data-raw/createTables2019.R
In validmeasures/ppitables: Lookup Tables to Generate Poverty Likelihoods and Rates using the Poverty Probability Index (PPI)
################################################################################
#
# Load libraries to use for data extraction from PDF lookup tables from PPI
#
################################################################################
library(readxl)
library(stringr)
library(tibble)
################################################################################
#
# PPI tables are now provided as Excel files. This started with Ghana PPI
#
################################################################################
## Ghana PPI ###################################################################
gha <- read_xlsx(path = "data-raw/sources/ghana2019.xlsx",
sheet = "Look-up Tables",
range = "A9:T110")
gha <- data.frame(gha)
gha[ , 2:ncol(gha)] <- gha[ , 2:ncol(gha)] * 100
names(gha) <- c("score", "nl100", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100",
"ppp1500", "ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiGHA2019 <- tibble::tibble(gha)
usethis::use_data(ppiGHA2019, overwrite = TRUE)
## Mozambique PPI ##############################################################
moz <- read_xlsx(path = "data-raw/sources/mozambique2019.xlsx",
sheet = "Look-up Tables",
range = "A9:O110")
moz <- data.frame(moz)
moz[ , 2:ncol(moz)] <- moz[ , 2:ncol(moz)] * 100
names(moz) <- c("score", "nl100", "nl150", "nl200",
"ppp190", "ppp320", "ppp550", "ppp800", "ppp1100",
"ppp1500", "ppp2170", "percentile20", "percentile40",
"percentile60", "percentile80")
ppiMOZ2019 <- tibble::tibble(moz)
usethis::use_data(ppiMOZ2019, overwrite = TRUE)
## Myanmar 2019 ################################################################
mmr <- read_xlsx(path = "data-raw/sources/myanmar2019.xlsx",
sheet = "Look-up Tables",
range = "A9:T110")
mmr <- data.frame(mmr)
mmr[ , 2:ncol(mmr)] <- mmr[ , 2:ncol(mmr)] * 100
names(mmr) <- c("score", "nl100", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100",
"ppp1500", "ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiMMR2019 <- tibble::tibble(mmr)
usethis::use_data(ppiMMR2019, overwrite = TRUE)
## Rwanda 2019 #################################################################
rwa <- read_xlsx(path = "data-raw/sources/rwanda2019.xlsx",
sheet = "Look-up Tables",
range = "A10:T111")
rwa <- data.frame(rwa)
rwa[ , 2:ncol(rwa)] <- rwa[ , 2:ncol(rwa)] * 100
names(rwa) <- c("score", "nl100", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100",
"ppp1500", "ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiRWA2019 <- tibble::tibble(rwa)
usethis::use_data(ppiRWA2019, overwrite = TRUE)
## Malawi 2020 #################################################################
mwi <- read_xlsx(path = "data-raw/sources/malawi2020.xlsx",
sheet = "Look-up Tables",
range = "B10:Q110")
mwi <- data.frame(mwi)
mwi[ , 2:ncol(mwi)] <- mwi[ , 2:ncol(mwi)] * 100
names(mwi) <- c("score", "nl100", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550",
"ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiMWI2020 <- tibble::tibble(mwi)
usethis::use_data(ppiMWI2020, overwrite = TRUE)
## Indonesia 2020 ##############################################################
idn <- read_xlsx(path = "data-raw/sources/indonesia2020.xlsx",
sheet = "Look-up Tables",
range = "B10:U110")
idn <- data.frame(idn)
idn[ , 2:ncol(idn)] <- idn[ , 2:ncol(idn)] * 100
names(idn) <- c("score", "nl100", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100", "ppp1500", "ppp2170",
"ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiIDN2020 <- tibble::tibble(idn)
usethis::use_data(ppiIDN2020, overwrite = TRUE)
## Tanzania 2022 ###############################################################
tza <- read_xlsx(path = "data-raw/sources/tanzania2022.xlsx",
sheet = "Look-up Tables",
range = "A10:U110")
tza <- data.frame(tza)
tza[ , 2:ncol(tza)] <- tza[ , 2:ncol(tza)] * 100
names(tza) <- c("score", "nl_upper", "nl_lower", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100",
"ppp1500", "ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiTZA2022 <- tibble::tibble(tza)
usethis::use_data(ppiTZA2022, overwrite = TRUE)
## Uganda 2022 ###############################################################
uga <- read_xlsx(path = "data-raw/sources/uganda2022.xlsx",
sheet = "Look-up Tables",
range = "B11:N111")
uga <- data.frame(uga)
