------------------------------------------------------ # rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", dimensions = list(`weo-subject` = "NGDP_RPCH"), simplify = TRUE) ## ---- eval = FALSE------------------------------------------------------------ # rdb_series(provider_code = "IMF", dataset_code = c("WEO:2019-10", "WEOAGG:2019-10"), query = "NGDP_RPCH") ## ---- eval = TRUE, echo = FALSE----------------------------------------------- DT <- rdbnomics:::rdbnomics_df024 DT <- DT[order(-`Number of series`)] DT <- head(DT, 5) display_table(DT) ## ---- eval = FALSE------------------------------------------------------------ # Error in open.connection(con, "rb") : # Could not resolve host: api.db.nomics.world ## ---- eval = FALSE------------------------------------------------------------ # h <- list( # proxy = "", # proxyport = , # proxyusername = "", # proxypassword = "" # ) ## ---- eval = FALSE------------------------------------------------------------ # options(rdbnomics.curl_config = h) ## ---- eval = FALSE------------------------------------------------------------ # hndl <- curl::new_handle() # curl::handle_setopt(hndl, .list = getOption("rdbnomics.curl_config")) # curl::curl_fetch_memory(url = <...>, handle = hndl) ## ---- eval = FALSE------------------------------------------------------------ # df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ## ---- eval = FALSE------------------------------------------------------------ # options(rdbnomics.curl = NULL) ## ---- eval = FALSE------------------------------------------------------------ # df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h) ## ---- eval = FALSE------------------------------------------------------------ # options(rdbnomics.use_readLines = TRUE) ## ---- eval = FALSE------------------------------------------------------------ # df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ## ---- eval = FALSE------------------------------------------------------------ # options(rdbnomics.use_readLines = FALSE) ## ---- eval = FALSE------------------------------------------------------------ # df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE) ## ---- eval = FALSE------------------------------------------------------------ # filters <- list( # code = "interpolate", # parameters = list(frequency = "monthly", method = "spline") # ) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb( # ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"), # filters = filters # ) ## ---- eval = FALSE------------------------------------------------------------ # filters <- list( # list( # code = "interpolate", # parameters = list(frequency = "monthly", method = "spline") # ), # list( # code = "aggregate", # parameters = list(frequency = "bi-annual", method = "end_of_period") # ) # ) # # df <- rdb( # ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"), # filters = filters # ) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df013 data.table::setDT(df) ## ---- echo = FALSE------------------------------------------------------------ df <- df[order(filtered, series_name, period)] df <- reorder_cols(df) display_table(df) ## ---- echo = FALSE, fig.align = 'center'-------------------------------------- # ggplot(df[!is.na(value)], aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() df <- df[!is.na(value)] i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 4, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) rdbnomics/inst/doc/rdbnomics.Rmd0000644000176200001440000011442513744125415016415 0ustar liggesusers--- title: "DBnomics R client" author: "Sébastien Galais^[Banque de France, [https://github.com/s915](https://github.com/s915)], Thomas Brand^[CEPREMAP]" output: html_document: highlight: default theme: simplex smart: false toc: true toc_float: true number_sections: true rmarkdown::html_vignette: highlight: default theme: simplex smart: false toc: true toc_float: true number_sections: true vignette: > %\VignetteIndexEntry{DBnomics R client} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # DBnomics: the world's economic database Explore all the economic data from different providers (national and international statistical institutes, central banks, etc.), for free, following the link [db.nomics.world](https://db.nomics.world) (*N.B.: in the examples, data have already been retrieved on april 6th 2020*). [![](dbnomics001.png)](https://db.nomics.world) # Fetch time series by `ids` First, let's assume that we know which series we want to download. A series identifier (`ids`) is defined by three values, formatted like this: `provider_code`/`dataset_code`/`series_code`. ## Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider ```{r, echo = FALSE} library <- function(...) { suppressWarnings( suppressPackageStartupMessages(base::library(..., quietly = TRUE)) ) } ``` ```{r} library(data.table) library(rdbnomics) ``` ```{r, echo = FALSE} reorder_cols <- function(x) { data.table::setDT(x) cols <- c( "provider_code", "dataset_code", "dataset_name", "series_code", "series_name", "original_period", "period", "original_value", "value", "@frequency" ) if ("unit" %in% colnames(x)) { cols <- c(cols, "unit", "Unit") } if ("geo" %in% colnames(x)) { cols <- c(cols, "geo", "Country") } if ("freq" %in% colnames(x)) { cols <- c(cols, "freq", "Frequency") } cols_add <- setdiff(colnames(x), cols) cols <- c(cols, cols_add) cols <- cols[cols %in% colnames(x)] cols <- match(cols, colnames(x)) x[, .SD, .SDcols = cols] } knitr::opts_chunk$set(dev.args = list(bg = "transparent")) display_table <- function(DT) { DT <- head(DT) DT <- as.data.table( lapply(DT, function(x) { if (is.character(x)) { ifelse( nchar(x) > 16, paste0(substr(x, 1, 16), "..."), x ) } else { x } }) ) DT[] } ``` ```{r, eval = FALSE} df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") df <- df[!is.na(value)] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df001 data.table::setDT(df) ``` In such `data.table`, you will always find at least ten columns: - `provider_code` - `dataset_code` - `dataset_name` - `series_code` - `series_name` - `original_period` (character string) - `period` (date of the first day of `original_period`) - `original_value` (character string) - `value` - `@frequency` (harmonized frequency generated by DBnomics) The other columns depend on the provider and on the dataset. They always come in pairs (for the code and the name). In the data.frame `df`, you have: - `unit` (code) and `Unit` (name) - `geo` (code) and `Country` (name) - `freq` (code) and `Frequency` (name) ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` In the event that you only use the argument `ids`, you can drop it and run: ```{r, eval = FALSE} df <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ``` ## Fetch two series from dataset 'Unemployment rate' (ZUTN) of AMECO provider ```{r, eval = FALSE} df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN")) df <- df[!is.na(value)] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df002 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.7, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` ## Fetch two series from different datasets of different providers ```{r, eval = FALSE} df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "Eurostat/une_rt_q/Q.SA.Y15-24.PC_ACT.T.EA19")) df <- df[!is.na(value)] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df003 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics(legend.text = element_text(size = 7)) i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) legend_text[2] <- sapply( legend_text[2], function(y) { paste0( paste0( strsplit(y, "active ")[[1]], collapse = "active\n" ), "\n" ) } ) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` # Fetch time series by `mask` The code mask notation is a very concise way to select one or many time series at once. ## Fetch one series from dataset 'Balance of Payments' (BOP) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR") df <- df[!is.na(value)] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df004 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value), max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` In the event that you only use the arguments `provider_code`, `dataset_code` and `mask`, you can drop the name `mask` and run: ```{r, eval = FALSE} df <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR") ``` ## Fetch two series from dataset 'Balance of Payments' (BOP) of IMF You just have to add a `+` between two different values of a dimension. ```{r, eval = FALSE} df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") df <- df[!is.na(value)] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df005 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 2*10^4, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "s") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` ## Fetch all series along one dimension from dataset 'Balance of Payments' (BOP) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR") df <- df[!is.na(value)] df <- df[order(-period, REF_AREA)] df <- head(df, 100) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df006 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ## Fetch series along multiple dimensions from dataset 'Balance of Payments' (BOP) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR") df <- df[!is.na(value)] df <- df[order(period), head(.SD, 50), by = INDICATOR] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df007 