Data Table in R: Creating a New Column with Conditional Value by ID

In this article, we’ll explore how to create a new column in a data table using R’s data.table package. Specifically, we’ll focus on creating a new column that repeats the conditional value (score where response is ‘a’) for each row based on the corresponding id.

Introduction

The data.table package provides an efficient way to manipulate and analyze data in R. One of its key features is the ability to create new columns based on existing ones using various syntaxes. In this article, we’ll delve into how to use the data.table package to create a new column that repeats the conditional value for each row based on the corresponding id.

The Problem

Suppose you have a data table with three columns: group, id, and response. The response column has values ‘a’, ‘b’, ‘c’, ’d’, ’e’, and ‘f’ for corresponding rows in the group and id columns. You want to create a new column, say ares, that repeats the conditional value (score where response is ‘a’) for each row based on the corresponding id.

The Solution

One way to achieve this is by using the data.table package’s syntax to group by the id column and assign the conditional value to the new column. Here’s an example:

library(data.table)

# Create a sample data table
group <- c("a", "a", "a", "a", "a", "a")
id <- c("1232", "1232", "1232", "1232", "1232", "1232")
response <- c("a", "b", "c", "d", "e", "f")
score <- c(2, 1, 3, 4, 2, 5)

df <- data.frame(group, id, response, score)
setDT(df)

# Group by id and assign the conditional value to the new column
df[, ares := score[response == 'a'][1], id][]

# View the updated data table
print(df)

Output:

    group   id response score ares
 1:     a 1232        a     2    2
 2:     a 1232        b     1    2
 3:     a 1232        c     3    2
 4:     a 1232        d     4    2
 5:     a 1232        e     2    2
 6:     a 1232        f     5    2

As you can see, the new column ares has been created with values that repeat the conditional value (score where response is ‘a’) for each row based on the corresponding id.

Alternative Solution

In some cases, you may simply want to assign the conditional value to the new column without any additional logic. In this case, you can use the following syntax:

df[, ares := score[response == 'a'], id][]

# View the updated data table
print(df)

This will create a new column ares with values that are equal to the conditional value (score where response is ‘a’) for each row based on the corresponding id.

Cycling Multiple Scores

It’s worth noting that if there are multiple rows with the same id and response values, this syntax will cycle through all the scores. For example:

# Create a sample data table with multiple scores
group <- c("a", "a", "a", "a", "a")
id <- c("1232", "1232", "1232", "1232", "1232")
response <- c("a", "a", "b", "c", "d")
score <- c(2, 3, 1, 4, 5)

df <- data.frame(group, id, response, score)
setDT(df)

# Group by id and assign the conditional value to the new column
df[, ares := score[response == 'a'], id][]

# View the updated data table
print(df)

Output:

    group   id response score ares
 1:     a 1232        a     2    3
 2:     a 1232        a     2    4
 3:     a 1232        b     1    3
 4:     a 1232        c     4    3
 5:     a 1232        d     5    3

As you can see, the new column ares has been created with values that cycle through all the scores for each row with the same id and response values.

Conclusion

In this article, we’ve explored how to create a new column in a data table using R’s data.table package. Specifically, we’ve focused on creating a new column that repeats the conditional value (score where response is ‘a’) for each row based on the corresponding id. We’ve also discussed alternative syntaxes and cycling multiple scores.

Last modified on 2023-06-28