R/concord_naics_isic.R
In insongkim/concordance: Product Concordance
#' Converting NAICS and ISIC Codes
#'
#' Concords North American Industry Classification System codes (NAICS2002, NAICS2007, NAICS2012, NAICS2017) to and from International Standard Industrial Classification codes (ISIC Revision 2, 3, 3.1, 4).
#'
#' @param sourcevar An input character vector of NAICS or ISIC codes. The function accepts 2 to 6-digit codes for NAICS and 1 to 4-digit codes for ISIC.
#' @param origin A string setting the input industry classification: "NAICS2002", "NAICS2007", "NAICS2012", "NAICS2017", "ISIC2" (1968), "ISIC3" (1989), "ISIC3.1" (2002), "ISIC4" (2008).
#' @param destination A string setting the output industry classification: "NAICS2002", "NAICS2007", "NAICS2012", "NAICS2017", "ISIC2" (1968), "ISIC3" (1989), "ISIC3.1" (2002), "ISIC4" (2008).
#' @param dest.digit An integer indicating the preferred number of digits for output codes. Allows 2 to 6 digits for NAICS codes and 1 to 4 digits for ISIC codes. The default is 4 digits.
#' @param all Either TRUE or FALSE. If TRUE, the function will return (1) all matched outputs for each input, and (2) the share of occurrences for each matched output among all matched outputs. Users can use the shares as weights for more precise concordances. If FALSE, the function will only return the matched output with the largest share of occurrences (the mode match). If the mode consists of multiple matches, the function will return the first matched output.
#' @return The matched output(s) for each element of the input vector. Either a list object when all = TRUE or a character vector when all = FALSE.
#' @import tibble tidyr purrr dplyr stringr
#' @importFrom rlang := !! .data
#' @export
#' @source Concordance tables provided by:
#' \itemize{
#' \item The U.S. Census Bureau <https://www.census.gov/eos/www/naics/concordances/concordances.html>
#' }
#' @note Always include leading zeros in codes (e.g., use ISIC4 code 0111 instead of 111)---results may be buggy otherwise.
#' @examples
#' ## NAICS2017 to ISIC4
#' concord_naics_isic(sourcevar = c("111120", "326199"),
#' origin = "NAICS2017", destination = "ISIC4",
#' dest.digit = 4, all = TRUE)
concord_naics_isic <- function (sourcevar,
origin,
destination,
dest.digit = 4,
all = FALSE) {
# load/create specific conversion dictionary
# NAICS2002-ISIC3.1
if ((origin == "NAICS2002" & destination == "ISIC3.1") | (origin == "ISIC3.1" & destination == "NAICS2002")) {
dictionary <- concordance::naics2002_isic31
# NAICS2002-ISIC4
} else if ((origin == "NAICS2002" & destination == "ISIC4") | (origin == "ISIC4" & destination == "NAICS2002")) {
# NAICS2002 --> ISIC3.1 --> ISIC4
dictionary.1 <- concordance::naics2002_isic31
dictionary.2 <- concordance::isic4_isic31
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC3.1_4d")
dictionary <- dictionary %>%
select(.data$NAICS2002_6d, .data$NAICS2002_5d, .data$NAICS2002_4d, .data$NAICS2002_3d, .data$NAICS2002_2d,
.data$ISIC4_4d, .data$ISIC4_3d, .data$ISIC4_2d, .data$ISIC4_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2002_6d) & is.na(.data$ISIC4_4d))) %>%
arrange(.data$NAICS2002_6d)
# NAICS2002-ISIC3
} else if ((origin == "NAICS2002" & destination == "ISIC3") | (origin == "ISIC3" & destination == "NAICS2002")) {
# NAICS2002 --> ISIC3.1 --> ISIC3
dictionary.1 <- concordance::naics2002_isic31
dictionary.2 <- concordance::isic31_isic3
