library(dplyr)
library(readxl)
# This is set up to be run from within the eesectors_data_extract folder. All
# paths are relative to that. You may need to source the utils.R script before
# running this script.
# Extract the entire worksheet from excel
SIC91 <- readxl::read_excel(
'../OFFICIAL/dcms/OFFICIAL_working_file_dcms_V13.xlsm',
sheet = 'SIC 91 Sales Data',
col_names = FALSE
)
# Name columns
colnames(SIC91) <- c('SIC','description','year','gva','code')
# Extract columns of interest
SIC91 <- SIC91 %>%
dplyr::select(SIC, year, gva)
# Drop rows that are completely NA from the bottom of the dataset/
SIC91 <- SIC91 %>%
dplyr::filter(!(is.na(SIC) & is.na(year) & is.na(gva)))
# Save out to file
SIC91 %>% saveRDS('../OFFICIAL/cleaned/OFFICIAL_SIC91_data.Rds')
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