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adjust_inflation_CPI.R
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## Script to adjust costs for inflation
## written by Anna J. Turbelin, November 14, 2022
library(dplyr)
library(ggplot2)
library(devtools)
library(mgcv)
library(ggpubr)
library(readxl)
library(tidyverse)
library(ggpmisc)
install.packages("wbstats")
library(wbstats)
# library(countrycode)
# library(ISOcodes)
options(stringsAsFactors=FALSE)
## function 2.0 to adjust costs for inflation with option to have a 'neutral' fixed year' original CPI column
##' @param costdb Name of database to pull costs from that need to be converted.
##' @param costcolumn Name of the cost column to use in \code{costdb}
##' @param yearcolumn Name of the year column to use. Specify \code{NULL} when costs have already been adjusted for inflation.
##' @param year Year of the required cost value / CPI value, YYYY format. For example if you want to standardize costs to 2017 USD values type 2017.
##' @param country_iso3 ISO3 code of the country from which the currency used to standardize costs is.
##' ISO3 codes are three-letter country codes defined in ISO 3166-1 (International Organization for Standardization (ISO)).
##' E.g United States of America (USA); France (FRA); Benin (BEN)
##' @param originalyear Year of the current cost value - use when @param yearcolumn is null because costs have already been standardized to a given year.
##' @param method Name of the method to use to adjust costs for inflation - currently only CPI available.
adjustInflation <- function(costdb, costcolumn, yearcolumn = NULL, year = "2020", country_iso3 = "USA", originalyear = NULL, method = "CPI"){
if(is.null(costcolumn))
{
stop("Add column name of raw costs to adjust for inflation")
}
if(!(costcolumn %in% colnames(costdb)))
{
stop("The 'costcolumn' does not exist in the database, please check spelling.")
}
if(is.null(yearcolumn)) {
if(is.null(originalyear))
{
stop("Add value to field 'originalyear' (lacks original CPI year) or add value to 'yearcolumn'")
}
## load CPI world bank data from wbstats package
CPI <- wb_data("FP.CPI.TOTL",country = "all") ## get CPI data from world bank data
CPI <- dplyr::filter(CPI, iso3c == country_iso3) ## filter to only keep only relevant CPI values for given country - here would be USA
CPI_new <- CPI[which(CPI$date == year & CPI$iso3c == country_iso3, arr.ind=TRUE), which(colnames(CPI)=="FP.CPI.TOTL")] # rename variable this is the CPI value we want so in this case 2020
CPI_new <- CPI_new[[1]] # get single value
raw.cost = costdb[which(colnames(costdb)==costcolumn)] # get cost column
CPI.original <- CPI[which(CPI$date == originalyear & CPI$iso3c == country_iso3, arr.ind=TRUE), which(colnames(CPI)=="FP.CPI.TOTL")] # rename variable this is the CPI value we want so in this case 2020
CPI.original <- CPI.original[[1]] # get single value
adjvalue <- (1+((CPI_new - CPI.original)/CPI.original))*raw.cost
colnames(adjvalue) <- paste0("cost_", year)
return(df <- data.frame(dplyr::bind_cols(costdb, adjvalue))) #this returns a new data frame - probably best to add to current dataset but not 100% how to do it
}
else {
if(!(yearcolumn %in% colnames(costdb)))
{
stop("The 'yearcolumn' does not exist in the database, please check spelling.")
}
## load CPI world bank data from wbstats package
CPI <- wb_data("FP.CPI.TOTL",country = "all") ## get CPI data from world bank data
CPI <- dplyr::filter(CPI, iso3c == country_iso3) ## filter to only keep only relevant CPI values for given country - here would be USA
CPI_new <- CPI[which(CPI$date == year & CPI$iso3c == country_iso3, arr.ind=TRUE), which(colnames(CPI)=="FP.CPI.TOTL")] # rename variable this is the CPI value we want so in this case 2020
CPI_new <- CPI_new[[1]] # get single value
raw.cost = costdb[which(colnames(costdb)==costcolumn)] # get cost column
CPI.year = costdb[which(colnames(costdb)==yearcolumn)] # get year column
CPI.year <- left_join(CPI.year, CPI, by=c('Year'='date')) # join year to match CPI value
CPI.original <- CPI.year[which(colnames(CPI.year)=="FP.CPI.TOTL")] # exxtract CPI value for equation
adjvalue <- (1+((CPI_new - CPI.original)/CPI.original))*raw.cost
colnames(adjvalue) <- paste0("cost_", year)
return(df <- data.frame(dplyr::bind_cols(costdb, adjvalue))) #this returns a new data frame - probably best to add to current dataset but not 100% how to do it
}
}