rdbnomics/0000755000176200001440000000000013616610265012240 5ustar liggesusersrdbnomics/NAMESPACE0000644000176200001440000000031413616554211013453 0ustar liggesusers# Generated by roxygen2: do not edit by hand export(dbnomics) export(rdb) export(rdb_by_api_link) export(rdb_last_updates) export(rdb_providers) import(curl) import(data.table) import(jsonlite) rdbnomics/README.md0000644000176200001440000003436613604427673013541 0ustar liggesusers# rdbnomics [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/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, ...). 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: ```r vignette("rdbnomics") ``` ## Examples ### Fetch time series by `ids`: ```r # Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider: df1 <- rdb(ids = "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")) ``` In the event that you only use the argument `ids`, you can drop it and run: ```r df <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ``` ### Fetch time series by `mask` : ```r # Fetch one series from dataset 'Balance of Payments' (BOP) of IMF: df1 <- rdb("IMF", "BOP", mask = "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") ``` In the event that you only use the arguments `provider_code`, `dataset_code` and `mask`, you can drop the name `mask` and run: ```r df4 <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR") ``` ### Fetch time series by `dimensions`: ```r # 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: df3 <- rdb("WB", "DB", dimensions = list(country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"))) # or df3 <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}') ``` ### Fetch time series with a `query`: ```r # Fetch one series from dataset 'WEO by countries' (WEO) of IMF provider: df1 <- rdb("IMF", "WEO", query = "France current account balance percent") # Fetch series from dataset 'WEO by countries' (WEO) of IMF provider: df2 <- rdb("IMF", "WEO", query = "current account balance percent") ``` ### Fetch one series from the dataset 'Doing Business' of WB provider with the link: ```r df1 <- 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") ``` In the event that you only use the argument `api_link`, you can drop the name and run: ```r df1 <- 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") ``` ## Proxy configuration or connection error `Could not resolve host` When using the function `rdb`, you may come across the following error: ```r Error in open.connection(con, "rb") : Could not resolve host: api.db.nomics.world ``` To get round this situation, you have two possibilities: 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 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 options(rdbnomics.curl_config = h) ``` When fetching the data, the following command is executed: ```r 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 df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ``` This option of the package can be disabled with: ```r 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 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 options(rdbnomics.use_readLines = TRUE) ``` And then use the standard function as follows: ```r df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") ``` This configuration can be disabled with: ```r 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 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 filters <- list( code = "interpolate", parameters = list(frequency = "monthly", method = "spline") ) 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 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. ## Transform the `data.table` object into a `xts` object For some analysis, it is more convenient to have a `xts` object instead of a `data.table` object. To transform it, you can use the following functions: ```r library(xts) library(data.table) library(rdbnomics) to_xts <- function( x, needed_columns = c("period", "series_code", "series_name", "value"), series_columns = c("series_code", "series_name") ) { 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() ) } x <- data.table::as.xts.data.table(x) xts::xtsAttributes(x) <- list(codename = attr_names) x } rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") #> ... original_value period provider_code REF_AREA Reference Area series_code ... #> 1: NA 1940-01-01 IMF ES Spain A.ES.BCA_BP6_EUR #> 2: NA 1941-01-01 IMF ES Spain A.ES.BCA_BP6_EUR #> --- ... ... ... ... ... ... #> 159: -15136.8 2018-01-01 IMF FR France A.FR.BCA_BP6_EUR #> 160: NA 2019-01-01 IMF FR France A.FR.BCA_BP6_EUR to_xts(rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR")) #> A.ES.BCA_BP6_EUR A.FR.BCA_BP6_EUR #> 1940-01-01 NA NA #> 1941-01-01 NA NA #> 1942-01-01 NA NA #> ... ... ... #> 2017-01-01 31086 -16397.700 #> 2018-01-01 23283 -15136.800 #> 2019-01-01 NA NA ``` In the `xts` object, the series codes are used as column names. If you prefer the series names (or apply a function to them), you can utilize the function: ```r library(magrittr) rdb_rename_xts <- function(x, fun = NULL, ...) { 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 } rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") %>% to_xts() %>% rdb_rename_xts() #> Annual – Spain – Current Account, Total, Net, Euros Annual – France – Current Account, Total, Net, Euros #> 1940-01-01 NA NA #> 1941-01-01 NA NA #> 1942-01-01 NA NA #> ... ... ... #> 2017-01-01 31086 -16397.700 #> 2018-01-01 23283 -15136.800 #> 2019-01-01 NA NA rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") %>% to_xts() %>% rdb_rename_xts(stringr::word, start = 3) #> Spain France #> 1940-01-01 NA NA #> 1941-01-01 