R/bis_import_functions.R
In MichaelGurkov/MiscImport:
Defines functions
import_bis_policy_rates
Documented in
import_bis_policy_rates
#' This function imports policy rates from BIS format
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearmon
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @import magrittr
#'
#' @importFrom stringr str_replace_all
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @export
#'
import_bis_policy_rates = function(file_path){
. = NULL
filtered_df = read_csv(file_path,col_types = cols()) %>%
select(matches("[0-9]+"), `Reference area`) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = "reference_area") %>%
mutate(country = str_replace_all(country, "\\s", "_")) %>%
pivot_longer(-country,names_to = "date",values_to = "policy_rate") %>%
filter(complete.cases(.)) %>%
mutate(date = as.yearmon(date))
return(filtered_df)
}
#' This function imports credit to GDP ratios from BIS format
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @param my_frequency time frequency of the data (default is Quarterly),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_borrowing_sector (default is Private non-financial sector),
#' available options are:
#' \itemize{
#' \item Private non-financial sector
#' }
#'
#' @param my_lending_sector (default is All sectors),
#' available options are:
#' \itemize{
#' \item All sectors
#' }
#'
#' @export
#'
import_bis_credit_to_gdp_ratios = function(file_path,
my_frequency = "Quarterly",
my_borrowing_sector = "Private non-financial sector",
my_lending_sector = "All sectors") {
. = NULL
raw_df = read_csv(file_path,col_types = cols())
named_df = raw_df %>%
select(
-FREQ,
-TC_BORROWERS,
-TC_LENDERS,
-`Time Period`,
-CG_DTYPE
) %>%
rename_all(~ str_replace_all(., " ", "_")) %>%
rename_all(~ str_remove_all(., "'")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = borrowers_country) %>%
rename(country_code = borrowers_cty)
filtered_df = named_df %>%
filter(frequency == my_frequency) %>%
select(-frequency) %>%
filter(borrowing_sector == my_borrowing_sector) %>%
select(-borrowing_sector) %>%
filter(lending_sector == my_lending_sector) %>%
select(-lending_sector)
clean_df = filtered_df %>%
mutate(credit_gap_data_type = str_remove_all(credit_gap_data_type,"\\(.*\\)")) %>%
mutate(credit_gap_data_type = str_remove_all(credit_gap_data_type,"Credit-to-GDP")) %>%
mutate(credit_gap_data_type = str_remove_all(credit_gap_data_type,"\\s")) %>%
pivot_longer(-c(country,
country_code,
credit_gap_data_type), names_to = "date",
values_to = "credit_gdp") %>%
filter(complete.cases(.)) %>%
mutate(date = as.yearqtr(date, format = "%Y-Q%q")) %>%
mutate(country = str_replace_all(country, "\\s", "_"))
return(clean_df)
}
#' This function imports total credit to private sector from BIS format
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_borrowing_sector (default is NULL),
#' available options are:
#' \itemize{
#' \item Non financial sector
#' \item General government
#' \item Households & NPISHs
#' \item Non-financial corporations
#' \item Private non-financial sector
#' }
#'
#' @param my_lending_sector (default is NULL),
#' available options are:
#' \itemize{
#' \item All sectors
#' \item Banks, domestic
#' }
#'
#' @param my_valuation_method (default is NULL), toggles the
#' difference between credit and debt. available options are:
#' \itemize{
#' \item Nominal value
#' \item Market value
#' }
#'
#' @param my_unit_type (default is NULL),
#' available options are:
#' \itemize{
#' \item US Dollar
#' \item Percentage of GDP
#' \item Percentage of GDP-PPP
#' \item Domestic currency
#' }
#'
#'
#' @param my_adjustment (default is NULL),
#' available options are:
#' \itemize{
#' \item Adjusted for breaks
#' \item Unadjusted for breaks
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#' @export
#'
import_bis_total_credit = function(file_path = NULL,
my_frequency = NULL,
my_borrowing_sector = NULL,
my_lending_sector = NULL,
my_valuation_method = NULL,
my_unit_type = NULL,
my_adjustment = NULL,
pivot_to_long = FALSE) {
long_currency_str = paste0("Domestic currency \\(incl\\. conversion",
" to current currency made using",
" a fix parity\\)")
filtered_df = read_csv(file_path, show_col_types = FALSE) %>%
select(-matches("^[A-Z_]+$", ignore.case = FALSE)) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = "borrowers'_country") %>%
mutate(country = str_replace_all(country, "\\s", "_")) %>%
mutate(unit_type = str_replace_all(unit_type,
long_currency_str,
"Domestic currency"))
for (filter_name in c(
"my_frequency",
"my_borrowing_sector",
"my_lending_sector",
"my_valuation_method",
"my_unit_type",
"my_adjustment"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
select(-series)
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "total_credit"
)
}
return(filtered_df)
}
#' This function imports debt securities from BIS format
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_measure (default is NULL),
#' available options are:
#' \itemize{
#' \item Amounts outstanding
#' \item Net issues
#' \item Gross issues
#' }
#'
#' @param my_issuer_residence (default is NULL)
#' The residence of the issuer is the country where the issuer is incorporated.