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` # Fetch time series by `dimensions` Searching by `dimensions` is a less concise way to select time series than using the code `mask`, but it works with all the different providers. You have a "*Description of series code*" at the bottom of each dataset page on the [DBnomics website](https://db.nomics.world). ## Fetch one value of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider ```{r, eval = FALSE} df <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea19")) df <- df[!is.na(value))] # or # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}') # df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df008 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` ## Fetch two values of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider ```{r, eval = FALSE} df <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea19", "dnk"))) df <- df[!is.na(value))] # or # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}') # df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df009 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` ## Fetch several values of several dimensions from dataset 'Doing business' (DB) of World Bank ```{r, eval = FALSE} df <- rdb("WB", "DB", dimensions = list(country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"))) df <- df[!is.na(value))] # or # df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}') # df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df010 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(series_name, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 7, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` # Fetch time series with a `query` The query is a Google-like search that will filter/select time series from a provider's dataset. ## Fetch one series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "WEO:2019-10", query = "France current account balance percent") df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df014 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` ## Fetch series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "WEO:2019-10", query = "current account balance percent") df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df015 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = `WEO Country`)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # ggtitle("Current account balance (% GDP)") + # dbnomics(legend.direction = "horizontal") i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value), max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` # Fetch time series found on the web site When you don't know the codes of the dimensions, provider, dataset or series, you can: - go to the page of a dataset on [DBnomics website](https://db.nomics.world), for example [Doing Business](https://db.nomics.world/WB/DB), - select some dimensions by using the input widgets of the left column, ![](dbnomics002.png) - click on "*Copy API link*" in the menu of the "*Download*" button, ![](dbnomics003.png) - use the `rdb(api_link = ...)` function such as below. ```{r, eval = FALSE} df <- rdb(api_link = "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0") df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df011 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(period, series_name)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "s") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "s") points(x3, y3, col = cols[3], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` In the event that you only use the argument `api_link`, you can drop the name and run: ```{r, eval = FALSE} df <- rdb("https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0") ``` # Fetch time series from the cart On the cart page of the [DBnomics website](https://db.nomics.world), click on "*Copy API link*" and copy-paste it as an argument of the `rdb(api_link = ...)` function. Please note that when you update your cart, you have to copy this link again, because the link itself contains the ids of the series in the cart.
![](dbnomics005.png)
