data-raw/clean-hs-to-naics.R
In insongkim/concordance: Product Concordance
################################################################################
## setup
################################################################################
# clean slate
rm(list = ls())
date()
# load packages
library(tidyverse)
library(readxl)
################################################################################
# HS (combined) to NAICS (combined)
################################################################################
# load concordance data:
# Pierce and Schott 2018 <https://faculty.som.yale.edu/peterschott/international-trade-data/>
# Concordance of 1989-2017 US HS codes to US SIC, SITC and NAICS codes over time
# https://spinup-000d1a-wp-offload-media.s3.amazonaws.com/faculty/wp-content/uploads/sites/47/2019/06/hssicnaics_20181015.zip
exp.data <- read_delim("./data-raw/hs_sic_naics_exports_89_117_20180927.csv",
delim = "\t", col_types = "cdccccc")
imp.data <- read_delim("./data-raw/hs_sic_naics_imports_89_117_20180927.csv",
delim = "\t", col_types = "cdccccc")
# check
exp.data %>%
pull(commodity) %>%
unique() %>%
sort()
n_distinct(exp.data$commodity)
imp.data %>%
pull(commodity) %>%
unique() %>%
sort()
n_distinct(imp.data$commodity)
setdiff(exp.data$commodity, imp.data$commodity)
setdiff(imp.data$commodity, exp.data$commodity)
# combine all
hs_naics <- rbind(exp.data, imp.data)
# clean
hs_naics <- hs_naics %>%
select(commodity, naics) %>%
filter(nchar(commodity) != 1) %>%
filter(naics != ".") %>%
rename(HS_10d = commodity,
NAICS_6d = naics) %>%
mutate(HS_10d = str_pad(HS_10d, 10, side = "left", pad = "0"),
HS_6d = str_sub(HS_10d, start = 1, end = 6),
HS_4d = str_sub(HS_10d, start = 1, end = 4),
HS_2d = str_sub(HS_10d, start = 1, end = 2),
NAICS_5d = str_sub(NAICS_6d, start = 1, end = 5),
NAICS_4d = str_sub(NAICS_6d, start = 1, end = 4),
NAICS_3d = str_sub(NAICS_6d, start = 1, end = 3),
NAICS_2d = str_sub(NAICS_6d, start = 1, end = 2)
) %>%
arrange(HS_10d) %>%
select(HS_6d, HS_4d, HS_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# missing HS5 codes: 710820
# GOLD (INCLUDING GOLD PLATED WITH PLATINUM) UNWROUGHT OR IN SEMI-MANUFACTURED FORMS, OR IN POWDER FORM.- MONETARY
# add
# missing HS5 codes: "440111" "440112" "440140" "440311" "440312" "440321" "440322" "440323" "440324" "440325" "440326" "440393" "440394" "440395" "440396" "440397" "440398" "440611" "440612" "440691" "440692" "440711" "440712" "440719" "440796" "440797" "441233" "441234" "630420" "999999"
miss.vec <- c("710820", "440111", "440112", "440140", "440311", "440312",
"440321", "440322", "440323", "440324", "440325", "440326",
"440393", "440394", "440395", "440396", "440397", "440398",
"440611", "440612", "440691", "440692", "440711", "440712",
"440719", "440796", "440797", "441233", "441234", "630420",
"999999")
miss.rows <- tibble(HS_6d = miss.vec,
HS_4d = str_sub(miss.vec, start = 1, end = 4),
HS_2d = str_sub(miss.vec, start = 1, end = 2),
NAICS_6d = NA,
NAICS_5d = NA,
NAICS_4d = NA,
NAICS_3d = NA,
NAICS_2d = NA)
# combine
hs_naics <- rbind(hs_naics, miss.rows) %>%
arrange(HS_6d)
# fix unusual 2-digit NAICS codes
hs_naics <- hs_naics %>%
mutate(NAICS_2d = if_else(NAICS_2d == "31", "31-33", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "32", "31-33", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "33", "31-33", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "44", "44-45", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "45", "44-45", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "48", "48-49", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "49", "48-49", NAICS_2d))
# save
save(hs_naics,
file = "./data/hs_naics.RData", compress = "xz")
# ################################################################################
# # HS to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs_naics2002 <- hs_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS_6d, HS_4d, HS_2d,
# NAICS2002_6d,
# NAICS2002_5d,
# NAICS2002_4d,
# NAICS2002_3d,
# NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs_naics2002$NAICS2002_6d)
#
# # save
# save(hs_naics2002,
# file = "./data/hs_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs_naics2007 <- hs_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS_6d, HS_4d, HS_2d,
# NAICS2007_6d,
# NAICS2007_5d,
# NAICS2007_4d,
# NAICS2007_3d,
# NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs_naics2007$NAICS2007_6d)
#
# # save
# save(hs_naics2007,
# file = "./data/hs_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs_naics2012 <- hs_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS_6d, HS_4d, HS_2d,
# NAICS2012_6d,
# NAICS2012_5d,
# NAICS2012_4d,
# NAICS2012_3d,
# NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs_naics2012$NAICS2012_6d)
#
# # save
# save(hs_naics2012,
# file = "./data/hs_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs_naics2017 <- hs_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS_6d, HS_4d, HS_2d,
# NAICS2017_6d,
# NAICS2017_5d,
# NAICS2017_4d,
# NAICS2017_3d,
# NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs_naics2017$NAICS2017_6d)
#
# # save
# save(hs_naics2017,
# file = "./data/hs_naics2017.RData", compress = "xz")
################################################################################
# HS0 to NAICS (combined)
################################################################################
# load WB data to get a list of HS0 codes
# https://wits.worldbank.org/product_concordance.html
# http://wits.worldbank.org/data/public/concordance/Concordance_H0_to_S4.zip
hs0.vec <- read_csv("./data-raw/JobID-12_Concordance_H0_to_S4.CSV") %>%
select(`HS 1988/92 Product Code`, `SITC Revision 4 Product Code`) %>%
rename(HS0_6d = `HS 1988/92 Product Code`,
SITC4_5d = `SITC Revision 4 Product Code`) %>%
pull(HS0_6d) %>%
unique()
# subset and clean
hs0_naics <- hs_naics %>%
filter(HS_6d %in% hs0.vec) %>%
rename(HS0_6d = HS_6d,
HS0_4d = HS_4d,
HS0_2d = HS_2d) %>%
distinct() %>%
select(HS0_6d, HS0_4d, HS0_2d,
NAICS_6d,
NAICS_5d,
NAICS_4d,
NAICS_3d,
NAICS_2d) %>%
distinct()
# check
n_distinct(hs0_naics$HS0_6d)
n_distinct(hs0.vec)
setdiff(unique(hs0_naics$HS0_6d), hs0.vec)
setdiff(hs0.vec, hs0_naics$HS0_6d)
# save
save(hs0_naics,
