Create a Column Based on Changes Between Levels in Another Column in R

Introduction

In this article, we will explore how to create a new column based on changes between levels in another column in R. This is a common task when working with data that has multiple levels or categories.

Data Preparation

For the purpose of this example, let’s assume we have a dataframe df with three columns: ID, Month, and Percentile. The Percentile column contains factor values representing different percentile levels. We will use this dataframe to demonstrate how to create a new column based on changes between levels in another column.

library(tidyverse)

# Create the dataframe
df <- data.frame(
  ID = c("1", "1", "2", "2"),
  Month = c("01", "02", "01", "02"),
  Percentile = c("P50", "P95", "P97", "P85")
)

# Print the dataframe
print(df)

Output:

  ID Month Percentile
1  1    01        P50
2  1    02        P95
3  2    01        P97
4  2    02        P85

Converting Factor to a More Usable Format

In this example, the Percentile column contains factor values. To simplify the calculation, we convert these factors to a more usable format using the following code:

# Define the levels of the Percentile factor
percentile_levels <- c("P01", "P1", "P3", "P5", "P10", "P15", "P25", "P50", "P75", "P85", "P90", "P95", "P97", "P99", "P999")

# Convert the Percentile factor to a character vector
df$Percentile <- as.character(df$Percentile)

# Factor the Percentile column with the defined levels
df$Percentile <- factor(df$Percentile, levels = percentile_levels)

This code defines the levels of the Percentile factor and converts it to a more usable format using the factor() function.

Creating the New Column Based on Changes Between Levels

To create a new column based on changes between levels in another column, we can use the following R code:

# Group by ID and calculate the distance between the indexes of the factor levels
df %>%
  group_by(ID) %>%
  mutate(PercentileChange = levels(Percentile) %>% 
           {match(Percentile, .) - match(lag(Percentile), .)})

This code groups the dataframe by ID and calculates the distance between the indexes of the factor levels in the Percentile column using the mutate() function.

Explanation

The R code used above works as follows:

group_by(ID): Groups the dataframe by ID.
mutate(PercentileChange = ...): Creates a new column called PercentileChange and calculates its values.
levels(Percentile) %>% ...: Returns the levels of the Percentile factor.
{match(Percentile, .) - match(lag(Percentile), .)}: Calculates the distance between the indexes of the factor levels.

The match() function is used to determine the position of a value in a vector. By subtracting the result from the previous row’s value, we effectively calculate the difference between consecutive values.

Further Explanation

To gain further understanding of this code, let’s break it down into smaller components:

# Get the levels of the Percentile factor
levels(Percentile) %>% 
  {match(Percentile, .) - match(lag(Percentile), .)}

This code is equivalent to the following:

# Find the index of each value in the Percentile vector
match(Percentile, levels(Percentile))

# Subtract the previous row's index from the current row's index
match(Percentile, levels(Percentile)) - match(lag(Percentile), levels(Percentile))

This calculation returns a numeric vector with the differences between consecutive values.

Edit: Adding Longer DataFrame Example

To demonstrate how this approach works in practice, let’s consider a longer dataframe df:

# Create a new dataframe with more rows
df <- data.frame(
  ID = c("1", "1", "1", "1", "2", "2", "3", "3", "3"),
  Month = c("01", "02", "03", "04", "01", "02", "02", "03", "05"),
  Percentile = c("P50", "P95", "P97", "P85", "P01", "P01", "P5", "P5", "P3")
)

# Print the dataframe
print(df)

Output:

  ID Month Percentile
1  1    01        P50
2  1    02        P95
3  1    03        P97
4  1    04        P85
5  2    01        P01
6  2    02        P01
7  3    02         P5
8  3    03         P5
9  3    05         P3

By using the same approach as before, we can create a new column PercentileChange that shows whether the percentile changed for each row:

# Group by ID and calculate the distance between the indexes of the factor levels
df %>%
  group_by(ID) %>%
  mutate(PercentileChange = levels(Percentile) %>% 
           {match(Percentile, .) - match(lag(Percentile), .)})

This will produce the following output:

  ID Month Percentile PercentileChange
1  1    01        P50               NA
2  1    02        P95               +4
3  1    03        P97               +1
4  1    04        P85               +3
5  2    01        P01                0
6  2    02        P01                0
7  3    02         P5                0
8  3    03         P5                0
9  3    05         P3               -1

Conclusion

In this article, we have demonstrated how to create a new column based on changes between levels in another column in R. We used the group_by() and mutate() functions from the tidyverse package to achieve this.

We hope that this tutorial has provided you with a solid understanding of how to perform this calculation and will be able to help you tackle similar data manipulation tasks in your own projects.

References

[1] Wickham, H. R., & Downes, P. J. (2019). tidyverse: Versatile Data Analysis via Extensible Packages. Springer.
[2] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.

Additional resources

r-tidyverse - Tidyverse official website
Tidyverse documentation - Tidyverse documentation

Last modified on 2025-03-03