Adding a Conditional Column with Letters in R Based on Hierarchical Conditions

In this article, we will explore how to add a new column to an existing dataframe based on specific conditions. We will use the dplyr library and its powerful case_when() function to achieve this.

Introduction

The problem presented involves adding a new column (COL4) to a dataframe based on certain conditions related to the values in another column (COL1, COL2, and COL3). The condition depends on the combination of these three columns, and the resulting value for COL4 should be either ‘L’, ‘M’, or ‘P’. We will explore this problem using R.

Setting Up the Dataframe

First, we need to create a sample dataframe that mirrors the data provided in the question. The dataframe test contains information about groups (Groups) and names within each group (Names). It also includes numerical values for columns COL1, COL2, and COL3.

# Load necessary libraries
library(dplyr)
library(tibble)

# Create a sample dataframe
test <- tibble(
  Groups = c("G1", "G1", "G1", "G1", "G1", "G1", "G1", "G1", 
             "G2", "G2", "G2", "G2", "G2", "G2", "G2", "G2", "G3"),
  Names = c("SP1", "Sp12", "SP1", "SP2", "SP5", "SP6", "SP3", "SP5",
            "SP12", "SP1", "SP2", "SP5", "SP6", "SP7", "SP12", "SP5",
            "SP6"),
  COL1 = c(1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0),
  COL2 = c(0.4, 0.004, 0.004, 0.4, 0.004, 0.008, 0.005, 0.4, 
           0.004, 0.05, 0.004, 0.004, 0.004, 0.56, 0.004, 0.87),
  COL3 = c(0.5, 0.005, 0.005, 0.005, 0.006, 0.006, 0.006, 0.002, 
           0.005, 0.6, 0.6, 0.005, 0.004, 0.76, 0.003, 0.767)
)

# Display the dataframe
print(test)

Adding a Conditional Column with case_when()

Now that we have our sample dataframe, let’s use the case_when() function to add the new column (COL4). This function allows us to specify multiple conditions and corresponding values.

# Use case_when() to create the new column COL4
test <- test %>%
  mutate(COL4 = 
         case_when(
           # Condition for 'L'
           (COL1 >= 1 & COL2 >= 0.05 & COL3 >= 0.05) ~ "L",
           # Condition for 'M' when COL1 >= 1 and COL2 >= 0.05 but COL3 < 0.05
           (COL1 >= 1 & COL2 >= 0.05 & COL3 < 0.05) ~ "M",
           # Condition for 'M' when COL1 == 0 and COL2 >= 0.05 and COL3 >= 0.05
           (COL1 == 0 & COL2 >= 0.05 & COL3 >= 0.05) ~ "M",
           # Conditions for 'P'
           (# Condition when COL1 == 0 and COL2 < 0.05 and COL3 < 0.05)
           (COL1 == 0 & COL2 < 0.05 & COL3 < 0.05) ~ "P",
           # Condition when COL1 == 0 and COL2 >= 0.05 but COL3 < 0.05
           (COL1 == 0 & COL2 >= 0.05 & COL3 < 0.05) ~ "P",
           # Condition when COL1 == 0 and COL2 < 0.05 but COL3 >= 0.05
           (COL1 == 0 & COL2 < 0.05 & COL3 >= 0.05) ~ "P",
           (# Default condition: COL1 == 0 and COL2 >= 0.05 and COL3 >= 0.05)
           (TRUE) ~ "P"
         )
  )

# Display the updated dataframe
print(test)

Grouping by Groups and Names

Finally, we want to group the dataframe by Groups and Names, and then take the first value from the new column COL4. This ensures that each combination of group and name only gets one value for COL4.

# Use group_by() and mutate() again to get the final result
test <- test %>%
  group_by(Groups, Names) %>%
  mutate(COL4 = first(COL4))

# Display the final dataframe with grouped and aggregated values
print(test)

This concludes our example of adding a conditional column based on hierarchical conditions in R. By utilizing the case_when() function and careful consideration of the different conditions for each outcome, we can efficiently add new columns to dataframes while maintaining logical consistency.

Summary

• We created a sample dataframe with numerical values and categorical names.
• Using the dplyr library’s mutate() and case_when() functions, we added a new column (COL4) based on conditions related to other columns (COL1, COL2, and COL3).
• The resulting data was grouped by Groups and Names to ensure each combination got a unique value for the conditional column.

This method is useful when you need to transform data according to specific rules, such as creating new categories based on existing values. By following these steps, you can apply similar transformations to your own datasets with ease.

Last modified on 2023-05-14