#'
#' @param my_issuer_nationality (default is NULL)
#' The nationality of the issuer is the country where the issuer's parent is headquartered
#'
#' @param my_issuer_sector_immediate_borrower (default is NULL),
#' available options are:
#' \itemize{
#' \item All issuers
#' \item Financial corporations
#' \item Private banks
#' \item International institutions
#' \item Public banks
#' \item General government
#' \item Non-financial corporations
#' \item Private other financial institutions
#' \item Central bank
#' \item Public other financial institutions
#' }
#'
#' @param my_issuer_sector_ultimate_borrower (default is NULL),
#' available options are:
#' \itemize{
#' \item All issuers
#' \item Financial corporations
#' \item Private banks
#' \item International institutions
#' \item Public banks
#' \item General government
#' \item Non-financial corporations
#' \item Private other financial institutions
#' \item Central bank
#' \item Public other financial institutions
#' }
#'
#' @param my_issue_market (default is NULL),
#' available options are:
#' \itemize{
#' \item All markets
#' \item Domestic market
#' \item International markets
#' }
#'
#' @param my_issue_type (default is NULL),
#' available options are:
#' \itemize{
#' \item All issue types
#' }
#'
#'
#' @param my_issue_currency_group (default is NULL),
#' available options are:
#' \itemize{
#' \item All currencies
#' \item Foreign currencies
#' \item Domestic currency
#' }
#' @param my_issue_currency (default is NULL),
#' There are 65 available options, the special are:
#' \itemize{
#' \item Total all currencies
#' \item Sum of ECU, Euro and legacy currencies now included in the Euro
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#'
#' @export
#'
import_bis_debt_sec = function(file_path,
my_frequency = NULL,
my_measure = NULL,
my_issuer_residence = NULL,
my_issuer_nationality = NULL,
my_issuer_sector_immediate_borrower = NULL,
my_issuer_sector_ultimate_borrower = NULL,
my_issue_market = NULL,
my_issue_type = NULL,
my_issue_currency_group = NULL,
my_issue_currency = NULL,
my_original_maturity = NULL,
my_remaining_maturity = NULL,
my_rate_type = NULL,
my_default_risk = NULL,
my_collateral_type = NULL,
pivot_to_long = FALSE) {
. = NULL
filtered_df = read_csv(file_path, col_types = cols()) %>%
select(-matches("^[A-Z_]+$", ignore.case = FALSE)) %>%
select(-`Time Period`) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
mutate(issuer_residence = str_replace_all(issuer_residence,"\\s","_"))
for (filter_val in c(
"my_frequency",
"my_measure",
"my_issuer_residence",
"my_issuer_nationality",
"my_issuer_sector_immediate_borrower",
"my_issuer_sector_ultimate_borrower",
"my_issue_market",
"my_issue_type",
"my_issue_currency_group",
"my_issue_currency",
"my_original_maturity",
"my_remaining_maturity",
"my_rate_type",
"my_default_risk",
"my_collateral_type"
)) {
if(!is.null(get(filter_val))){
filter_name = filter_val %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% !!sym(filter_val))
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "debt_credit"
) %>%
mutate(date = as.yearqtr(date, format = "%Y-Q%q"))
}
return(filtered_df)
}
#' This function imports locational banking statistics from BIS format
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_measure (default is NULL),
#' available options are:
#' \itemize{
#' \item FX and break adjusted change (BIS calculated)
#' \item Amounts outstanding / Stocks
#' }
#'
#' @param my_balance_sheet_position (default is NULL)
#' available options are:
#' \itemize{
#' \item Total claims
#' \item Total liabilities
#' }
#'
#' @param my_type_of_instruments (default is NULL)
#' available options are:
#' \itemize{
#' \item All instruments
#' \item Debt securities
#' \item Loans and deposits
#' \item Other instruments
#' \item Unallocated by instrument
#' \item Debt securities, short-term
#' }
#'
#' @param my_currency_denomination (default is NULL),
#' available options are:
#' \itemize{
#' \item Swiss Franc
#' \item Euro
#' \item Pound Sterling
#' \item Japanese Yen
#' \item US Dollar
#' \item All currencies
#' \item All currencies excluding USD, EUR, JPY, CHF and GBP
#' \item Unallocated currencies
#' }
#'
#' @param my_currency_type_of_reporting_country (default is NULL),
#' available options are:
#' \itemize{
#' \item All currencies (=D+F+U)
#' \item Foreign currency (ie currencies foreign to bank location country)
#' \item Domestic currency (ie currency of bank location country)