NA NA #> 1942-01-01 NA NA #> ... ... ... #> 2017-01-01 31086 -16397.700 #> 2018-01-01 23283 -15136.800 #> 2019-01-01 NA NA ``` ## P.S. Visit https://db.nomics.world/ ! rdbnomics/man/0000755000176200001440000000000013616545645013024 5ustar liggesusersrdbnomics/man/rdbnomics.Rd0000644000176200001440000000035213616554211015260 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_by_api_link.Rd0000644000176200001440000001203313604436273016412 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/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/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.Rd0000644000176200001440000002106013604436273014052 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' (WEO) from IMF : df1 <- rdb("IMF", "WEO", query = "France current account balance percent") # Fetch series from dataset 'WEO by countries' (WEO) from IMF : df2 <- rdb("IMF", "WEO", 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/ABW.BCA", "IMF/WEO/ABW.BCA_NGDPD"), filters = list( code = "interpolate", parameters = list(frequency = "daily", method = "spline") ) ) # Two filters df1 <- rdb( ids = c("IMF/WEO/ABW.BCA", "IMF/WEO/ABW.BCA_NGDPD"), 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/0000755000176200001440000000000013616545645014470 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}} } \author{ Sebastien Galais } rdbnomics/man/rdb_providers.Rd0000644000176200001440000000364313604436273016156 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}} } \author{ Sebastien Galais } rdbnomics/man/dbnomics.Rd0000644000176200001440000000167413604436273015112 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{\link[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", 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/DESCRIPTION0000644000176200001440000000170713616610265013753 0ustar liggesusersPackage: rdbnomics Type: Package Title: Download DBnomics Data Version: 0.5.2 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://github.com/dbnomics/rdbnomics BugReports: https://github.com/dbnomics/rdbnomics/issues Encoding: UTF-8 LazyData: true Imports: curl, jsonlite, data.table RoxygenNote: 7.0.2 Suggests: knitr, rmarkdown, dplyr, magrittr, ggplot2, DT, testthat VignetteBuilder: knitr NeedsCompilation: no Packaged: 2020-02-05 15:01:57 UTC; sebas Author: Sebastien Galais [cre, ctb], Thomas Brand [aut] Maintainer: Sebastien Galais Repository: CRAN Date/Publication: 2020-02-05 19:00:05 UTC rdbnomics/build/0000755000176200001440000000000013616554345013345 5ustar liggesusersrdbnomics/build/vignette.rds0000644000176200001440000000032113616554345015700 0ustar liggesusersb```b`fcb`b2 1# '-JIL. MAtq))$d敠)C(AF2aD 0!)eKM-FZ]?4-ީE0=(jؠjX2sRad9.nP&c0Gq?gQ~poݣ9JI,IK+\Wrdbnomics/tests/0000755000176200001440000000000013616545645013413 5ustar liggesusersrdbnomics/tests/testthat/0000755000176200001440000000000013616610265015242 5ustar liggesusersrdbnomics/tests/testthat/test-rdb.R0000644000176200001440000000545313574256275017132 0ustar liggesuserscontext("Right combination of arguments for rdb") library(rdbnomics) test_that("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")) }) test_that("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")) }) test_that("Fetch by mask with wrong provider_code", { expect_error(rdb(mask = "x", provider_code = "y", dataset_code = "z")) }) test_that("Fetch by ids", { expect_error(rdb(ids = character())) }) test_that("Nothing provided", { expect_error(rdb()) }) test_that("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())) }) test_that("api_link is not a character string", { expect_error(rdb(api_link = 0)) }) test_that("use_readLines is NULL", { expect_error(rdb(api_link = "url", use_readLines = NULL)) }) test_that("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())) }) test_that("use_readLines is not a logical", { expect_error(rdb(api_link = "url", use_readLines = "TRUE")) }) test_that("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))) }) test_that("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/tests/testthat/test-rdb_last_updates.R0000644000176200001440000000033713442307062021657 0ustar liggesuserscontext("Right arguments for rdb_last_updates") library(rdbnomics) test_that("all is logical and of length one", { expect_error(rdb_last_updates(all = logical())) expect_error(rdb_last_updates(all = "TRUE")) }) rdbnomics/tests/testthat/test-rdb_providers.R0000644000176200001440000000033113442307054021177 0ustar liggesuserscontext("Right arguments for rdb_providers") library(rdbnomics) test_that("code is logical and of length one", { expect_error(rdb_providers(code = logical())) expect_error(rdb_providers(code = "TRUE")) }) rdbnomics/tests/testthat.R0000644000176200001440000000010213363162572015360 0ustar liggesuserslibrary(testthat) library(rdbnomics) test_check("rdbnomics") rdbnomics/vignettes/0000755000176200001440000000000013616554345014256 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<[$g!}{;njR eG:#ITҫwmNUL媉+iţS&8Cߺۣʝ>?O"7!{ԵVMbGQJ2;AO϶9dxde`OvUzz?uVݽcLj-hz)֙wG9kƌ0/>ovyCE?!.6#|uCo\oѐ!#[tQ_YUι|)`=%#qzUn 9= 3b&LQofdI(9#W\OZ{Λ! 