```{r, eval = FALSE} df <- rdb(api_link = "https://api.db.nomics.world/v22/series?observations=1&series_ids=BOE/6008/RPMTDDC,BOE/6231/RPMTBVE") df <- df[!is.na(value))] ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df012 data.table::setDT(df) ``` ```{r, echo = FALSE} df[ , series_name := sapply( series_name, function(y) { paste0( paste0( strsplit(y, "institutions' ")[[1]], collapse = "institutions'\n" ), "\n" ) } ) ] ``` ```{r, echo = FALSE} df <- df[order(period, series_name)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 4*10^3, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` # Fetch the available datasets of a provider When fetching series from [DBnomics](https://db.nomics.world), you need to give a provider and a dataset before specifying correct dimensions. With the function `rdb_datasets`, you can download the list of the available datasets for a provider. For example, to fetch the **IMF** datasets, you have to use: ```{r, eval = FALSE} rdb_datasets(provider_code = "IMF") ``` The result is a named list (its name is **IMF**) with one element which is a `data.table`: ```{r, eval = TRUE, echo = FALSE} str(rdbnomics:::rdbnomics_df016) ``` With the same function, if you want to fetch the available datasets for multiple providers, you can give a vector of providers and get a named list. ```{r, eval = FALSE} rdb_datasets(provider_code = c("IMF", "BDF")) ``` ```{r, eval = TRUE, echo = FALSE} str(rdbnomics:::rdbnomics_df018) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df018 DT <- sapply(DT, function(y) { paste0(": ", nrow(y)) }) DT <- paste0("Number of datasets for ", names(DT), " ", unname(DT)) cat(DT, sep = "\n") ``` In the event that you only request the datasets for one provider, if you define `simplify = TRUE`, then the result will be a `data.table` not a named list. ```{r, eval = FALSE} rdb_datasets(provider_code = "IMF", simplify = TRUE) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df017 data.table::setDT(DT) display_table(DT) ``` The extent of datasets gathered by [DBnomics](https://db.nomics.world) can be appreciate by using the function with the argument `provider_code` set to `NULL`: ```{r, eval = FALSE} options(rdbnomics.progress_bar_datasets = TRUE) rdb_datasets() options(rdbnomics.progress_bar_datasets = FALSE) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df019 DT <- data.table(Provider = names(DT), `Number of datasets` = sapply(DT, nrow)) DT <- DT[order(Provider)] display_table(DT) ``` # Fetch the possible dimensions of available datasets of a provider When fetching series from [DBnomics](https://db.nomics.world), it can be interesting and especially useful to specify dimensions for a particular dataset to download only the series you want to analyse. With the function `rdb_dimensions`, you can download these dimensions and their meanings. For example, for the dataset **WEO:2019-10** of the **IMF**, you may use: ```{r, eval = FALSE} rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10") ``` The result is a nested named list (its names are **IMF**, **WEO:2019-10** and the dimensions names) with a `data.table` at the end of each branch: ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10` DT <- paste0("Number of dimensions for IMF/WEO:2019-10 : ", length(DT)) cat(DT, sep = "\n") ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[1]] display_table(DT) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[2]] display_table(DT) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[3]] display_table(DT) ``` In the event that you only request the dimensions for one dataset for one provider, if you define `simplify = TRUE`, then the result will be a named list `data.table` not a nested named list. ```{r, eval = FALSE} rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE) ``` ```{r, eval = TRUE, echo = FALSE} str(rdbnomics:::rdbnomics_df021) ``` You can measure the vast extent of datasets gathered by [DBnomics](https://db.nomics.world) by downloading all the possible dimensions. To do this, you have to set the arguments `provider_code` and `dataset_code` to `NULL`. ⚠ It's relatively long to run and heavy to show so we display the first 100. ```{r, eval = FALSE} options(rdbnomics.progress_bar_datasets = TRUE) rdb_dimensions() options(rdbnomics.progress_bar_datasets = FALSE) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df022 DT <- DT[order(Provider, Dataset)] DT <- head(DT, 100) display_table(DT) # rdbnomics_df022 %>% # sapply(function(u) { # sapply( # u, # function(x) { # sapply( # x, # function(y) { # nrow(y) # }, # simplify = FALSE # ) %>% # { # data.table(Dimension = names(.), `Number of codes` = unname(.)) # } # }, # simplify = FALSE # ) %>% # rbindlist(idcol = "Dataset") # }, # simplify = FALSE # ) %>% # rbindlist(idcol = "Provider") ``` # Fetch the series codes and names of available datasets of a provider You can download the list of series, and especially their codes, of a dataset's provider by using the function `rdb_series`. The result is a nested named list with a `data.table` at the end of each branch. If you define `simplify = TRUE`, then the result will be a `data.table` not a nested named list. For example, for the **IMF** provider and the dataset **WEO:2019-10**, the command is (only first 100): ```{r, eval = FALSE} rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE) ``` ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df023 DT <- head(DT, 100) display_table(DT) ``` Like