When we want to focus on a group within our dataset, it is common to filter it.
Suppose we have a data table showing how many points 4 players earned in a game.
Table 1: Game Scores for 4 Players
| Age | Nationality | Score |
|---|---|---|
| 17 | English | 80 |
| 18 | French | 40 |
| 20 | German | 60 |
| 24 | Irish | 100 |
The team captain is picking top players for the world championships, but cannot include anyone who is not an adult yet. If we filter this table to only have players above the age of 18 we obtain a new table 2.
Table 2: Filtered Table to Only Include Adults
| Age | Nationality | Score |
|---|---|---|
| 18 | French | 40 |
| 20 | German | 60 |
| 24 | Irish | 100 |
Sometimes, we might want to instead add a new variable to our table to indicate the filter condition. This might be helpful to instead see who should compete in an Adult division and who should in a Junior division.
Table 3: Table with an additional column to indicate adults.
| Age | Nationality | Score | Adult |
|---|---|---|---|
| 17 | English | 80 | FALSE |
| 18 | French | 40 | TRUE |
| 20 | German | 60 | TRUE |
| 24 | Irish | 100 | TRUE |
Is there a name for soft-filtering like this?
At work I refer to it as shallow-filtering, because it does select a group without removing data from the original table (only adding). But perhaps there is some established terminology I'm missing here.