#' \item Unclassified currency
#' }
#'
#' @param my_parent_country string
#'
#' @param my_reporting_country string
#'
#' @param my_type_of_reporting_institutions (default is NULL),
#' available options are:
#' \itemize{
#' \item All reporting banks/institutions (domestic, foreign, consortium and unclassified)
#' \item Foreign branches
#' \item Domestic banks
#' \item Foreign subsidiaries
#' }
#'
#' @param my_counterparty_sector (default is NULL),
#' available options are:
#' \itemize{
#' \item All sectors
#' \item Banks, total
#' \item Non-financial corporations
#' \item Non-bank financial institutions
#' \item General government
#' \item Households and NPISHs
#' \item Banks, related offices
#' \item Banks, central banks
#' \item Non-banks, total
#' \item Non-financial sectors
#' \item Unallocated by sector
#'
#' }
#'
#' @param counterparty_country string
#'
#'
#' @param my_position_type (default is NULL),
#' available options are:
#' \itemize{
#' \item All
#' \item Cross-border
#' \item Local
#' \item Unallocated
#' }
#'
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#'
#' @export
#'
import_bis_lbs = function(file_path,
my_frequency = NULL,
my_measure = NULL,
my_balance_sheet_position = NULL,
my_type_of_instruments = NULL,
my_currency_denomination = NULL,
my_currency_type_of_reporting_country = NULL,
my_parent_country = NULL,
my_type_of_reporting_institutions = NULL,
my_reporting_country = NULL,
my_counterparty_sector = NULL,
my_counterparty_country = NULL,
my_position_type = NULL,
pivot_to_long = FALSE) {
filtered_df = read_csv(file_path, show_col_types = FALSE) %>%
select(-matches("^[A-Z_]+$", ignore.case = FALSE)) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]"))
for (filter_name in c(
"my_frequency",
"my_measure",
"my_balance_sheet_position",
"my_type_of_instruments",
"my_currency_denomination",
"my_currency_type_of_reporting_country",
"my_parent_country",
"my_type_of_reporting_institutions",
"my_reporting_country",
"my_counterparty_sector",
"my_counterparty_country",
"my_position_type"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "balance"
) %>%
mutate(date = as.yearqtr(date, format = "%Y-Q%q"))
}
return(filtered_df)
}
#' @title This function imports BIS residential property prices
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_unit_of_measure (default is NULL),
#' available options are:
#' \itemize{
#' \item Index, 2010 = 100
#' \item Year-on-year changes, in per cent
#' }
#'
#'
#' @param my_value (default is NULL),
#' available options are:
#' \itemize{
#' \item Real
#' \item Nominal
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#' @export
#'
#'
import_bis_selected_property_prices = function(file_path,
my_frequency = NULL,
my_unit_of_measure = NULL,
my_value = NULL,
pivot_to_long = FALSE) {
. = NULL
filtered_df = read_csv(file_path, col_types = cols()) %>%
select(-`Time Period`) %>%
select(-matches("^[A-Z_]+$", ignore.case = FALSE)) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = reference_area) %>%
mutate(country = str_replace_all(country, "\\s", "_")) %>%
mutate(across(where(is.character), ~str_remove_all(.,"^[A-Z0-9]+:")))
for (filter_name in c(
"my_frequency",
"my_unit_of_measure",
"my_value"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "property_price"
) %>%
mutate(date = as.yearqtr(date, format = "%Y-Q%q"))
}
return(filtered_df)
}
#' @title This function imports BIS FX rates
#'
#' @importFrom readr read_csv cols
#'
#'
#' @importFrom stats complete.cases
#'
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Daily
#' \item Quarterly
#' \item Annual
#' }
#'
#' @param my_collection (default is NULL),
#' available options are:
#' \itemize{
#' \item Average of observations through period
#' \item End of period
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#' @export
#'
#'
import_bis_fx_rates = function(file_path,
my_frequency = NULL,
my_currency = NULL,
my_collection = NULL,
pivot_to_long = FALSE) {
. = NULL
filtered_df = read_csv(file_path, col_types = cols()) %>%
select(matches("[0-9]"),Frequency,CURRENCY,
Collection, `Reference area`) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
# rename_all( ~ str_replace_all(., "_-_", "_")) %>%
# rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = reference_area) %>%
mutate(country = str_replace_all(country, "\\s", "_"))
for (filter_name in c(
"my_frequency",
"my_collection"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "fx_rate") %>%
filter(complete.cases(.))