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC3.1_4d")
dictionary <- dictionary %>%
select(.data$NAICS2002_6d, .data$NAICS2002_5d, .data$NAICS2002_4d, .data$NAICS2002_3d, .data$NAICS2002_2d,
.data$ISIC3_4d, .data$ISIC3_3d, .data$ISIC3_2d, .data$ISIC3_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2002_6d) & is.na(.data$ISIC3_4d))) %>%
arrange(.data$NAICS2002_6d)
# NAICS2002-ISIC2
} else if ((origin == "NAICS2002" & destination == "ISIC2") | (origin == "ISIC2" & destination == "NAICS2002")) {
# NAICS2002 --> ISIC3.1 --> ISIC2
dictionary.1 <- concordance::naics2002_isic31
dictionary.2 <- concordance::isic31_isic2
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC3.1_4d")
dictionary <- dictionary %>%
select(.data$NAICS2002_6d, .data$NAICS2002_5d, .data$NAICS2002_4d, .data$NAICS2002_3d, .data$NAICS2002_2d,
.data$ISIC2_4d, .data$ISIC2_3d, .data$ISIC2_2d, .data$ISIC2_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2002_6d) & is.na(.data$ISIC2_4d))) %>%
arrange(.data$NAICS2002_6d)
# NAICS2007
} else if ((origin == "NAICS2007" & destination == "ISIC4") | (origin == "ISIC4" & destination == "NAICS2007")) {
dictionary <- concordance::naics2007_isic4
# NAICS2007-ISIC3.1
} else if ((origin == "NAICS2007" & destination == "ISIC3.1") | (origin == "ISIC3.1" & destination == "NAICS2007")) {
# NAICS2007 --> ISIC4 --> ISIC3.1
dictionary.1 <- concordance::naics2007_isic4
dictionary.2 <- concordance::isic4_isic31
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2007_6d, .data$NAICS2007_5d, .data$NAICS2007_4d, .data$NAICS2007_3d, .data$NAICS2007_2d,
.data$ISIC3.1_4d, .data$ISIC3.1_3d, .data$ISIC3.1_2d, .data$ISIC3.1_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2007_6d) & is.na(.data$ISIC3.1_4d))) %>%
arrange(.data$NAICS2007_6d)
# NAICS2007-ISIC3
} else if ((origin == "NAICS2007" & destination == "ISIC3") | (origin == "ISIC3" & destination == "NAICS2007")) {
# NAICS2007 --> ISIC4 --> ISIC3
dictionary.1 <- concordance::naics2007_isic4
dictionary.2 <- concordance::isic4_isic3
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2007_6d, .data$NAICS2007_5d, .data$NAICS2007_4d, .data$NAICS2007_3d, .data$NAICS2007_2d,
.data$ISIC3_4d, .data$ISIC3_3d, .data$ISIC3_2d, .data$ISIC3_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2007_6d) & is.na(.data$ISIC3_4d))) %>%
arrange(.data$NAICS2007_6d)
# NAICS2007-ISIC2
} else if ((origin == "NAICS2007" & destination == "ISIC2") | (origin == "ISIC2" & destination == "NAICS2007")) {
# NAICS2007 --> ISIC4 --> ISIC2
dictionary.1 <- concordance::naics2007_isic4
dictionary.2 <- concordance::isic4_isic2
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2007_6d, .data$NAICS2007_5d, .data$NAICS2007_4d, .data$NAICS2007_3d, .data$NAICS2007_2d,
.data$ISIC2_4d, .data$ISIC2_3d, .data$ISIC2_2d, .data$ISIC2_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2007_6d) & is.na(.data$ISIC2_4d))) %>%
arrange(.data$NAICS2007_6d)
# NAICS2012
} else if ((origin == "NAICS2012" & destination == "ISIC4") | (origin == "ISIC4" & destination == "NAICS2012")) {
dictionary <- concordance::naics2012_isic4
# NAICS2012-ISIC3.1
} else if ((origin == "NAICS2012" & destination == "ISIC3.1") | (origin == "ISIC3.1" & destination == "NAICS2012")) {
# NAICS2012 --> ISIC4 --> ISIC3.1
dictionary.1 <- concordance::naics2012_isic4
dictionary.2 <- concordance::isic4_isic31
