[R] Levels of the datasets

In R, factors are used to represent categorical variables, and levels are the distinct categories within a factor. To manipulate the levels of a factor variable in a dataset, you can use various functions and techniques. Here are some common ways to work with factor levels:

Viewing Levels: Use the levels() function to see the levels of a factor variable.

levels(dataset$factor_variable)

Changing Level Names: You can directly assign new level names using the levels() function.

levels(dataset$factor_variable) <- c("new_level1", "new_level2", "new_level3")

 Reordering Levels: Use the factor() function with the levels argument to reorder the levels.

dataset$factor_variable <- factor(dataset$factor_variable, levels = c("level2", "level1", "level3"))

        What will happen if I use factor but some of the name of the levels are different from the original ones? 

If you use the factor() function and specify levels that are different from the original ones, R will match the specified levels to the existing data based on their order. Any data that doesn't match the specified levels will be converted to NA (missing values). Here's an example to illustrate this:

# Original factor variable
original_factor <- factor(c("apple", "banana", "orange", "banana", "apple"))

# Converting to factor with different levels
new_factor <- factor(original_factor, levels = c("banana", "apple", "grape"))

# Result
new_factor
# [1] <NA>    banana apple  banana <NA>   
# Levels: banana apple grape

In this example, the original factor had levels "apple", "banana", and "orange". When converting it to a new factor with levels "banana", "apple", and "grape", the following happens:

• "banana" and "apple" are matched to their corresponding levels in the new factor.
• "orange" does not have a corresponding level in the new factor, so it is converted to NA.
• "grape" is a new level in the specified levels, but there is no matching data in the original factor, so it remains unused.
• So it's important to be careful when specifying levels to ensure that they match the data you have, or you may end up with unexpected NA values in your factor variable.

Adding Levels: To add a new level to a factor, you can use the levels() function and concatenate the new level.

levels(dataset$factor_variable) <- c(levels(dataset$factor_variable), "new_level")

Adding a new level to a factor variable in R can be useful in several scenarios:

1. Preparing for New Data: If you know that your dataset will be updated with new categories in the future, you can add these levels in advance to ensure consistency in your analyses. This way, when the new data arrives, the factor variable will already have the necessary levels defined.

2. Consolidating Datasets: When merging or combining datasets with similar categorical variables, you might need to add levels to ensure that the factor variable encompasses all possible categories from both datasets.

3. Setting a Fixed Set of Categories: In some analyses, you might want to define a fixed set of categories for a factor variable, even if some of the categories are not present in the current data. This can be useful for standardizing categories across different analyses or datasets.

4. Creating Dummy Variables: When creating dummy variables for regression analysis, you might add a level to represent a baseline or reference category.

# Original factor variable
colors <- factor(c("red", "blue", "green"))

# Adding a new level "yellow"
levels(colors) <- c(levels(colors), "yellow")

# Updated factor variable
colors
# [1] red   blue  green
# Levels: red blue green yellow

 

Dropping Levels: Use the droplevels() function to remove unused levels from a factor.

dataset$factor_variable <- droplevels(dataset$factor_variable)

Recoding Levels: The recode() function from the dplyr package or the fct_recode() function from the forcats package can be used to recode levels.

dataset$factor_variable <- recode(dataset$factor_variable, "old_level1" = "new_level1", "old_level2" = "new_level2")

Combining Levels: You can combine levels by recoding them to the same new level.

dataset$factor_variable <- recode(dataset$factor_variable, "level1" = "combined_level", "level2" = "combined_level")