Subsetting Data by Conjunction of Two Columns

In data analysis, subsetting data refers to the process of selecting a subset of rows from a larger dataset based on specific conditions or criteria. One common scenario where subsetting is required is when working with multiple variables that need to be considered simultaneously.

This article will delve into the world of subsetting data by conjunction of two columns using the popular R programming language and the dplyr library, which provides an efficient and expressive way to perform data manipulation operations.

Background

In this scenario, we have a dataset called Gamedat that contains information about various games played, including Games, People, Hoursplayed, and other variables. We want to create a subset of this data where the game is “goldeneye” and there exists another player who has played the same number of hours as the goldeneye player.

The initial approach involves using the grep function to select rows containing “goldeneye” in the Games column, followed by subsetting the resulting dataset based on the existence of another row with matching values for People and Hoursplayed. However, this method yields an inconsistent result that does not align with our desired output.

Introduction to dplyr

The dplyr library provides a set of functions for efficient data manipulation. It is built on top of the base R functionality but offers a more concise and expressive syntax for common data operations.

In this example, we will utilize the inner_join function from dplyr to join our dataset with itself based on matching values in the People and Hoursplayed columns.

Step-by-Step Guide to Subsetting Data by Conjunction of Two Columns

Step 1: Load the dplyr Library

To begin, we need to load the dplyr library using the library() function.

library(dplyr)

Step 2: Retrieve Rows Where Game is Goldeneye

Next, we use the filter() function from dplyr to select rows where the game is “goldeneye”.

Gamedat %>% 
  filter(Games == "goldeneye")

This step isolates our dataset to only include games that match the condition specified.

Step 3: Inner Join with Original Dataset

We then perform an inner join with the original Gamedat dataset using the inner_join() function, specifying People and Hoursplayed as the common columns.

%>% 
  inner_join(Gamedat, by = c("People", "Hoursplayed"))

The inner_join() operation combines rows from both datasets where there is a match in the specified columns.

Step 4: Select Desired Columns

To achieve our desired output format, we use the select() function to specify only the Games, People, and Hoursplayed columns.

%>% 
  select(Games = Games.y, People, Hoursplayed)

Note that in this step, we assign a new alias (Games.y) to the original Games column. This is necessary because when selecting only specific columns from another dataset using the select() function, R requires an explicit reference to the source column.

Step 5: Arrange by People (Optional)

Finally, we use the arrange() function to arrange our result in ascending order based on the People column.

%>% 
  arrange(People)

This ensures that the players are listed in alphabetical order within each row.

Example Output

The final dataset after these operations is as follows:

          Games   People Hoursplayed
1     goldeneye    Clint           2
2        tetris    Clint           2
3     goldeneye   Dexter          12
4     goldeneye   Dexter          15
5        pacman   Dexter           2
6     goldeneye   Dexter           2
7     goldeneye  Michael           5
8        pacman  Michael           5
9     goldeneye Thatcher           8
10    goldeneye Thatcher          12
11 ageofempires Thatcher          12

In this output, we can see that the subset includes all rows where the game is “goldeneye” and there exists another player who has played the same number of hours. The dplyr approach efficiently handles this complex subsetting task.

Conclusion

Subsetting data by conjunction of two columns can be a challenging task, especially when working with multiple variables that need to be considered simultaneously. This article demonstrated how using the dplyr library in R enables an elegant solution to such problems through the effective use of inner joins and column selection operations.

Last modified on 2025-01-25