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2012_6d, .data$NAICS2012_5d, .data$NAICS2012_4d, .data$NAICS2012_3d, .data$NAICS2012_2d,
.data$ISIC3.1_4d, .data$ISIC3.1_3d, .data$ISIC3.1_2d, .data$ISIC3.1_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2012_6d) & is.na(.data$ISIC3.1_4d))) %>%
arrange(.data$NAICS2012_6d)
# NAICS2012-ISIC3
} else if ((origin == "NAICS2012" & destination == "ISIC3") | (origin == "ISIC3" & destination == "NAICS2012")) {
# NAICS2012 --> ISIC4 --> ISIC3
dictionary.1 <- concordance::naics2012_isic4
dictionary.2 <- concordance::isic4_isic3
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2012_6d, .data$NAICS2012_5d, .data$NAICS2012_4d, .data$NAICS2012_3d, .data$NAICS2012_2d,
.data$ISIC3_4d, .data$ISIC3_3d, .data$ISIC3_2d, .data$ISIC3_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2012_6d) & is.na(.data$ISIC3_4d))) %>%
arrange(.data$NAICS2012_6d)
# NAICS2012-ISIC2
} else if ((origin == "NAICS2012" & destination == "ISIC2") | (origin == "ISIC2" & destination == "NAICS2012")) {
# NAICS2012 --> ISIC4 --> ISIC2
dictionary.1 <- concordance::naics2012_isic4
dictionary.2 <- concordance::isic4_isic2
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2012_6d, .data$NAICS2012_5d, .data$NAICS2012_4d, .data$NAICS2012_3d, .data$NAICS2012_2d,
.data$ISIC2_4d, .data$ISIC2_3d, .data$ISIC2_2d, .data$ISIC2_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2012_6d) & is.na(.data$ISIC2_4d))) %>%
arrange(.data$NAICS2012_6d)
# NAICS2017-ISIC4
} else if ((origin == "NAICS2017" & destination == "ISIC4") | (origin == "ISIC4" & destination == "NAICS2017")) {
dictionary <- concordance::naics2017_isic4
# NAICS2017-ISIC3.1
} else if ((origin == "NAICS2017" & destination == "ISIC3.1") | (origin == "ISIC3.1" & destination == "NAICS2017")) {
# NAICS2017 --> ISIC4 --> ISIC3.1
dictionary.1 <- concordance::naics2017_isic4
dictionary.2 <- concordance::isic4_isic31
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2017_6d, .data$NAICS2017_5d, .data$NAICS2017_4d, .data$NAICS2017_3d, .data$NAICS2017_2d,
.data$ISIC3.1_4d, .data$ISIC3.1_3d, .data$ISIC3.1_2d, .data$ISIC3.1_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2017_6d) & is.na(.data$ISIC3.1_4d))) %>%
arrange(.data$NAICS2017_6d)
# NAICS2017-ISIC3
} else if ((origin == "NAICS2017" & destination == "ISIC3") | (origin == "ISIC3" & destination == "NAICS2017")) {
# NAICS2017 --> ISIC4 --> ISIC3
dictionary.1 <- concordance::naics2017_isic4
dictionary.2 <- concordance::isic4_isic3
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2017_6d, .data$NAICS2017_5d, .data$NAICS2017_4d, .data$NAICS2017_3d, .data$NAICS2017_2d,
.data$ISIC3_4d, .data$ISIC3_3d, .data$ISIC3_2d, .data$ISIC3_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2017_6d) & is.na(.data$ISIC3_4d))) %>%
arrange(.data$NAICS2017_6d)
# NAICS2017-ISIC2
} else if ((origin == "NAICS2017" & destination == "ISIC2") | (origin == "ISIC2" & destination == "NAICS2017")) {
# NAICS2017 --> ISIC4 --> ISIC2
dictionary.1 <- concordance::naics2017_isic4
dictionary.2 <- concordance::isic4_isic2
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2017_6d, .data$NAICS2017_5d, .data$NAICS2017_4d, .data$NAICS2017_3d, .data$NAICS2017_2d,
.data$ISIC2_4d, .data$ISIC2_3d, .data$ISIC2_2d, .data$ISIC2_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2017_6d) & is.na(.data$ISIC2_4d))) %>%
arrange(.data$NAICS2017_6d)
} else {
stop("Conversion dictionary not available.")