file = "./data/hs0_naics.RData", compress = "xz")
# ################################################################################
# # HS0 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs0_naics2002 <- hs0_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS0_6d, HS0_4d, HS0_2d,
# NAICS2002_6d,
# NAICS2002_5d,
# NAICS2002_4d,
# NAICS2002_3d,
# NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs0_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs0_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs0_naics2002$NAICS2002_6d)
#
# # save
# save(hs0_naics2002,
# file = "./data/hs0_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS0 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs0_naics2007 <- hs0_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS0_6d, HS0_4d, HS0_2d,
# NAICS2007_6d,
# NAICS2007_5d,
# NAICS2007_4d,
# NAICS2007_3d,
# NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs0_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs0_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs0_naics2007$NAICS2007_6d)
#
# # save
# save(hs0_naics2007,
# file = "./data/hs0_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS0 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs0_naics2012 <- hs0_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS0_6d, HS0_4d, HS0_2d,
# NAICS2012_6d,
# NAICS2012_5d,
# NAICS2012_4d,
# NAICS2012_3d,
# NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs0_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs0_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs0_naics2012$NAICS2012_6d)
#
# # save
# save(hs0_naics2012,
# file = "./data/hs0_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS0 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs0_naics2017 <- hs0_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS0_6d, HS0_4d, HS0_2d,
# NAICS2017_6d,
# NAICS2017_5d,
# NAICS2017_4d,
# NAICS2017_3d,
# NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs0_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs0_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs0_naics2017$NAICS2017_6d)
#
# # save
# save(hs0_naics2017,
# file = "./data/hs0_naics2017.RData", compress = "xz")
################################################################################
# HS1 to NAICS (combined)
################################################################################
# load WB data to get a list of HS1 codes
# https://wits.worldbank.org/product_concordance.html
# http://wits.worldbank.org/data/public/concordance/Concordance_H1_to_S4.zip
hs1.vec <- read_csv("./data-raw/JobID-25_Concordance_H1_to_S4.CSV") %>%
select(`HS 1996 Product Code`, `SITC Revision 4 Product Code`) %>%
rename(HS1_6d = `HS 1996 Product Code`,
SITC4_5d = `SITC Revision 4 Product Code`) %>%
pull(HS1_6d) %>%
unique()
# subset and clean
hs1_naics <- hs_naics %>%
filter(HS_6d %in% hs1.vec) %>%
rename(HS1_6d = HS_6d,
HS1_4d = HS_4d,
HS1_2d = HS_2d) %>%
distinct() %>%
select(HS1_6d, HS1_4d, HS1_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# check
n_distinct(hs1_naics$HS1_6d)
n_distinct(hs1.vec)
setdiff(unique(hs1_naics$HS1_6d), hs1.vec)
setdiff(hs1.vec, hs1_naics$HS1_6d)
# save
save(hs1_naics,
file = "./data/hs1_naics.RData", compress = "xz")
# ################################################################################
# # HS1 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs1_naics2002 <- hs1_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS1_6d, HS1_4d, HS1_2d,
# NAICS2002_6d, NAICS2002_5d, NAICS2002_4d, NAICS2002_3d, NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs1_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs1_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs1_naics2002$NAICS2002_6d)
#
# # save
# save(hs1_naics2002,
# file = "./data/hs1_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS1 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs1_naics2007 <- hs1_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS1_6d, HS1_4d, HS1_2d,
# NAICS2007_6d, NAICS2007_5d, NAICS2007_4d, NAICS2007_3d, NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs1_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs1_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs1_naics2007$NAICS2007_6d)
#
# # save
# save(hs1_naics2007,
# file = "./data/hs1_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS1 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs1_naics2012 <- hs1_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS1_6d, HS1_4d, HS1_2d,
# NAICS2012_6d, NAICS2012_5d, NAICS2012_4d, NAICS2012_3d, NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs1_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs1_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs1_naics2012$NAICS2012_6d)
#
# # save
# save(hs1_naics2012,
# file = "./data/hs1_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS1 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs1_naics2017 <- hs1_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS1_6d, HS1_4d, HS1_2d,
# NAICS2017_6d, NAICS2017_5d, NAICS2017_4d, NAICS2017_3d, NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs1_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs1_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs1_naics2017$NAICS2017_6d)
#
# # save
# save(hs1_naics2017,
# file = "./data/hs1_naics2017.RData", compress = "xz")
################################################################################
# HS2 to NAICS (combined)
################################################################################
# load WB data to get a list of HS1 codes
# https://wits.worldbank.org/product_concordance.html
# http://wits.worldbank.org/data/public/concordance/Concordance_H2_to_S4.zip
hs2.vec <- read_csv("./data-raw/JobID-39_Concordance_H2_to_S4.CSV") %>%
select(`HS 2002 Product Code`, `SITC Revision 4 Product Code`) %>%
rename(HS2_6d = `HS 2002 Product Code`,
SITC4_5d = `SITC Revision 4 Product Code`) %>%
pull(HS2_6d) %>%
unique()
# subset and clean
hs2_naics <- hs_naics %>%
filter(HS_6d %in% hs2.vec) %>%
rename(HS2_6d = HS_6d,
HS2_4d = HS_4d,
HS2_2d = HS_2d) %>%
distinct() %>%
select(HS2_6d, HS2_4d, HS2_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# check
n_distinct(hs2_naics$HS2_6d)
