BISdata
Functions for downloading data from the Bank for International Settlements (BIS; https://www.bis.org/) in Basel. Supported are only full datasets in (typically) CSV format. The package is lightweight and without dependencies; suggested packages are used only if data is to be transformed into particular data structures, for instance into ‘zoo’ objects. Downloaded data can optionally be cached, to avoid repeated downloads of the same files.
Installation
To install the package from a running R session, type:
install.packages('BISdata',
repos = c('http://enricoschumann.net/R',
getOption('repos')))or clone/build the GitHub version.
Examples
datasets
Fetch a list of all datasets.
library("BISdata")
datasets()filename description updated 1 full_bis_lbs_diss_csv.zip Locational banking statistics (CSV) 2021-10-27 2 full_bis_cbs_csv.zip Consolidated banking statistics (CSV) 2021-10-27 3 full_bis_debt_sec2_csv.zip Debt securities statistics (CSV) 2021-09-20 4 full_bis_total_credit_csv.zip Credit to the non-financial sector (CSV) 2021-09-20 5 full_webstats_credit_gap_dataflow_csv.zip Credit-to-GDP gaps (CSV) 2021-09-20 6 full_bis_dsr_csv.zip Debt service ratios for the private non-financial sector (CSV) 2021-09-20 7 full_bis_gli_csv.zip Global liquidity indicators (CSV) 2021-10-27 8 full_bis_xtd_csv.zip Exchange-traded derivatives statistics (CSV) 2021-09-20 9 full_bis_otc_csv.zip OTC derivatives outstanding (CSV) 2021-11-15 10 full_webstats_xru_current_dataflow_csv.zip US dollar exchange rates (monthly, quarterly and annual) (CSV) 2021-11-17 11 full_webstats_xru_current_d_dataflow_csv_col.zip US dollar exchange rates (daily, horizontal time axis) (CSV) 2021-11-17 12 full_webstats_xru_current_d_dataflow_csv_row.zip US dollar exchange rates (daily, vertical time axis) (CSV) 2021-11-17 13 full_bis_eer_csv.zip Effective exchange rate indices (monthly) (CSV) 2021-11-17 14 full_webstats_eer_d_dataflow_csv_col.zip Effective exchange rate indices (daily, horizontal time axis) (CSV) 2021-11-17 15 full_webstats_eer_d_dataflow_csv_row.zip Effective exchange rate indices (daily, vertical time axis) (CSV) 2021-11-17 16 full_BIS_DER_csv.zip Triennial Survey statistics on turnover (CSV) 2019-12-08 17 full_bis_selected_pp_csv.zip Property prices: selected series (CSV) 2021-08-26 18 full_bis_long_pp_csv.zip Property prices: long series (CSV) (from May 2019, updated in the selected series dataset) <NA> 19 full_bis_rb_csv.zip Payments and financial market infrastructures statistics (CSV) 2021-03-25 20 full_bis_rb_sdmx_ml21.zip Payments and financial market infrastructures statistics (SDMX-ML 2.1) 2021-03-25 21 full_webstats_long_cpi_dataflow_csv.zip Consumer prices (CSV) 2021-10-28 22 full_webstats_cbpol_m_dataflow_csv.zip Policy rates (monthly) (CSV) 2021-11-17 23 full_webstats_cbpol_d_dataflow_csv_col.zip Policy rates (daily, horizontal time axis) (CSV) 2021-11-17 24 full_webstats_cbpol_d_dataflow_csv_row.zip Policy rates (daily, vertical time axis) (CSV) 2021-11-17
fetch_dataset
Fetch a particular dataset.