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Explore all the economic data from different providers (national and international statistical institutes, central banks, etc.), for free, following the link [db.nomics.world](https://db.nomics.world) (*N.B.: in the examples, data have already been retrieved on december 11rd 2019*). [![](dbnomics001.png)](https://db.nomics.world) # Fetch time series by `ids` First, let's assume that we know which series we want to download. A series identifier (`ids`) is defined by three values, formatted like this: `provider_code`/`dataset_code`/`series_code`. ## Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider ```{r, echo = FALSE} library <- function(...) { suppressWarnings( suppressPackageStartupMessages(base::library(..., quietly = TRUE)) ) } ``` ```{r} library(magrittr) library(dplyr) library(ggplot2) library(rdbnomics) ``` ```{r, echo = FALSE} reorder_cols <- function(x) { cols <- c( "provider_code", "dataset_code", "dataset_name", "series_code", "series_name", "original_period", "period", "original_value", "value", "@frequency" ) if ("unit" %in% colnames(x)) { cols <- c(cols, "unit", "Unit") } if ("geo" %in% colnames(x)) { cols <- c(cols, "geo", "Country") } if ("freq" %in% colnames(x)) { cols <- c(cols, "freq", "Frequency") } cols_add <- setdiff(colnames(x), cols) cols <- c(cols, cols_add) cols <- cols[cols %in% colnames(x)] cols <- match(cols, colnames(x)) dplyr::select(x, cols) } knitr::opts_chunk$set(dev.args = list(bg = "transparent")) dbnomics <- function(color_palette = "Set1", ...) { # Check if ggplot2 is installed. ggplot2_ok <- try(utils::packageVersion("ggplot2"), silent = TRUE) if (inherits(ggplot2_ok, "try-error")) { stop( "Please run install.packages('ggplot2') to use dbnomics().", call. = FALSE ) } # DBnomics vignette theme 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)) { result <- c( result, list(ggplot2::scale_color_brewer(palette = color_palette)) ) } result } display_table <- function(DT) { DT_ok <- FALSE if ( "rmarkdown" %in% installed.packages()[, "Package"] & "DT" %in% installed.packages()[, "Package"] ) { if (rmarkdown::pandoc_available()) { if (rmarkdown::pandoc_version() >= numeric_version("1.12.3")) { DT_ok <- TRUE } } } if (DT_ok) { DT::datatable( DT, rownames = FALSE, options = list(pageLength = 5, scrollX = TRUE) ) } else { dplyr::as.tbl(DT) } } ``` ```{r, eval = FALSE} df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df001 ``` In such data.frame (data.table or tibble), 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() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` 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")) %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df002 ``` ```{r, echo = FALSE} df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` ## 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.TOTAL.PC_ACT.T.EA19")) %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df003 ``` ```{r, echo = FALSE} df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ``` ```{r, 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)) ``` # 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") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df004 ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_step(size = 1.2) + geom_point(size = 2) + dbnomics() ``` 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") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df005 ``` ```{r, echo = FALSE} df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_step(size = 1.2) + geom_point(size = 2) + dbnomics() ``` ## 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") %>% filter(!is.na(value)) %>% arrange(desc(period), REF_AREA) %>% head(100) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df006 ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` ## 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") %>% filter(!is.na(value)) %>% group_by(INDICATOR) %>% top_n(n = 50, wt = period) ``` ```{r, eval = TRUE, echo = FALSE} df <- ungroup(rdbnomics:::rdbnomics_df007) ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` # 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")) %>% filter(!is.na(value)) # or # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}') %>% # filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df008 ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` ## 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"))) %>% filter(!is.na(value)) # or # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}') %>% # filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df009 ``` ```{r, echo = FALSE} df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` ## 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"))) %>% filter(!is.na(value)) # or # df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}') %>% # filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df010 ``` ```{r, echo = FALSE} df %>% arrange(series_name, period) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` # 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' (WEO) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "WEO", query = "France current account balance percent") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df014 ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` ## Fetch series from dataset 'WEO by countries' (WEO) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "WEO", query = "current account balance percent") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df015 ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` ```{r, 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") ``` # 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") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df011 ``` ```{r, echo = FALSE} df %>% arrange(period, series_name) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_step(size = 1.2) + geom_point(size = 2) + dbnomics() ``` 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") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df012 ``` ```{r, echo = FALSE} df %<>% mutate( series_name = sapply( series_name, function(y) { paste0( paste0( strsplit(y, "institutions' ")[[1]], collapse = "institutions'\n" ), "\n" ) } ) ) ``` ```{r, echo = FALSE} df %>% arrange(period, series_name) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` # 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.frame (data.table or tibble) 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 ``` ```{r, echo = FALSE} df %>% arrange(filtered, series_name, period) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(filter(df, !is.na(value)), aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` # Appendix ## ggplot2 function `dbnomics()` used in the vignette We show the function `dbnomics()` as an information. ```{r, eval = FALSE} dbnomics <- function(color_palette = "Set1", ...) { # Check if ggplot2 is installed. ggplot2_ok <- try(utils::packageVersion("ggplot2"), silent = TRUE) if (inherits(ggplot2_ok, "try-error")) { stop( "Please run install.packages('ggplot2') to use dbnomics().", call. = FALSE ) } # DBnomics vignette theme 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)) { result <- c( result, list(ggplot2::scale_color_brewer(palette = color_palette)) ) } result } ``` 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$c==.2n9Oe-wsܼ}7/zM7XV{; 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Sebastien Galais #' @export dbnomics <- function(color_palette = "Set1", ...) { # Check if ggplot2 is installed. ggplot2_ok <- try(utils::packageVersion("ggplot2"), silent = TRUE) if (inherits(ggplot2_ok, "try-error")) { stop( "Please run install.packages('ggplot2') to use dbnomics().", call. = FALSE ) } # DBnomics vignette theme result <- list( ggplot2::scale_x_date(expand = c(0, 0)), ggplot2::scale_y_continuous( labels = function(x) { format(x, big.mark = " ") } ), ggplot2::xlab(""), ggplot2::ylab(""), ggplot2::theme_bw(), ggplot2::theme( legend.position = "bottom", legend.direction = "vertical", legend.background = ggplot2::element_rect( fill = "transparent", colour = NA ), legend.key = ggplot2::element_blank(), panel.background = ggplot2::element_rect( fill = "transparent", colour = NA ), plot.background = ggplot2::element_rect( fill = "transparent", colour = NA ), legend.title = ggplot2::element_blank() ), ggplot2::theme(...), ggplot2::annotate( geom = "text", label = "DBnomics ", 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Only readLines if (as.numeric(R.Version()$major) >= 3) { if (as.numeric(R.Version()$minor) < 2) { suppressMessages(suppressWarnings(utils::setInternet2(TRUE))) } } verb_warn_rl <- getOption("rdbnomics.verbose_warning_readLines") check_argument(verb_warn_rl, "logical") if (verb_warn_rl) { response <- try(readLines(x), silent = TRUE) } else { response <- try(suppressWarnings(readLines(x)), silent = TRUE) } if (inherits(response, "try-error")) { stop("BAD REQUEST", call. = FALSE) } else { jsonlite::fromJSON(response) } } else { # With curl if (!is.null(curl_args)) { if (inherits(curl_args, "curl_handle")) { curl_args <- list(handle = curl_args) } if (!inherits(curl_args, "list")) { stop( paste0( "Argument 'curl_config' or option 'rdbnomics.curl_config' can ", "only be of class 'curl_handle' or 'list'." ), call. = FALSE ) } if (inherits(curl_args, "list")) { if (is.null(names(curl_args))) { stop("The list 'curl_config' must be named.", call. = FALSE) } if (length(curl_args) <= 0) { stop("The list 'curl_config' is empty.", call. = FALSE) } nm <- names(curl_args) nm <- no_empty_char(nm) if (length(curl_args) != length(nm)) { stop("All elements of 'curl_config' must be named.", call. = FALSE) } } } try(curl::handle_reset(tmp_curl), silent = TRUE) if (!list_has_curl_handle(curl_args)) { tmp_curl <- list(handle = curl::new_handle()) if (!is.null(curl_args)) { curl::handle_setopt(tmp_curl$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)$errors)) { stop( "\n", jsonlite::fromJSON(response)$errors$message, " : ", jsonlite::fromJSON(response)$errors$provider_code, "/", jsonlite::fromJSON(response)$errors$dataset_code, "/", jsonlite::fromJSON(response)$errors$series_code, call. = FALSE ) } jsonlite::fromJSON(response) } else { errormessage <- try( paste0( "\n", check_x, "\n", jsonlite::fromJSON(rawToChar(response$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_, '"]]') c(codes[i], ref_elt, eval(parse(text = paste0("x", elt_)))) }) }, 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 { 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 } } rdbnomics/R/zzz.R0000644000176200001440000000323013521150705013410 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$", rdbnomics.curl_config = NULL, rdbnomics.rdb_no_arg = TRUE, rdbnomics.metadata = TRUE, rdbnomics.filters = NULL, rdbnomics.progress_bar = TRUE, rdbnomics.translate_codes = TRUE ) 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.R0000644000176200001440000001300213604412433015661 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/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/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.R0000644000176200001440000004274513604412310013333 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' (WEO) from IMF : #' df1 <- rdb("IMF", "WEO", query = "France current account balance percent") #' # Fetch series from dataset 'WEO by countries' (WEO) from IMF : #' df2 <- rdb("IMF", "WEO", 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/ABW.BCA", "IMF/WEO/ABW.BCA_NGDPD"), #' filters = list( #' code = "interpolate", #' parameters = list(frequency = "daily", method = "spline") #' ) #' ) #' #' # Two filters #' df1 <- rdb( #' ids = c("IMF/WEO/ABW.BCA", "IMF/WEO/ABW.BCA_NGDPD"), #' 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/rdbnomics.R0000644000176200001440000000067413604412702014544 