n_distinct(hs2.vec)
setdiff(unique(hs2_naics$HS2_6d), hs2.vec)
setdiff(hs2.vec, hs2_naics$HS2_6d)
# save
save(hs2_naics,
file = "./data/hs2_naics.RData", compress = "xz")
# ################################################################################
# # HS2 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs2_naics2002 <- hs2_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS2_6d, HS2_4d, HS2_2d,
# NAICS2002_6d, NAICS2002_5d, NAICS2002_4d, NAICS2002_3d, NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs2_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs2_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs2_naics2002$NAICS2002_6d)
#
# # save
# save(hs2_naics2002,
# file = "./data/hs2_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS2 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs2_naics2007 <- hs2_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS2_6d, HS2_4d, HS2_2d,
# NAICS2007_6d, NAICS2007_5d, NAICS2007_4d, NAICS2007_3d, NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs2_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs2_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs2_naics2007$NAICS2007_6d)
#
# # save
# save(hs2_naics2007,
# file = "./data/hs2_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS2 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs2_naics2012 <- hs2_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS2_6d, HS2_4d, HS2_2d,
# NAICS2012_6d, NAICS2012_5d, NAICS2012_4d, NAICS2012_3d, NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs2_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs2_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs2_naics2012$NAICS2012_6d)
#
# # save
# save(hs2_naics2012,
# file = "./data/hs2_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS2 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs2_naics2017 <- hs2_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS2_6d, HS2_4d, HS2_2d,
# NAICS2017_6d, NAICS2017_5d, NAICS2017_4d, NAICS2017_3d, NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs2_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs2_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs2_naics2017$NAICS2017_6d)
#
# # save
# save(hs2_naics2017,
# file = "./data/hs2_naics2017.RData", compress = "xz")
################################################################################
# HS3 to NAICS (combined)
################################################################################
# load WB data to get a list of HS1 codes
# https://wits.worldbank.org/product_concordance.html
# http://wits.worldbank.org/data/public/concordance/Concordance_H3_to_S4.zip
hs3.vec <- read_csv("./data-raw/JobID-54_Concordance_H3_to_S4.CSV") %>%
select(`HS 2007 Product Code`, `SITC Revision 4 Product Code`) %>%
rename(HS3_6d = `HS 2007 Product Code`,
SITC4_5d = `SITC Revision 4 Product Code`) %>%
pull(HS3_6d) %>%
unique()
# subset and clean
hs3_naics <- hs_naics %>%
filter(HS_6d %in% hs3.vec) %>%
rename(HS3_6d = HS_6d,
HS3_4d = HS_4d,
HS3_2d = HS_2d) %>%
distinct() %>%
select(HS3_6d, HS3_4d, HS3_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# check
n_distinct(hs3_naics$HS3_6d)
n_distinct(hs3.vec)
setdiff(unique(hs3_naics$HS3_6d), hs3.vec)
setdiff(hs3.vec, hs3_naics$HS3_6d)
# save
save(hs3_naics,
file = "./data/hs3_naics.RData", compress = "xz")
# ################################################################################
# # HS3 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs3_naics2002 <- hs3_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS3_6d, HS3_4d, HS3_2d,
# NAICS2002_6d, NAICS2002_5d, NAICS2002_4d, NAICS2002_3d, NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs3_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs3_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs3_naics2002$NAICS2002_6d)
#
# # save
# save(hs3_naics2002,
# file = "./data/hs3_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS3 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs3_naics2007 <- hs3_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS3_6d, HS3_4d, HS3_2d,
# NAICS2007_6d, NAICS2007_5d, NAICS2007_4d, NAICS2007_3d, NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs3_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs3_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs3_naics2007$NAICS2007_6d)
#
# # save
# save(hs3_naics2007,
# file = "./data/hs3_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS3 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs3_naics2012 <- hs3_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS3_6d, HS3_4d, HS3_2d,
# NAICS2012_6d, NAICS2012_5d, NAICS2012_4d, NAICS2012_3d, NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs3_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs3_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs3_naics2012$NAICS2012_6d)
#
# # save
# save(hs3_naics2012,
# file = "./data/hs3_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS3 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs3_naics2017 <- hs3_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS3_6d, HS3_4d, HS3_2d,
# NAICS2017_6d, NAICS2017_5d, NAICS2017_4d, NAICS2017_3d, NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs3_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs3_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs3_naics2017$NAICS2017_6d)
#
# # save
# save(hs3_naics2017,
# file = "./data/hs3_naics2017.RData", compress = "xz")
################################################################################
# HS4 to NAICS (combined)
################################################################################
# load UN data to get HS codes
# https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp
# https://unstats.un.org/unsd/trade/classifications/tables/HS%202012%20to%20SITC%20Rev.4%20Correlation%20and%20conversion%20tables.xls
hs4.vec <- read_excel("./data-raw/HS 2012 to SITC Rev.4 Correlation and conversion tables.xls",
sheet = 2, col_names = TRUE, skip = 1) %>%
rename(HS4_6d = `HS 2012`,
SITC4_5d = `SITC Rev. 4`) %>%
mutate(HS4_6d = str_replace_all(HS4_6d, "\\.", "")) %>%
pull(HS4_6d)
# subset and clean
hs4_naics <- hs_naics %>%
filter(HS_6d %in% hs4.vec) %>%
rename(HS4_6d = HS_6d,
