1 DBnomics : the world’s economic database

You can explore all the economic data from different providers by following the link db.nomics.world 3.

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, you will always find at least nine columns:

  • provider_code
  • dataset_code
  • dataset_name
  • series_code
  • series_name
  • original_period (a character string)
  • period (a date of the first day of original_period)
  • 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 = 2) +
  dbnomics()

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 = 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 = 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. It is compatible only with some providers : BIS, ECB, Eurostat, FED, ILO, IMF, INSEE, OECD, WTO.

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 = 2) +
  dbnomics()

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 = 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 dimension 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 = '{"geo": ["ea19"]}') %>%
  filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_code)) +
  geom_line(size = 2) +
  dbnomics()

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

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 = 2) +
  dbnomics()

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

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 = 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/v21/series?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%2C%22indicator%22%3A%5B%22IC.REG.PROC.FE.NO%22%5D%7D&provider_code=WB&dataset_code=DB&format=json") %>%
  filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_step(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/v21/series?series_ids=BOE%2F8745%2FLPMB23A%2CBOE%2F8745%2FLPMB26A&format=json") %>%
  filter(!is.na(value))
ggplot(df, aes(x = period, y = value, color = series_name)) +
  geom_line(size = 2) +
  scale_y_continuous(labels = function(x) { format(x, big.mark = " ") }) +
  dbnomics()

  1. CEPREMAP↩

  2. Banque de France↩

  3. In the examples, data have already been retrieved on november 19th 2018.↩