Visualizing with ggplot2

Introduction to the Tidyverse

David Robinson

Chief Data Scientist, DataCamp

Variable Assignment

gapminder_2007 <- gapminder %>%
  filter(year == 2007)

gapminder_2007

# A tibble: 142 x 6
       country continent  year lifeExp       pop  gdpPercap
         <fct>     <fct> <int>   <dbl>     <int>      <dbl>
 1 Afghanistan      Asia  2007  43.828  31889923   974.5803
 2     Albania    Europe  2007  76.423   3600523  5937.0295
 3     Algeria    Africa  2007  72.301  33333216  6223.3675
 4      Angola    Africa  2007  42.731  12420476  4797.2313
 5   Argentina  Americas  2007  75.320  40301927 12779.3796
 6   Australia   Oceania  2007  81.235  20434176 34435.3674
 7     Austria    Europe  2007  79.829   8199783 36126.4927
 8     Bahrain      Asia  2007  75.635    708573 29796.0483
 9  Bangladesh      Asia  2007  64.062 150448339  1391.2538
10     Belgium    Europe  2007  79.441  10392226 33692.6051
# ... with 132 more rows

Visualizing with ggplot2

library(ggplot2)

ggplot(gapminder_2007, aes(x = gdpPerCap, y = lifeExp)) +
  geom_point()

Let's practice!

Introduction to the Tidyverse