uga[ , 2:ncol(uga)] <- uga[ , 2:ncol(uga)] * 100
names(uga) <- c("score", "ppp100", "ppp190", "ppp320", "ppp550", "ppp800",
"ppp1100", "ppp1500", "ppp2170",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiUGA2022 <- tibble::tibble(uga)
usethis::use_data(ppiUGA2022, overwrite = TRUE)
## Benin PPI ###################################################################
ben11 <- read_xlsx(
path = "data-raw/sources/benin2022.xlsx",
sheet = "Look-up Tables 11Q",
range = "A9:N110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(ben11) <- c(
"score", "nl100", "nl150", "nl200",
"ppp190", "ppp320", "ppp550", "ppp215", "ppp365", "ppp685",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiBEN2022_11q <- ben11
usethis::use_data(ppiBEN2022_11q, overwrite = TRUE, compress = "xz")
ben6 <- read_xlsx(
path = "data-raw/sources/benin2022.xlsx",
sheet = "Look-up Tables 6Q",
range = "A9:N110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(ben6) <- c(
"score", "nl100", "nl150", "nl200",
"ppp190", "ppp320", "ppp550", "ppp215", "ppp365", "ppp685",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiBEN2022_6q <- ben6
usethis::use_data(ppiBEN2022_6q, overwrite = TRUE, compress = "xz")
## Bolivia PPI #################################################################
bol <- read_xlsx(
path = "data-raw/sources/bolivia2023.xlsx",
sheet = "Look-up Table",
range = "B9:P110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(bol) <- c(
"score", "nl100", "nl_extreme", "nl150", "nl200",
"ppp190", "ppp320", "ppp550", "ppp215", "ppp365", "ppp685",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiBOL2023 <- bol
usethis::use_data(ppiBOL2023, overwrite = TRUE, compress = "xz")
## Burkina Faso PPI ############################################################
bfa <- read_xlsx(
path = "data-raw/sources/burkinafaso2023.xlsx",
sheet = "Look-up Table",
range = "B9:O110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(bfa) <- c(
"score", "nl100", "nl150", "nl200",
"ppp215", "ppp365", "ppp685", "ppp190", "ppp320", "ppp550",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiBFA2023 <- bfa
usethis::use_data(ppiBFA2023, overwrite = TRUE, compress = "xz")
## Cambodia PPI ################################################################
khm <- read_xlsx(
path = "data-raw/sources/cambodia2023.xlsx",
sheet = "Look-up Tables",
range = "A9:N110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(khm) <- c(
"score", "nl100", "nl150", "nl200",
"ppp550", "ppp800", "ppp1100", "ppp1500", "ppp2170", "ppp685",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiKHM2023 <- khm
usethis::use_data(ppiKHM2023, overwrite = TRUE, compress = "xz")
## Ecuador PPI #################################################################
ecu <- read_xlsx(
path = "data-raw/sources/ecuador2022.xlsx",
sheet = "Look-up Table (10Q)",
range = "A9:T110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Lines`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(ecu) <- c(
"score", "nl100", "nl_extreme", "nl150", "nl200",
"ppp215", "ppp365", "ppp685", "ppp100", "ppp190", "ppp320", "ppp550",
"ppp800", "ppp1100", "ppp1500", "ppp2170",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiECU2022 <- ecu
usethis::use_data(ppiECU2022, overwrite = TRUE, compress = "xz")
## El Salvador PPI #############################################################
slv <- read_xlsx(
path = "data-raw/sources/elsalvador2021.xlsx",
sheet = "Look-up Tables (10Q)",
range = "B9:V110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(slv) <- c(
"score", "nl100", "nl_extreme",
"ppp215", "ppp365", "ppp685", "ppp100", "ppp190", "ppp320", "ppp550",
"ppp800", "ppp1100", "ppp1500", "ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiSLV2021 <- slv
usethis::use_data(ppiSLV2021, overwrite = TRUE, compress = "xz")
## Ethiopia PPI ################################################################
eth <- read_xlsx(
path = "data-raw/sources/ethiopia2023.xlsx",
sheet = "Look-up Tables",
range = "A9:T110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(eth) <- c(
"score", "nl100", "nl_extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100", "ppp1500",
"ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiETH2023 <- eth
usethis::use_data(ppiETH2023, overwrite = TRUE, compress = "xz")
## Guatemala PPI ###############################################################
gtm <- read_xlsx(
path = "data-raw/sources/guatemala2023.xlsx",