the function `rdb()`, you can add features to `rdb_series()`. You can ask for the series with specific `dimensions`: ```{r, eval = FALSE} rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", dimensions = list(`weo-subject` = "NGDP_RPCH"), simplify = TRUE) ``` or with a `query`: ```{r, eval = FALSE} rdb_series(provider_code = "IMF", dataset_code = c("WEO:2019-10", "WEOAGG:2019-10"), query = "NGDP_RPCH") ``` ⚠ We ask the user to use this function parsimoniously because there are a huge amount of series per dataset. Please only fetch for one dataset if you need it or visit the website [https://db.nomics.world](https://db.nomics.world). For example, for the **IMF** provider, the number of series is (only first 5): ```{r, eval = TRUE, echo = FALSE} DT <- rdbnomics:::rdbnomics_df024 DT <- DT[order(-`Number of series`)] DT <- head(DT, 5) display_table(DT) ``` # Proxy configuration or connection error `Could not resolve host` When using the function `rdb`, you may come across the following error: ```{r, eval = FALSE} Error in open.connection(con, "rb") : Could not resolve host: api.db.nomics.world ``` To get round this situation, you have two options: 1. configure **curl** to use a specific and authorized proxy. 2. use the default R internet connection i.e. the Internet Explorer proxy defined in *internet2.dll*. ## Configure **curl** to use a specific and authorized proxy In **rdbnomics**, by default the function `curl_fetch_memory` (of the package **curl**) is used to fetch the data. If a specific proxy must be used, it is possible to define it permanently with the package option `rdbnomics.curl_config` or on the fly through the argument `curl_config`. Because the object is a named list, its elements are passed to the connection (the `curl_handle` object created internally with `new_handle()`) with `handle_setopt()` before using `curl_fetch_memory`. To see the available parameters, run `names(curl_options())` in *R* or visit the website https://curl.haxx.se/libcurl/c/curl_easy_setopt.html. Once they are chosen, you define the curl object as follows: ```{r, eval = FALSE} h <- list( proxy = "", proxyport = , proxyusername = "", proxypassword = "" ) ``` ### Set the connection up for a session The curl connection can be set up for a session by modifying the following package option: ```{r, eval = FALSE} options(rdbnomics.curl_config = h) ``` When fetching the data, the following command is executed: ```{r, eval = FALSE} hndl <- curl::new_handle() curl::handle_setopt(hndl, .list = getOption("rdbnomics.curl_config")) curl::curl_fetch_memory(url = <...>, handle = hndl) ``` After configuration, just use the standard functions of **rdbnomics** e.g.: ```{r, eval = FALSE} df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ``` This option of the package can be disabled with: ```{r, eval = FALSE} options(rdbnomics.curl = NULL) ``` ### Use the connection only for a function call If a complete configuration is not needed but just an "on the fly" execution, then use the argument `curl_config` of the function `rdb`: ```{r, eval = FALSE} df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h) ``` ## Use the default R internet connection To retrieve the data with the default R internet connection, **rdbnomics** will use the base function `readLines`. ### Set the connection up for a session To activate this feature for a session, you need to enable an option of the package: ```{r, eval = FALSE} options(rdbnomics.use_readLines = TRUE) ``` And then use the standard function as follows: ```{r, eval = FALSE} df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ``` This configuration can be disabled with: ```{r, eval = FALSE} options(rdbnomics.use_readLines = FALSE) ``` ### Use the connection only for a function call If you just want to do it once, you may use the argument `use_readLines` of the function `rdb`: ```{r, eval = FALSE} df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE) ``` # Transform time series with filters The **rdbnomics** package can interact with the *Time Series Editor* of DBnomics to transform time series by applying filters to them. Available filters are listed on the filters page [https://editor.nomics.world/filters](https://editor.nomics.world/filters). Here is an example of how to proceed to interpolate two annual time series with a monthly frequency, using a spline interpolation: ```{r, eval = FALSE} filters <- list( code = "interpolate", parameters = list(frequency = "monthly", method = "spline") ) ``` The request is then: ```{r, eval = FALSE} df <- rdb( ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"), filters = filters ) ``` If you want to apply more than one filter, the `filters` argument will be a list of valid