Animated visualization of popular names using GGAnimate

In this post, I will create a racing bar chart using ggplot2 for the most popular names in the last decade.

The data comes from the “Babynames” package by Hadley Wickham.

Tip

You can find my github code repository here.

1 Load libraries

# For loading Tidy Tuesday data
library(babynames)

# EDA
library(tidyverse)
library(DT)

# Interactive visualization
library(gganimate)

2 Load data

I am using the “babynames” dataset from the “Babynames” package.

babynames

# A tibble: 1,924,665 × 5
    year sex   name          n   prop
   <dbl> <chr> <chr>     <int>  <dbl>
 1  1880 F     Mary       7065 0.0724
 2  1880 F     Anna       2604 0.0267
 3  1880 F     Emma       2003 0.0205
 4  1880 F     Elizabeth  1939 0.0199
 5  1880 F     Minnie     1746 0.0179
 6  1880 F     Margaret   1578 0.0162
 7  1880 F     Ida        1472 0.0151
 8  1880 F     Alice      1414 0.0145
 9  1880 F     Bertha     1320 0.0135
10  1880 F     Sarah      1288 0.0132
# … with 1,924,655 more rows

datatable(babynames %>% 
  head())

3 Clean and rank the data

ranked_by_year <- babynames %>% 
  mutate(prop = round(prop*100, 2)) %>% 
  select(sex, year, name, var = prop) %>% 
  group_by(sex, year) %>% 
  arrange(year, -var) %>% 
  mutate(rank = 1:n()) %>% 
  filter(rank <= 10)

4 Function to filter the data

data_filter <- function(ranked_by_year, sex_filter = "M", year_min = 1998) {
  ranked_by_year %>% 
    filter(sex == sex_filter,
           year >= year_min)
}

5 Function to create the ggplot for filtered variables

plot_rect_function <- function(data, title_text) {
  data %>% 
    ggplot() +
    aes(xmin = 0,
        xmax = var) +
    aes(ymin = rank - 0.5,
        ymax = rank + 0.5,
        y = rank) +
    theme_minimal() +
    facet_wrap(~ year) +
    geom_rect(alpha = 0.3, color = "black") +
    aes(fill = name) +
    scale_x_continuous(
      limits = c(-2, 1.4)
    ) +
    geom_label(col = "gray12",
              hjust = "right",
              aes(label = name, fill = name),
              x = -0.2) +
    scale_y_reverse() +
    labs(fill = NULL) +
    labs(title = title_text,
         y = "",
         x = "Percentage of names") +
    facet_null() +
    scale_x_continuous(
      limits =c(-0.7, 2)
    ) +
    geom_text(x = 1.6 , y = -10,
              family = "Times",
              aes(label = as.character(year)),
              size = 20, col = "grey18") +
    aes(group = name) +
    theme(legend.position = "none",
          axis.text.y = element_blank()) 
}

6 Create plots for filtered Male and Female names

plot_rect_M <- ranked_by_year %>% 
  data_filter("M", 1998) %>% 
  plot_rect_function(title_text = "Most popular male baby names between 1998-2017")

plot_rect_F <- ranked_by_year %>% 
  data_filter("F", 1998) %>% 
  plot_rect_function(title_text = "Most popular female baby names between 1998-2017")

7 Animate with GGAnimate over time

While the above plot looks extremely scrambled, applying gganimate and looking at it over time will clean it up.

7.1 Male names between 1998 and 2017

The name “Jacob” went from rank 1 to rank 10 by 2017 but stayed on the charts throughout!

names_prop_animated_M <- plot_rect_M  +
  gganimate::transition_time(year)

animate(names_prop_animated_M, duration =30, fps = 10, width = 900, height = 600, renderer = gifski_renderer(), end_pause = 100, start_pause = 30)

anim_save(file = "popular_names_Male.gif")

7.2 Female names between 1998 and 2017

The name “Emily” went from rank 1 to not surviving the top 10 chart by 2017.

names_prop_animated_F <- plot_rect_F  +
  gganimate::transition_time(year)

animate(names_prop_animated_F, duration =30, fps = 10, width = 900, height = 600, renderer = gifski_renderer(), end_pause = 100, start_pause = 30)

anim_save(file = "popular_names_Female.gif")