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.
You can install the package from CRAN or GitHub.
library(devtools)
install_github("stefanangrick/BIS") # GitHub
install.packages("BIS") # CRANTo 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.
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"))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.
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.