}
return(filtered_df)
}
#' @title This function imports BIS CPI index
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Monthly
#' \item Annual
#' }
#'
#' @param unit_of_measure (default is NULL),
#' available options are:
#' \itemize{
#' \item Index, 2010 = 100
#' \item Year-on-year changes, in per cent
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#' @export
#'
#'
import_bis_cpi_index = function(file_path,
my_frequency = NULL,
my_unit_of_measure = NULL,
pivot_to_long = FALSE) {
. = NULL
filtered_df = read_csv(file_path, col_types = cols()) %>%
select(-matches("^[A-Z_]+$", ignore.case = FALSE)) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = reference_area) %>%
mutate(country = str_replace_all(country, "\\s", "_"))
for (filter_name in c(
"my_frequency",
"my_unit_of_measure"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "cpi"
)
filtered_df = filtered_df %>%
select(country, date, cpi) %>%
filter(complete.cases(.))
}
return(filtered_df)
}
#' This function imports global liquidity indicators from BIS format
#'
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_currency_of_denomination currency denomination
#' (default is NULL),
#' available options are:
#' \itemize{
#' \item Euro
#' \item Yen
#' \item All currencies
#' \item US dollar
#' }
#'
#' @param my_country borrowers country (default is NULL),
#'
#' @param my_borrowers_sector (default is NULL),
#' available options are:
#' \itemize{
#' \item All sectors
#' \item Banks, total
#' \item General government
#' \item Non-banks, total
#' \item Non-financial sectors
#' \item Non-financial private sector
#' }
#'
#' @param my_lending_sector (default is NULL),
#' available options are:
#' \itemize{
#' \item All sectors
#' \item Banks, total
#' }
#'
#' @param my_position_type (default is NULL), toggles the
#' difference between cross border and local position.
#' available options are:
#' \itemize{
#' \item All
#' \item Cross-border & Local in FCY
#' \item Cross-border
#' \item Local
#' }
#'
#' @param my_type_of_instruments (default is NULL),
#' available options are:
#' \itemize{
#' \item All instruments
#' \item Credit (loans & debt securities)
#' \item Debt securities
#' \item Loans and deposits
#' }
#'
#'
#' @param my_unit_of_measure (default is NULL),
#' available options are:
#' \itemize{
#' \item Year-on-year changes, in per cent
#' \item Euro
#' \item Yen
#' \item US dollar
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#' @export
#'
import_bis_liqiudity_indicators = function(file_path,
my_frequency = NULL,
my_currency_of_denomination = NULL,
my_borrowers_country = NULL,
my_borrowers_sector = NULL,
my_lending_sector = NULL,
my_position_type = NULL,
my_type_of_instruments = NULL,
my_unit_of_measure = NULL,
pivot_to_long = FALSE) {
. = NULL
filtered_df = read_csv(file_path, col_types = cols()) %>%
select(-`Time Period`,-Title,
-`Unit Multiplier`, -Decimals,
-`Collection Indicator`, -Availability) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_all(~ str_remove_all(., "\\'")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
mutate(borrowers_country = str_replace_all(borrowers_country,
"\\s", "_")) %>%
mutate(across(where(is.character), ~str_remove_all(.,"^[A-Z0-9]+:")))
for (filter_name in c(
"my_frequency",
"my_currency_of_denomination",
"my_borrowers_country",
"my_borrowers_sector",
"my_lending_sector",
"my_position_type",
"my_type_of_instruments",
"my_unit_of_measure"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "value"
)
}
return(filtered_df)
}
MichaelGurkov/MiscImport documentation built on June 6, 2023, 10:53 p.m.