sheet = "Look-up Tables",
range = "B9:L110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `$1.90/day 2011 PPP \r\n(Bottom 7th Percentile)*`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(gtm) <- c(
"score", "ppp190", "ppp320", "ppp550", "ppp215", "ppp365", "ppp685",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiGTM2023 <- gtm
usethis::use_data(ppiGTM2023, overwrite = TRUE, compress = "xz")
## Honduras PPI ################################################################
hnd <- read_xlsx(
path = "data-raw/sources/honduras2023.xlsx",
sheet = "Look-up Table",
range = "B9:S110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(hnd) <- c(
"score", "nl100", "nl_extreme",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100", "ppp1500",
"ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiHND2023 <- hnd
usethis::use_data(ppiHND2023, overwrite = TRUE, compress = "xz")
## Indonesia PPI ###############################################################
idn <- read_xlsx(
path = "data-raw/sources/indonesia2023.xlsx",
sheet = "Look-up Table",
range = "A9:J110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(idn) <- c(
"score", "nl100", "ppp365", "ppp685", "ppp320", "ppp550",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiIDN2023 <- idn
usethis::use_data(ppiIDN2023, overwrite = TRUE, compress = "xz")
## Papua New Guinea PPI ########################################################
png <- read_xlsx(
path = "data-raw/sources/papuanewguinea2023.xlsx",
sheet = "Look-up Tables",
range = "B9:J110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `Bottom 20th percentile WI`:`Bottom 80th percentile WI Urban/Rural`,
.fns = ~.x * 100
)
)
names(png) <- c(
"score",
"percentile20_wi", "percentile40_wi", "percentile60_wi", "percentile80_wi",
"percentile20_wi_ur", "percentile40_wi_ur", "percentile60_wi_ur",
"percentile80_wi_ur"
)
ppiPNG2023 <- png
usethis::use_data(ppiPNG2023, overwrite = TRUE, compress = "xz")
## Papua New Guinea PPI ########################################################
phl <- read_xlsx(
path = "data-raw/sources/philippines2023.xlsx",
sheet = "Look-up Table",
range = "B9:N110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(phl) <- c(
"score",
"nl100", "food", "ppp215", "ppp365", "ppp685", "ppp190", "ppp320", "ppp550",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiPHL2023 <- phl
usethis::use_data(ppiPHL2023, overwrite = TRUE, compress = "xz")
## South Africa PPI ############################################################
zaf <- read_xlsx(
path = "data-raw/sources/southafrica2023.xlsx",
sheet = "Look-up Tables",
range = "B9:G110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `Wealth Index Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(zaf) <- c(
"score",
"wealth_index", "percentile20", "percentile40", "percentile60", "percentile80"
)
ppiZAF2023 <- zaf
usethis::use_data(ppiZAF2023, overwrite = TRUE, compress = "xz")
## Togo PPI ####################################################################
tgo <- read_xlsx(
path = "data-raw/sources/togo2023.xlsx",
sheet = "Look-up Table",
range = "A9:N110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(tgo) <- c(
"score",
"nl100", "nl150", "nl200",
"ppp215", "ppp365", "ppp685", "ppp190", "ppp320", "ppp550",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiTGO2023 <- tgo
usethis::use_data(ppiTGO2023, overwrite = TRUE, compress = "xz")
## Vietnam PPI #################################################################
vnm <- read_xlsx(
path = "data-raw/sources/vietnam2023.xlsx",
sheet = "Look-up Table",
range = "B9:F110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `Bottom 20th Percentile`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(vnm) <- c(
"score",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiVNM2023 <- vnm
usethis::use_data(ppiVNM2023, overwrite = TRUE, compress = "xz")
## Malawi PPI ##################################################################
mwi <- read_xlsx(
path = "data-raw/sources/malawi2023.xlsx",
sheet = "Look-up Tables",
range = "A9:M110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(mwi) <- c(
"score", "nl100", "food",
"ppp215", "ppp365", "ppp685", "ppp190", "ppp320", "ppp550",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiMWI2023 <- mwi
usethis::use_data(ppiMWI2023, overwrite = TRUE, compress = "xz")
validmeasures/ppitables documentation built on April 23, 2024, 8:15 p.m.