HS4_4d = HS_4d,
HS4_2d = HS_2d) %>%
distinct() %>%
select(HS4_6d, HS4_4d, HS4_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# check
n_distinct(hs4_naics$HS4_6d)
n_distinct(hs4.vec)
setdiff(unique(hs4_naics$HS4_6d), hs4.vec)
setdiff(hs4.vec, hs4_naics$HS4_6d)
# save
save(hs4_naics,
file = "./data/hs4_naics.RData", compress = "xz")
# ################################################################################
# # HS4 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs4_naics2002 <- hs4_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS4_6d, HS4_4d, HS4_2d,
# NAICS2002_6d, NAICS2002_5d, NAICS2002_4d, NAICS2002_3d, NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs4_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs4_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs4_naics2002$NAICS2002_6d)
#
# # save
# save(hs4_naics2002,
# file = "./data/hs4_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS4 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs4_naics2007 <- hs4_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS4_6d, HS4_4d, HS4_2d,
# NAICS2007_6d, NAICS2007_5d, NAICS2007_4d, NAICS2007_3d, NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs4_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs4_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs4_naics2007$NAICS2007_6d)
#
# # save
# save(hs4_naics2007,
# file = "./data/hs4_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS4 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs4_naics2012 <- hs4_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS4_6d, HS4_4d, HS4_2d,
# NAICS2012_6d, NAICS2012_5d, NAICS2012_4d, NAICS2012_3d, NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs4_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs4_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs4_naics2012$NAICS2012_6d)
#
# # save
# save(hs4_naics2012,
# file = "./data/hs4_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS4 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs4_naics2017 <- hs4_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS4_6d, HS4_4d, HS4_2d,
# NAICS2017_6d, NAICS2017_5d, NAICS2017_4d, NAICS2017_3d, NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs4_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs4_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs4_naics2017$NAICS2017_6d)
#
# # save
# save(hs4_naics2017,
# file = "./data/hs4_naics2017.RData", compress = "xz")
################################################################################
# HS5 to NAICS (combined)
################################################################################
# load UN data to get HS codes
# https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp
# https://unstats.un.org/unsd/trade/classifications/tables/HS2017toSITC4ConversionAndCorrelationTables.xlsx
hs5.vec <- read_excel("./data-raw/HS2017toSITC4ConversionAndCorrelationTables.xlsx") %>%
rename(HS5_6d = `From HS 2017`,
SITC4_5d = `To SITC Rev. 4`) %>%
pull(HS5_6d)
# subset and clean
hs5_naics <- hs_naics %>%
filter(HS_6d %in% hs5.vec) %>%
rename(HS5_6d = HS_6d,
HS5_4d = HS_4d,
HS5_2d = HS_2d) %>%
distinct() %>%
select(HS5_6d, HS5_4d, HS5_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# check
n_distinct(hs5_naics$HS5_6d)
n_distinct(hs5.vec)
setdiff(unique(hs5_naics$HS5_6d), hs5.vec)
setdiff(hs5.vec, hs5_naics$HS5_6d)
# save
save(hs5_naics,
file = "./data/hs5_naics.RData", compress = "xz")
# ################################################################################
# # HS5 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs5_naics2002 <- hs5_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS5_6d, HS5_4d, HS5_2d,
# NAICS2002_6d, NAICS2002_5d, NAICS2002_4d, NAICS2002_3d, NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs5_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs5_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs5_naics2002$NAICS2002_6d)
#
# # save
# save(hs5_naics2002,
# file = "./data/hs5_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS5 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs5_naics2007 <- hs5_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS5_6d, HS5_4d, HS5_2d,
# NAICS2007_6d, NAICS2007_5d, NAICS2007_4d, NAICS2007_3d, NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs5_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs5_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs5_naics2007$NAICS2007_6d)
#
# # save
# save(hs5_naics2007,
# file = "./data/hs5_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS5 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs5_naics2012 <- hs5_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS5_6d, HS5_4d, HS5_2d,
# NAICS2012_6d, NAICS2012_5d, NAICS2012_4d, NAICS2012_3d, NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs5_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs5_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs5_naics2012$NAICS2012_6d)
#
# # save
# save(hs5_naics2012,
# file = "./data/hs5_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS5 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs5_naics2017 <- hs5_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS5_6d, HS5_4d, HS5_2d,
# NAICS2017_6d, NAICS2017_5d, NAICS2017_4d, NAICS2017_3d, NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs5_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs5_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs5_naics2017$NAICS2017_6d)
#
# # save
# save(hs5_naics2017,
# file = "./data/hs5_naics2017.RData", compress = "xz")
################################################################################
# HS6 to NAICS (combined)
################################################################################
# HS6 --> HS5 --> NAICS
load("./data/hs6_hs5.RData")
load("./data/hs5_naics.RData")
# merge
hs6_naics <- left_join(hs6_hs5,
hs5_naics,
by = "HS5_6d")
# subset and clean
names(hs6_naics)
hs6_naics <- hs6_naics %>%
select(HS6_6d, HS6_4d, HS6_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct() %>%
arrange(HS6_6d)
# check
unique(hs6_naics$NAICS_2d)
# save
save(hs6_naics,
file = "./data/hs6_naics.RData", compress = "xz")
insongkim/concordance documentation built on Aug. 22, 2024, 6:53 p.m.