}
# sanity check
if (length(sourcevar) == 0) {return(character(0))}
# check whether input codes have the same digits
# NAICS code has some unusual 2-digit codes, exclude them when counting digits
exempt.naics <- c("31-33", "44-45", "48-49")
sourcevar.sub <- sourcevar[!sourcevar %in% exempt.naics]
# avoid errors in the case where users only put in unusal 2-digit codes
if(all(length(sourcevar.sub) == 0 & sourcevar %in% exempt.naics)) {
sourcevar.sub <- "31"
}
# get the number of unique digits, excluding NAs
digits <- unique(nchar(sourcevar.sub))
digits <- digits[!is.na(digits)]
# check whether input codes have the same digits
if (length(digits) > 1) {stop("'sourcevar' has codes with different number of digits. Please ensure that input codes are at the same length.")}
# set acceptable digits for inputs and outputs
if (str_detect(origin, "NAICS") & str_detect(destination, "ISIC")){
origin.digits <- seq(2, 6, 1)
if (!(digits %in% origin.digits)) {stop("'sourcevar' only accepts 2 to 6-digit inputs for NAICS codes.")}
destination.digits <- c(1, 2, 3, 4)
if ((!dest.digit %in% destination.digits)) {stop("'dest.digit' only accepts 1, 2, 3, 4-digit outputs for ISIC codes.")}
} else if (str_detect(origin, "ISIC") & str_detect(destination, "NAICS")) {
origin.digits <- c(1, 2, 3, 4)
if (!(digits %in% origin.digits)) {stop("'sourcevar' only accepts 1, 2, 3, 4-digit inputs for ISIC codes.")}
destination.digits <- seq(2, 6, 1)
if ((!dest.digit %in% destination.digits)) {stop("'dest.digit' only accepts 2 to 6-digit outputs for NAICS codes.")}
} else {
stop("Concordance not supported.")
}
# get column names of dictionary
origin.codes <- names(dictionary)
destination.codes <- names(dictionary)
# allow origin / destination to be entered in any case
origin.var <- paste(toupper(origin), "_", digits, "d", sep = "")
destination.var <- paste(toupper(destination), "_", dest.digit, "d", sep = "")
if (!origin.var %in% origin.codes){stop("Origin code not supported.")}
if (!destination.var %in% destination.codes){stop("Destination code not supported.")}
# check if concordance is available for sourcevar
all.origin.codes <- dictionary %>%
pull(!!as.name(origin.var))
if (!all(sourcevar %in% all.origin.codes)){
no.code <- sourcevar[!sourcevar %in% all.origin.codes]
no.code <- paste0(no.code, collapse = ", ")
warning(paste("Matches for ", str_extract(origin, "[^_]+"), " code(s): ", no.code, " not found and returned NA. Please double check input code and classification.\n", sep = ""))
}
# match
matches <- which(all.origin.codes %in% sourcevar)
dest.var <- dictionary[matches, c(origin.var, destination.var)]
# calculate weights for matches
dest.var <- dest.var %>%
rename(!!origin := 1,
!!destination := 2) %>%
# if input is NA then match should be NA
mutate(!!as.name(destination) := if_else(is.na(!!as.name(origin)), NA_character_, !!as.name(destination))) %>%
group_by(!!as.name(origin), !!as.name(destination)) %>%
mutate(n = length(!!as.name(destination)),
n = ifelse(is.na(!!as.name(destination)), NA, n)) %>%
distinct() %>%
filter(!(is.na(n) & sum(!is.na(n)) > 0)) %>%
group_by(!!as.name(origin)) %>%
mutate(n_sum = sum(.data$n, na.rm = TRUE),
weight = .data$n/.data$n_sum) %>%
arrange(dplyr::desc(.data$weight)) %>%
ungroup() %>%
select(-n, -.data$n_sum) %>%
rename(match = !!as.name(destination))
# keep info on all matches and weights?
if (all == TRUE){
# merge matches/weights according to input
out.merge <- nest_join(tibble(!!origin := sourcevar),
dest.var,
by = origin)
names(out.merge$dest.var) <- sourcevar
# fill NAs when there is no match
out <- map(out.merge$dest.var, function(x){
if(nrow(x) == 0){
out.sub <- list(match = NA_character_,
weight = NA)
} else {
out.sub <- list(match = x$match,
weight = x$weight)
}
})
} else {
# keep match with largest weight
# if multiple matches have the same weights, keep first match
dest.var.sub <- dest.var %>%
group_by(!!as.name(origin)) %>%
slice(1) %>%
ungroup() %>%
select(-.data$weight)
# handle repeated inputs
out <- dest.var.sub[match(sourcevar, dest.var.sub %>% pull(!!as.name(origin))), "match"] %>%
pull(match)
}
return(out)
}
insongkim/concordance documentation built on Jan. 25, 2023, 4:55 p.m.
