R/etl_international.R

In schoulten/macroview: Dashboard: Macroeconomic Data of Brazil

#' ETL International economy
#'
#' @encoding UTF-8
#' @import dplyr
#' @importFrom utils lsf.str
#' @return RDATA
#' @export
#'
etl_international <- function(){

### International economy ###


# This R code provides the extraction and data wrangling of macroeconomic
# variables referring to International economy


# Packages ----------------------------------------------------------------


# Install/load packages
# if (!require("pacman")) install.packages("pacman")
# pacman::p_load("OECD", "tidyverse")



# Parameters --------------------------------------------------------------


# Parameters used in the code to import, download or cleaning data


# # List of parameters to get data from OECD API
# api_oecd <- list(
#
#   # Quarterly National Accounts (code to get dataset)
#   dataset_qna = "QNA",
#
#   # Main Economic Indicators (code to get  dataset)
#   dataset_mei = "MEI",
#
#   # Labour statistics (code to get dataset)
#   dataset_stlabour = "STLABOUR",
#
#
#   # GDP (list of parameters to filter the QNA dataset)
#   filter_gdp = "AUS+CAN+CHL+FRA+DEU+ITA+JPN+MEX+ESP+TUR+USA+ARG+BRA+CHN+CRI+IND+RUS.B1_GE.GYSA.A+Q",
#
#   # Unemployment (list of parameters to filter the STLABOUR dataset)
#   filter_unemployment = "AUS+CAN+CHL+FRA+DEU+ITA+JPN+MEX+ESP+TUR+USA.LRHUTTTT.STSA.M",
#
#   # Inflation (list of parameters to filter the MEI dataset)
#   filter_inflation = "AUS+CAN+CHL+FRA+DEU+ITA+JPN+MEX+ESP+TUR+USA+ARG+BRA+CHN+CRI+IND+RUS+BRIICS.CPALTT01.GY.M",
#
#   # Interest rate (list of parameters to filter the MEI dataset)
#   filter_int_rate = "AUS+CAN+CHL+FRA+DEU+ITA+JPN+MEX+ESP+TUR+USA+ARG+BRA+CHN+CRI+IND+RUS+BRIICS.IRSTCB01+IRSTCI01.ST+STSA.M"
#
#   )
#
#
#
# # Import data -------------------------------------------------------------
#
#
# # This section performs the import of data from OECD datasets (economic indicators for many countries)
#
#
# # GDP
# str_gdp <- OECD::get_data_structure(api_oecd$dataset_qna) # dataset structure information
# raw_gdp_oecd <- OECD::get_dataset(
#   dataset       = api_oecd$dataset_qna,
#   filter        = list(api_oecd$filter_gdp),
#   pre_formatted = TRUE
#   )
#
#
# # Unemployment Rate
# str_unemployment <- OECD::get_data_structure(api_oecd$dataset_stlabour) # dataset structure information
# raw_unemployment_oecd <- OECD::get_dataset(
#   dataset       = api_oecd$dataset_stlabour,
#   filter        = list(api_oecd$filter_unemployment),
#   pre_formatted = TRUE
#   )
#
#
# # Inflation
# str_inflation <- OECD::get_data_structure(api_oecd$dataset_mei) # dataset structure information
# raw_inflation_oecd <- OECD::get_dataset(
#   dataset       = api_oecd$dataset_mei,
#   filter        = list(api_oecd$filter_inflation),
#   pre_formatted = TRUE
#   )
#
#
# # Interest rate
# raw_int_rate <- OECD::get_dataset(
#   dataset       = api_oecd$dataset_mei,
#   filter        = list(api_oecd$filter_int_rate),
#   pre_formatted = TRUE
#   )
#
#
#
#
# # Data wrangling ----------------------------------------------------------
#
#
# # This section performs data wrangling
#
#
# # GDP
# gdp_oecd <- raw_gdp_oecd %>%
#   filter(FREQUENCY == "Q") %>%
#   select(
#     date     = obsTime,
#     location = LOCATION,
#     value    = obsValue,
#     status   = contains("OBS_STATUS")
#     ) %>%
#   left_join(
#     str_gdp$LOCATION,
#     by = c("location" = "id")
#   ) %>%
#   mutate(
#     across(
#       any_of("status"),
#       ~recode(
#         status,
#         "E" = "Estimated value",
#         "P" = "Provisional value"
#         )
#       )
#     )
#
#
# # Inflation
# inflation_oecd <- raw_inflation_oecd %>%
#   select(
#     date     = obsTime,
#     location = LOCATION,
#     value    = obsValue,
#     status   = contains("OBS_STATUS")
#   ) %>%
#   left_join(
#     str_inflation$LOCATION,
#     by = c("location" = "id")
#     ) %>%
#   mutate(
#     date = paste0(date, "-01") %>% stringr::str_replace_all("-", "/"),
#     across(
#       any_of("status"),
#       ~recode(
#         status,
#         "E" = "Estimated value",
#         "P" = "Provisional value"
#         )
#       )
#     )
#
#
# # Unemployment rate
# unemployment_oecd <- raw_unemployment_oecd %>%
#   select(
#     date     = obsTime,
#     location = LOCATION,
#     value    = obsValue,
#     status   = contains("OBS_STATUS")
#   ) %>%
#   left_join(
#     str_unemployment$LOCATION,
#     by = c("location" = "id")
#     ) %>%
#   mutate(
#     date = paste0(date, "-01") %>% stringr::str_replace_all("-", "/"),
#     across(
#       any_of("status"),
#       ~recode(
#         status,
#         "E" = "Estimated value",
#         "P" = "Provisional value",
#         "B" = "Break"
#         )
#       )
#     )
#
#
# # Interest rate
# int_rate_oecd <- raw_int_rate %>%
#   filter(SUBJECT == "IRSTCI01") %>%
#   select(
#     date     = obsTime,
#     location = LOCATION,
#     value    = obsValue,
#     status   = contains("OBS_STATUS")
#   ) %>%
#   left_join(
#     str_inflation$LOCATION,
#     by = c("location" = "id")
#   ) %>%
#   mutate(
#     date = paste0(date, "-01") %>% stringr::str_replace_all("-", "/"),
#     across(
#       any_of("status"),
#       ~recode(
#         status,
#         "E" = "Estimated value",
#         "P" = "Provisional value",
#         "B" = "Break"
#         )
#       )
#     )
#
#
#
#
# # Save data ---------------------------------------------------------------
#
#
# if (0L %in% purrr::map_dbl(mget(ls()), length)) {
#
#   stop("Some objects are zero in length.", call. = FALSE)
#
#   } else
#     {
#     # Aggregate data
#     imported_data_international <- mget(ls(pattern = "raw_|api_|str_"))
#
#
#     # Remove unnecessary objects
#     rm(
#       list  = c(lsf.str(), ls(pattern = "raw_|api_|str_")),
#       envir = environment()
#       )
#
#
#     # Save RDATA file
#     save(
#       list  = ls(),
#       file  = file.path(file.path("./inst/extdata"), "international.Rdata"),
#       envir = environment()
#       )
#     }

}