idswb is primarily a data package enabling an efficient method for
working with International Debt Statistics from World
Bank in R.
You can install the development version of idswb from GitHub with:
# setting options
options(timeout = 600, scipen = 999)
# install.packages("devtools")
devtools::install_github("Teal-Insights/idswb")This package will likely never be published on CRAN because the data file sizes are too big.
This is a basic example which shows you how to solve a common problem:
library(idswb)
library(knitr)
library(tidyverse)- unique_counterpart_area: information on counterparts (creditors)
unique_counterpart_area %>% head()
#> # A tibble: 6 × 3
#> ids_counterpart_area_id ids_counterpart_area_name iso3c
#> <chr> <chr> <chr>
#> 1 1 Austria AUT
#> 2 10 Sweden SWE
#> 3 11 Switzerland CHE
#> 4 12 United Kingdom GBR
#> 5 130 Algeria DZA
#> 6 133 Libya LBY- unique_country: information on debtors
unique_country %>% head()
#> # A tibble: 6 × 3
#> ids_country_id ids_country_name iso3c
#> <chr> <chr> <chr>
#> 1 AFG Afghanistan AFG
#> 2 ALB Albania ALB
#> 3 DZA Algeria DZA
#> 4 AGO Angola AGO
#> 5 ARG Argentina ARG
#> 6 ARM Armenia ARM- unique_series: information on series
unique_series %>% head()
#> # A tibble: 6 × 2
#> ids_series_id ids_series_name
#> <chr> <chr>
#> 1 BM.GSR.TOTL.CD Imports of goods, services and primary income (current US$)
#> 2 BN.CAB.XOKA.CD Current account balance (current US$)
#> 3 BX.GRT.EXTA.CD.DT Grants, excluding technical cooperation (current US$)
#> 4 BX.GRT.TECH.CD.DT Technical cooperation grants (current US$)
#> 5 BX.GSR.TOTL.CD Exports of goods, services and primary income (current US$)
#> 6 BX.KLT.DINV.CD.DT Foreign direct investment, net inflows in reporting economy…- unique_time: information on years
unique_time %>% head()
#> # A tibble: 6 × 2
#> ids_time_id ids_time_name
#> <chr> <dbl>
#> 1 YR1970 1970
#> 2 YR1971 1971
#> 3 YR1972 1972
#> 4 YR1973 1973
#> 5 YR1974 1974
#> 6 YR1975 1975We create an ETL object by specifying a string that defines the object’s
class, and use the package idswb to gain access to the relevant data.
# ceating an object
# idswb_ob <-etl("idswb", dir = getwd())The first step here is to acquire data
# idswb_ob %>% etl_extract()This data might require conversion from its original format into files that are appropriate for SQL import, typically in CSV format.
