Nobel Prize Winners

Using week 2019-20 TidyTuesday Nobel Prize Winners dataset this week I took the opportunity to explore ggridge plots, plotting age of nobel prize winners for each of the six Nobel Prize Winner categories Economics, Literature, Peace, Medicine, Chemistry and Physics. I also had a look at themes in ggplot2 provided by additional packages ggthemes, ggdark and hrbrthemes, which have been retained but commented out in the code.

library(tidyverse)
library(lubridate)
library(ggplot2)
library(ggridges)
library(ggthemes)
library(viridis)
library(janitor)

nobel_winners_raw <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-14/nobel_winners.csv")

Firstly we are only interested in individuals who have won a prize along with the year they won and their date of birth, so that we work out how old they were when they won. We can exclude organisation winners at this stage.

nobel_winners <- nobel_winners_raw %>% 
  filter(laureate_type == "Individual") %>% 
  select(prize_year, category, birth_date) 

Lets work out the age each individual was when they were awarded a Nobel prize. We only have the year of the prize and so won’t worry too much if a person had their birthday before the date in the year, ignoring that we may overestimate their age by a year. Also the few individuals with missing birth dates are removed.

nobel_winners <- nobel_winners %>% 
  filter(!is.na(birth_date)) %>% 
  mutate(age = prize_year - year(birth_date)) 

The range of years for each Nobel proze category can be displayed using a ggridge plot. First group the categories so that the science categories are next to each other

categories <- c("Physics", "Chemistry", "Medicine", "Peace", "Literature", "Economics")
nobel_winners <- nobel_winners %>% 
  mutate(category = factor(category, levels = categories))

Now create the plot using ggplot2 with ggridges geometry, theme from ggthemes package and using a colour palette from the viridis package.

nobel_plot <- ggplot(nobel_winners, aes(x = age, y = category, fill = category)) +
  geom_density_ridges2(rel_min_height = 0.01,
                       scale = 2.4,
                       center_axis_labels = TRUE,
                       colour = 'white',
                       size = 1) +
  scale_y_discrete(expand = expand_scale(add = c(0.2, 0.5))) +
  scale_x_continuous(expand = expand_scale(add = c(0, 5)),
                     breaks = seq(0, 100, 10)) +
  theme_economist() +
  theme(axis.ticks.x = element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.grid.major.y = element_line(linetype = 2),
        axis.line.x = element_blank(),
        axis.text = element_text(size = 20, colour = 'white', face = 'bold'),
        axis.text.x = element_text(vjust = 2),
        axis.text.y = element_text(hjust = 1),
        plot.title = element_text(size = 24, colour = 'white', face = 'italic', hjust = -30),
        plot.subtitle = element_text(size = 16, colour = 'white', face = 'italic', hjust = -0.5),
        plot.caption = element_text(size = 12, colour = 'white', face = 'italic'),
        legend.position = "none") +
  labs(title = "Age ranges of Nobel Laureates",
       subtitle = "TidyTuesday 2019-05-14",
       caption = "Kaggle: Nobel Laureates, 1901-Present") +
  scale_fill_viridis(discrete = TRUE)
nobel_plot

Trying out additional ggplot themes

nobel_plot <- ggplot(nobel_winners, aes(x = age, y = category, fill = category)) +
  geom_density_ridges2(rel_min_height = 0.01,
                       scale = 2.4,
                       center_axis_labels = TRUE,
                       colour = 'white',
                       size = 1) +
  scale_y_discrete(expand = expand_scale(add = c(0.2, 0.5))) +
  scale_x_continuous(expand = expand_scale(add = c(0, 5)),
                     breaks = seq(0, 100, 10)) +
  # ggplot2::theme_linedraw() +
  # ggdark::dark_theme_linedraw() +
  # ggplot2::theme_classic() +
  # ggdark::dark_theme_classic() +
  # ggthemes::theme_base() + # base R graphics theme
  # ggthemes::theme_clean() + # clean ggplot theme
  # ggdark::dark_mode(theme_clean()) + # clean ggplot theme
  # ggthemes::theme_economist_white(gray_bg = FALSE) + # The Economist style
  # ggthemes::theme_tufte() + # tufte theme
  # ggthemes::theme_map() + # theme for displaying maps
  # ggthemes::theme_economist() + # The Economist style
  # ggthemes::theme_solarized(light = FALSE) + # Solarized palette (dark)
  # ggthemes::theme_solarized() + # Solarized palette (light)
  # hrbrthemes::theme_modern_rc() +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank()) +  labs(title = "Age ranges of Nobel Laureates",
       subtitle = "TidyTuesday 2019-05-14",
       caption = "Kaggle: Nobel Laureates, 1901-Present") +
  scale_fill_viridis(discrete = TRUE)

ggsave(filename = "dark_theme_classic.png",
       plot = nobel_plot,
       width = 10)

ggsave(filename = "nobel_prize_winners.png",
       plot = nobel_plot,
       width = 10)

It looks like the winners of the three science categories tend to be younger than the winners of the Peace, Literature or Economics categories.