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################################################################################
#
# Load libraries to use for data extraction from PDF lookup tables from PPI
#
################################################################################
library(readxl)
library(stringr)
library(tibble)
################################################################################
#
# PPI tables are now provided as Excel files. This started with Ghana PPI
#
################################################################################
## Ghana PPI ###################################################################
gha <- read_xlsx(path = "data-raw/sources/ghana2019.xlsx",
sheet = "Look-up Tables",
range = "A9:T110")
gha <- data.frame(gha)
gha[ , 2:ncol(gha)] <- gha[ , 2:ncol(gha)] * 100
names(gha) <- c("score", "nl100", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100",
"ppp1500", "ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiGHA2019 <- tibble::tibble(gha)
usethis::use_data(ppiGHA2019, overwrite = TRUE)
## Mozambique PPI ##############################################################
moz <- read_xlsx(path = "data-raw/sources/mozambique2019.xlsx",
sheet = "Look-up Tables",
range = "A9:O110")
moz <- data.frame(moz)
moz[ , 2:ncol(moz)] <- moz[ , 2:ncol(moz)] * 100
names(moz) <- c("score", "nl100", "nl150", "nl200",
"ppp190", "ppp320", "ppp550", "ppp800", "ppp1100",
"ppp1500", "ppp2170", "percentile20", "percentile40",
"percentile60", "percentile80")
ppiMOZ2019 <- tibble::tibble(moz)
usethis::use_data(ppiMOZ2019, overwrite = TRUE)
## Myanmar 2019 ################################################################
mmr <- read_xlsx(path = "data-raw/sources/myanmar2019.xlsx",
sheet = "Look-up Tables",
range = "A9:T110")
mmr <- data.frame(mmr)
mmr[ , 2:ncol(mmr)] <- mmr[ , 2:ncol(mmr)] * 100
names(mmr) <- c("score", "nl100", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100",
"ppp1500", "ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiMMR2019 <- tibble::tibble(mmr)
usethis::use_data(ppiMMR2019, overwrite = TRUE)
## Rwanda 2019 #################################################################
rwa <- read_xlsx(path = "data-raw/sources/rwanda2019.xlsx",
sheet = "Look-up Tables",
range = "A10:T111")
rwa <- data.frame(rwa)
rwa[ , 2:ncol(rwa)] <- rwa[ , 2:ncol(rwa)] * 100
names(rwa) <- c("score", "nl100", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100",
"ppp1500", "ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiRWA2019 <- tibble::tibble(rwa)
usethis::use_data(ppiRWA2019, overwrite = TRUE)
## Malawi 2020 #################################################################
mwi <- read_xlsx(path = "data-raw/sources/malawi2020.xlsx",
sheet = "Look-up Tables",
range = "B10:Q110")
mwi <- data.frame(mwi)
mwi[ , 2:ncol(mwi)] <- mwi[ , 2:ncol(mwi)] * 100
names(mwi) <- c("score", "nl100", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550",
"ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiMWI2020 <- tibble::tibble(mwi)
usethis::use_data(ppiMWI2020, overwrite = TRUE)
## Indonesia 2020 ##############################################################
idn <- read_xlsx(path = "data-raw/sources/indonesia2020.xlsx",
sheet = "Look-up Tables",
range = "B10:U110")
idn <- data.frame(idn)
idn[ , 2:ncol(idn)] <- idn[ , 2:ncol(idn)] * 100
names(idn) <- c("score", "nl100", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100", "ppp1500", "ppp2170",
"ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiIDN2020 <- tibble::tibble(idn)
usethis::use_data(ppiIDN2020, overwrite = TRUE)
## Tanzania 2022 ###############################################################
tza <- read_xlsx(path = "data-raw/sources/tanzania2022.xlsx",
sheet = "Look-up Tables",
range = "A10:U110")
tza <- data.frame(tza)
tza[ , 2:ncol(tza)] <- tza[ , 2:ncol(tza)] * 100
names(tza) <- c("score", "nl_upper", "nl_lower", "extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100",
"ppp1500", "ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiTZA2022 <- tibble::tibble(tza)
usethis::use_data(ppiTZA2022, overwrite = TRUE)
## Uganda 2022 ###############################################################
uga <- read_xlsx(path = "data-raw/sources/uganda2022.xlsx",
sheet = "Look-up Tables",
range = "B11:N111")
uga <- data.frame(uga)
uga[ , 2:ncol(uga)] <- uga[ , 2:ncol(uga)] * 100
names(uga) <- c("score", "ppp100", "ppp190", "ppp320", "ppp550", "ppp800",
