2022-03-08.R

library(tidyverse)
library(countrycode)
library(ggDoubleHeat)
library(patchwork)
theme_set(theme_bw())

# load and clean up the data

erasmus <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-03-08/erasmus.csv') %>%
  select(-education_level,
         -field_of_education,
         -participant_profile,
         -group_leader) %>%
  mutate(sending_country_code = fct_recode(sending_country_code, 
                                           "GB" = "UK",
                                           "GR" = "EL"),
         receiving_country_code = fct_recode(receiving_country_code, 
                                           "GB" = "UK",
                                           "GR" = "EL")) %>%
  mutate(sending_country = countrycode(sending_country_code, origin = "iso2c", destination = "country.name"),
         receiving_country = countrycode(receiving_country_code, origin = "iso2c", destination = "country.name"),
         year = as.numeric(str_remove(academic_year, "-.+$")))


# Plot 1

erasmus %>%
  filter(participant_gender %in% c("Male", "Female"),
         fct_lump(sending_country, n = 9) != "Other") %>%
  group_by(year, participant_gender, sending_country) %>%
  summarize(participant_total = sum(participants)) %>%
  ungroup() %>%
  mutate(sending_country = fct_reorder(sending_country, -participant_total, sum)) %>%
  ggplot(aes(year, participant_total, color = participant_gender)) +
  geom_line(size = 1) +
  geom_point() +
  facet_wrap(~sending_country) +
  labs(x = "",
       y = "# of participants",
       color = NULL,
       title = "Yearly # of Program Participants",
       subtitle = "9 largest sending countries selected") +
  theme(strip.text = element_text(size = 15),
        plot.title = element_text(size = 18)) 


#ggsave("plot1.png", width = 10, height = 8)

# Plot 2


erasmus %>%
  filter(participant_age > 0,
         participant_age < 60) %>% 
  ggplot(aes(year, participant_age, group = year)) +
  geom_boxplot(aes(fill = participant_gender), outlier.alpha = 0.5) +
  facet_wrap(~participant_gender) +
  theme(strip.text = element_text(size = 15),
        plot.title = element_text(size = 18),
        legend.position = "none") +
  labs(x = "",
       y = "age",
       title = "Participant Age Distribution") 

#ggsave("plot2.png", width = 10, height = 8)

# Plot 3

p31 <- erasmus %>%
  mutate(abroad = if_else(sending_country == receiving_country,
                          "Domestic",
                          "Abroad")) %>%
  filter(!is.na(abroad)) %>%
  group_by(sending_country, abroad, year) %>%
  summarize(n = n()) %>%
  ungroup() %>%
  arrange(desc(n)) %>%
  group_by(abroad) %>%
  slice_max(n, n = 20) %>%
  pivot_wider(names_from = "abroad",
              values_from = "n") %>%
  replace_na(list(Abroad = 0,
                  Domestic = 0)) %>%
  
  ggplot(aes(year, sending_country)) +
  geom_heat_grid(outside = Abroad,
                 inside = Domestic) +
  theme(panel.grid = element_blank(),
        legend.position = "bottom",
        plot.title = elemnet_text(size = 18)) +
  coord_fixed() +
  labs(x = NULL,
       y = "sending country",
       title = "Where do participants come from and go to?") 



p32 <- erasmus %>%
  mutate(abroad = if_else(sending_country == receiving_country,
                          "Domestic",
                          "Abroad")) %>%
  filter(!is.na(abroad)) %>%
  group_by(receiving_country, abroad, year) %>%
  summarize(n = n()) %>%
  ungroup() %>%
  arrange(desc(n)) %>%
  group_by(abroad) %>%
  slice_max(n, n = 20) %>%
  pivot_wider(names_from = "abroad",
              values_from = "n") %>%
  replace_na(list(Abroad = 0,
                  Domestic = 0)) %>%
  
  ggplot(aes(year, receiving_country)) +
  geom_heat_grid(outside = Abroad,
                 inside = Domestic) +
  theme(panel.grid = element_blank(),
        legend.position = "bottom") +
  coord_fixed() +
  labs(x = NULL,
       y = "receiving country") 

p31 + p32

#ggsave("plot3.png", width = 12, height = 8)

# Plot 4

erasmus %>%
  count(sending_city, receiving_city, sort = T) %>% View()

by_city <- erasmus %>%
  group_by(year, sending_city) %>%
  mutate(yearly_city_sending = n()) %>% 
  ungroup() %>%
  group_by(year, receiving_city) %>%
  mutate(yearly_city_receiving = n()) %>%
  ungroup() %>%
  distinct(year, sending_city, receiving_city, yearly_city_sending, yearly_city_receiving)

p41 <- by_city %>%
  distinct(year, sending_city, .keep_all = T) %>%
  group_by(year) %>%
  slice_max(yearly_city_sending, n = 1) %>%
  ungroup() %>%
  ggplot(aes(year, yearly_city_sending, fill = sending_city)) +
  geom_col(show.legend = F) +
  geom_text(aes(label = sending_city), vjust = 0) +
  scale_x_continuous(breaks = 2014:2019) +
  labs(x = NULL,
       y = "# of sending participants",
       title = "The Annual City Sending the Most Participants") 


p42 <- by_city %>%
  distinct(year, sending_city, .keep_all = T) %>%
  group_by(year) %>%
  slice_max(yearly_city_receiving, n = 1, with_ties = F) %>%
  ungroup() %>%
  ggplot(aes(year, yearly_city_receiving, fill = sending_city)) +
  geom_col(show.legend = F) +
  geom_text(aes(label = sending_city), vjust = 0) +
  scale_x_continuous(breaks = 2014:2019) +
  labs(x = NULL,
       y = "# of receiving participants",
       title = "The Annual City Receiving the Most Participants") 

p41 / p42

#ggsave("plot4.png", width = 10, height = 8)