1 DBnomics: the world’s economic database

You can explore all the economic data from different providers by following the link db.nomics.world
(N.B.: in the examples, data have already been retrieved on september 23^rd 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 nine 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_code)) +
  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_code)) +
  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_code)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

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 ‘Consumer Price Index’ (CPI) of IMF

df <- rdb('IMF', 'CPI', mask = 'M.DE.PCPIEC_WT') %>%
  filter(!is.na(value))

ggplot(df, aes(x = period, y = value, color = series_code)) +
  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', 'CPI', 'M.DE.PCPIEC_WT')

3.2 Fetch two series from dataset ‘Consumer Price Index’ (CPI) of IMF

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

df <- rdb('IMF', 'CPI', mask = 'M.DE+FR.PCPIEC_WT') %>%
  filter(!is.na(value))

ggplot(df, aes(x = period, y = value, color = series_code)) +
  geom_step(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

3.3 Fetch all series along one dimension from dataset ‘Consumer Price Index’ (CPI) of IMF

df <- rdb('IMF', 'CPI', mask = 'M..PCPIEC_WT') %>%
  filter(!is.na(value)) %>%
  arrange(desc(period), REF_AREA) %>%
  head(100)

3.4 Fetch series along multiple dimensions from dataset ‘Consumer Price Index’ (CPI) of IMF

df <- rdb('IMF', 'CPI', mask = 'M..PCPIEC_IX+PCPIA_IX') %>%
  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_code)) +
  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_code)) +
  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 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_by_api_link function such as below.

df <- rdb_by_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()

6 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_by_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_by_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) +
  scale_y_continuous(labels = function(x) { format(x, big.mark = " ") }) +
  dbnomics()

## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

7 Proxy configuration or connection error `Could not resolve host`

When using the functions rdb or 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:

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.

7.1 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>"
)

7.1.1 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)

7.1.2 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 functions rdb and rdb_...:

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

7.2 Use the default R internet connection

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

7.2.1 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)

7.2.2 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 functions rdb and rdb_...:

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

8 Transform time series with filters

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

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

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

The request is then:

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

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

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

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

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

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

The content of two columns are modified:

series_code: same as before for original series, but the suffix _filtered is added for filtered series.
series_name: same as before for original series, but the suffix (filtered) is added for filtered series.

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

9 Appendix

9.1 ggplot2 function `dbnomics()` used in the vignette

We show the function dbnomics() as an information. It’s not implemented in the package.

dbnomics <- function() {
  list(
    scale_x_date(expand = c(0, 0)),
    scale_y_continuous(labels = function(x) { format(x, big.mark = " ") }),
    xlab(""),
    ylab(""),
    theme_bw(),
    theme(
      legend.position = "bottom", legend.direction = "vertical",
      legend.background = element_rect(fill = "transparent", colour = NA),
      legend.key = element_blank(),
      panel.background = element_rect(fill = "transparent", colour = NA),
      plot.background = element_rect(fill = "transparent", colour = NA),
      legend.title = element_blank()
    ),
    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"
    )
  )
}

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

DBnomics R client

Sébastien Galais¹, Thomas Brand²

1 DBnomics: the world’s economic database

2 Fetch time series by `ids`

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

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

2.3 Fetch two series from different datasets of different providers

3 Fetch time series by `mask`

3.1 Fetch one series from dataset ‘Consumer Price Index’ (CPI) of IMF

3.2 Fetch two series from dataset ‘Consumer Price Index’ (CPI) of IMF

3.3 Fetch all series along one dimension from dataset ‘Consumer Price Index’ (CPI) of IMF

3.4 Fetch series along multiple dimensions from dataset ‘Consumer Price Index’ (CPI) of IMF

4 Fetch time series by `dimensions`

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

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

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

5 Fetch time series found on the web site

6 Fetch time series from the cart

7 Proxy configuration or connection error `Could not resolve host`

7.1 Configure curl to use a specific and authorized proxy

7.1.1 Set the connection up for a session

7.1.2 Use the connection only for a function call

7.2 Use the default R internet connection

7.2.1 Set the connection up for a session

7.2.2 Use the connection only for a function call

8 Transform time series with filters

9 Appendix

9.1 ggplot2 function `dbnomics()` used in the vignette

DBnomics R client

Sébastien Galais1, Thomas Brand2

1 DBnomics: the world’s economic database

2 Fetch time series by ids

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

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

2.3 Fetch two series from different datasets of different providers

3 Fetch time series by mask

3.1 Fetch one series from dataset ‘Consumer Price Index’ (CPI) of IMF

3.2 Fetch two series from dataset ‘Consumer Price Index’ (CPI) of IMF

3.3 Fetch all series along one dimension from dataset ‘Consumer Price Index’ (CPI) of IMF

3.4 Fetch series along multiple dimensions from dataset ‘Consumer Price Index’ (CPI) of IMF

4 Fetch time series by dimensions

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

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

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

5 Fetch time series found on the web site

6 Fetch time series from the cart

7 Proxy configuration or connection error Could not resolve host

7.1 Configure curl to use a specific and authorized proxy

7.1.1 Set the connection up for a session

7.1.2 Use the connection only for a function call

7.2 Use the default R internet connection

7.2.1 Set the connection up for a session

7.2.2 Use the connection only for a function call

8 Transform time series with filters

9 Appendix

9.1 ggplot2 function dbnomics() used in the vignette

Sébastien Galais¹, Thomas Brand²

2 Fetch time series by `ids`

3 Fetch time series by `mask`

4 Fetch time series by `dimensions`

7 Proxy configuration or connection error `Could not resolve host`

9.1 ggplot2 function `dbnomics()` used in the vignette