"ppp1100", "ppp1500", "ppp2170",
"percentile20", "percentile40", "percentile60", "percentile80")
ppiUGA2022 <- tibble::tibble(uga)
usethis::use_data(ppiUGA2022, overwrite = TRUE)
## Benin PPI ###################################################################
ben11 <- read_xlsx(
path = "data-raw/sources/benin2022.xlsx",
sheet = "Look-up Tables 11Q",
range = "A9:N110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(ben11) <- c(
"score", "nl100", "nl150", "nl200",
"ppp190", "ppp320", "ppp550", "ppp215", "ppp365", "ppp685",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiBEN2022_11q <- ben11
usethis::use_data(ppiBEN2022_11q, overwrite = TRUE, compress = "xz")
ben6 <- read_xlsx(
path = "data-raw/sources/benin2022.xlsx",
sheet = "Look-up Tables 6Q",
range = "A9:N110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(ben6) <- c(
"score", "nl100", "nl150", "nl200",
"ppp190", "ppp320", "ppp550", "ppp215", "ppp365", "ppp685",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiBEN2022_6q <- ben6
usethis::use_data(ppiBEN2022_6q, overwrite = TRUE, compress = "xz")
## Bolivia PPI #################################################################
bol <- read_xlsx(
path = "data-raw/sources/bolivia2023.xlsx",
sheet = "Look-up Table",
range = "B9:P110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(bol) <- c(
"score", "nl100", "nl_extreme", "nl150", "nl200",
"ppp190", "ppp320", "ppp550", "ppp215", "ppp365", "ppp685",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiBOL2023 <- bol
usethis::use_data(ppiBOL2023, overwrite = TRUE, compress = "xz")
## Burkina Faso PPI ############################################################
bfa <- read_xlsx(
path = "data-raw/sources/burkinafaso2023.xlsx",
sheet = "Look-up Table",
range = "B9:O110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(bfa) <- c(
"score", "nl100", "nl150", "nl200",
"ppp215", "ppp365", "ppp685", "ppp190", "ppp320", "ppp550",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiBFA2023 <- bfa
usethis::use_data(ppiBFA2023, overwrite = TRUE, compress = "xz")
## Cambodia PPI ################################################################
khm <- read_xlsx(
path = "data-raw/sources/cambodia2023.xlsx",
sheet = "Look-up Tables",
range = "A9:N110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(khm) <- c(
"score", "nl100", "nl150", "nl200",
"ppp550", "ppp800", "ppp1100", "ppp1500", "ppp2170", "ppp685",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiKHM2023 <- khm
usethis::use_data(ppiKHM2023, overwrite = TRUE, compress = "xz")
## Ecuador PPI #################################################################
ecu <- read_xlsx(
path = "data-raw/sources/ecuador2022.xlsx",
sheet = "Look-up Table (10Q)",
range = "A9:T110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Lines`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(ecu) <- c(
"score", "nl100", "nl_extreme", "nl150", "nl200",
"ppp215", "ppp365", "ppp685", "ppp100", "ppp190", "ppp320", "ppp550",
"ppp800", "ppp1100", "ppp1500", "ppp2170",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiECU2022 <- ecu
usethis::use_data(ppiECU2022, overwrite = TRUE, compress = "xz")
## El Salvador PPI #############################################################
slv <- read_xlsx(
path = "data-raw/sources/elsalvador2021.xlsx",
sheet = "Look-up Tables (10Q)",
range = "B9:V110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(slv) <- c(
"score", "nl100", "nl_extreme",
"ppp215", "ppp365", "ppp685", "ppp100", "ppp190", "ppp320", "ppp550",
"ppp800", "ppp1100", "ppp1500", "ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiSLV2021 <- slv
usethis::use_data(ppiSLV2021, overwrite = TRUE, compress = "xz")
## Ethiopia PPI ################################################################
eth <- read_xlsx(
path = "data-raw/sources/ethiopia2023.xlsx",
sheet = "Look-up Tables",
range = "A9:T110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(eth) <- c(
"score", "nl100", "nl_extreme", "nl150", "nl200",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100", "ppp1500",
"ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiETH2023 <- eth
usethis::use_data(ppiETH2023, overwrite = TRUE, compress = "xz")
## Guatemala PPI ###############################################################
gtm <- read_xlsx(
path = "data-raw/sources/guatemala2023.xlsx",
sheet = "Look-up Tables",
