Introduction to the Tidyverse
David Robinson
Chief Data Scientist, DataCamp
gapminder %>%
mutate(pop = pop / 1000000)
# A tibble: 1,704 x 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.801 8.425333 779.4453
2 Afghanistan Asia 1957 30.332 9.240934 820.8530
3 Afghanistan Asia 1962 31.997 10.267083 853.1007
4 Afghanistan Asia 1967 34.020 11.537966 836.1971
5 Afghanistan Asia 1972 36.088 13.079460 739.9811
6 Afghanistan Asia 1977 38.438 14.880372 786.1134
7 Afghanistan Asia 1982 39.854 12.881816 978.0114
8 Afghanistan Asia 1987 40.822 13.867957 852.3959
9 Afghanistan Asia 1992 41.674 16.317921 649.3414
10 Afghanistan Asia 1997 41.763 22.227415 635.3414
# ... with 1,694 more rows
gapminder %>%
mutate(gdp = gdpPercap * pop)
# A tibble: 1,704 x 7
country continent year lifeExp pop gdpPercap gdp
<fct> <fct> <int> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.801 8425333 779.4453 6567086330
2 Afghanistan Asia 1957 30.332 9240934 820.8530 7585448670
3 Afghanistan Asia 1962 31.997 10267083 853.1007 8758855797
4 Afghanistan Asia 1967 34.020 11537966 836.1971 9648014150
5 Afghanistan Asia 1972 36.088 13079460 739.9811 9678553274
6 Afghanistan Asia 1977 38.438 14880372 786.1134 11697659231
7 Afghanistan Asia 1982 39.854 12881816 978.0114 12598563401
8 Afghanistan Asia 1987 40.822 13867957 852.3959 11820990309
9 Afghanistan Asia 1992 41.674 16317921 649.3414 10595901589
10 Afghanistan Asia 1997 41.763 22227415 635.3414 14121995875
# ... with 1,694 more rows
gapminder %>%
mutate(gdp = gdpPercap * pop) %>%
filter(year == 2007) %>%
arrange(desc(gdp))
# A tibble: 142 x 7
country continent year lifeExp pop gdpPercap gdp
<fct> <fct> <int> <dbl> <dbl> <dbl> <dbl>
1 United States Americas 2007 78.242 301139947 42951.653 1.293446e+13
2 China Asia 2007 72.961 1318683096 4959.115 6.539501e+12
3 Japan Asia 2007 82.603 127467972 31656.068 4.035135e+12
4 India Asia 2007 64.698 1110396331 2452.210 2.722925e+12
5 Germany Europe 2007 79.406 82400996 32170.374 2.650871e+12
6 United Kingdom Europe 2007 79.425 60776238 33203.261 2.017969e+12
7 France Europe 2007 80.657 61083916 30470.017 1.861228e+12
8 Brazil Americas 2007 72.390 190010647 9065.801 1.722599e+12
9 Italy Europe 2007 80.546 58147733 28569.720 1.661264e+12
10 Mexico Americas 2007 76.195 108700891 11977.575 1.301973e+12
# ... with 132 more rows
Introduction to the Tidyverse