The filter verb
Introduction to the Tidyverse
David Robinson
Chief Data Scientist, DataCamp
The filter verb
Filtering for one year
gapminder %>%
filter(year == 2007)
# A tibble: 142 x 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <dbl> <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
Filtering for one country
gapminder %>%
filter(country == "United States")
# A tibble: 12 x 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <dbl> <dbl>
1 United States Americas 1952 68.440 157553000 13990.48
2 United States Americas 1957 69.490 171984000 14847.13
3 United States Americas 1962 70.210 186538000 16173.15
4 United States Americas 1967 70.760 198712000 19530.37
5 United States Americas 1972 71.340 209896000 21806.04
6 United States Americas 1977 73.380 220239000 24072.63
7 United States Americas 1982 74.650 232187835 25009.56
8 United States Americas 1987 75.020 242803533 29884.35
9 United States Americas 1992 76.090 256894189 32003.93
10 United States Americas 1997 76.810 272911760 35767.43
11 United States Americas 2002 77.310 287675526 39097.10
12 United States Americas 2007 78.242 301139947 42951.65
Filtering for two variables
gapminder %>%
filter(year == 2007, country == "United States")
# A tibble: 1 x 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <dbl> <dbl>
1 United States Americas 2007 78.242 301139947 42951.65
Let's practice!
Introduction to the Tidyverse
