data-raw/asm_ex.R

In jameelalsalam/naicsmatch: Crosswalks and Handling of NAICS Sector Codes

# read asm example data

library(tidyverse)
library(glue)

# example NAICS for changes 2007 -> 2012
example_naics <- c(
  "333512", "333513",  # machine tools 2007 join
  "333517",            # to be one in 2012

  "311822", "311823", # flour and pasta mftring join, but don't appear in ASM
  "31182M",           # cooke, cracker and pasta mftring - rollup code in ASM'09

  "311830", #tortillas same in both
  "31183",  #tortillas, 5-digit code
  "3118",   # bakeries and tortilla mftring

  "334113", # computer terminals in 2007 changes
  "334118", # new 2012 naics

  "333315", # 2007 photocopiers also join with some of other computers

  "334119",  # 2007 computers split into computers and cameras
  "333316", "334118", # into these in 2012
  "33331N",  # roll-up in ASM09, 'all other commercial and service indus machinery mftring

  "33211",  # forging and stamping
  "332114", # custom roll forming
  "332117", # powder metallurgy
  "33211N", # forging
  "33211P"  # crown, closure, and metal stamping manufacturing
)

naics_cats <- c(
  "3118", # bakeries and tortillas
  "33211", # forging and stamping
  "33331", # commercial and service equip (incl. photocopiers)
  "33351" # computers
)

naics_cat_regex <- glue("(^{naics_cats})") %>% glue_collapse(sep = "|")


# from ASM 2009

asm09 <- read_csv(
  "data-raw/ASM_2009_31GS101.csv") %>%
  select(5,6,8,33) # includes corrected 2008 data and 2009 data
  #select(4,5, 6, 7, 24, 26, 27, 29, 30, 31)

asm09 <- asm09[-1, ] # first row (after names) are descriptions

asm09 <- asm09 %>%
  mutate(vos09 = as.numeric(RCPTOT)) %>%
  rename(naics_2007 = NAICS.id,
         naics_label_2007 = `NAICS.display-label`) %>%
  select(-RCPTOT) %>%

  filter(YEAR.id == 2009) %>%
  rename(year = YEAR.id)

ex_asm09 <- asm09 %>%
  filter(str_detect(naics_2007, naics_cat_regex))

usethis::use_data(asm09, overwrite = TRUE)
usethis::use_data(ex_asm09, overwrite = TRUE)

# from ASM 2015

asm15 <- read_csv(
  "data-raw/ASM_2015_31GS101.csv") %>%
  select(5,6,8,47)
# NAICS ID, NAICS title, year, and total value of shipments ($1000s)

asm15 <- asm15[-1, ] # first row (after names) are descriptions

asm15 <- asm15 %>%
  mutate(vos15 = as.numeric(RCPTOT)) %>%
  rename(naics_2012 = NAICS.id,
         naics_label_2012 = `NAICS.display-label`) %>%
  select(-RCPTOT) %>%

  filter(YEAR.id == 2015) %>%
  rename(year = YEAR.id)

ex_asm15 <- asm15 %>%
  filter(str_detect(naics_2012, naics_cat_regex))

usethis::use_data(asm15, overwrite = TRUE)
usethis::use_data(ex_asm15, overwrite = TRUE)