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Defines functions import_bis_policy_rates
Documented in import_bis_policy_rates
#' This function imports policy rates from BIS format
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearmon
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @import magrittr
#'
#' @importFrom stringr str_replace_all
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @export
#'
import_bis_policy_rates = function(file_path){
. = NULL
filtered_df = read_csv(file_path,col_types = cols()) %>%
select(matches("[0-9]+"), `Reference area`) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = "reference_area") %>%
mutate(country = str_replace_all(country, "\\s", "_")) %>%
pivot_longer(-country,names_to = "date",values_to = "policy_rate") %>%
filter(complete.cases(.)) %>%
mutate(date = as.yearmon(date))
return(filtered_df)
}
#' This function imports credit to GDP ratios from BIS format
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @param my_frequency time frequency of the data (default is Quarterly),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_borrowing_sector (default is Private non-financial sector),
#' available options are:
#' \itemize{
#' \item Private non-financial sector
#' }
#'
#' @param my_lending_sector (default is All sectors),
#' available options are:
#' \itemize{
#' \item All sectors
#' }
#'
#' @export
#'
import_bis_credit_to_gdp_ratios = function(file_path,
my_frequency = "Quarterly",
my_borrowing_sector = "Private non-financial sector",
my_lending_sector = "All sectors") {
. = NULL
raw_df = read_csv(file_path,col_types = cols())
named_df = raw_df %>%
select(
-FREQ,
-TC_BORROWERS,
-TC_LENDERS,
-`Time Period`,
-CG_DTYPE
) %>%
rename_all(~ str_replace_all(., " ", "_")) %>%
rename_all(~ str_remove_all(., "'")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = borrowers_country) %>%
rename(country_code = borrowers_cty)
filtered_df = named_df %>%
filter(frequency == my_frequency) %>%
select(-frequency) %>%
filter(borrowing_sector == my_borrowing_sector) %>%
select(-borrowing_sector) %>%
filter(lending_sector == my_lending_sector) %>%
select(-lending_sector)
clean_df = filtered_df %>%
mutate(credit_gap_data_type = str_remove_all(credit_gap_data_type,"\\(.*\\)")) %>%
mutate(credit_gap_data_type = str_remove_all(credit_gap_data_type,"Credit-to-GDP")) %>%
mutate(credit_gap_data_type = str_remove_all(credit_gap_data_type,"\\s")) %>%
pivot_longer(-c(country,
country_code,
credit_gap_data_type), names_to = "date",
values_to = "credit_gdp") %>%
filter(complete.cases(.)) %>%
mutate(date = as.yearqtr(date, format = "%Y-Q%q")) %>%
mutate(country = str_replace_all(country, "\\s", "_"))
return(clean_df)
}
#' This function imports total credit to private sector from BIS format
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_borrowing_sector (default is NULL),
#' available options are:
#' \itemize{
#' \item Non financial sector
#' \item General government
#' \item Households & NPISHs
#' \item Non-financial corporations
#' \item Private non-financial sector
#' }
#'
#' @param my_lending_sector (default is NULL),
#' available options are:
#' \itemize{
#' \item All sectors
#' \item Banks, domestic
#' }
#'
#' @param my_valuation_method (default is NULL), toggles the
#' difference between credit and debt. available options are:
#' \itemize{
#' \item Nominal value
#' \item Market value
#' }
#'
#' @param my_unit_type (default is NULL),
#' available options are:
#' \itemize{
#' \item US Dollar
#' \item Percentage of GDP
#' \item Percentage of GDP-PPP
#' \item Domestic currency
#' }
#'
#'
#' @param my_adjustment (default is NULL),
#' available options are:
#' \itemize{
#' \item Adjusted for breaks
#' \item Unadjusted for breaks
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#' @export
#'
import_bis_total_credit = function(file_path = NULL,
my_frequency = NULL,
my_borrowing_sector = NULL,
my_lending_sector = NULL,
my_valuation_method = NULL,
my_unit_type = NULL,
my_adjustment = NULL,
pivot_to_long = FALSE) {
long_currency_str = paste0("Domestic currency \\(incl\\. conversion",
" to current currency made using",
" a fix parity\\)")
filtered_df = read_csv(file_path, show_col_types = FALSE) %>%
select(-matches("^[A-Z_]+$", ignore.case = FALSE)) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = "borrowers'_country") %>%
mutate(country = str_replace_all(country, "\\s", "_")) %>%
mutate(unit_type = str_replace_all(unit_type,
long_currency_str,
"Domestic currency"))
for (filter_name in c(
"my_frequency",
"my_borrowing_sector",
"my_lending_sector",
"my_valuation_method",
"my_unit_type",
"my_adjustment"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
select(-series)
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "total_credit"
)
}
return(filtered_df)
}
#' This function imports debt securities from BIS format
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_measure (default is NULL),
#' available options are:
#' \itemize{
#' \item Amounts outstanding
#' \item Net issues
#' \item Gross issues
#' }
#'
#' @param my_issuer_residence (default is NULL)
#' The residence of the issuer is the country where the issuer is incorporated.
