BIS

The BIS package provides an R interface to data hosted by the Bank for International Settlements, specifically the single-file data sets available on the BIS homepage.

Installing the package

You can install the package from CRAN or GitHub.

library(devtools)
# install_github("stefanangrick/BIS")
install.packages("BIS", repos = c('https://stefanangrick.r-universe.dev', 'https://cloud.r-project.org'))  # GitHub
install.packages("BIS")              # CRAN

Example usage

To start using the package, load it into your R session.

library("BIS")

Next, retrieve a list of available data sets using the get_datasets() function.

ds <- get_datasets()
head(ds, 20)

## # A tibble: 20 × 3
##    name                                                              id    url  
##    <chr>                                                             <chr> <chr>
##  1 Locational banking statistics (CSV, flat)10 Dec 2024              WS_L… http…
##  2 Consolidated banking statistics (CSV, flat)10 Dec 2024            WS_C… http…
##  3 Debt securities statistics (CSV, flat)10 Dec 2024                 WS_N… http…
##  4 International debt securities (BIS-compiled) (CSV, flat)10 Dec 2… WS_D… http…
##  5 Credit to the non-financial sector (CSV, flat)10 Dec 2024         WS_T… http…
##  6 Credit-to-GDP gaps (CSV, flat)10 Dec 2024                         WS_C… http…
##  7 Debt service ratios (CSV, flat)10 Dec 2024                        WS_D… http…
##  8 Global liquidity indicators (CSV, flat)10 Dec 2024                WS_G… http…
##  9 Exchange traded derivatives (CSV, flat)10 Dec 2024                WS_X… http…
## 10 OTC derivatives statistics (CSV, flat)10 Dec 2024                 WS_O… http…
## 11 Triennial survey (CSV, flat)5 Dec 2022                            WS_D… http…
## 12 Selected residential property prices (CSV, flat)19 Dec 2024       WS_S… http…
## 13 Detailed residential property prices (CSV, flat)19 Dec 2024       WS_D… http…
## 14 Commercial property prices (CSV, flat)19 Dec 2024                 WS_C… http…
## 15 Consumer prices index (CSV, flat)19 Dec 2024                      WS_L… http…
## 16 Bilateral exchange rates (CSV, flat)3 Jan 2025                    WS_X… http…
## 17 Effective exchange rates (CSV, flat)3 Jan 2025                    WS_E… http…
## 18 Central bank total assets (CSV, flat)28 Nov 2024                  WS_C… http…
## 19 Central bank policy rates (CSV, flat)3 Jan 2025                   WS_C… http…
## 20 CPMI comparative tables type 1 (CSV, flat)                        WS_C… http…

The get_datasets() function returns a tibble data frame listing available data sets. Use the url column as input for the get_bis() function to download, parse, and import the corresponding data set.

For example, to import monthly-frequency data on central banks’ policy rates, use the following code:

rates <- get_bis(ds$url[ds$id == "WS_CBPOL_csv_flat"])
head(rates)

## # A tibble: 6 × 18
##   structure structure_id             action freq  ref_area time_period obs_value
##   <chr>     <chr>                    <chr>  <chr> <chr>    <chr>           <dbl>
## 1 dataflow  BIS:WS_CBPOL(1.0): Cent… I      M: M… BE: Bel… 1945-01           1.5
## 2 dataflow  BIS:WS_CBPOL(1.0): Cent… I      M: M… BE: Bel… 1945-02           1.5
## 3 dataflow  BIS:WS_CBPOL(1.0): Cent… I      M: M… BE: Bel… 1945-03           1.5
## 4 dataflow  BIS:WS_CBPOL(1.0): Cent… I      M: M… BE: Bel… 1945-04           1.5
## 5 dataflow  BIS:WS_CBPOL(1.0): Cent… I      M: M… BE: Bel… 1945-05           1.5
## 6 dataflow  BIS:WS_CBPOL(1.0): Cent… I      M: M… BE: Bel… 1945-06           1.5
## # ℹ 11 more variables: unit_measure <chr>, unit_mult <chr>, time_format <chr>,
## #   compilation <chr>, decimals <chr>, source_ref <chr>,
## #   supp_info_breaks <chr>, title <chr>, obs_status <chr>, obs_conf <chr>,
## #   obs_pre_break <chr>

To plot the data with ggplot2, run the following:

library("dplyr")
library("ggplot2")
library("zoo")

rates_plot <- subset(rates, ref_area %in% c("US", "XM", "JP", "GB", "CH", "CA"))
rates_plot <- subset(rates, ref_area %in% c("US: United States",
                                            "XM: Euro area",
                                            "JP: Japan",
                                            "GB: United Kingdom",
                                            "CH: Switzerland",
                                            "CA: Canada"))
rates_plot <- mutate(rates_plot, time_period =
                       as.Date(as.yearmon(time_period, format = "%Y-%m")))

ggplot(rates_plot, aes(time_period, obs_value, color = ref_area)) +
  geom_line(show.legend = FALSE) +
  facet_wrap(~ref_area) +
  labs(title = "Central bank policy rates",
       subtitle = "% per annum", x = NULL, y = NULL)

Note that BIS data sets use various time formats. The zoo package (e.g., as.yearmon()) can handle most of these formats.

Reading locally stored files

In some cases, the BIS homepage may only be accessible through a web browser, preventing the programmatic retrieval of data sets directly within R. When this occurs, users can manually download the files and use the read_bis() function to parse them.

To read a locally stored CSV file, use the following code:

df <- read_bis("WS_CBPOL_csv_flat.csv")

To read a locally stored ZIP file, use this code:

df <- read_bis(.unzip_file("WS_CBPOL_csv_flat.zip"))

Retrieving individual data series

To retrieve individual data series instead of full data sets, consider using the BIS SDMX RESTful API. The rsdmx R package supports processing SDMX data in R. The latest development version of rsdmx includes a BIS connector to streamline the process.

Note

This package is neither officially related to nor endorsed by the Bank for International Settlements. It is based on a fork of CC0-licensed code by expersso. Please avoid overloading the BIS servers with unnecessary requests.