Packages

In order to create this chart, we need to load the following packages, as well as some fonts:

extrafont::loadfonts()
library(tidyverse)
library(ggridges)
library(viridis)
library(extrafont)
library(cowplot)
library(showtext)

# Loading fonts
font_add_google("DM Serif Display", "abril")
font_add_google("Tajawal", "tawa")
showtext_auto()

# Set theme
theme_set(theme_light())

Dataset

The data consists of a CSV file containing baby names, which is loaded into a data frame called babynames. The data is then filtered to find the 50 most popular female and male names of all time, and the results are stored in the top_female and top_male vectors, respectively.

The babynames data frame is then filtered again to include only the top 50 female and male names for each year, and the results are stored in the female_names and male_names data frames. The name column in both data frames is converted to a factor with levels based on the order of popularity.

The female_names and male_names data frames are grouped by year and name, and the total number of occurrences for each name is summarized in the n column.

# Loading data
babynames <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-03-22/babynames.csv")

# 50 most popular female names over all time
top_female <- babynames |>
  filter(sex == "F") |>
  group_by(name) |>
  summarise(total = sum(n)) |>
  slice_max(total, n = 50) |>
  mutate(
    rank = 1:50,
    name = forcats::fct_reorder(name, -total)
  ) |>
  pull(name)

# 50 most popular male names over all time
top_male <- babynames |>
  filter(sex == "M") |>
  group_by(name) |>
  summarise(total = sum(n)) |>
  slice_max(total, n = 50) |>
  mutate(
    rank = 1:50,
    name = forcats::fct_reorder(name, -total)
  ) |>
  pull(name)

# filter female names data
female_names <- babynames |>
  filter(
    sex == "F",
    name %in% top_female
  ) |>
  mutate(name = factor(name, levels = levels(top_female))) |>
  group_by(year, name) |>
  summarise(n = sum(n))

# filter top males
male_names <- babynames |>
  filter(
    sex == "M",
    name %in% top_male
  ) |>
  mutate(name = factor(name, levels = levels(top_male))) |>
  group_by(year, name) |>
  summarise(n = sum(n))

Ridgeline with female names

We start by creating a single ridgeline plot using only female names.

The ridgeline plot is made via the geom_ridgeline() function from the ggridges package.

plot1 <- ggplot(female_names, aes(year,
  y = fct_reorder(name, n), height = n / 50000,
  group = name, scale = 2
)) +
  geom_ridgeline(
    alpha = 0.5, scale = 4.5, linewidth = 0,
    fill = "#05595B", color = "white"
  ) +
  xlim(1900, NA) +
  labs(title = "Female", y = "", x = "") +
  theme(
    plot.title = element_text(hjust = 0, family = "Bahnschrift", size = 20),
    axis.ticks.y = element_blank(),
    axis.text = element_text(family = "Bahnschrift", size = 15),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.y = element_line(size = 0.5),
    panel.border = element_blank()
  ) +
  geom_segment(aes(x = 1967, xend = 1967, y = 56.7, yend = 52), color = "#404040") +
  geom_segment(aes(x = 1967, xend = 1964, y = 56.7, yend = 56.7), color = "#404040") +
  annotate(
    geom = "text", x = 1970, y = 57, label = "73,982 babies called\n'Mary' in 1921", hjust = "left",
    size = 7, color = "#404040", family = "Bahnschrift"
  )

plot1

ggsave("img/graph/ridgeline-1.png")

Ridgeline with male names

Now we create the same chart but using the male names only. The code is pretty much the same as above:

plot2 <- ggplot(male_names, aes(year,
  y = fct_reorder(name, n), height = n / 50000,
  group = name, scale = 2
)) +
  geom_ridgeline(
    alpha = 0.5, scale = 4.5, linewidth = 0,
    fill = "#603601", color = "white"
  ) +
  xlim(1900, NA) +
  labs(title = "Male", y = "", x = "") +
  theme(
    plot.title = element_text(hjust = 0, family = "Bahnschrift", size = 20),
    axis.ticks.y = element_blank(),
    axis.text = element_text(family = "Bahnschrift", size = 15),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.y = element_line(size = 0.5),
    panel.border = element_blank(),
    panel.background = element_rect(fill = "white"),
    plot.background = element_rect(fill = "white")
  ) +
  geom_segment(aes(x = 1975, xend = 1975, y = 58, yend = 53.1), color = "#404040") +
  geom_segment(aes(x = 1975, xend = 1972, y = 58, yend = 58), color = "#404040") +
  annotate(
    geom = "text", x = 1977, y = 57.6, label = "94,756 babies called\n'James' in 1947", hjust = "left",
    size = 7, color = "#404040", family = "Bahnschrift"
  )

plot2

ggsave("img/graph/ridgeline-2.png")

Combine plots

Now that we have the charts that we needed, we can concatenate them into a single one thanks to the plot_grid() function:

title_theme <- ggdraw() +
  draw_label("50 most common given names in the US since 1900",
    fontfamily = "Gadugi",
    fontface = "bold",
    size = 45,
    hjust = 0.4
  )

# caption
caption <- ggdraw() +
  draw_label("Source: {babynames} from Hadley Wickham | Graphic: Laura Navarro Soler",
    fontfamily = "Bahnschrift",
    size = 25,
    hjust = 0.5,
    color = "#6B6B6B"
  )

gridofplots <- plot_grid(plot1, plot2, nrow = 1)
plot_grid(title_theme,
  gridofplots,
  caption,
  ncol = 1, rel_heights = c(0.2, 1.5, 0.1)
)

ggsave("img/graph/ridgeline-3.png")

Double ridgeline plot

Packages

Dataset

Ridgeline with female names

Ridgeline with male names

Combine plots

Going further

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