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" ) utils::globalVariables(unique(vars)) }rdbnomics/R/dot_rdb.R0000644000176200001440000002745713574256420014222 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 == 21) { data_elt <- "data" } else 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, ... if (!getOption("rdbnomics.translate_codes")) { additional_geo_column <- additional_geo_mapping <- NULL } else { additional_geo_column <- get_geo_colname(DBlist) additional_geo_mapping <- get_geo_names(DBlist, 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$dataset_code[1] u2 <- colnames(u)[2:3] u2 <- paste0(u2, collapse = "|") paste0(u1, "|", u2) }) additional_geo_mapping <- stats::setNames(additional_geo_mapping, check_agm) keep <- intersect(check_agc, check_agm) if (length(keep) == 0) { additional_geo_column <- additional_geo_mapping <- NULL } else { additional_geo_column <- additional_geo_column[sort(keep)] additional_geo_mapping <- additional_geo_mapping[sort(keep)] } } } } } # Extracting data DBdata <- list(DBlist$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=", i * limit) # Fetching data DBlist <- get_data(tmp_api_link, use_readLines, curl_config) # Extracting data DBlist$series[[data_elt]] }) DBdata <- append(DBdata, DBdata0, 0) rm(DBdata0) } # Transform data.frames inside DBdata if (list_has_dataframe(DBdata)) { DBdata <- lapply(DBdata, dataframe_to_columns) } # Transforming into data.table DBdata <- lapply(DBdata, data.table::setDT) # Gathering data DBdata <- data.table::rbindlist(DBdata, use.names = TRUE, fill = TRUE) # Expanding list columns DBdata <- deploy(DBdata) # To reproduce the Python module, we copy the 'value' element DBdata[ , original_value := format( value, scientific = FALSE, decimal.mark = ".", big.mark = "", trim = TRUE, drop0trailing = TRUE ) ] # Transforming date format and timestamp format transform_date_timestamp(DBdata) # Modifying column names tryCatch({ data.table::setnames(DBdata, "period", "original_period") }, error = function(e) { stop( paste0( "The retrieved dataset doesn't have a column named 'period', it's not ", "normal please check ." ), call. = FALSE ) }) tryCatch({ data.table::setnames(DBdata, "period_start_day", "period") }, error = function(e) { stop( paste0( "The retrieved dataset doesn't have a column named ", "'period_start_day', it's not normal please check ." ), call. = FALSE ) }) # DBnomics editor if (!is.null(filters)) { if (check_filter == "notlist") { filters <- list(filters) } # Filters are applied by 'series_code' codes <- unique(DBdata$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 ) }) # 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)) { 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.R0000644000176200001440000000532713604412577015442 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}} #' @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.R0000644000176200001440000000644213604413025016101 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}} #' @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 == 21) { updates <- updates$datasets data.table::setDT(updates) } else 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/NEWS.md0000644000176200001440000000454413616554077013355 0ustar liggesusers# 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/MD50000644000176200001440000000350413616610265012552 0ustar liggesusersea7bac5280ca6e92edb02da94889c02a *DESCRIPTION e382fd94b131156a8f7c628bdd76dcc8 *NAMESPACE a91776d680f8944051147568704209ab *NEWS.md 5141b95cc468772793f9dc084215383f *R/dbnomics.R a32f71e3fd46e5cf35667d14613cc9f6 *R/dot_rdb.R 479cf12e9b7e5f7a500734ba88675865 *R/rdb.R df0c048ebd28d9c2d402e3396a8dbadc *R/rdb_by_api_link.R 1a91ca82d8d309a64d1ca5eae9597fc8 *R/rdb_last_updates.R ae5d791ae4ac127a6ca17358fad37ea2 *R/rdb_providers.R 4ba920927b28c01085b4c7c202173070 *R/rdbnomics.R d4c8432e0c0760f4cd5b81845afabd7a *R/sysdata.rda 62b49dc1da4e754e60c00bbb6646e2c1 *R/utils.R 39aaba834a2dd721bccc3dcbdcbf737a *R/zzz.R d793796e88c38ccffb506a8f55cb49f6 *README.md b064f45ddabb14f8a586becdf0355cc3 *build/vignette.rds faa2935314b4671ed7183794d2edb021 *inst/doc/rdbnomics.R 8587a8239e22874a02dc5d1f375b2527 *inst/doc/rdbnomics.Rmd 03b3937135f19e92c1f14a13d18f0f12 *inst/doc/rdbnomics.html 51f0fe4504ec8e0c634b448d32216b58 *man/dbnomics.Rd 54193b888f996476a11b1e5968e1ae04 *man/figures/logo.png 4a723f1afc60b97d8c1a03b8e14bb7ee *man/rdb.Rd 8a9ef40c5114f2c0be1c6fae63af9b76 *man/rdb_by_api_link.Rd 90ba31aee2b88d77c3677c98c8e23387 *man/rdb_last_updates.Rd 6962c6209acb27c8a9b545c5ce8b1251 *man/rdb_providers.Rd b34afbed06fa3c99f3883ad3881fa4ea *man/rdbnomics.Rd 326c31d08a11df871813c2aa0128759e *tests/testthat.R 8de9ebd7dab2bbfc9ef5163f10a3fd8b *tests/testthat/test-rdb.R fcbaf04293dfff6555324693fc7dcbdd *tests/testthat/test-rdb_last_updates.R be63d85138d790a464646dbe7650b4b8 *tests/testthat/test-rdb_providers.R e98953a1eb427a74175b91efdad549e1 *vignettes/dbnomics001.png 0c459913b70e2638e3667fece3af2c8a *vignettes/dbnomics002.png de513c0de5c2be3212c475914c98615d *vignettes/dbnomics003.png 34946e93869403ca124b2ed6c5dab526 *vignettes/dbnomics004.png fab6a1c58f5207ef75d20afbf2f987a1 *vignettes/dbnomics005.png 8587a8239e22874a02dc5d1f375b2527 *vignettes/rdbnomics.Rmd rdbnomics/inst/0000755000176200001440000000000013616554345013223 5ustar liggesusersrdbnomics/inst/doc/0000755000176200001440000000000013616554345013770 5ustar liggesusersrdbnomics/inst/doc/rdbnomics.html0000644000176200001440001217765313616554345016664 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 december 11rd 2019).