# idswb_ob %>% etl_transform()Fill the SQL database with the processed data.
# idswb_ob %>% etl_load()We can still perform the steps above at once
# ceating an object
idswb_ob <-etl("idswb", dir = getwd())
idswb_ob %>%
# extract
etl_extract() %>%
# transform
etl_transform() %>%
# load
etl_load()# idswb_ob %>% etl_create()We can now query debt of a given country that it owes to both China and the World as follows
Query 1:
# input specifications
series <- c("DT.DOD.BLAT.CD","DT.DOD.PBND.CD",'DT.DOD.MLAT.CD',"DT.DOD.PROP.CD","DT.DOD.PCBK.CD","DT.DOD.DPPG.CD")
debtor <- "Kenya"
creditor <- c("China","World")
# Querying data
ids_query_data(etl_object = idswb_ob, debtor = debtor, creditor = creditor, series = series) %>%
dplyr::filter(year == "2022")
#> api_code data_last_updated
#> 1 DT.DOD.BLAT.CD 2024-02-29
#> 2 DT.DOD.DPPG.CD 2024-02-29
#> 3 DT.DOD.PROP.CD 2024-02-29
#> 4 DT.DOD.MLAT.CD 2024-02-29
#> 5 DT.DOD.PCBK.CD 2024-02-29
#> 6 DT.DOD.DPPG.CD 2024-02-29
#> 7 DT.DOD.PBND.CD 2024-02-29
#> 8 DT.DOD.PROP.CD 2024-02-29
#> 9 DT.DOD.BLAT.CD 2024-02-29
#> series_full_name
#> 1 PPG, bilateral (DOD, current US$)
#> 2 External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)
#> 3 PPG, other private creditors (DOD, current US$)
#> 4 PPG, multilateral (DOD, current US$)
#> 5 PPG, commercial banks (DOD, current US$)
#> 6 External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)
#> 7 PPG, bonds (DOD, current US$)
#> 8 PPG, other private creditors (DOD, current US$)
#> 9 PPG, bilateral (DOD, current US$)
#> wb_debtor_country_name wb_debtor_country_id wb_creditor_name wb_creditor_id
#> 1 Kenya KEN China 730
#> 2 Kenya KEN China 730
#> 3 Kenya KEN China 730
#> 4 Kenya KEN World WLD
#> 5 Kenya KEN World WLD
#> 6 Kenya KEN World WLD
#> 7 Kenya KEN World WLD
#> 8 Kenya KEN World WLD
#> 9 Kenya KEN World WLD
#> year debt
#> 1 2022 6685750503
#> 2 2022 6685750503
#> 3 2022 NA
#> 4 2022 16719778200
#> 5 2022 1097776462
#> 6 2022 35037185972
#> 7 2022 7100000000
#> 8 2022 NA
#> 9 2022 10119631310Query 2:
# input specifications
series <- c("DT.DOD.BLAT.CD","DT.DOD.PBND.CD",'DT.DOD.MLAT.CD',"DT.DOD.PROP.CD","DT.DOD.PCBK.CD","DT.DOD.DPPG.CD")
debtor <- "Nigeria"
creditor <- c("China","World")
# Querying data
ids_query_data(etl_object = idswb_ob, debtor = debtor, creditor = creditor, series = series) %>%
dplyr::filter(year == "2022")
#> api_code data_last_updated
#> 1 DT.DOD.BLAT.CD 2024-02-29
#> 2 DT.DOD.BLAT.CD 2024-02-29
#> 3 DT.DOD.DPPG.CD 2024-02-29
#> 4 DT.DOD.DPPG.CD 2024-02-29
#> 5 DT.DOD.MLAT.CD 2024-02-29
#> 6 DT.DOD.PBND.CD 2024-02-29
#> 7 DT.DOD.PCBK.CD 2024-02-29
#> 8 DT.DOD.PCBK.CD 2024-02-29
#> 9 DT.DOD.PROP.CD 2024-02-29
#> series_full_name
#> 1 PPG, bilateral (DOD, current US$)
#> 2 PPG, bilateral (DOD, current US$)
#> 3 External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)
#> 4 External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)
#> 5 PPG, multilateral (DOD, current US$)
#> 6 PPG, bonds (DOD, current US$)
#> 7 PPG, commercial banks (DOD, current US$)
#> 8 PPG, commercial banks (DOD, current US$)
#> 9 PPG, other private creditors (DOD, current US$)
#> wb_debtor_country_name wb_debtor_country_id wb_creditor_name wb_creditor_id
#> 1 Nigeria NGA China 730
#> 2 Nigeria NGA World WLD
#> 3 Nigeria NGA China 730
#> 4 Nigeria NGA World WLD
#> 5 Nigeria NGA World WLD
#> 6 Nigeria NGA World WLD
#> 7 Nigeria NGA China 730
#> 8 Nigeria NGA World WLD
#> 9 Nigeria NGA World WLD
#> year debt
#> 1 2022 4291730985
#> 2 2022 5169011908
#> 3 2022 4291730985
#> 4 2022 40003719240
#> 5 2022 16992036718
#> 6 2022 16364972000
#> 7 2022 NA
#> 8 2022 1477698613
#> 9 2022 NA