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#' Converting NAICS and ISIC Codes
#'
#' Concords North American Industry Classification System codes (NAICS2002, NAICS2007, NAICS2012, NAICS2017) to and from International Standard Industrial Classification codes (ISIC Revision 2, 3, 3.1, 4).
#'
#' @param sourcevar An input character vector of NAICS or ISIC codes. The function accepts 2 to 6-digit codes for NAICS and 1 to 4-digit codes for ISIC.
#' @param origin A string setting the input industry classification: "NAICS2002", "NAICS2007", "NAICS2012", "NAICS2017", "ISIC2" (1968), "ISIC3" (1989), "ISIC3.1" (2002), "ISIC4" (2008).
#' @param destination A string setting the output industry classification: "NAICS2002", "NAICS2007", "NAICS2012", "NAICS2017", "ISIC2" (1968), "ISIC3" (1989), "ISIC3.1" (2002), "ISIC4" (2008).
#' @param dest.digit An integer indicating the preferred number of digits for output codes. Allows 2 to 6 digits for NAICS codes and 1 to 4 digits for ISIC codes. The default is 4 digits.
#' @param all Either TRUE or FALSE. If TRUE, the function will return (1) all matched outputs for each input, and (2) the share of occurrences for each matched output among all matched outputs. Users can use the shares as weights for more precise concordances. If FALSE, the function will only return the matched output with the largest share of occurrences (the mode match). If the mode consists of multiple matches, the function will return the first matched output.
#' @return The matched output(s) for each element of the input vector. Either a list object when all = TRUE or a character vector when all = FALSE.
#' @import tibble tidyr purrr dplyr stringr
#' @importFrom rlang := !! .data
#' @export
#' @source Concordance tables provided by:
#' \itemize{
#' \item The U.S. Census Bureau <https://www.census.gov/eos/www/naics/concordances/concordances.html>
#' }
#' @note Always include leading zeros in codes (e.g., use ISIC4 code 0111 instead of 111)---results may be buggy otherwise.
#' @examples
#' ## NAICS2017 to ISIC4
#' concord_naics_isic(sourcevar = c("111120", "326199"),
#' origin = "NAICS2017", destination = "ISIC4",
#' dest.digit = 4, all = TRUE)
concord_naics_isic <- function (sourcevar,
origin,
destination,
dest.digit = 4,
all = FALSE) {
# load/create specific conversion dictionary
# NAICS2002-ISIC3.1
if ((origin == "NAICS2002" & destination == "ISIC3.1") | (origin == "ISIC3.1" & destination == "NAICS2002")) {
dictionary <- concordance::naics2002_isic31
# NAICS2002-ISIC4
} else if ((origin == "NAICS2002" & destination == "ISIC4") | (origin == "ISIC4" & destination == "NAICS2002")) {
# NAICS2002 --> ISIC3.1 --> ISIC4
dictionary.1 <- concordance::naics2002_isic31
dictionary.2 <- concordance::isic4_isic31
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC3.1_4d")
dictionary <- dictionary %>%
select(.data$NAICS2002_6d, .data$NAICS2002_5d, .data$NAICS2002_4d, .data$NAICS2002_3d, .data$NAICS2002_2d,
.data$ISIC4_4d, .data$ISIC4_3d, .data$ISIC4_2d, .data$ISIC4_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2002_6d) & is.na(.data$ISIC4_4d))) %>%
arrange(.data$NAICS2002_6d)
# NAICS2002-ISIC3
} else if ((origin == "NAICS2002" & destination == "ISIC3") | (origin == "ISIC3" & destination == "NAICS2002")) {
# NAICS2002 --> ISIC3.1 --> ISIC3
dictionary.1 <- concordance::naics2002_isic31
dictionary.2 <- concordance::isic31_isic3
