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:
install.packages("rdbnomics")
library(rdbnomics)To install rdbnomics from github:
remotes::install_github("dbnomics/rdbnomics", build_vignettes = TRUE, force = TRUE)
library(rdbnomics)After installation, a vignette is available to the user:
vignette("rdbnomics")Examples
Fetch time series by ids:
# 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:
df <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
Fetch time series by mask :
# 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:
df4 <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR")
Fetch time series 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:
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:
# 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:
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:
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")Fetch the available datasets of a provider
# Example with the IMF datasets:
df_datasets <- rdb_datasets(provider_code = "IMF")
# Example with the IMF and BDF datasets:
df_datasets <- rdb_datasets(provider_code = c("IMF", "BDF"))In the event that you only request the datasets for one provider, if you define
simplify = TRUE, then the result will be a data.table not a named list.
df_datasets <- rdb_datasets(provider_code = "IMF", simplify = TRUE)Fetch the possible dimensions of available datasets of a provider
# Example for the dataset WEO of the IMF:
df_dimensions <- rdb_dimensions(provider_code = "IMF", dataset_code = "WEO")In the event that you only request the dimensions for one dataset for one
provider, if you define simplify = TRUE, then the result will be a named list
data.table not a nested named list.
df_dimensions <- rdb_dimensions(provider_code = "IMF", dataset_code = "WEO", simplify = TRUE)Fetch the number of series of available datasets of a provider
# Example for the dataset WEOAGG of the IMF:
df_series <- rdb_series(provider_code = "IMF", dataset_code = "WEOAGG")
df_series <- rdb_series(provider_code = "IMF", dataset_code = "WEOAGG", simplify = TRUE)
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.worldTo get round this situation, you have two possibilities:
-
configure curl to use a specific and authorized proxy.
-
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:
h <- list(
proxy = "<proxy>",
proxyport = <port>,
proxyusername = "<username>",
proxypassword = "<password>"
)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)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)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:
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)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)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")
)
df <- rdb(
ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"),
filters = filters
)If you want to apply more than one filter, the filters argument will be a list of valid filters:
filters <- list(
list(
code = "interpolate",
parameters = list(frequency = "monthly", method = "spline")
),
list(
code = "aggregate",
parameters = list(frequency = "bi-annual", method = "end_of_period")
)
)
df <- rdb(
ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"),
filters = filters
)The data.table columns change a little bit when filters are used. There are two new columns:
-
period_middle_day: the middle day oforiginal_period(can be useful when you compare graphically interpolated series and original ones). -
filtered(boolean):TRUEif the series is filtered,FALSEotherwise.
The content of two columns are modified:
-
series_code: same as before for original series, but the suffix_filteredis 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:
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()
)
}
if (!inherits(x[[1]], "Date")) {
stop(
paste0(
"The first needed column '", needed_columns[1], "' is not of class ",
"'Date' which is a problem for data.table::as.xts.data.table()."
),
"\n",
paste0(
"Please check with packageVersion(\"rdbnomics\") that your version is ",
"greater or equal to '0.5.2'. Otherwise update rdbnomics or contact ",
"s915.stem@gmail.com."
),
call. = FALSE
)
}
x <- data.table::as.xts.data.table(x)
xts::xtsAttributes(x) <- list(codename = attr_names)
x
}
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 NAIn 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:
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 NAP.S.
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