city_race_chart

library(tidyverse)
library(gganimate)
library(tweenr)
library(stringi)
library(countrycode)

#read in data
#see JBM's original post for links
data <- read.csv("./city_populations.csv",
                 encoding = "UTF-8", stringsAsFactors = FALSE) %>%
  #select out relevant columns
  select(country_id = Country.Code, country = Country.or.area,
         city_id = City.Code, city = Urban.Agglomeration,
         X1950, X1955, X1960, X1965, X1970, X1975, X1980, X1985, X1990,
         X1995, X2000, X2005, X2010, X2015, X2020, X2025, X2030) %>%
  #melt the data to long format
  reshape2::melt(id.vars = c("country_id", "country", "city_id", "city"),
                 variable.name = "year", value.name = "population") %>%
  #conver the data into usuable numbers
  mutate(year = as.numeric(gsub("^X", "", year)),
         population = as.numeric(gsub(",", "", population)),
         #convert the text into utf-8 readable
         city = stri_trans_general(city, "latin-ascii")) %>%
  #extract the english names for cities
  mutate(city_name = case_when(
    grepl("\\(", city) ~ str_extract(city,  "(?<=\\().+?(?=\\))"),
    grepl("-", city) ~ gsub("-.*", "", city),
    TRUE ~ as.character(city)
  )) %>%
  #group by and find order at any point
  group_by(year) %>%
  arrange(-population) %>%
  mutate(order = row_number()) %>%
  ungroup()

#get the id data for each unique city
id_data <- data %>%
  select(city_id, city_name, country_id, country) %>%
  unique() %>%
  #find the continent of each city
  mutate(continent = countrycode(.$country, origin = "country.name", destination = "continent"))

#the number of frames the output will contain
frames <- 100

#use tweenr to manually make the naimation frame data
frame_data <- data %>%
  group_by(year) %>%
  arrange(-population) %>%
  mutate(order = row_number()) %>%
  #tweenr stuff here
  select(city_id, year, population, order) %>%
  mutate(ease = "linear") %>%
  tween_elements(., "year", "city_id", "ease", nframes= frames) %>%
  #select out columns
  select(population, order, year, .frame, city_id = .group) %>%
  #merge in id data
  merge(., id_data, by = 'city_id') %>%
  #munger population numbers
  mutate(pop = round(population/1000, 2))

p <- frame_data %>%
  #only want to plot the top 10
  filter(order < 10.8) %>%
  ggplot(aes(y = order, x = pop)) +
  #hack to plot the moving bars
  #from v helpful answer at
  #https://stackoverflow.com/questions/53162821/
  #animated-sorted-bar-chart-with-bars-overtaking-each-other/53163549
  geom_tile(aes(x = pop/2, width = pop, fill = continent),
            alpha = 0.8, colour = "black", height = 0.9) +
  geom_text(aes(label = sprintf("%1.2f",pop)), hjust = 1) +
  geom_text(aes(x = 0, label = paste(city_name, " ")),
            vjust = 0.2, hjust = 1) +
  #add labels to plot
  labs(title='{round(as.numeric(closest_state))}',
       x = "Population (millions)", y = "") +
  #y limits at 0-10.5
  #don't clip as will screw the labels outside the plot
  coord_cartesian(ylim = c(0,10.5), clip = "off") +
  #flip the y axis
  scale_y_reverse(position = "left") +
  #theme stuff
  #taken from same stackoverflow answer
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0, size = 22),
        axis.ticks.y = element_blank(),
        axis.text.y  = element_blank(),
        #make sure labels will be visible
        plot.margin = margin(0,0,0,2.5, "cm")) +
  #transition by our calculated year
  transition_states(year, transition_length = 1, state_length = 0) +
  #scale x axis as pop increases
  view_follow(fixed_y = TRUE) +
  #fade as bares enter and exit the plot
  exit_fade() +
  enter_fade()

#save the gif
gif <- animate(p, frames)
anim_save("city_gif.gif", gif)