#'
#' @param my_issuer_nationality (default is NULL)
#' The nationality of the issuer is the country where the issuer's parent is headquartered
#'
#' @param my_issuer_sector_immediate_borrower (default is NULL),
#' available options are:
#' \itemize{
#' \item All issuers
#' \item Financial corporations
#' \item Private banks
#' \item International institutions
#' \item Public banks
#' \item General government
#' \item Non-financial corporations
#' \item Private other financial institutions
#' \item Central bank
#' \item Public other financial institutions
#' }
#'
#' @param my_issuer_sector_ultimate_borrower (default is NULL),
#' available options are:
#' \itemize{
#' \item All issuers
#' \item Financial corporations
#' \item Private banks
#' \item International institutions
#' \item Public banks
#' \item General government
#' \item Non-financial corporations
#' \item Private other financial institutions
#' \item Central bank
#' \item Public other financial institutions
#' }
#'
#' @param my_issue_market (default is NULL),
#' available options are:
#' \itemize{
#' \item All markets
#' \item Domestic market
#' \item International markets
#' }
#'
#' @param my_issue_type (default is NULL),
#' available options are:
#' \itemize{
#' \item All issue types
#' }
#'
#'
#' @param my_issue_currency_group (default is NULL),
#' available options are:
#' \itemize{
#' \item All currencies
#' \item Foreign currencies
#' \item Domestic currency
#' }
#' @param my_issue_currency (default is NULL),
#' There are 65 available options, the special are:
#' \itemize{
#' \item Total all currencies
#' \item Sum of ECU, Euro and legacy currencies now included in the Euro
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#'
#' @export
#'
import_bis_debt_sec = function(file_path,
my_frequency = NULL,
my_measure = NULL,
my_issuer_residence = NULL,
my_issuer_nationality = NULL,
my_issuer_sector_immediate_borrower = NULL,
my_issuer_sector_ultimate_borrower = NULL,
my_issue_market = NULL,
my_issue_type = NULL,
my_issue_currency_group = NULL,
my_issue_currency = NULL,
my_original_maturity = NULL,
my_remaining_maturity = NULL,
my_rate_type = NULL,
my_default_risk = NULL,
my_collateral_type = NULL,
pivot_to_long = FALSE) {
. = NULL
filtered_df = read_csv(file_path, col_types = cols()) %>%
select(-matches("^[A-Z_]+$", ignore.case = FALSE)) %>%
select(-`Time Period`) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
mutate(issuer_residence = str_replace_all(issuer_residence,"\\s","_"))
for (filter_val in c(
"my_frequency",
"my_measure",
"my_issuer_residence",
"my_issuer_nationality",
"my_issuer_sector_immediate_borrower",
"my_issuer_sector_ultimate_borrower",
"my_issue_market",
"my_issue_type",
"my_issue_currency_group",
"my_issue_currency",
"my_original_maturity",
"my_remaining_maturity",
"my_rate_type",
"my_default_risk",
"my_collateral_type"
)) {
if(!is.null(get(filter_val))){
filter_name = filter_val %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% !!sym(filter_val))
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "debt_credit"
) %>%
mutate(date = as.yearqtr(date, format = "%Y-Q%q"))
}
return(filtered_df)
}
#' This function imports locational banking statistics from BIS format
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_measure (default is NULL),
#' available options are:
#' \itemize{
#' \item FX and break adjusted change (BIS calculated)
#' \item Amounts outstanding / Stocks
#' }
#'
#' @param my_balance_sheet_position (default is NULL)
#' available options are:
#' \itemize{
#' \item Total claims
#' \item Total liabilities
#' }
#'
#' @param my_type_of_instruments (default is NULL)
#' available options are:
#' \itemize{
#' \item All instruments
#' \item Debt securities
#' \item Loans and deposits
#' \item Other instruments
#' \item Unallocated by instrument
#' \item Debt securities, short-term
#' }
#'
#' @param my_currency_denomination (default is NULL),
#' available options are:
#' \itemize{
#' \item Swiss Franc
#' \item Euro
#' \item Pound Sterling
#' \item Japanese Yen
#' \item US Dollar
#' \item All currencies
#' \item All currencies excluding USD, EUR, JPY, CHF and GBP
#' \item Unallocated currencies
#' }
#'
#' @param my_currency_type_of_reporting_country (default is NULL),
#' available options are:
#' \itemize{
#' \item All currencies (=D+F+U)
#' \item Foreign currency (ie currencies foreign to bank location country)
#' \item Domestic currency (ie currency of bank location country)
