You can use the
imf_data function to download the data the IMF makes available via its API. To do this you will need at least the following information:
database_id: the ID of the specific database you wish to download the data series from. You can find the list of IDs and their description using the
indicator: the IMF indicators of the variables you want to download. One way to find these is to:
database_id for the database you want to access with the
imf_codelist function to find the code list of the database.
Then using the indicator code (usually
imf_codes, you can find the data series indicator codes in that database.
Tip: if you have a number of country identifiers that are not in ISO2C format, you can use the helpful countrycode package to convert them.
country: one or more ISO two letter country codes for the countries you would like to download the data for. If
country = 'all' then all available countries will be downloaded.
end: the start and end years for which you would like to download the data.
freq: the frequency of the series you want to download. Often series are available annually, quarterly, and monthly.
Imagine that we want to download Effective Exchange Rate (CPI base) for China and the UK for 2013:
library(imfr) real_ex <- imf_data(database_id = 'IFS', indicator = 'EREER_IX', country = c('CN', 'GB'), freq = 'A', start = 2013, end = current_year())
## iso2c year EREER_IX ## 1 CN 2013 115.2979 ## 2 CN 2014 118.9859 ## 3 CN 2015 131.6297 ## 4 GB 2013 105.7684 ## 5 GB 2014 113.7014 ## 6 GB 2015 121.7981
While many quantities of interest from the IMF database are in simple
country-time-variable format, many are not. For example, Direction of Trade
Statistics include country-year-variable and a "counterpart area". By default,
imf_data would only return the first, but not the last.
Because of the many possible data structures available from the imf,
allows you to return the entire API call as a list. From this list you can then
extract the requested data. To do this use the
return_raw = TRUE argument, e.g.:
data_list <- imf_data(database_id = "DOT", indicator = "TXG_FOB_USD", country = "US", return_raw = TRUE)
Then extract the data series (it is typically contained in
data_df <- data_list$CompactData$DataSet$Series names(data_df)
##  "@FREQ" "@REF_AREA" "@INDICATOR" ##  "@COUNTERPART_AREA" "@UNIT_MULT" "@TIME_FORMAT" ##  "Obs"
You can then subset and clean up
data_df to suit your purposes.
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