get operaciones interbancarias

DESCRIPTION

@@ -19,7 +19,8 @@ Imports:
     stringr,
     usethis,
     tidyr,
-    yaml
+    yaml,
+    readr
 Suggests:
     urca,
     tsibble,

NAMESPACE

@@ -22,6 +22,7 @@ export(get_pib_gasto)
 export(get_pib_sectores)
 export(get_prestamos_osd)
 export(get_remesas)
+export(get_tasa_interbancaria)
 export(get_tasas_activas)
 export(get_tasas_pasivas)
 export(get_tc)

R/operaciones_interbancarias.R

@@ -0,0 +1,101 @@
+#' Get average interbank interest rates by maturity
+#'
+#' Downloads and processes the official Average Interbank Rates by Maturity
+#' file published by the Central Bank of the Dominican Republic.
+#'
+#' The function:
+#' \itemize{
+#'   \item Downloads the .xlsm file from the institutional CDN.
+#'   \item Cleans headers and aggregated rows (cumulative values).
+#'   \item Converts amounts and interest rates to numeric format.
+#'   \item Builds time variables (`year`, `mes`, `date`).
+#' }
+#'
+#' @return A monthly-frequency `tibble` including:
+#' \describe{
+#'   \item{date}{Date object (first day of the month).}
+#'   \item{year}{Numeric year.}
+#'   \item{mes}{Numeric month (1–12).}
+#'   \item{monto_operaciones_interbancarias}{Total interbank transaction amount.}
+#'   \item{tasa_promedio_ponderado}{Weighted average interbank interest rate.}
+#'   \item{monto_depositos_vista}{Demand deposits amount.}
+#'   \item{tasa_depositos_vista}{Demand deposits interest rate.}
+#'   \item{monto_d_*}{Amounts by maturity bucket (days).}
+#'   \item{tasa_d_*}{Interest rates by maturity bucket (days).}
+#' }
+#'
+#' @details
+#' Requires the source file structure to remain unchanged (11 initial rows skipped
+#' and fixed column order). If the format published by the Central Bank changes,
+#' the function may fail.
+#'
+#' @source Central Bank of the Dominican Republic.
+#'
+#' @examples
+#' \dontrun{
+#' rates <- get_tasa_interbancaria()
+#' dplyr::glimpse(rates)
+#' }
+#'
+#' @export
+get_tasa_interbancaria <- function() {
+  url <- paste0(
+    "https://cdn.bancentral.gov.do/documents/",
+    "estadisticas/sector-monetario-y-financiero/",
+    "documents/Tasas_Interbancarias_Promedio_por_Plazos.xlsm"
+  )
+
+  file <- tempfile(fileext = ".xlsm")
+
+  tryCatch(
+    download.file(url, file, mode = "wb", quiet = TRUE),
+    error = function(e) {
+      stop("No se pudo descargar el archivo de tasas interbancarias.")
+    }
+  )
+
+  headers_oi <- c(
+    "year_mes",
+    "monto_operaciones_interbancarias",
+    "tasa_promedio_ponderado",
+    "monto_depositos_vista",
+    "tasa_depositos_vista",
+    "monto_d_1_7",        "tasa_d_1_7",
+    "monto_d_8_30",       "tasa_d_8_30",
+    "monto_d_31_60",      "tasa_d_31_60",
+    "monto_d_61_90",      "tasa_d_61_90",
+    "monto_d_91_120",     "tasa_d_91_120",
+    "monto_d_121_180",    "tasa_d_121_180",
+    "monto_d_181_365",    "tasa_d_181_365",
+    "monto_mas_365_dias", "tasa_mas_365_dias"
+  )
+
+  readxl::read_excel(
+    file,
+    skip = 11,
+    col_names = FALSE
+  ) |>
+    stats::setNames(headers_oi) |>
+    dplyr::filter(!is.na(year_mes)) |>
+    dplyr::mutate(
+      year = as.integer(stringr::str_extract(year_mes, "20\\d{2}")),
+      year_mes = stringr::str_remove(year_mes, "\\*")
+    ) |>
+    tidyr::fill(year) |>
+    dplyr::filter(!stringr::str_detect(year_mes, "Acumulad")) |>
+    dplyr::mutate(
+      dplyr::across(
+        -c(year_mes, year),
+        readr::parse_number
+      )
+    ) |>
+    dplyr::filter(!is.na(monto_operaciones_interbancarias)) |>
+    dplyr::distinct(year, year_mes, .keep_all = TRUE) |>
+    dplyr::mutate(
+      mes = databcrd::crear_mes(year_mes),
+      date = lubridate::make_date(year, mes, 1)
+    ) |>
+    dplyr::relocate(date, year, mes) |>
+    suppressMessages() |>
+    suppressWarnings()
+}

