BIS

Install package

The package can be installed from GitHub.

library(devtools)
install_github("stefanangrick/BIS")  # GitHub

Example usage

To import data, first load the package:

library("BIS")

Next, run the get_datasets() function to obtain a list of available data sets:

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

## # A tibble: 20 × 3
##    name                                                          id        url  
##    <chr>                                                         <chr>     <chr>
##  1 Locational banking statistics                                 full_lbs… http…
##  2 Consolidated banking statistics                               full_cbs… http…
##  3 Debt securities statistics                                    full_deb… http…
##  4 Credit to the non-financial sector                            full_tc_… http…
##  5 Credit-to-GDP gaps                                            full_cre… http…
##  6 Debt service ratios for the private non-financial sector      full_dsr… http…
##  7 Global liquidity indicators                                   full_gli… http…
##  8 Exchange-traded derivatives statistics                        full_xtd… http…
##  9 OTC derivatives outstanding                                   full_otc… http…
## 10 US dollar exchange rates (monthly, quarterly and annual)      full_xru… http…
## 11 US dollar exchange rates (daily, horizontal time axis)        full_xru… http…
## 12 US dollar exchange rates (daily, vertical time axis)          full_xru… http…
## 13 Effective exchange rate indices (monthly)                     full_eer… http…
## 14 Effective exchange rate indices (daily, horizontal time axis) full_eer… http…
## 15 Effective exchange rate indices (daily, vertical time axis)   full_eer… http…
## 16 Triennial Survey statistics on turnover                       full_der… http…
## 17 Property prices: selected series                              full_spp… http…
## 18 Property prices: long series                                  full_bis… http…
## 19 Payments and financial market infrastructures statistics      full_bis… http…
## 20 Consumer prices                                               full_lon… http…

The get_datasets() function returns a tibble data frame listing the available data sets. The column url can be used as input for the get_bis() function which downloads, parses and imports the corresponding data set.

To import monthly-frequency data on central banks’ policy rates, run:

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

## # A tibble: 6 × 15
##   freq  freque…¹ ref_a…² refer…³ time_…⁴ time_…⁵ compi…⁶ decim…⁷ decim…⁸ sourc…⁹
##   <chr> <chr>    <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  
## 1 M     Monthly  AR      Argent… <NA>    <NA>    From 0… 4       Four    Centra…
## 2 M     Monthly  AR      Argent… <NA>    <NA>    From 0… 4       Four    Centra…
## 3 M     Monthly  AR      Argent… <NA>    <NA>    From 0… 4       Four    Centra…
## 4 M     Monthly  AR      Argent… <NA>    <NA>    From 0… 4       Four    Centra…
## 5 M     Monthly  AR      Argent… <NA>    <NA>    From 0… 4       Four    Centra…
## 6 M     Monthly  AR      Argent… <NA>    <NA>    From 0… 4       Four    Centra…
## # … with 5 more variables: supp_info_breaks <chr>, title <chr>, series <chr>,
## #   date <chr>, obs_value <dbl>, and abbreviated variable names ¹​frequency,
## #   ²​ref_area, ³​reference_area, ⁴​time_format, ⁵​time_format.1, ⁶​compilation,
## #   ⁷​decimals, ⁸​decimals.1, ⁹​source_ref

To plot the data using 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 <- mutate(rates_plot, date = as.Date(as.yearmon(date, format = "%Y-%m")))

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

Note that BIS data sets come with a number of different time formats. The zoo package (e.g. as.yearmon()) should be able to parse most formats.

Large data sets

Large data sets (e.g. the Locational banking statistics and Debt securities statistics) may cause get_bis() to fail if the amount of available memory is insufficient for executing a required pivot operation. As a workaround, users may wish to set auto_pivot = FALSE when calling get_bis(), then subset the data and run pivot_longer_bis() manually.

options(timeout = 600)
lbs <- get_bis(ds$url[(ds$id == "full_lbs_d_pub_csv")], auto_pivot = FALSE)
lbs <- subset(lbs, l_parent_cty %in% c("US", "DE", "JP"))
lbs <- pivot_longer_bis(lbs)

Retrieve individual data series

To retrieve individual data series instead of full data sets, consider using the BIS SDMX RESTful API. The rsdmx R package is able to process SDMX data within R. The latest rsdmx development version contains a BIS connector that streamlines the process.

Note

This package is in no way officially related to or endorsed by the Bank for International Settlements. It’s based on a fork of CC0-licensed code by expersso. Please don’t abuse the BIS’s servers with unnecessary calls.