# Nettoyage et mise en forme des donnees au niveau departemental
prenoms_dpt <-
read_csv2("data-raw/dpt2018.csv", na = c("XX", "XXXX")) %>%
rename(
n = nombre,
sex = sexe,
year = annais,
name = preusuel,
dpt_num = dpt
) %>%
filter(
!is.na(year),
name != "_PRENOMS_RARES"
) %>%
mutate(
name = stringr::str_to_title(name),
n = as.integer(n),
sex = if_else(sex == 1, "M", "F")
) %>%
left_join(read_csv("data-raw/tools/corresp_dpt.csv")) %>%
arrange(year, sex, name, dpt_num) %>%
select(year, name, sex, dpt_num, dpt_name, n)
save(prenoms_dpt, file = "data/prenoms_dpt.rda", compress = "xz")
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