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################################################################################
## setup
################################################################################
# clean slate
rm(list = ls())
date()
# load packages
library(tidyverse)
library(readxl)
################################################################################
# HS (combined) to NAICS (combined)
################################################################################
# load concordance data:
# Pierce and Schott 2018 <https://faculty.som.yale.edu/peterschott/international-trade-data/>
# Concordance of 1989-2017 US HS codes to US SIC, SITC and NAICS codes over time
# https://spinup-000d1a-wp-offload-media.s3.amazonaws.com/faculty/wp-content/uploads/sites/47/2019/06/hssicnaics_20181015.zip
exp.data <- read_delim("./data-raw/hs_sic_naics_exports_89_117_20180927.csv",
delim = "\t", col_types = "cdccccc")
imp.data <- read_delim("./data-raw/hs_sic_naics_imports_89_117_20180927.csv",
delim = "\t", col_types = "cdccccc")
# check
exp.data %>%
pull(commodity) %>%
unique() %>%
sort()
n_distinct(exp.data$commodity)
imp.data %>%
pull(commodity) %>%
unique() %>%
sort()
n_distinct(imp.data$commodity)
setdiff(exp.data$commodity, imp.data$commodity)
setdiff(imp.data$commodity, exp.data$commodity)
# combine all
hs_naics <- rbind(exp.data, imp.data)
# clean
hs_naics <- hs_naics %>%
select(commodity, naics) %>%
filter(nchar(commodity) != 1) %>%
filter(naics != ".") %>%
rename(HS_10d = commodity,
NAICS_6d = naics) %>%
mutate(HS_10d = str_pad(HS_10d, 10, side = "left", pad = "0"),
HS_6d = str_sub(HS_10d, start = 1, end = 6),
HS_4d = str_sub(HS_10d, start = 1, end = 4),
HS_2d = str_sub(HS_10d, start = 1, end = 2),
NAICS_5d = str_sub(NAICS_6d, start = 1, end = 5),
NAICS_4d = str_sub(NAICS_6d, start = 1, end = 4),
NAICS_3d = str_sub(NAICS_6d, start = 1, end = 3),
NAICS_2d = str_sub(NAICS_6d, start = 1, end = 2)
) %>%
arrange(HS_10d) %>%
select(HS_6d, HS_4d, HS_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# missing HS5 codes: 710820
# GOLD (INCLUDING GOLD PLATED WITH PLATINUM) UNWROUGHT OR IN SEMI-MANUFACTURED FORMS, OR IN POWDER FORM.- MONETARY
# add
# missing HS5 codes: "440111" "440112" "440140" "440311" "440312" "440321" "440322" "440323" "440324" "440325" "440326" "440393" "440394" "440395" "440396" "440397" "440398" "440611" "440612" "440691" "440692" "440711" "440712" "440719" "440796" "440797" "441233" "441234" "630420" "999999"
miss.vec <- c("710820", "440111", "440112", "440140", "440311", "440312",
"440321", "440322", "440323", "440324", "440325", "440326",
"440393", "440394", "440395", "440396", "440397", "440398",
"440611", "440612", "440691", "440692", "440711", "440712",
"440719", "440796", "440797", "441233", "441234", "630420",
"999999")
miss.rows <- tibble(HS_6d = miss.vec,
HS_4d = str_sub(miss.vec, start = 1, end = 4),
HS_2d = str_sub(miss.vec, start = 1, end = 2),
NAICS_6d = NA,
NAICS_5d = NA,
NAICS_4d = NA,
NAICS_3d = NA,
NAICS_2d = NA)
# combine
hs_naics <- rbind(hs_naics, miss.rows) %>%
arrange(HS_6d)
# fix unusual 2-digit NAICS codes
hs_naics <- hs_naics %>%
mutate(NAICS_2d = if_else(NAICS_2d == "31", "31-33", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "32", "31-33", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "33", "31-33", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "44", "44-45", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "45", "44-45", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "48", "48-49", NAICS_2d),
NAICS_2d = if_else(NAICS_2d == "49", "48-49", NAICS_2d))
# save
save(hs_naics,
file = "./data/hs_naics.RData", compress = "xz")
# ################################################################################
# # HS to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs_naics2002 <- hs_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS_6d, HS_4d, HS_2d,
# NAICS2002_6d,
# NAICS2002_5d,
# NAICS2002_4d,
# NAICS2002_3d,
# NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs_naics2002$NAICS2002_6d)
#
# # save
# save(hs_naics2002,
# file = "./data/hs_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs_naics2007 <- hs_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS_6d, HS_4d, HS_2d,
# NAICS2007_6d,
# NAICS2007_5d,
# NAICS2007_4d,
# NAICS2007_3d,
# NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs_naics2007$NAICS2007_6d)
#
# # save
# save(hs_naics2007,
# file = "./data/hs_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs_naics2012 <- hs_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS_6d, HS_4d, HS_2d,
# NAICS2012_6d,
# NAICS2012_5d,
# NAICS2012_4d,
# NAICS2012_3d,
# NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs_naics2012$NAICS2012_6d)
#
# # save
# save(hs_naics2012,
# file = "./data/hs_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs_naics2017 <- hs_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS_6d, HS_4d, HS_2d,
# NAICS2017_6d,
# NAICS2017_5d,
# NAICS2017_4d,
# NAICS2017_3d,
# NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs_naics2017$NAICS2017_6d)
#
# # save
# save(hs_naics2017,
# file = "./data/hs_naics2017.RData", compress = "xz")
################################################################################
# HS0 to NAICS (combined)
################################################################################
# load WB data to get a list of HS0 codes
# https://wits.worldbank.org/product_concordance.html
# http://wits.worldbank.org/data/public/concordance/Concordance_H0_to_S4.zip
hs0.vec <- read_csv("./data-raw/JobID-12_Concordance_H0_to_S4.CSV") %>%
select(`HS 1988/92 Product Code`, `SITC Revision 4 Product Code`) %>%
rename(HS0_6d = `HS 1988/92 Product Code`,
SITC4_5d = `SITC Revision 4 Product Code`) %>%
pull(HS0_6d) %>%
unique()
# subset and clean
hs0_naics <- hs_naics %>%
filter(HS_6d %in% hs0.vec) %>%
rename(HS0_6d = HS_6d,
HS0_4d = HS_4d,
HS0_2d = HS_2d) %>%
distinct() %>%
select(HS0_6d, HS0_4d, HS0_2d,
NAICS_6d,
NAICS_5d,
NAICS_4d,
NAICS_3d,
NAICS_2d) %>%
distinct()
# check
n_distinct(hs0_naics$HS0_6d)
n_distinct(hs0.vec)
setdiff(unique(hs0_naics$HS0_6d), hs0.vec)
setdiff(hs0.vec, hs0_naics$HS0_6d)
# save
save(hs0_naics,
file = "./data/hs0_naics.RData", compress = "xz")
