9.7 Exercise 4

Let’s do a small exercise to practice this conversion.

  1. Read in file DataViz_source_files-main/files/stats_countries_barcelona_2013-2023.csv
correction 
stats_countries <- read_csv("DataViz_source_files-main/files/stats_countries_barcelona_2013-2023.csv")


2. Columns 2013 to 2022 contain observations. Convert this wide format into a long format. Save into a new object.

correction 
# Option 1: list all columns
stats_countries_long <- pivot_longer(stats_countries, 
                                     cols=c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"),
                                     values_to = "Population",
                                     names_to = "Years")

# Option 2: select columns that start with "20"
stats_countries_long <- pivot_longer(stats_countries, 
                                     cols=starts_with("20"),
                                     values_to = "Population",
                                     names_to = "Years")

# Option 3: select all numeric columns
stats_countries_long <- pivot_longer(stats_countries, 
                                     cols=where(is.numeric),
                                     values_to = "Population",
                                     names_to = "Years")


3. Do the 3 following tasks linked by a %>% (pipe):

  • Keep only rows that
    • match continent “America”
    • have a population >= 50
  • Remove column Continent.
  • Produce a barplot with “Years” in the x-axis, Population in the y-axis, and split by Country.
correction 
filter(stats_countries_long, Continent=="America" & Population >= 50) %>%
  select(-Continent) %>%
  ggplot(mapping=aes(x=Years, y=Population, fill=Country)) + geom_bar(stat = "identity", position = "dodge")