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC3.1_4d")
dictionary <- dictionary %>%
select(.data$NAICS2002_6d, .data$NAICS2002_5d, .data$NAICS2002_4d, .data$NAICS2002_3d, .data$NAICS2002_2d,
.data$ISIC3_4d, .data$ISIC3_3d, .data$ISIC3_2d, .data$ISIC3_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2002_6d) & is.na(.data$ISIC3_4d))) %>%
arrange(.data$NAICS2002_6d)
# NAICS2002-ISIC2
} else if ((origin == "NAICS2002" & destination == "ISIC2") | (origin == "ISIC2" & destination == "NAICS2002")) {
# NAICS2002 --> ISIC3.1 --> ISIC2
dictionary.1 <- concordance::naics2002_isic31
dictionary.2 <- concordance::isic31_isic2
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC3.1_4d")
dictionary <- dictionary %>%
select(.data$NAICS2002_6d, .data$NAICS2002_5d, .data$NAICS2002_4d, .data$NAICS2002_3d, .data$NAICS2002_2d,
.data$ISIC2_4d, .data$ISIC2_3d, .data$ISIC2_2d, .data$ISIC2_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2002_6d) & is.na(.data$ISIC2_4d))) %>%
arrange(.data$NAICS2002_6d)
# NAICS2007
} else if ((origin == "NAICS2007" & destination == "ISIC4") | (origin == "ISIC4" & destination == "NAICS2007")) {
dictionary <- concordance::naics2007_isic4
# NAICS2007-ISIC3.1
} else if ((origin == "NAICS2007" & destination == "ISIC3.1") | (origin == "ISIC3.1" & destination == "NAICS2007")) {
# NAICS2007 --> ISIC4 --> ISIC3.1
dictionary.1 <- concordance::naics2007_isic4
dictionary.2 <- concordance::isic4_isic31
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2007_6d, .data$NAICS2007_5d, .data$NAICS2007_4d, .data$NAICS2007_3d, .data$NAICS2007_2d,
.data$ISIC3.1_4d, .data$ISIC3.1_3d, .data$ISIC3.1_2d, .data$ISIC3.1_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2007_6d) & is.na(.data$ISIC3.1_4d))) %>%
arrange(.data$NAICS2007_6d)
# NAICS2007-ISIC3
} else if ((origin == "NAICS2007" & destination == "ISIC3") | (origin == "ISIC3" & destination == "NAICS2007")) {
# NAICS2007 --> ISIC4 --> ISIC3
dictionary.1 <- concordance::naics2007_isic4
dictionary.2 <- concordance::isic4_isic3
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2007_6d, .data$NAICS2007_5d, .data$NAICS2007_4d, .data$NAICS2007_3d, .data$NAICS2007_2d,
.data$ISIC3_4d, .data$ISIC3_3d, .data$ISIC3_2d, .data$ISIC3_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2007_6d) & is.na(.data$ISIC3_4d))) %>%
arrange(.data$NAICS2007_6d)
# NAICS2007-ISIC2
} else if ((origin == "NAICS2007" & destination == "ISIC2") | (origin == "ISIC2" & destination == "NAICS2007")) {
# NAICS2007 --> ISIC4 --> ISIC2
dictionary.1 <- concordance::naics2007_isic4
dictionary.2 <- concordance::isic4_isic2
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2007_6d, .data$NAICS2007_5d, .data$NAICS2007_4d, .data$NAICS2007_3d, .data$NAICS2007_2d,
.data$ISIC2_4d, .data$ISIC2_3d, .data$ISIC2_2d, .data$ISIC2_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2007_6d) & is.na(.data$ISIC2_4d))) %>%
arrange(.data$NAICS2007_6d)
# NAICS2012
} else if ((origin == "NAICS2012" & destination == "ISIC4") | (origin == "ISIC4" & destination == "NAICS2012")) {
dictionary <- concordance::naics2012_isic4
# NAICS2012-ISIC3.1
} else if ((origin == "NAICS2012" & destination == "ISIC3.1") | (origin == "ISIC3.1" & destination == "NAICS2012")) {
# NAICS2012 --> ISIC4 --> ISIC3.1
dictionary.1 <- concordance::naics2012_isic4
dictionary.2 <- concordance::isic4_isic31
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2012_6d, .data$NAICS2012_5d, .data$NAICS2012_4d, .data$NAICS2012_3d, .data$NAICS2012_2d,