#' \item Unclassified currency
#' }
#'
#' @param my_parent_country string
#'
#' @param my_reporting_country string
#'
#' @param my_type_of_reporting_institutions (default is NULL),
#' available options are:
#' \itemize{
#' \item All reporting banks/institutions (domestic, foreign, consortium and unclassified)
#' \item Foreign branches
#' \item Domestic banks
#' \item Foreign subsidiaries
#' }
#'
#' @param my_counterparty_sector (default is NULL),
#' available options are:
#' \itemize{
#' \item All sectors
#' \item Banks, total
#' \item Non-financial corporations
#' \item Non-bank financial institutions
#' \item General government
#' \item Households and NPISHs
#' \item Banks, related offices
#' \item Banks, central banks
#' \item Non-banks, total
#' \item Non-financial sectors
#' \item Unallocated by sector
#'
#' }
#'
#' @param counterparty_country string
#'
#'
#' @param my_position_type (default is NULL),
#' available options are:
#' \itemize{
#' \item All
#' \item Cross-border
#' \item Local
#' \item Unallocated
#' }
#'
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#'
#' @export
#'
import_bis_lbs = function(file_path,
my_frequency = NULL,
my_measure = NULL,
my_balance_sheet_position = NULL,
my_type_of_instruments = NULL,
my_currency_denomination = NULL,
my_currency_type_of_reporting_country = NULL,
my_parent_country = NULL,
my_type_of_reporting_institutions = NULL,
my_reporting_country = NULL,
my_counterparty_sector = NULL,
my_counterparty_country = NULL,
my_position_type = NULL,
pivot_to_long = FALSE) {
filtered_df = read_csv(file_path, show_col_types = FALSE) %>%
select(-matches("^[A-Z_]+$", ignore.case = FALSE)) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]"))
for (filter_name in c(
"my_frequency",
"my_measure",
"my_balance_sheet_position",
"my_type_of_instruments",
"my_currency_denomination",
"my_currency_type_of_reporting_country",
"my_parent_country",
"my_type_of_reporting_institutions",
"my_reporting_country",
"my_counterparty_sector",
"my_counterparty_country",
"my_position_type"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "balance"
) %>%
mutate(date = as.yearqtr(date, format = "%Y-Q%q"))
}
return(filtered_df)
}
#' @title This function imports BIS residential property prices
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_unit_of_measure (default is NULL),
#' available options are:
#' \itemize{
#' \item Index, 2010 = 100
#' \item Year-on-year changes, in per cent
#' }
#'
#'
#' @param my_value (default is NULL),
#' available options are:
#' \itemize{
#' \item Real
#' \item Nominal
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#' @export
#'
#'
import_bis_selected_property_prices = function(file_path,
my_frequency = NULL,
my_unit_of_measure = NULL,
my_value = NULL,
pivot_to_long = FALSE) {
. = NULL
filtered_df = read_csv(file_path, col_types = cols()) %>%
select(-`Time Period`) %>%
select(-matches("^[A-Z_]+$", ignore.case = FALSE)) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = reference_area) %>%
mutate(country = str_replace_all(country, "\\s", "_")) %>%
mutate(across(where(is.character), ~str_remove_all(.,"^[A-Z0-9]+:")))
for (filter_name in c(
"my_frequency",
"my_unit_of_measure",
"my_value"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "property_price"
) %>%
mutate(date = as.yearqtr(date, format = "%Y-Q%q"))
}
return(filtered_df)
}
#' @title This function imports BIS FX rates
#'
#' @importFrom readr read_csv cols
#'
#'
#' @importFrom stats complete.cases
#'
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Daily
#' \item Quarterly
#' \item Annual
#' }
#'
#' @param my_collection (default is NULL),
#' available options are:
#' \itemize{
#' \item Average of observations through period
#' \item End of period
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#' @export
#'
#'
import_bis_fx_rates = function(file_path,
my_frequency = NULL,
my_currency = NULL,
my_collection = NULL,
pivot_to_long = FALSE) {
. = NULL
filtered_df = read_csv(file_path, col_types = cols()) %>%
select(matches("[0-9]"),Frequency,CURRENCY,
Collection, `Reference area`) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
# rename_all( ~ str_replace_all(., "_-_", "_")) %>%
# rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = reference_area) %>%
mutate(country = str_replace_all(country, "\\s", "_"))
for (filter_name in c(
"my_frequency",
"my_collection"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "fx_rate") %>%
filter(complete.cases(.))
