1 DBnomics: the world’s economic database

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

2 Fetch time series by ids

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

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

library(data.table)
library(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)
## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(cols)` instead of `cols` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
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 Fetch the available datasets of a provider

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

rdb_datasets(provider_code = "IMF")

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

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

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

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

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

rdb_datasets(provider_code = "IMF", simplify = TRUE)

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

options(rdbnomics.progress_bar_datasets = TRUE)
rdb_datasets()
options(rdbnomics.progress_bar_datasets = FALSE)

9 Fetch the possible dimensions of available datasets of a provider

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

rdb_dimensions(provider_code = "IMF", dataset_code = "WEO")

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

## Number of dimensions for IMF/WEO : 2

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

rdb_dimensions(provider_code = "IMF", dataset_code = "WEO", simplify = TRUE)
## List of 2
##  $ weo-country:Classes 'data.table' and 'data.frame':    194 obs. of  2 variables:
##   ..$ weo-country: chr [1:194] "ABW" "AFG" "AGO" "ALB" ...
##   ..$ WEO Country: chr [1:194] "Aruba" "Afghanistan" "Angola" "Albania" ...
##   ..- attr(*, ".internal.selfref")=<externalptr> 
##  $ weo-subject:Classes 'data.table' and 'data.frame':    45 obs. of  2 variables:
##   ..$ weo-subject: chr [1:45] "BCA" "BCA_NGDPD" "FLIBOR6" "GGR" ...
##   ..$ WEO Subject: chr [1:45] "Current account balance - U.S. dollars" "Current account balance - Percent of GDP" "Six-month London interbank offered rate (LIBOR) - Percent" "General government revenue - National currency" ...
##   ..- attr(*, ".internal.selfref")=<externalptr>

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

options(rdbnomics.progress_bar_datasets = TRUE)
rdb_dimensions()
options(rdbnomics.progress_bar_datasets = FALSE)

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

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

rdb_series(provider_code = "IMF", dataset_code = "WEO", simplify = TRUE)

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

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

or with a query:

rdb_series(provider_code = "IMF", dataset_code = c("WEO", "WEOAGG"), query = "NGDP_RPCH")

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

11 Proxy configuration or connection error Could not resolve host

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

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

To get round this situation, you have two options:

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

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

11.1 Configure curl to use a specific and authorized proxy

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

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

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

11.1.1 Set the connection up for a session

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

options(rdbnomics.curl_config = h)

When fetching the data, the following command is executed:

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

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

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

This option of the package can be disabled with:

options(rdbnomics.curl = NULL)

11.1.2 Use the connection only for a function call

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

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

11.2 Use the default R internet connection

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

11.2.1 Set the connection up for a session

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

options(rdbnomics.use_readLines = TRUE)

And then use the standard function as follows:

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

This configuration can be disabled with:

options(rdbnomics.use_readLines = FALSE)

11.2.2 Use the connection only for a function call

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

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

12 Transform time series with filters

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

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

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

The request is then:

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

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

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

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

The data.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()

13 Appendix

13.1 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↩︎