.data$ISIC3.1_4d, .data$ISIC3.1_3d, .data$ISIC3.1_2d, .data$ISIC3.1_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2012_6d) & is.na(.data$ISIC3.1_4d))) %>%
arrange(.data$NAICS2012_6d)
# NAICS2012-ISIC3
} else if ((origin == "NAICS2012" & destination == "ISIC3") | (origin == "ISIC3" & destination == "NAICS2012")) {
# NAICS2012 --> ISIC4 --> ISIC3
dictionary.1 <- concordance::naics2012_isic4
dictionary.2 <- concordance::isic4_isic3
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2012_6d, .data$NAICS2012_5d, .data$NAICS2012_4d, .data$NAICS2012_3d, .data$NAICS2012_2d,
.data$ISIC3_4d, .data$ISIC3_3d, .data$ISIC3_2d, .data$ISIC3_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2012_6d) & is.na(.data$ISIC3_4d))) %>%
arrange(.data$NAICS2012_6d)
# NAICS2012-ISIC2
} else if ((origin == "NAICS2012" & destination == "ISIC2") | (origin == "ISIC2" & destination == "NAICS2012")) {
# NAICS2012 --> ISIC4 --> ISIC2
dictionary.1 <- concordance::naics2012_isic4
dictionary.2 <- concordance::isic4_isic2
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2012_6d, .data$NAICS2012_5d, .data$NAICS2012_4d, .data$NAICS2012_3d, .data$NAICS2012_2d,
.data$ISIC2_4d, .data$ISIC2_3d, .data$ISIC2_2d, .data$ISIC2_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2012_6d) & is.na(.data$ISIC2_4d))) %>%
arrange(.data$NAICS2012_6d)
# NAICS2017-ISIC4
} else if ((origin == "NAICS2017" & destination == "ISIC4") | (origin == "ISIC4" & destination == "NAICS2017")) {
dictionary <- concordance::naics2017_isic4
# NAICS2017-ISIC3.1
} else if ((origin == "NAICS2017" & destination == "ISIC3.1") | (origin == "ISIC3.1" & destination == "NAICS2017")) {
# NAICS2017 --> ISIC4 --> ISIC3.1
dictionary.1 <- concordance::naics2017_isic4
dictionary.2 <- concordance::isic4_isic31
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2017_6d, .data$NAICS2017_5d, .data$NAICS2017_4d, .data$NAICS2017_3d, .data$NAICS2017_2d,
.data$ISIC3.1_4d, .data$ISIC3.1_3d, .data$ISIC3.1_2d, .data$ISIC3.1_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2017_6d) & is.na(.data$ISIC3.1_4d))) %>%
arrange(.data$NAICS2017_6d)
# NAICS2017-ISIC3
} else if ((origin == "NAICS2017" & destination == "ISIC3") | (origin == "ISIC3" & destination == "NAICS2017")) {
# NAICS2017 --> ISIC4 --> ISIC3
dictionary.1 <- concordance::naics2017_isic4
dictionary.2 <- concordance::isic4_isic3
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2017_6d, .data$NAICS2017_5d, .data$NAICS2017_4d, .data$NAICS2017_3d, .data$NAICS2017_2d,
.data$ISIC3_4d, .data$ISIC3_3d, .data$ISIC3_2d, .data$ISIC3_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2017_6d) & is.na(.data$ISIC3_4d))) %>%
arrange(.data$NAICS2017_6d)
# NAICS2017-ISIC2
} else if ((origin == "NAICS2017" & destination == "ISIC2") | (origin == "ISIC2" & destination == "NAICS2017")) {
# NAICS2017 --> ISIC4 --> ISIC2
dictionary.1 <- concordance::naics2017_isic4
dictionary.2 <- concordance::isic4_isic2
# merge
dictionary <- full_join(dictionary.1,
dictionary.2,
by = "ISIC4_4d")
dictionary <- dictionary %>%
select(.data$NAICS2017_6d, .data$NAICS2017_5d, .data$NAICS2017_4d, .data$NAICS2017_3d, .data$NAICS2017_2d,
.data$ISIC2_4d, .data$ISIC2_3d, .data$ISIC2_2d, .data$ISIC2_1d) %>%
distinct() %>%
filter(!(is.na(.data$NAICS2017_6d) & is.na(.data$ISIC2_4d))) %>%
arrange(.data$NAICS2017_6d)
} else {
stop("Conversion dictionary not available.")