range = "B9:L110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `$1.90/day 2011 PPP \r\n(Bottom 7th Percentile)*`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(gtm) <- c(
"score", "ppp190", "ppp320", "ppp550", "ppp215", "ppp365", "ppp685",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiGTM2023 <- gtm
usethis::use_data(ppiGTM2023, overwrite = TRUE, compress = "xz")
## Honduras PPI ################################################################
hnd <- read_xlsx(
path = "data-raw/sources/honduras2023.xlsx",
sheet = "Look-up Table",
range = "B9:S110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(hnd) <- c(
"score", "nl100", "nl_extreme",
"ppp100", "ppp190", "ppp320", "ppp550", "ppp800", "ppp1100", "ppp1500",
"ppp2170", "ppp125", "ppp250", "ppp500",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiHND2023 <- hnd
usethis::use_data(ppiHND2023, overwrite = TRUE, compress = "xz")
## Indonesia PPI ###############################################################
idn <- read_xlsx(
path = "data-raw/sources/indonesia2023.xlsx",
sheet = "Look-up Table",
range = "A9:J110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(idn) <- c(
"score", "nl100", "ppp365", "ppp685", "ppp320", "ppp550",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiIDN2023 <- idn
usethis::use_data(ppiIDN2023, overwrite = TRUE, compress = "xz")
## Papua New Guinea PPI ########################################################
png <- read_xlsx(
path = "data-raw/sources/papuanewguinea2023.xlsx",
sheet = "Look-up Tables",
range = "B9:J110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `Bottom 20th percentile WI`:`Bottom 80th percentile WI Urban/Rural`,
.fns = ~.x * 100
)
)
names(png) <- c(
"score",
"percentile20_wi", "percentile40_wi", "percentile60_wi", "percentile80_wi",
"percentile20_wi_ur", "percentile40_wi_ur", "percentile60_wi_ur",
"percentile80_wi_ur"
)
ppiPNG2023 <- png
usethis::use_data(ppiPNG2023, overwrite = TRUE, compress = "xz")
## Papua New Guinea PPI ########################################################
phl <- read_xlsx(
path = "data-raw/sources/philippines2023.xlsx",
sheet = "Look-up Table",
range = "B9:N110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(phl) <- c(
"score",
"nl100", "food", "ppp215", "ppp365", "ppp685", "ppp190", "ppp320", "ppp550",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiPHL2023 <- phl
usethis::use_data(ppiPHL2023, overwrite = TRUE, compress = "xz")
## South Africa PPI ############################################################
zaf <- read_xlsx(
path = "data-raw/sources/southafrica2023.xlsx",
sheet = "Look-up Tables",
range = "B9:G110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `Wealth Index Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(zaf) <- c(
"score",
"wealth_index", "percentile20", "percentile40", "percentile60", "percentile80"
)
ppiZAF2023 <- zaf
usethis::use_data(ppiZAF2023, overwrite = TRUE, compress = "xz")
## Togo PPI ####################################################################
tgo <- read_xlsx(
path = "data-raw/sources/togo2023.xlsx",
sheet = "Look-up Table",
range = "A9:N110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(tgo) <- c(
"score",
"nl100", "nl150", "nl200",
"ppp215", "ppp365", "ppp685", "ppp190", "ppp320", "ppp550",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiTGO2023 <- tgo
usethis::use_data(ppiTGO2023, overwrite = TRUE, compress = "xz")
## Vietnam PPI #################################################################
vnm <- read_xlsx(
path = "data-raw/sources/vietnam2023.xlsx",
sheet = "Look-up Table",
range = "B9:F110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `Bottom 20th Percentile`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(vnm) <- c(
"score",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiVNM2023 <- vnm
usethis::use_data(ppiVNM2023, overwrite = TRUE, compress = "xz")
## Malawi PPI ##################################################################
mwi <- read_xlsx(
path = "data-raw/sources/malawi2023.xlsx",
sheet = "Look-up Tables",
range = "A9:M110"
) |>
dplyr::mutate(
dplyr::across(
.cols = `National Poverty Line`:`Bottom 80th Percentile`,
.fns = ~.x * 100
)
)
names(mwi) <- c(
"score", "nl100", "food",
"ppp215", "ppp365", "ppp685", "ppp190", "ppp320", "ppp550",
"percentile20", "percentile40", "percentile60", "percentile80"
)
ppiMWI2023 <- mwi
usethis::use_data(ppiMWI2023, overwrite = TRUE, compress = "xz")
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