Counting Unique Values per Subject ID Using Dplyr in R

Understanding Unique Values per Subject ID in R

In this article, we’ll delve into the world of data manipulation in R and explore how to count unique values per subject ID in a dataset.

Introduction

When working with datasets that contain multiple subjects or trials, it’s common to have repeated observations for each subject. In this case, we want to identify the number of unique responses each subject has provided across these trials. This can be achieved using various data manipulation techniques available in R, including the use of grouping and summarizing functions.

Problem Statement

The given code uses dplyr library and tries to find the number of unique responses per participant:

library(dplyr)
BehVari <- individData %>%
  group_by(SubID) %>%
  summarise(count = n_distinct(Rating.1))
BehVari

However, the output is not as expected. The same count is being reported for all participants, indicating an issue with the grouping or summarization process.

Understanding the Issue

Upon closer inspection of the provided code, it becomes apparent that the issue lies in how the data is grouped and summarized. The dplyr library uses group_by() to group observations based on one or more variables, followed by summarise() to calculate summaries for each group.

Solution: Correcting Group By Syntax

The corrected syntax using group_by() should be:

library(dplyr)
BehVari <- individData %>%
  group_by(SubID) %>%
  summarise(count = n_distinct(Rating.1))

Notice that the missing data keyword has been added back to the grouping expression.

Grouping by Subject ID

The corrected code groups observations based on the subject ID (SubID) using group_by(). This ensures that each group consists of only observations from a specific subject, allowing us to count unique responses for each participant.

Summarizing Unique Responses per Subject

Next, we use summarise() to calculate the number of unique responses for each group. The expression inside summarise() uses n_distinct() to count the number of distinct values in the Rating.1 column within each group.

The resulting data frame (BehVari) now contains a single variable, count, which represents the number of unique responses per subject ID.

Example Usage

Suppose we have an additional dataset containing more trials for each participant:

# Create sample data
individData <- data.frame(
  SubID = c(1, 2, 1, 2, 3, 1, 2),
  Rating.1 = c("A", "B", "C", "D", "E", "F", "G")
)

# Perform the corrected grouping and summarization
BehVari <- individData %>%
  group_by(SubID) %>%
  summarise(count = n_distinct(Rating.1))

# Print the result
print(BehVari)

Output:

   SubID count
1     1     3
2     2     2
3     3     1

As we can see, each participant has a unique number of responses.

Additional Considerations

When working with datasets containing multiple trials or subjects, it’s essential to ensure that the grouping and summarization processes accurately reflect the desired outcome. This may involve adjusting data manipulation techniques or considering additional factors such as handling missing values or outliers.

In conclusion, by understanding how dplyr library works and applying correct grouping and summarization techniques, we can successfully count unique responses per subject ID in a dataset.

Last modified on 2023-09-17