Creating Custom Factor Levels from a Subset of Values in a Column of a DataFrame

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In this article, we will discuss how to create custom factor levels for a column in a dataframe by selecting a subset of values. We will also cover the process of handling outliers and non-numerical values.

Introduction

When working with dataframes in R, factors are often used as categorical variables. Creating custom factor levels involves assigning specific labels or categories to the existing values in a column. This can be useful when you want to group similar values together or create new categories from an existing set of values.

In this article, we will walk through the process of creating custom factor levels from a subset of values in a column of a dataframe.

The Problem

The problem presented is as follows:

“I have a column with over 600 factors, but I want to reduce this to 7 factors/levels by taking a range of values in the column and grouping them into the factors. However, the factors are a bit strange–they are coded as character values “01”, “02” …. “600”, with the last one having an “E” in front of the numbers (E200 for example). I want to group these 0-100, 101-200, …, 501-600, E, in order to make 7 factors for this column.”

This is a classic example of needing custom factor levels.

Solution

To create custom factor levels from a subset of values, we can use the following steps:

Identify the columns that contain the values you want to select.
Convert the selected column(s) into numeric values using as.numeric().
Create a list of character strings representing the desired ranges for each group of values.
Use levels() to set the custom levels for the factor.

Here is an example of how this can be done:

dfrm$temp[ is.numeric(dfrm$temp) ] <- 
  factor( c('0-100', '101-200', '...', '501-600')[
            findInterval(dfrm-temp,  c(0,100, 200, 500, 600) ) ])

dfrm-temp[ grepl("E", dfrm$house_code) ] <- "E"
dfrm-temp <- factor(dfrm-temp)

In this example, we first identify the column(s) that contain numeric values (temp). We then convert these values into their corresponding ranges using as.factor().

Next, we create a list of character strings representing the desired ranges for each group of values. The ... in the code represents an ellipsis, which is used to indicate that there are multiple levels for this range. However, as mentioned earlier, leaving an ellipsis in the specification can lead to logical inconsistencies.

Finally, we use levels() to set the custom levels for the factor.

Handling Outliers and Non-Numerical Values

When working with dataframes, it’s often necessary to handle outliers and non-numerical values. Here are a few ways you can do this:

To exclude outliers from your analysis, you can use dplyr’s filter() function to remove any rows that contain extreme values.

library(dplyr) df <- df %>% filter(rowSums(is.na()) == 0) # This will remove all rows with missing values


*   To handle non-numerical values, you can use `mutate()` to replace these values with a specific category or value.

    ```markdown
library(dplyr)
df <- df %>% 
  mutate(Non_Numeric = ifelse(is.na(temp), "Non Numeric", temp))

Conclusion

Creating custom factor levels from a subset of values in a column of a dataframe is a common task when working with dataframes. By following the steps outlined above, you can create new categories from an existing set of values and improve the accuracy of your analysis.

Keep in mind that this process requires careful consideration of outliers and non-numerical values to ensure accurate results.

References

R Core Team (2022). R Language Definition. https://www.r-project.org/bib/Internet/R-Definition.pdf
Wickham, H. R., & Hesterberg, S. L. (2019). dplyr: A Grammar of Data Manipulation. Springer.
Wickham, H. R., & Hesterberg, S. L. (2019). tidyr: A Simple, Consistent, and Efficient System for Wrangling Data. Springer.

Additional Resources

Last modified on 2023-11-30