library(dplyr)
library(tidyr)
library(readr)
# Get filenames of the original tables
pop_files <- list.files("data-raw/extracted/", pattern = "*.csv", full.names = TRUE)
# Map region codes to English names
region_names <- c(
"1" = "Scandinavia",
"2" = "England and Wales",
"3" = "Scotland",
"4" = "Ireland",
"5" = "The Netherlands",
"6" = "Belgium",
"7" = "Germany",
"8" = "France",
"9" = "Switzerland",
"10" = "Northern Italy",
"11" = "Central Italy",
"12" = "Southern Italy",
"13" = "Spain",
"14" = "Portugal",
"15" = "Austria and Czechoslovakia",
"16" = "Poland"
)
# Read each table and bind together
europop <- lapply(pop_files, function(x) read_csv(x, col_types = "cciiiiiii", na = "UNK")) %>%
bind_rows() %>%
# Reshape into long table
gather(year, population, `1500`:`1800`) %>%
arrange(year) %>%
# Set the correct variable types
mutate(
year = as.integer(year),
region = plyr::revalue(Code, region_names)) %>%
# Set final variables
select(city = City, region, year, population)
write_csv(europop, path = "data-raw/europop.csv")
devtools::use_data(europop, overwrite = TRUE)
# Read in coordinates file
city_coords <- read_csv("data-raw/city_coords.csv", col_types = "ddc")
devtools::use_data(city_coords, overwrite = TRUE)
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