The filter() function
filter(my_data_frame, condition)
filter(my_data_frame, condition_one, condition_two, ...)The filter() function takes a data frame and one or more filtering expressions as input parameters. It processes the data frame and keeps only the rows that fulfill the defined filtering expressions. These expressions can be seen as rules for the evaluation and keeping of rows. In the majority of the cases, they are based on relational operators. As an example, we could filter the pres_results data frame and keep only the rows, where the state variable is equal to "CA" (California):
filter(pres_results, state == "CA")
# A tibble: 11 x 6
year state total_votes dem rep other
<dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 1976 CA 7803770 0.480 0.497 0.0230
2 1980 CA 8582938 0.359 0.527 0.114
3 1984 CA 9505041 0.413 0.575 0.0122
4 1988 CA 9887065 0.476 0.511 0.0131
5 1992 CA 11131721 0.460 0.326 0.213
6 1996 CA 10019469 0.511 0.382 0.107
7 2000 CA 10965822 0.534 0.417 0.0490
8 2004 CA 12421353 0.543 0.444 0.0117
9 2008 CA 13561900 0.610 0.370 0.0188
10 2012 CA 13038547 0.602 0.371 0.0246
11 2016 CA 14181595 0.617 0.316 0.0581In the output, we can compare the election results in California for different years.
As another example, we could filter the pres_results data frame and keep only those rows, where the dem variable (percentage of votes for the Democratic Party) is greater than 0.85:
filter(pres_results, dem > 0.85)
# A tibble: 7 x 6 year state total_votes dem rep other <dbl> <chr> <dbl> <dbl> <dbl> <dbl> 1 1984 DC 211288 0.854 0.137 0.00886 2 1996 DC 185726 0.852 0.0934 0.0513 3 2000 DC 201894 0.852 0.0895 0.0563 4 2004 DC 227586 0.892 0.0934 0.0125 5 2008 DC 265853 0.925 0.0653 0.00582 6 2012 DC 293764 0.909 0.0728 0.0155 7 2016 DC 312575 0.905 0.0407 0.0335
In the output we can see for each election year the states where the Democratic Party got over 85% of the votes. Based on the results, we could say that the Democratic Party has a solid voter base in the District of Columbia (known as Washington, D.C.).