Nobel Prize Winners.R

# Loading in required libraries
install.packages("tidyverse")
install.packages("lubridate")
library(tidyverse)

# Reading in the Nobel Prize data
nobel <- read_csv("data/nobel.csv")

# Taking a look at the first couple of winners
head(nobel)



# Counting the number of (possibly shared) Nobel Prizes handed
# out between 1901 and 2016
nobel %>% count()



# Counting the number of prizes won by male and female recipients.
nobel %>%
  count(sex)



# Counting the number of prizes won by different nationalities.
nobel %>%
  count(birth_country) %>%
  arrange(desc(n)) %>%
  head(20)



# Calculating the proportion of USA born winners per decade
prop_usa_winners <- nobel %>% 
  mutate(
    usa_born_winner = birth_country == "United States of America",
    decade = floor(year / 10) * 10
  ) %>%
  group_by(decade) %>%
  summarize(proportion = mean(usa_born_winner, na.rm = TRUE))

# Display the proportions of USA born winners per decade
prop_usa_winners



# Setting the size of plots in this notebook
options(repr.plot.width=7, repr.plot.height=4)

# Plotting USA born winners
ggplot(prop_usa_winners, aes(x = decade, y = proportion)) +
  geom_line(color = "skyblue") +
  geom_point(color = "red") +
  scale_y_continuous(labels = scales::percent, limits = 0:1, expand = c(0, 0))
ggsave("img/plot01.png", dpi=1000)



# Calculating the proportion of female laureates per decade
prop_female_winners <- nobel %>%
  mutate(
    female_winner = sex == "Female",
    decade = floor(year / 10) * 10
  ) %>%
  group_by(decade, category) %>%
  summarize(proportion = mean(female_winner, na.rm = TRUE))

# Plotting the proportion of female laureates per decade
ggplot(prop_female_winners, aes(x = decade, y = proportion, color = category)) +
  geom_line() +
  geom_point() +
  scale_y_continuous(labels = scales::percent, limits = 0:1, expand = c(0, 0))
ggsave("img/plot02.png", dpi=1000)



# Picking out the first woman to win a Nobel Prize
nobel %>%
  filter(sex == "Female") %>%
  top_n(1, desc(year))



# Selecting the laureates that have received 2 or more prizes.
nobel %>%
  count(full_name) %>%
  filter(n > 1)



# Loading the lubridate package
# library(lubridate)

# Calculating the age of Nobel Prize winners
nobel_age <- nobel %>%
  mutate(age = year - year(birth_date))

# Plotting the age of Nobel Prize winners
ggplot(nobel_age, aes(x = age, y = year)) +
  geom_point() +
  geom_smooth()
ggsave("img/plot03.png", dpi=1000)


# Same plot as above, but faceted by the category of the Nobel Prize
ggplot(nobel_age, aes(x = age, y = year)) +
  geom_point() +
  geom_smooth(se = FALSE) +
  facet_wrap(~ category)
ggsave("img/plot04.png", dpi=1000)


# The oldest winner of a Nobel Prize as of 2016
nobel_age %>% top_n(1, age)

# The youngest winner of a Nobel Prize as of 2016
nobel_age %>% top_n(1, desc(age))



# The name of the youngest winner of the Nobel Prize as of 2016
youngest_winner <- "Malala Yousafzai"
youngest_winner