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.

You can also retrieve all the economic data through the rdbnomics package here. This blog post describes the different ways to do so.

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

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')

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()

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.Y15-74.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() + 
  theme(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

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_line(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')

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_line(size = 1.2) +
  geom_point(size = 2) +
  dbnomics()

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)