}
return(filtered_df)
}
#' @title This function imports BIS CPI index
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Monthly
#' \item Annual
#' }
#'
#' @param unit_of_measure (default is NULL),
#' available options are:
#' \itemize{
#' \item Index, 2010 = 100
#' \item Year-on-year changes, in per cent
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#' @export
#'
#'
import_bis_cpi_index = function(file_path,
my_frequency = NULL,
my_unit_of_measure = NULL,
pivot_to_long = FALSE) {
. = NULL
filtered_df = read_csv(file_path, col_types = cols()) %>%
select(-matches("^[A-Z_]+$", ignore.case = FALSE)) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
rename(country = reference_area) %>%
mutate(country = str_replace_all(country, "\\s", "_"))
for (filter_name in c(
"my_frequency",
"my_unit_of_measure"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "cpi"
)
filtered_df = filtered_df %>%
select(country, date, cpi) %>%
filter(complete.cases(.))
}
return(filtered_df)
}
#' This function imports global liquidity indicators from BIS format
#'
#'
#' @importFrom readr read_csv cols
#'
#' @importFrom zoo as.yearqtr
#'
#' @importFrom stats complete.cases
#'
#' @importFrom rlang .data
#'
#' @importFrom tidyr pivot_longer
#'
#' @importFrom stringr str_replace_all str_remove_all
#'
#' @import magrittr
#'
#' @import dplyr
#'
#' @param file_path string a file path to the source data file
#'
#' @param my_frequency time frequency of the data (default is NULL),
#' available options are:
#' \itemize{
#' \item Quarterly
#' }
#'
#' @param my_currency_of_denomination currency denomination
#' (default is NULL),
#' available options are:
#' \itemize{
#' \item Euro
#' \item Yen
#' \item All currencies
#' \item US dollar
#' }
#'
#' @param my_country borrowers country (default is NULL),
#'
#' @param my_borrowers_sector (default is NULL),
#' available options are:
#' \itemize{
#' \item All sectors
#' \item Banks, total
#' \item General government
#' \item Non-banks, total
#' \item Non-financial sectors
#' \item Non-financial private sector
#' }
#'
#' @param my_lending_sector (default is NULL),
#' available options are:
#' \itemize{
#' \item All sectors
#' \item Banks, total
#' }
#'
#' @param my_position_type (default is NULL), toggles the
#' difference between cross border and local position.
#' available options are:
#' \itemize{
#' \item All
#' \item Cross-border & Local in FCY
#' \item Cross-border
#' \item Local
#' }
#'
#' @param my_type_of_instruments (default is NULL),
#' available options are:
#' \itemize{
#' \item All instruments
#' \item Credit (loans & debt securities)
#' \item Debt securities
#' \item Loans and deposits
#' }
#'
#'
#' @param my_unit_of_measure (default is NULL),
#' available options are:
#' \itemize{
#' \item Year-on-year changes, in per cent
#' \item Euro
#' \item Yen
#' \item US dollar
#' }
#'
#' @param pivot_to_long reshape the data to long format? Default is FALSE
#'
#' @export
#'
import_bis_liqiudity_indicators = function(file_path,
my_frequency = NULL,
my_currency_of_denomination = NULL,
my_borrowers_country = NULL,
my_borrowers_sector = NULL,
my_lending_sector = NULL,
my_position_type = NULL,
my_type_of_instruments = NULL,
my_unit_of_measure = NULL,
pivot_to_long = FALSE) {
. = NULL
filtered_df = read_csv(file_path, col_types = cols()) %>%
select(-`Time Period`,-Title,
-`Unit Multiplier`, -Decimals,
-`Collection Indicator`, -Availability) %>%
rename_all( ~ str_replace_all(., " ", "_")) %>%
rename_all( ~ str_replace_all(., "_-_", "_")) %>%
rename_all(~ str_remove_all(., "_\\(.*\\)$")) %>%
rename_all(~ str_remove_all(., "\\'")) %>%
rename_with(tolower, matches("^[A-Za-z]")) %>%
mutate(borrowers_country = str_replace_all(borrowers_country,
"\\s", "_")) %>%
mutate(across(where(is.character), ~str_remove_all(.,"^[A-Z0-9]+:")))
for (filter_name in c(
"my_frequency",
"my_currency_of_denomination",
"my_borrowers_country",
"my_borrowers_sector",
"my_lending_sector",
"my_position_type",
"my_type_of_instruments",
"my_unit_of_measure"
)) {
filter_val = get(filter_name)
if(!is.null(filter_val)){
filter_name = filter_name %>%
str_remove("my_")
filtered_df = filtered_df %>%
filter(!!sym(filter_name) %in% filter_val)
if(length(filter_val) == 1){
filtered_df = filtered_df %>%
select(-!!sym(filter_name))
}
}
}
if(pivot_to_long){
filtered_df = filtered_df %>%
pivot_longer(matches("^[0-9]"),
names_to = "date",
values_to = "value"
)
}
return(filtered_df)
}
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