# ------------------------------------------------------------------------
#
# Title : French popular firstnames
# By : Victor
# Date : 2018-05-18
#
# ------------------------------------------------------------------------
# Packages ----------------------------------------------------------------
library( r2d3maps )
library( rnaturalearth )
library( dplyr )
# Boys in 1992 ------------------------------------------------------------
# map data
fr_dept <- ne_states(country = "france", returnclass = "sf")
fr_dept <- fr_dept[fr_dept$type_en %in% "Metropolitan department", ]
# firstnames data
data("prenoms_fr", package = "r2d3maps")
head(prenoms_fr)
prenoms_fr_92 <- prenoms_fr %>%
filter(annais == 1992, sexe == 1) %>%
group_by(preusuel) %>%
mutate(n = n()) %>%
ungroup() %>%
mutate(prenom = if_else(n <= 2, "AUTRE", preusuel))
fr_dept <- left_join(
x = fr_dept,
y = prenoms_fr_92,
by = "adm1_code"
)
# map
d3_map(shape = fr_dept) %>%
add_discrete_scale(var = "prenom", palette = "viridis") %>%
add_tooltip(value = "<b>{name}</b>: {prenom}", .na = NULL) %>%
add_legend(title = "Prénoms") %>%
add_labs(
title = "Prénoms masculins les plus attribués en 1992",
caption = "Data: Insee"
)
# Girls in 1989 -----------------------------------------------------------
# map data
fr_dept <- ne_states(country = "france", returnclass = "sf")
fr_dept <- fr_dept[fr_dept$type_en %in% "Metropolitan department", ]
# firstnames data
data("prenoms_fr", package = "r2d3maps")
head(prenoms_fr)
prenoms_fr_89 <- prenoms_fr %>%
filter(annais == 1989, sexe == 2) %>%
group_by(preusuel) %>%
mutate(n = n()) %>%
ungroup() %>%
mutate(prenom = if_else(n < 2, "AUTRE", preusuel))
fr_dept <- left_join(
x = fr_dept,
y = prenoms_fr_89,
by = "adm1_code"
)
# map
d3_map(shape = fr_dept) %>%
add_discrete_scale(
var = "prenom", palette = "Set2",
labels_order = c(setdiff(unique(na.omit(fr_dept$prenom)), "AUTRE"), "AUTRE")
) %>%
add_tooltip(value = "<b>{name}</b>: {prenom}", .na = NULL) %>%
add_legend(title = "Prénoms") %>%
add_labs(
title = "Prénoms féminins les plus attribués en 1989",
caption = "Data: Insee"
)
d3_map(shape = fr_dept) %>%
add_discrete_scale2(
var = "prenom",
values = list(
"ELODIE" = "indianred",
"JULIE" = "cornflowerblue",
"LAURA" = "gold",
"MARION" = "mediumpurple",
"AURÉLIE" = "forestgreen",
"AUTRE" = "grey"
)
) %>%
add_tooltip(value = "<b>{name}</b>: {prenom}", .na = NULL) %>%
add_legend(title = "Prénoms") %>%
add_labs(
title = "Prénoms féminins les plus attribués en 1989",
caption = "Data: Insee"
)
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