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(magrittr)
library(dplyr)
library(ggplot2)
library(rdbnomics)
df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") %>%
  filter(!is.na(value))

In such data.frame (data.table or tibble), 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)
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

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")) %>%
  filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

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.TOTAL.PC_ACT.T.EA19")) %>%
  filter(!is.na(value))
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))

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") %>%
  filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_step(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

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") %>%
  filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_step(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

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") %>%
  filter(!is.na(value)) %>%
  arrange(desc(period), REF_AREA) %>%
  head(100)

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") %>%
  filter(!is.na(value)) %>%
  group_by(INDICATOR) %>%
  top_n(n = 50, wt = period)

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")) %>%
  filter(!is.na(value))
# or
# df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}') %>%
#   filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

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"))) %>%
  filter(!is.na(value))
# or
# df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}') %>%
#   filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

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"))) %>%
  filter(!is.na(value))
# or
# df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}') %>%
#   filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

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’ (WEO) of IMF

df <- rdb("IMF", "WEO", query = "France current account balance percent") %>%
  filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

5.2 Fetch series from dataset ‘WEO by countries’ (WEO) of IMF

df <- rdb("IMF", "WEO", query = "current account balance percent") %>%
  filter(!is.na(value))
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")

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") %>%
  filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_step(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

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") %>%
  filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

8 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.

8.1 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>"
)

8.1.1 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)

8.1.2 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)

8.2 Use the default R internet connection

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

8.2.1 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)

8.2.2 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)

9 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.frame (data.table or tibble) 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.
ggplot(filter(df, !is.na(value)), aes(x = period, y = value, color = series_name)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

10 Appendix

10.1 ggplot2 function dbnomics() used in the vignette

We show the function dbnomics() as an information.

dbnomics <- function(color_palette = "Set1", ...) {
  # Check if ggplot2 is installed.
  ggplot2_ok <- try(utils::packageVersion("ggplot2"), silent = TRUE)
  if (inherits(ggplot2_ok, "try-error")) {
    stop(
      "Please run install.packages('ggplot2') to use dbnomics().",
      call. = FALSE
    )
  }

  # DBnomics vignette theme
  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 <https://db.nomics.world>", 
      x = structure(Inf, class = "Date"), y = -Inf,
      hjust = 1.1, vjust = -0.4, col = "grey", 
      fontface = "italic"
    )
  )

  if (!is.null(color_palette)) {
    result <- c(
      result,
      list(ggplot2::scale_color_brewer(palette = color_palette))
    )
  }

