R/turismo.R

In Johan-rosa/bcdata: Descarga los datos publicados por el Banco Central de RD

# get_llegadas_aereas <- function() {
#
# }
#
# `%>%` <- magrittr::`%>%`
#
# # LLegadas de según aropuerto y estatus de residencia
# urls <- paste0(
#     "https://cdn.bancentral.gov.do/documents/",
#     "estadisticas/sector-turismo/documents/",
#     "lleg_total_", 1996:2020,
#     ".xls")
#
# temporal_paths <- purrr::map(
#     1996:2020,
#     ~tempfile(fileext = ".xls")
# )
#
# purrr::map2(
#     urls, temporal_paths,
#     ~download.file(url = .x, destfile = .y, mode = "wb", quiet = TRUE)
#     )
#
# llegadas1 <- purrr::map(
#     temporal_paths[1:7],
#     ~readxl::read_excel(
#         path = .x, skip = 4
#     ) %>% janitor::clean_names() %>%
#         setNames(c("aeropuerto", "total", names(.)[-(1:2)])) %>%
#         janitor::remove_empty(which = "rows") %>%
#         janitor::remove_empty(which = "cols") %>%
#         dplyr::mutate(
#             residencia = stringr::str_extract(aeropuerto, "NO RESIDENTES|RESIDENTES|TOTAL"),
#             nacionalidad = stringr::str_extract(
#                 aeropuerto, "DOMINICANOS|EXTRANJEROS|NO RESIDENTES|RESIDENTES|TOTAL")
#         ) %>%
#         tidyr::fill(residencia, nacionalidad, .direction = "down") %>%
#         dplyr::filter(!aeropuerto %in% c("TOTAL PASAJEROS", "RESIDENTES",
#                                  "DOMINICANOS", "EXTRANJEROS", "NO RESIDENTES"))
# ) %>% setNames(1996:2002) %>%
#     dplyr::bind_rows(.id = "year") %>%
#     dplyr::select(-total) %>%
#     dplyr::filter(!residencia == nacionalidad)
#
#
# llegadas2 <- purrr::map(
#     temporal_paths[8:10],
#     ~readxl::read_excel(
#         path = .x, skip = 5
#     ) %>% janitor::clean_names() %>%
#         setNames(c("aeropuerto", "total", names(.)[-(1:2)]))%>%
#         janitor::remove_empty(which = "rows") %>%
#         janitor::remove_empty(which = "cols") %>%
#         dplyr::mutate(
#             residencia = stringr::str_extract(aeropuerto, "NO RESIDENTES|RESIDENTES|TOTAL"),
#             nacionalidad = stringr::str_extract(
#                 aeropuerto, "DOMINICANOS|EXTRANJEROS|NO RESIDENTES|RESIDENTES|TOTAL")
#         ) %>%
#         tidyr::fill(residencia, nacionalidad, .direction = "down") %>%
#         dplyr::filter(!aeropuerto %in% c("TOTAL PASAJEROS", "RESIDENTES",
#                                          "DOMINICANOS", "EXTRANJEROS", "NO RESIDENTES"))
# ) %>% setNames(2003:2005) %>%
#     dplyr::bind_rows(.id = "year") %>%
#     dplyr::select(-total) %>%
#     dplyr::filter(!residencia == nacionalidad)
#
#
# llegadas3 <-
#     purrr::map(
#     temporal_paths[11:length(temporal_paths)],
#     ~readxl::read_excel(
#         path = .x, skip = 3
#     ) %>% janitor::clean_names() %>%
#         setNames(c("aeropuerto", "total", stringr::str_extract(names(.)[-(1:2)], "^..."))) %>%
#         janitor::remove_empty(which = "rows") %>%
#         janitor::remove_empty(which = "cols") %>%
#         #dplyr::filter(!is.na(aeropuerto)) %>%
#         dplyr::mutate(
#             residencia = stringr::str_extract(aeropuerto, "NO RESIDENTES|RESIDENTES|TOTAL"),
#             nacionalidad = stringr::str_extract(
#                 aeropuerto, "DOMINICANOS|EXTRANJEROS|NO RESIDENTES|RESIDENTES|TOTAL")
#         ) %>%
#         tidyr::fill(residencia, nacionalidad, .direction = "down") %>%
#         dplyr::filter(!aeropuerto %in% c("TOTAL PASAJEROS", "RESIDENTES",
#                                          "DOMINICANOS", "EXTRANJEROS", "NO RESIDENTES"))
# ) %>% setNames(2006:2020) %>%