# ################################################################################
# # HS0 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs0_naics2002 <- hs0_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS0_6d, HS0_4d, HS0_2d,
# NAICS2002_6d,
# NAICS2002_5d,
# NAICS2002_4d,
# NAICS2002_3d,
# NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs0_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs0_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs0_naics2002$NAICS2002_6d)
#
# # save
# save(hs0_naics2002,
# file = "./data/hs0_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS0 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs0_naics2007 <- hs0_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS0_6d, HS0_4d, HS0_2d,
# NAICS2007_6d,
# NAICS2007_5d,
# NAICS2007_4d,
# NAICS2007_3d,
# NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs0_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs0_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs0_naics2007$NAICS2007_6d)
#
# # save
# save(hs0_naics2007,
# file = "./data/hs0_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS0 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs0_naics2012 <- hs0_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS0_6d, HS0_4d, HS0_2d,
# NAICS2012_6d,
# NAICS2012_5d,
# NAICS2012_4d,
# NAICS2012_3d,
# NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs0_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs0_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs0_naics2012$NAICS2012_6d)
#
# # save
# save(hs0_naics2012,
# file = "./data/hs0_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS0 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs0_naics2017 <- hs0_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS0_6d, HS0_4d, HS0_2d,
# NAICS2017_6d,
# NAICS2017_5d,
# NAICS2017_4d,
# NAICS2017_3d,
# NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs0_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs0_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs0_naics2017$NAICS2017_6d)
#
# # save
# save(hs0_naics2017,
# file = "./data/hs0_naics2017.RData", compress = "xz")
################################################################################
# HS1 to NAICS (combined)
################################################################################
# load WB data to get a list of HS1 codes
# https://wits.worldbank.org/product_concordance.html
# http://wits.worldbank.org/data/public/concordance/Concordance_H1_to_S4.zip
hs1.vec <- read_csv("./data-raw/JobID-25_Concordance_H1_to_S4.CSV") %>%
select(`HS 1996 Product Code`, `SITC Revision 4 Product Code`) %>%
rename(HS1_6d = `HS 1996 Product Code`,
SITC4_5d = `SITC Revision 4 Product Code`) %>%
pull(HS1_6d) %>%
unique()
# subset and clean
hs1_naics <- hs_naics %>%
filter(HS_6d %in% hs1.vec) %>%
rename(HS1_6d = HS_6d,
HS1_4d = HS_4d,
HS1_2d = HS_2d) %>%
distinct() %>%
select(HS1_6d, HS1_4d, HS1_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# check
n_distinct(hs1_naics$HS1_6d)
n_distinct(hs1.vec)
setdiff(unique(hs1_naics$HS1_6d), hs1.vec)
setdiff(hs1.vec, hs1_naics$HS1_6d)
# save
save(hs1_naics,
file = "./data/hs1_naics.RData", compress = "xz")
# ################################################################################
# # HS1 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs1_naics2002 <- hs1_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS1_6d, HS1_4d, HS1_2d,
# NAICS2002_6d, NAICS2002_5d, NAICS2002_4d, NAICS2002_3d, NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs1_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs1_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs1_naics2002$NAICS2002_6d)
#
# # save
# save(hs1_naics2002,
# file = "./data/hs1_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS1 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs1_naics2007 <- hs1_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS1_6d, HS1_4d, HS1_2d,
# NAICS2007_6d, NAICS2007_5d, NAICS2007_4d, NAICS2007_3d, NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs1_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs1_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs1_naics2007$NAICS2007_6d)
#
# # save
# save(hs1_naics2007,
# file = "./data/hs1_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS1 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs1_naics2012 <- hs1_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS1_6d, HS1_4d, HS1_2d,
# NAICS2012_6d, NAICS2012_5d, NAICS2012_4d, NAICS2012_3d, NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs1_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs1_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs1_naics2012$NAICS2012_6d)
#
# # save
# save(hs1_naics2012,
# file = "./data/hs1_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS1 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs1_naics2017 <- hs1_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS1_6d, HS1_4d, HS1_2d,
# NAICS2017_6d, NAICS2017_5d, NAICS2017_4d, NAICS2017_3d, NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs1_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs1_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs1_naics2017$NAICS2017_6d)
#
# # save
# save(hs1_naics2017,
# file = "./data/hs1_naics2017.RData", compress = "xz")
################################################################################
# HS2 to NAICS (combined)
################################################################################
# load WB data to get a list of HS1 codes
# https://wits.worldbank.org/product_concordance.html
# http://wits.worldbank.org/data/public/concordance/Concordance_H2_to_S4.zip
hs2.vec <- read_csv("./data-raw/JobID-39_Concordance_H2_to_S4.CSV") %>%
select(`HS 2002 Product Code`, `SITC Revision 4 Product Code`) %>%
rename(HS2_6d = `HS 2002 Product Code`,
SITC4_5d = `SITC Revision 4 Product Code`) %>%
pull(HS2_6d) %>%
unique()
# subset and clean
hs2_naics <- hs_naics %>%
filter(HS_6d %in% hs2.vec) %>%
rename(HS2_6d = HS_6d,
HS2_4d = HS_4d,
HS2_2d = HS_2d) %>%
distinct() %>%
select(HS2_6d, HS2_4d, HS2_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# check
n_distinct(hs2_naics$HS2_6d)
n_distinct(hs2.vec)
setdiff(unique(hs2_naics$HS2_6d), hs2.vec)
setdiff(hs2.vec, hs2_naics$HS2_6d)
# save
save(hs2_naics,
file = "./data/hs2_naics.RData", compress = "xz")