  result
}

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

  2. CEPREMAP↩

rdbnomics/inst/doc/rdbnomics.R0000644000176200001440000004463113616554343016101 0ustar liggesusers## ---- echo = FALSE------------------------------------------------------------ library <- function(...) { suppressWarnings( suppressPackageStartupMessages(base::library(..., quietly = TRUE)) ) } ## ----------------------------------------------------------------------------- library(magrittr) library(dplyr) library(ggplot2) library(rdbnomics) ## ---- echo = FALSE------------------------------------------------------------ reorder_cols <- function(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)) dplyr::select(x, cols) } knitr::opts_chunk$set(dev.args = list(bg = "transparent")) dbnomics <- function(color_palette = "Set1", ...) { # Check if ggplot2 is installed. ggplot2_ok <- try(utils::packageVersion("ggplot2"), silent = TRUE) if (inherits(ggplot2_ok, "try-error")) { stop( "Please run install.packages('ggplot2') to use dbnomics().", call. = FALSE ) } # DBnomics vignette theme 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)) { result <- c( result, list(ggplot2::scale_color_brewer(palette = color_palette)) ) } result } display_table <- function(DT) { DT_ok <- FALSE if ( "rmarkdown" %in% installed.packages()[, "Package"] & "DT" %in% installed.packages()[, "Package"] ) { if (rmarkdown::pandoc_available()) { if (rmarkdown::pandoc_version() >= numeric_version("1.12.3")) { DT_ok <- TRUE } } } if (DT_ok) { DT::datatable( DT, rownames = FALSE, options = list(pageLength = 5, scrollX = TRUE) ) } else { dplyr::as.tbl(DT) } } ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") %>% # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df001 ## ---- echo = FALSE------------------------------------------------------------ df %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- 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")) %>% # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df002 ## ---- echo = FALSE------------------------------------------------------------ df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "Eurostat/une_rt_q/Q.SA.TOTAL.PC_ACT.T.EA19")) %>% # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df003 ## ---- echo = FALSE------------------------------------------------------------ df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ## ---- 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)) ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR") %>% # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df004 ## ---- echo = FALSE------------------------------------------------------------ df %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(df, aes(x = period, y = value, color = series_name)) + geom_step(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR") ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") %>% # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df005 ## ---- echo = FALSE------------------------------------------------------------ df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(df, aes(x = period, y = value, color = series_name)) + geom_step(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR") %>% # filter(!is.na(value)) %>% # arrange(desc(period), REF_AREA) %>% # head(100) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df006 ## ---- echo = FALSE------------------------------------------------------------ df %>% reorder_cols() %>% display_table() ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR") %>% # filter(!is.na(value)) %>% # group_by(INDICATOR) %>% # top_n(n = 50, wt = period) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- ungroup(rdbnomics:::rdbnomics_df007) ## ---- echo = FALSE------------------------------------------------------------ df %>% reorder_cols() %>% display_table() ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea19")) %>% # filter(!is.na(value)) # # or # # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}') %>% # # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df008 ## ---- echo = FALSE------------------------------------------------------------ df %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea19", "dnk"))) %>% # filter(!is.na(value)) # # or # # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}') %>% # # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df009 ## ---- echo = FALSE------------------------------------------------------------ df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- 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"))) %>% # filter(!is.na(value)) # # or # # df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}') %>% # # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df010 ## ---- echo = FALSE------------------------------------------------------------ df %>% arrange(series_name, period) %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "WEO", query = "France current account balance percent") %>% # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df014 ## ---- echo = FALSE------------------------------------------------------------ df %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- eval = FALSE------------------------------------------------------------ # df <- rdb("IMF", "WEO", query = "current account balance percent") %>% # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df015 ## ---- echo = FALSE------------------------------------------------------------ df %>% reorder_cols() %>% display_table() ## ---- 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") ## ---- 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") %>% # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df011 ## ---- echo = FALSE------------------------------------------------------------ df %>% arrange(period, series_name) %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(df, aes(x = period, y = value, color = series_name)) + geom_step(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- 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") %>% # filter(!is.na(value)) ## ---- eval = TRUE, echo = FALSE----------------------------------------------- df <- rdbnomics:::rdbnomics_df012 ## ---- echo = FALSE------------------------------------------------------------ df %<>% mutate( series_name = sapply( series_name, function(y) { paste0( paste0( strsplit(y, "institutions' ")[[1]], collapse = "institutions'\n" ), "\n" ) } ) ) ## ---- echo = FALSE------------------------------------------------------------ df %>% arrange(period, series_name) %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- 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 ## ---- echo = FALSE------------------------------------------------------------ df %>% arrange(filtered, series_name, period) %>% reorder_cols() %>% display_table() ## ---- fig.align = 'center'---------------------------------------------------- ggplot(filter(df, !is.na(value)), aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ## ---- eval = FALSE------------------------------------------------------------ # dbnomics <- function(color_palette = "Set1", ...) { # # Check if ggplot2 is installed. # ggplot2_ok <- try(utils::packageVersion("ggplot2"), silent = TRUE) # if (inherits(ggplot2_ok, "try-error")) { # stop( # "Please run install.packages('ggplot2') to use dbnomics().", # call. = FALSE # ) # } # # # DBnomics vignette theme # 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)) { # result <- c( # result, # list(ggplot2::scale_color_brewer(palette = color_palette)) # ) # } # # result # } rdbnomics/inst/doc/rdbnomics.Rmd0000644000176200001440000005457613604711711016422 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 december 11rd 2019*). [![](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(magrittr) library(dplyr) library(ggplot2) library(rdbnomics) ``` ```{r, echo = FALSE} reorder_cols <- function(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)) dplyr::select(x, cols) } knitr::opts_chunk$set(dev.args = list(bg = "transparent")) dbnomics <- function(color_palette = "Set1", ...) { # Check if ggplot2 is installed. ggplot2_ok <- try(utils::packageVersion("ggplot2"), silent = TRUE) if (inherits(ggplot2_ok, "try-error")) { stop( "Please run install.packages('ggplot2') to use dbnomics().", call. = FALSE ) } # DBnomics vignette theme 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)) { result <- c( result, list(ggplot2::scale_color_brewer(palette = color_palette)) ) } result } display_table <- function(DT) { DT_ok <- FALSE if ( "rmarkdown" %in% installed.packages()[, "Package"] & "DT" %in% installed.packages()[, "Package"] ) { if (rmarkdown::pandoc_available()) { if (rmarkdown::pandoc_version() >= numeric_version("1.12.3")) { DT_ok <- TRUE } } } if (DT_ok) { DT::datatable( DT, rownames = FALSE, options = list(pageLength = 5, scrollX = TRUE) ) } else { dplyr::as.tbl(DT) } } ``` ```{r, eval = FALSE} df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df001 ``` In such data.frame (data.table or tibble), 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() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` 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")) %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df002 ``` ```{r, echo = FALSE} df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` ## 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.TOTAL.PC_ACT.T.EA19")) %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df003 ``` ```{r, echo = FALSE} df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ``` ```{r, 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)) ``` # 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") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df004 ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_step(size = 1.2) + geom_point(size = 2) + dbnomics() ``` 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") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df005 ``` ```{r, echo = FALSE} df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_step(size = 1.2) + geom_point(size = 2) + dbnomics() ``` ## 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") %>% filter(!is.na(value)) %>% arrange(desc(period), REF_AREA) %>% head(100) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df006 ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` ## 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") %>% filter(!is.na(value)) %>% group_by(INDICATOR) %>% top_n(n = 50, wt = period) ``` ```{r, eval = TRUE, echo = FALSE} df <- ungroup(rdbnomics:::rdbnomics_df007) ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` # 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")) %>% filter(!is.na(value)) # or # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}') %>% # filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df008 ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` ## 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"))) %>% filter(!is.na(value)) # or # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}') %>% # filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df009 ``` ```{r, echo = FALSE} df %>% arrange(series_code, period) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` ## 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"))) %>% filter(!is.na(value)) # or # df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}') %>% # filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df010 ``` ```{r, echo = FALSE} df %>% arrange(series_name, period) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` # 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' (WEO) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "WEO", query = "France current account balance percent") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df014 ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` ## Fetch series from dataset 'WEO by countries' (WEO) of IMF ```{r, eval = FALSE} df <- rdb("IMF", "WEO", query = "current account balance percent") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df015 ``` ```{r, echo = FALSE} df %>% reorder_cols() %>% display_table() ``` ```{r, 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") ``` # 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") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df011 ``` ```{r, echo = FALSE} df %>% arrange(period, series_name) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_step(size = 1.2) + geom_point(size = 2) + dbnomics() ``` 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") %>% filter(!is.na(value)) ``` ```{r, eval = TRUE, echo = FALSE} df <- rdbnomics:::rdbnomics_df012 ``` ```{r, echo = FALSE} df %<>% mutate( series_name = sapply( series_name, function(y) { paste0( paste0( strsplit(y, "institutions' ")[[1]], collapse = "institutions'\n" ), "\n" ) } ) ) ``` ```{r, echo = FALSE} df %>% arrange(period, series_name) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(df, aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` # 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.frame (data.table or tibble) 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 ``` ```{r, echo = FALSE} df %>% arrange(filtered, series_name, period) %>% reorder_cols() %>% display_table() ``` ```{r, fig.align = 'center'} ggplot(filter(df, !is.na(value)), aes(x = period, y = value, color = series_name)) + geom_line(size = 1.2) + geom_point(size = 2) + dbnomics() ``` # Appendix ## ggplot2 function `dbnomics()` used in the vignette We show the function `dbnomics()` as an information. ```{r, eval = FALSE} dbnomics <- function(color_palette = "Set1", ...) { # Check if ggplot2 is installed. ggplot2_ok <- try(utils::packageVersion("ggplot2"), silent = TRUE) if (inherits(ggplot2_ok, "try-error")) { stop( "Please run install.packages('ggplot2') to use dbnomics().", call. = FALSE ) } # DBnomics vignette theme 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)) { result <- c( result, list(ggplot2::scale_color_brewer(palette = color_palette)) ) } result } ```