}
# sanity check
if (length(sourcevar) == 0) {return(character(0))}
# check whether input codes have the same digits
# NAICS code has some unusual 2-digit codes, exclude them when counting digits
exempt.naics <- c("31-33", "44-45", "48-49")
sourcevar.sub <- sourcevar[!sourcevar %in% exempt.naics]
# avoid errors in the case where users only put in unusal 2-digit codes
if(all(length(sourcevar.sub) == 0 & sourcevar %in% exempt.naics)) {
sourcevar.sub <- "31"
}
# get the number of unique digits, excluding NAs
digits <- unique(nchar(sourcevar.sub))
digits <- digits[!is.na(digits)]
# check whether input codes have the same digits
if (length(digits) > 1) {stop("'sourcevar' has codes with different number of digits. Please ensure that input codes are at the same length.")}
# set acceptable digits for inputs and outputs
if (str_detect(origin, "NAICS") & str_detect(destination, "ISIC")){
origin.digits <- seq(2, 6, 1)
if (!(digits %in% origin.digits)) {stop("'sourcevar' only accepts 2 to 6-digit inputs for NAICS codes.")}
destination.digits <- c(1, 2, 3, 4)
if ((!dest.digit %in% destination.digits)) {stop("'dest.digit' only accepts 1, 2, 3, 4-digit outputs for ISIC codes.")}
} else if (str_detect(origin, "ISIC") & str_detect(destination, "NAICS")) {
origin.digits <- c(1, 2, 3, 4)
if (!(digits %in% origin.digits)) {stop("'sourcevar' only accepts 1, 2, 3, 4-digit inputs for ISIC codes.")}
destination.digits <- seq(2, 6, 1)
if ((!dest.digit %in% destination.digits)) {stop("'dest.digit' only accepts 2 to 6-digit outputs for NAICS codes.")}
} else {
stop("Concordance not supported.")
}
# get column names of dictionary
origin.codes <- names(dictionary)
destination.codes <- names(dictionary)
# allow origin / destination to be entered in any case
origin.var <- paste(toupper(origin), "_", digits, "d", sep = "")
destination.var <- paste(toupper(destination), "_", dest.digit, "d", sep = "")
if (!origin.var %in% origin.codes){stop("Origin code not supported.")}
if (!destination.var %in% destination.codes){stop("Destination code not supported.")}
# check if concordance is available for sourcevar
all.origin.codes <- dictionary %>%
pull(!!as.name(origin.var))
if (!all(sourcevar %in% all.origin.codes)){
no.code <- sourcevar[!sourcevar %in% all.origin.codes]
no.code <- paste0(no.code, collapse = ", ")
warning(paste("Matches for ", str_extract(origin, "[^_]+"), " code(s): ", no.code, " not found and returned NA. Please double check input code and classification.\n", sep = ""))
}
# match
matches <- which(all.origin.codes %in% sourcevar)
dest.var <- dictionary[matches, c(origin.var, destination.var)]
# calculate weights for matches
dest.var <- dest.var %>%
rename(!!origin := 1,
!!destination := 2) %>%
# if input is NA then match should be NA
mutate(!!as.name(destination) := if_else(is.na(!!as.name(origin)), NA_character_, !!as.name(destination))) %>%
group_by(!!as.name(origin), !!as.name(destination)) %>%
mutate(n = length(!!as.name(destination)),
n = ifelse(is.na(!!as.name(destination)), NA, n)) %>%
distinct() %>%
filter(!(is.na(n) & sum(!is.na(n)) > 0)) %>%
group_by(!!as.name(origin)) %>%
mutate(n_sum = sum(.data$n, na.rm = TRUE),
weight = .data$n/.data$n_sum) %>%
arrange(dplyr::desc(.data$weight)) %>%
ungroup() %>%
select(-n, -.data$n_sum) %>%
rename(match = !!as.name(destination))
# keep info on all matches and weights?
if (all == TRUE){
# merge matches/weights according to input
out.merge <- nest_join(tibble(!!origin := sourcevar),
dest.var,
by = origin)
names(out.merge$dest.var) <- sourcevar
# fill NAs when there is no match
out <- map(out.merge$dest.var, function(x){
if(nrow(x) == 0){
out.sub <- list(match = NA_character_,
weight = NA)
} else {
out.sub <- list(match = x$match,
weight = x$weight)
}
})
} else {
# keep match with largest weight
# if multiple matches have the same weights, keep first match
dest.var.sub <- dest.var %>%
group_by(!!as.name(origin)) %>%
slice(1) %>%
ungroup() %>%
select(-.data$weight)
# handle repeated inputs
out <- dest.var.sub[match(sourcevar, dest.var.sub %>% pull(!!as.name(origin))), "match"] %>%
pull(match)
}
return(out)
}
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