inst/WORDLIST

@@ -52,3 +52,8 @@ clase
 grupo
 subclase
 subgrupo
+interbank
+Interbank
+xlsm
+CDN
+

tests/testthat/test-operaciones_interbancarias.R

@@ -0,0 +1,83 @@
+data <- get_tasa_interbancaria() |>
+  dplyr::mutate(fecha = date)
+
+test_that("There aren't dates in the future", {
+  testthat::expect_true(max(data$fecha) <= lubridate::today())
+})
+
+test_that("There aren't haps between dates", {
+  test_nmonths <- data |>
+    dplyr::arrange(fecha) |>
+    dplyr::mutate(lag_fecha = dplyr::lag(fecha)) |>
+    dplyr::filter(!is.na(lag_fecha)) |>
+    dplyr::mutate(one_month_diff = fecha == lag_fecha + months(1))
+
+  testthat::expect_true(all(test_nmonths$one_month_diff))
+})
+
+test_that("No missing columns", {
+  any_missing_columns <- data |>
+    sapply(function(x) all(is.na(x))) |>
+    all()
+
+  testthat::expect_false(any_missing_columns)
+})
+
+test_that("No missing rows", {
+  empty_rows <- data |>
+    apply(MARGIN = 1, FUN = function(x) all(is.na(x))) |>
+    all()
+
+  testthat::expect_false(empty_rows)
+})
+
+test_that("Dates are unique", {
+  testthat::expect_equal(nrow(data), dplyr::n_distinct(data$fecha))
+})
+
+test_that("Dates are ordered after arrange", {
+  ordered_data <- data |> dplyr::arrange(fecha)
+  testthat::expect_true(all(diff(ordered_data$fecha) > 0))
+})
+
+test_that("Year and month are consistent with date", {
+  testthat::expect_true(
+    all(lubridate::year(data$fecha) == data$year)
+  )
+  testthat::expect_true(
+    all(lubridate::month(data$fecha) == data$mes)
+  )
+})
+
+test_that("Montos are non-negative", {
+  monto_cols <- names(data)[stringr::str_detect(names(data), "^monto")]
+
+  non_negative <- data |>
+    dplyr::select(dplyr::all_of(monto_cols)) |>
+    sapply(function(x) all(is.na(x) | x >= 0)) |>
+    all()
+
+  testthat::expect_true(non_negative)
+})
+
+test_that("Tasas are within reasonable bounds", {
+  tasa_cols <- names(data)[stringr::str_detect(names(data), "^tasa")]
+
+  reasonable_range <- data |>
+    dplyr::select(dplyr::all_of(tasa_cols)) |>
+    sapply(function(x) all(is.na(x) | (x >= 0 & x <= 100))) |>
+    all()
+
+  testthat::expect_true(reasonable_range)
+})
+
+test_that("No duplicated year-month combinations", {
+  testthat::expect_equal(
+    nrow(data),
+    dplyr::n_distinct(data$year, data$mes)
+  )
+})
+
+test_that("Dataset has at least 12 observations", {
+  testthat::expect_gte(nrow(data), 12)
+})