data <- fetch_dataset(dest.dir = "~/Downloads/BIS",
dataset = "full_bis_total_credit_csv.zip")
str(data)'data.frame': 1133 obs. of 339 variables: $ FREQ : chr "Q" "Q" "Q" "Q" ... $ Frequency : chr "Quarterly" "Quarterly" "Quarterly" "Quarterly" ... $ BORROWERS_CTY : chr "4T" "4T" "4T" "4T" ... $ Borrowers' country: chr "Emerging market economies (aggregate)" ... $ TC_BORROWERS : chr "C" "C" "C" "G" ... $ Borrowing sector : chr "Non financial sector" "Non financial sector" ... $ TC_LENDERS : chr "A" "A" "A" "A" ... $ Lending sector : chr "All sectors" "All sectors" "All sectors" "All sectors" ... $ VALUATION : chr "M" "M" "M" "N" ... $ Valuation : chr "Market value" "Market value" "Market value" "Nominal value" ... $ UNIT_TYPE : chr "770" "799" "USD" "770" ... $ Unit type : chr "Percentage of GDP" "Percentage of GDP (using PPP exchange rates)" ... $ TC_ADJUST : chr "A" "A" "A" "A" ... $ Type of adjustment: chr "Adjusted for breaks" "Adjusted for breaks" ... $ Time Period : chr "Q:4T:C:A:M:770:A" "Q:4T:C:A:M:799:A" "Q:4T:C:A:M:USD:A" ... $ 1940-Q2 : num NA NA NA NA NA NA NA NA NA NA ... $ 1940-Q3 : num NA NA NA NA NA NA NA NA NA NA ... $ 1940-Q4 : num NA NA NA NA NA NA NA NA NA NA ... $ 1941-Q1 : num NA NA NA NA NA NA NA NA NA NA ... $ 1941-Q2 : num NA NA NA NA NA NA NA NA NA NA ... $ 1941-Q3 : num NA NA NA NA NA NA NA NA NA NA ... ## .... $ 2017-Q3 : num 196.4 178.6 53645 50.4 48.3 ... $ 2017-Q4 : num 198.4 180.5 55857.1 51 49.1 ... $ 2018-Q1 : num 200.5 178.5 58616.2 51.6 48.7 ... $ 2018-Q2 : num 188.2 179.8 56539.5 48.5 49.6 ... $ 2018-Q3 : num 184.2 181 55717.4 47.8 50.5 ... $ 2018-Q4 : num 187.8 181.2 56927.6 49.2 50.8 ... $ 2019-Q1 : num 197.8 182.5 59666.9 51.6 50.7 ... $ 2019-Q2 : num 199.2 183.1 60127.3 52.6 51.3 ... $ 2019-Q3 : num 194.6 185.4 59244.3 51.7 52.6 ... $ 2019-Q4 : num 200.9 186.9 61777.3 53.6 53.3 ... $ 2020-Q1 : num 200.4 193.6 60906.7 52.8 54.7 ... $ 2020-Q2 : num 215.1 203.4 63401.2 57.6 58.6 ... $ 2020-Q3 : num 227.3 209 66627.7 61.6 61.2 ... $ 2020-Q4 : num 239.5 211.2 70888.6 66.3 63.2 ... $ 2021-Q1 : num 235.5 209.5 72400.9 65.2 62.7 ...
Transform data into zoo.
library("zoo")
data <- fetch_dataset(dest.dir = "~/Downloads/BIS",
dataset = "full_bis_total_credit_csv.zip",
return.class = "zoo")
summary(data) Index Q:4T:C:A:M:770:A Q:4T:C:A:M:799:A
Min. :1940 Min. :109.7 Min. :113.8
1st Qu.:1960 1st Qu.:122.2 1st Qu.:118.8
Median :1981 Median :141.9 Median :137.8
Mean :1981 Mean :151.9 Mean :146.2
3rd Qu.:2001 3rd Qu.:185.2 3rd Qu.:174.7
Max. :2021 Max. :239.5 Max. :211.2
NA's :246 NA's :246
Q:4T:C:A:M:USD:A Q:4T:G:A:N:770:A Q:4T:G:A:N:799:A
Min. : 6821 Min. :30.90 Min. :33.90
1st Qu.:13173 1st Qu.:38.00 1st Qu.:38.10
Median :29868 Median :40.60 Median :40.30
Mean :31788 Mean :43.69 Mean :43.68
3rd Qu.:46650 3rd Qu.:49.20 3rd Qu.:48.70
Max. :72401 Max. :66.30 Max. :63.20
NA's :246 NA's :271 NA's :271
## ....