filters: ```{r, eval = FALSE} filters <- list( list( code = "interpolate", parameters = list(frequency = "monthly", method = "spline") ), list( code = "aggregate", parameters = list(frequency = "bi-annual", method = "end_of_period") ) ) df <- rdb( ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"), filters = filters ) ``` The `data.table` columns change a little bit when filters are used. There are two new columns: - `period_middle_day`: the middle day of `original_period` (can be useful when you compare graphically interpolated series and original ones). - `filtered` (boolean): `TRUE` if the series is filtered, `FALSE` otherwise. The content of two columns are modified: - `series_code`: same as before for original series, but the suffix `_filtered` is added for filtered series. - `series_name`: same as before for original series, but the suffix ` (filtered)` is added for filtered series. ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df013 data.table::setDT(df) ``` ```{r, echo = FALSE} df <- df[order(filtered, series_name, period)] df <- reorder_cols(df) display_table(df) ``` ```{r, echo = FALSE, fig.align = 'center'} # ggplot(df[!is.na(value)], aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() df <- df[!is.na(value)] i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 4, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics ", side = 3, col = "grey", font = 3, adj = 1 ) ``` rdbnomics/inst/tinytest/0000755000176200001440000000000013744116260015076 5ustar liggesusersrdbnomics/inst/tinytest/test_rdb_providers.R0000644000176200001440000000030113653274703021124 0ustar liggesuserscat("\n", "Context: Right arguments for rdb_providers", "\n") # Code is logical and of length one expect_error(rdb_providers(code = logical())) expect_error(rdb_providers(code = "TRUE")) rdbnomics/inst/tinytest/test_rdb.R0000644000176200001440000000503613653274660017043 0ustar liggesuserscat("\n", "Context: Right combination of arguments for rdb", "\n") # Fetch by dimensions expect_error(rdb(dimensions = "x", provider_code = NULL, dataset_code = "y")) expect_error(rdb(dimensions = "x", provider_code = "y", dataset_code = NULL)) expect_error(rdb(dimensions = "x", provider_code = NULL, dataset_code = NULL)) expect_error(rdb(dimensions = c("x1", "x2"), provider_code = "y", dataset_code = "z")) expect_error(rdb(dimensions = list(), provider_code = "y", dataset_code = "z")) expect_error(rdb(dimensions = list("x1", "x2"), provider_code = "y", dataset_code = "z")) expect_error(rdb(dimensions = list(elt1 = "x1", "x2"), provider_code = "y", dataset_code = "z")) # Fetch by mask expect_error(rdb(mask = "IMF", provider_code = NULL, dataset_code = "y")) expect_error(rdb(mask = "IMF", provider_code = "y", dataset_code = NULL)) expect_error(rdb(mask = "IMF", provider_code = NULL, dataset_code = NULL)) expect_error(rdb(mask = c("x1", "x2"), provider_code = "y", dataset_code = "z")) # Fetch by mask with wrong provider_code expect_error(rdb(mask = "x", provider_code = "y", dataset_code = "z")) # Fetch by ids expect_error(rdb(ids = character())) # Nothing provided expect_error(rdb()) # api_link length is greater than one or equal to zero expect_error(rdb(api_link = c("url1", "url2"))) expect_error(rdb(api_link = character())) # api_link is not a character string expect_error(rdb(api_link = 0)) # use_readLines is NULL expect_error(rdb(api_link = "url", use_readLines = NULL)) # use_readLines length is greater than one or equal to zero expect_error(rdb(api_link = "url", use_readLines = c(TRUE, FALSE))) expect_error(rdb(api_link = "url", use_readLines = logical())) # use_readLines is not a logical expect_error(rdb(api_link = "url", use_readLines = "TRUE")) # curl_config is not a curl_handle object or a named list expect_error(rdb(api_link = "url", curl_config = TRUE)) expect_error(rdb(api_link = "url", curl_config = list(1))) # filters is not a valid list expect_error(rdb(api_link = "url", filters = TRUE)) expect_error(rdb(api_link = "url", filters = "filters")) expect_error(rdb(api_link = "url", filters = list())) expect_error(rdb(api_link = "url", filters = list(code = "interpolate", parameter = NULL))) expect_error(rdb(api_link = "url", filters = list(code = "interpolate", parameters = list()))) expect_error(rdb(api_link = "url", filters = list(list(code = "interpolate", parameters = list(frequency = "quarterly", method = "spline")), list(code = "aggregate", parameters = list())))) rdbnomics/inst/tinytest/test_rdb_last_updates.R0000644000176200001440000000030713653274672021612 0ustar liggesuserscat("\n", "Context: Right arguments for rdb_last_updates", "\n") # All is logical and of length one expect_error(rdb_last_updates(all = logical())) expect_error(rdb_last_updates(all = "TRUE"))