#     dplyr::bind_rows(.id = "year") %>%
#     dplyr::select(-total) %>%
#     dplyr::filter(!residencia == nacionalidad) %>%
#     dplyr::filter(!is.na(ene))
#
#
# dplyr::bind_rows(list(llegadas1, llegadas2, llegadas3)) %>%
#     tidyr::pivot_longer(cols = ene:dic, names_to = 'mes', values_to = 'pasajeros') %>%
#     janitor::remove_empty(which = 'cols')
#
#
# # Llegadas detalles -------------------------------------------------------
#
#
# get_turistas <- function() {
#
#     url <- paste0('https://cdn.bancentral.gov.do/',
#                  'documents/estadisticas/sector-turismo/',
#                  'documents/lleg_caracteristicas_', 2006:2021
#                  ,'.xls')
#     `%>%` <- magrittr::`%>%`
#
# detalles_year <- function(url) {
#         `%>%` <- magrittr::`%>%`
#         path <- tempfile(fileext = '.xls')
#         download.file(url, path, quiet = TRUE, mode = 'wb')
#         sheets <- readxl::excel_sheets(path) %>%
#             stringr::str_subset('^[0-9]+$', negate = TRUE)
#
#         headers <- c(
#             "pais", "sexo_total", "sexo_femenino", "sexo_masculino", "alojamiento_total",
#             "alojamiento_hotel", "alojamiento_otro", "edad_total", "edad_0a12",
#             "edad_13a20", "x21a35", "x36a49", "x50mas", "motivo_total", "motivo_recreacion",
#             "motivo_negocio", "motivo_conf", "motivo_estudio", "motivo_amigo_pareja",
#             "motivo_otro", 'aeropuerto')
#
#         pattern_region <- c('^America', 'Asia', 'Europa', 'Resto', 'AMERICA', 'ASIA',
#                             'EUROPA', 'RESTO') %>%
#             toupper() %>%
#             paste(collapse = '|^')
#
#         pattern_remove <- c('^TOTAL', 'RESIDENTES', 'NO RESIDENTES', 'Dominicanos', 'Extranjeros',
#                             'EXTRANJEROS', 'Ext. Residentes.', 'Dom. Residentes.', 'Dom. No Residentes.',
#                             'PAIS', 'Pais', 'pais') %>%
#             paste(collapse = '|^')
#
#         purrr::map(
#             sheets,
#             ~suppressMessages(readxl::read_excel(path, col_names = FALSE, sheet = .x)) %>%
#                 janitor::clean_names() %>%
#                 dplyr::select(x1:x20) %>%
#                 janitor::remove_empty(which = 'rows') %>%
#                 janitor::remove_empty(which = 'cols') %>%
#                 dplyr::mutate(aeropuerto = ifelse(stringr::str_detect(tolower(x1), 'aeropuerto'), x1, NA)) %>%
#                 tidyr::fill(aeropuerto, .direction = 'down') %>%
#                 tidyr::drop_na() %>%
#                 dplyr::filter(stringr::str_detect(x1, '^RESIDENCIA|^NACIONALIDAD', negate = TRUE)) %>%
#                 setNames(headers) %>%
#                 dplyr::mutate(region = ifelse(stringr::str_detect(pais, pattern_region), pais, NA)) %>%
#                 tidyr::fill(region, .direction = 'down') %>%
#                 dplyr::filter(stringr::str_detect(pais, pattern_remove, negate = TRUE)) %>%
#                 dplyr::filter(stringr::str_detect(pais, pattern_region, negate = TRUE)) %>%
#                 dplyr::mutate(mes = .x)
#         ) %>%
#             dplyr::bind_rows() %>%
#             dplyr::mutate(year = readr::parse_number(stringr::str_extract(url, '[0-9]+\\.xls$'))) %>%
#             dplyr::select(year, mes, aeropuerto, region, pais, dplyr::everything())
#
# }
#
# data_turistas <- purrr::map_df(
#     url,
#     detalles_year
# )
#
# return(data_turistas)
# }
#
# data_turistas <- get_turistas()
#
# data_turistas %>%
#     dplyr::select(year, mes, aeropuerto, region, pais, dplyr::contains('sexo'))
#
#