# ################################################################################
# # HS2 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs2_naics2002 <- hs2_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS2_6d, HS2_4d, HS2_2d,
# NAICS2002_6d, NAICS2002_5d, NAICS2002_4d, NAICS2002_3d, NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs2_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs2_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs2_naics2002$NAICS2002_6d)
#
# # save
# save(hs2_naics2002,
# file = "./data/hs2_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS2 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs2_naics2007 <- hs2_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS2_6d, HS2_4d, HS2_2d,
# NAICS2007_6d, NAICS2007_5d, NAICS2007_4d, NAICS2007_3d, NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs2_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs2_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs2_naics2007$NAICS2007_6d)
#
# # save
# save(hs2_naics2007,
# file = "./data/hs2_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS2 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs2_naics2012 <- hs2_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS2_6d, HS2_4d, HS2_2d,
# NAICS2012_6d, NAICS2012_5d, NAICS2012_4d, NAICS2012_3d, NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs2_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs2_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs2_naics2012$NAICS2012_6d)
#
# # save
# save(hs2_naics2012,
# file = "./data/hs2_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS2 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs2_naics2017 <- hs2_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS2_6d, HS2_4d, HS2_2d,
# NAICS2017_6d, NAICS2017_5d, NAICS2017_4d, NAICS2017_3d, NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs2_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs2_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs2_naics2017$NAICS2017_6d)
#
# # save
# save(hs2_naics2017,
# file = "./data/hs2_naics2017.RData", compress = "xz")
################################################################################
# HS3 to NAICS (combined)
################################################################################
# load WB data to get a list of HS1 codes
# https://wits.worldbank.org/product_concordance.html
# http://wits.worldbank.org/data/public/concordance/Concordance_H3_to_S4.zip
hs3.vec <- read_csv("./data-raw/JobID-54_Concordance_H3_to_S4.CSV") %>%
select(`HS 2007 Product Code`, `SITC Revision 4 Product Code`) %>%
rename(HS3_6d = `HS 2007 Product Code`,
SITC4_5d = `SITC Revision 4 Product Code`) %>%
pull(HS3_6d) %>%
unique()
# subset and clean
hs3_naics <- hs_naics %>%
filter(HS_6d %in% hs3.vec) %>%
rename(HS3_6d = HS_6d,
HS3_4d = HS_4d,
HS3_2d = HS_2d) %>%
distinct() %>%
select(HS3_6d, HS3_4d, HS3_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# check
n_distinct(hs3_naics$HS3_6d)
n_distinct(hs3.vec)
setdiff(unique(hs3_naics$HS3_6d), hs3.vec)
setdiff(hs3.vec, hs3_naics$HS3_6d)
# save
save(hs3_naics,
file = "./data/hs3_naics.RData", compress = "xz")
# ################################################################################
# # HS3 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs3_naics2002 <- hs3_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS3_6d, HS3_4d, HS3_2d,
# NAICS2002_6d, NAICS2002_5d, NAICS2002_4d, NAICS2002_3d, NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs3_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs3_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs3_naics2002$NAICS2002_6d)
#
# # save
# save(hs3_naics2002,
# file = "./data/hs3_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS3 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs3_naics2007 <- hs3_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS3_6d, HS3_4d, HS3_2d,
# NAICS2007_6d, NAICS2007_5d, NAICS2007_4d, NAICS2007_3d, NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs3_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs3_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs3_naics2007$NAICS2007_6d)
#
# # save
# save(hs3_naics2007,
# file = "./data/hs3_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS3 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs3_naics2012 <- hs3_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS3_6d, HS3_4d, HS3_2d,
# NAICS2012_6d, NAICS2012_5d, NAICS2012_4d, NAICS2012_3d, NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs3_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs3_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs3_naics2012$NAICS2012_6d)
#
# # save
# save(hs3_naics2012,
# file = "./data/hs3_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS3 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs3_naics2017 <- hs3_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS3_6d, HS3_4d, HS3_2d,
# NAICS2017_6d, NAICS2017_5d, NAICS2017_4d, NAICS2017_3d, NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs3_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs3_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs3_naics2017$NAICS2017_6d)
#
# # save
# save(hs3_naics2017,
# file = "./data/hs3_naics2017.RData", compress = "xz")
################################################################################
# HS4 to NAICS (combined)
################################################################################
# load UN data to get HS codes
# https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp
# https://unstats.un.org/unsd/trade/classifications/tables/HS%202012%20to%20SITC%20Rev.4%20Correlation%20and%20conversion%20tables.xls
hs4.vec <- read_excel("./data-raw/HS 2012 to SITC Rev.4 Correlation and conversion tables.xls",
sheet = 2, col_names = TRUE, skip = 1) %>%
rename(HS4_6d = `HS 2012`,
SITC4_5d = `SITC Rev. 4`) %>%
mutate(HS4_6d = str_replace_all(HS4_6d, "\\.", "")) %>%
pull(HS4_6d)
# subset and clean
hs4_naics <- hs_naics %>%
filter(HS_6d %in% hs4.vec) %>%
rename(HS4_6d = HS_6d,
HS4_4d = HS_4d,
HS4_2d = HS_2d) %>%
distinct() %>%
select(HS4_6d, HS4_4d, HS4_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# check
n_distinct(hs4_naics$HS4_6d)
n_distinct(hs4.vec)
setdiff(unique(hs4_naics$HS4_6d), hs4.vec)
setdiff(hs4.vec, hs4_naics$HS4_6d)
# save
save(hs4_naics,
file = "./data/hs4_naics.RData", compress = "xz")
# ################################################################################
# # HS4 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs4_naics2002 <- hs4_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS4_6d, HS4_4d, HS4_2d,
# NAICS2002_6d, NAICS2002_5d, NAICS2002_4d, NAICS2002_3d, NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs4_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs4_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs4_naics2002$NAICS2002_6d)
#
# # save
# save(hs4_naics2002,
# file = "./data/hs4_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS4 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs4_naics2007 <- hs4_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS4_6d, HS4_4d, HS4_2d,
# NAICS2007_6d, NAICS2007_5d, NAICS2007_4d, NAICS2007_3d, NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs4_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs4_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs4_naics2007$NAICS2007_6d)
#
# # save
# save(hs4_naics2007,
# file = "./data/hs4_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS4 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs4_naics2012 <- hs4_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS4_6d, HS4_4d, HS4_2d,
# NAICS2012_6d, NAICS2012_5d, NAICS2012_4d, NAICS2012_3d, NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs4_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs4_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs4_naics2012$NAICS2012_6d)
#
# # save
# save(hs4_naics2012,
# file = "./data/hs4_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS4 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs4_naics2017 <- hs4_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS4_6d, HS4_4d, HS4_2d,
# NAICS2017_6d, NAICS2017_5d, NAICS2017_4d, NAICS2017_3d, NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs4_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs4_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs4_naics2017$NAICS2017_6d)
#
# # save
# save(hs4_naics2017,
# file = "./data/hs4_naics2017.RData", compress = "xz")
################################################################################
# HS5 to NAICS (combined)
################################################################################
# load UN data to get HS codes
# https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp
# https://unstats.un.org/unsd/trade/classifications/tables/HS2017toSITC4ConversionAndCorrelationTables.xlsx
hs5.vec <- read_excel("./data-raw/HS2017toSITC4ConversionAndCorrelationTables.xlsx") %>%
rename(HS5_6d = `From HS 2017`,
SITC4_5d = `To SITC Rev. 4`) %>%
pull(HS5_6d)
# subset and clean
hs5_naics <- hs_naics %>%
filter(HS_6d %in% hs5.vec) %>%
rename(HS5_6d = HS_6d,
HS5_4d = HS_4d,
HS5_2d = HS_2d) %>%
distinct() %>%
select(HS5_6d, HS5_4d, HS5_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct()
# check
n_distinct(hs5_naics$HS5_6d)
n_distinct(hs5.vec)
setdiff(unique(hs5_naics$HS5_6d), hs5.vec)
setdiff(hs5.vec, hs5_naics$HS5_6d)
# save
save(hs5_naics,
file = "./data/hs5_naics.RData", compress = "xz")
# ################################################################################
# # HS5 to NAICS2002
# ################################################################################
# # load NAICS2002 codes
# load("./data/naics2002_desc.RData")
#
# naics2002.vec <- naics2002_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs5_naics2002 <- hs5_naics %>%
# filter(NAICS_6d %in% naics2002.vec) %>%
# rename(NAICS2002_6d = NAICS_6d,
# NAICS2002_5d = NAICS_5d,
# NAICS2002_4d = NAICS_4d,
# NAICS2002_3d = NAICS_3d,
# NAICS2002_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS5_6d, HS5_4d, HS5_2d,
# NAICS2002_6d, NAICS2002_5d, NAICS2002_4d, NAICS2002_3d, NAICS2002_2d) %>%
# distinct()
#
# # check
# n_distinct(hs5_naics2002$NAICS2002_6d)
# n_distinct(naics2002.vec)
# setdiff(unique(hs5_naics2002$NAICS2002_6d), naics2002.vec)
# setdiff(naics2002.vec, hs5_naics2002$NAICS2002_6d)
#
# # save
# save(hs5_naics2002,
# file = "./data/hs5_naics2002.RData", compress = "xz")
#
#
# ################################################################################
# # HS5 to NAICS2007
# ################################################################################
# # load NAICS2007 codes
# load("./data/naics2007_desc.RData")
#
# naics2007.vec <- naics2007_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs5_naics2007 <- hs5_naics %>%
# filter(NAICS_6d %in% naics2007.vec) %>%
# rename(NAICS2007_6d = NAICS_6d,
# NAICS2007_5d = NAICS_5d,
# NAICS2007_4d = NAICS_4d,
# NAICS2007_3d = NAICS_3d,
# NAICS2007_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS5_6d, HS5_4d, HS5_2d,
# NAICS2007_6d, NAICS2007_5d, NAICS2007_4d, NAICS2007_3d, NAICS2007_2d) %>%
# distinct()
#
# # check
# n_distinct(hs5_naics2007$NAICS2007_6d)
# n_distinct(naics2007.vec)
# setdiff(unique(hs5_naics2007$NAICS2007_6d), naics2007.vec)
# setdiff(naics2007.vec, hs5_naics2007$NAICS2007_6d)
#
# # save
# save(hs5_naics2007,
# file = "./data/hs5_naics2007.RData", compress = "xz")
#
#
# ################################################################################
# # HS5 to NAICS2012
# ################################################################################
# # load NAICS2012 codes
# load("./data/naics2012_desc.RData")
#
# naics2012.vec <- naics2012_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs5_naics2012 <- hs5_naics %>%
# filter(NAICS_6d %in% naics2012.vec) %>%
# rename(NAICS2012_6d = NAICS_6d,
# NAICS2012_5d = NAICS_5d,
# NAICS2012_4d = NAICS_4d,
# NAICS2012_3d = NAICS_3d,
# NAICS2012_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS5_6d, HS5_4d, HS5_2d,
# NAICS2012_6d, NAICS2012_5d, NAICS2012_4d, NAICS2012_3d, NAICS2012_2d) %>%
# distinct()
#
# # check
# n_distinct(hs5_naics2012$NAICS2012_6d)
# n_distinct(naics2012.vec)
# setdiff(unique(hs5_naics2012$NAICS2012_6d), naics2012.vec)
# setdiff(naics2012.vec, hs5_naics2012$NAICS2012_6d)
#
# # save
# save(hs5_naics2012,
# file = "./data/hs5_naics2012.RData", compress = "xz")
#
#
# ################################################################################
# # HS5 to NAICS2017
# ################################################################################
# # load NAICS2017 codes
# load("./data/naics2017_desc.RData")
#
# naics2017.vec <- naics2017_desc %>%
# filter(nchar(code) == 6) %>%
# pull(code) %>%
# unique()
#
# # subset and clean
# hs5_naics2017 <- hs5_naics %>%
# filter(NAICS_6d %in% naics2017.vec) %>%
# rename(NAICS2017_6d = NAICS_6d,
# NAICS2017_5d = NAICS_5d,
# NAICS2017_4d = NAICS_4d,
# NAICS2017_3d = NAICS_3d,
# NAICS2017_2d = NAICS_2d) %>%
# distinct() %>%
# select(HS5_6d, HS5_4d, HS5_2d,
# NAICS2017_6d, NAICS2017_5d, NAICS2017_4d, NAICS2017_3d, NAICS2017_2d) %>%
# distinct()
#
# # check
# n_distinct(hs5_naics2017$NAICS2017_6d)
# n_distinct(naics2017.vec)
# setdiff(unique(hs5_naics2017$NAICS2017_6d), naics2017.vec)
# setdiff(naics2017.vec, hs5_naics2017$NAICS2017_6d)
#
# # save
# save(hs5_naics2017,
# file = "./data/hs5_naics2017.RData", compress = "xz")
################################################################################
# HS6 to NAICS (combined)
################################################################################
# HS6 --> HS5 --> NAICS
load("./data/hs6_hs5.RData")
load("./data/hs5_naics.RData")
# merge
hs6_naics <- left_join(hs6_hs5,
hs5_naics,
by = "HS5_6d")
# subset and clean
names(hs6_naics)
hs6_naics <- hs6_naics %>%
select(HS6_6d, HS6_4d, HS6_2d,
NAICS_6d, NAICS_5d, NAICS_4d, NAICS_3d, NAICS_2d) %>%
distinct() %>%
arrange(HS6_6d)
# check
unique(hs6_naics$NAICS_2d)
# save
save(hs6_naics,
file = "./data/hs6_naics.RData", compress = "xz")
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