Conditional Cuts: A Step-by-Step Guide to Grouping and Age Ranges

Introduction

When working with datasets, it’s not uncommon to have multiple variables that share a common trait or characteristic. One such scenario is when we have data on age ranges from external sources like census data, which can be used to categorize our original dataset into groups based on those ranges.

In this article, we’ll delve into the specifics of how to achieve this task using R and the dplyr library. We’ll start by exploring how to create a list of breakpoints for each country and then walk through the steps required to convert these breakpoints into a format that can be used with our original dataset.

Understanding Breakpoints

A breakpoint is simply a value at which something changes or occurs. In this context, it’s the lower bound of each age range defined by the census data. For instance, if we’re looking at group A and its corresponding age ranges from 18 to 27, then these values represent our breakpoints.

Creating Breakpoints

To get started, let’s first create a list of breakpoints for each country using the sample() function in R.

# Sample countries and their respective age groups
age <- sample(18:50, 100, replace = TRUE)
group <- sample(c("group A", "group B", "group C"), 100, replace = TRUE)

# Create a list of breakpoints for each group
cutpoints <- list(
  group A = c(18, 27, 36, 45),
  group B = c(15, 24, 50),
  group C = c(30, 40, 50, 60, 70)
)

The Challenge: Error Message

We know that we’ve created our breakpoints and defined them in a list format. However, when we try to use the cut() function with these breakpoints, we get an error message that indicates the object cannot be coerced into the required type.

# The error message
Error in sort.int(as.double(breaks)) : (list) object cannot be coerced to type 'double'

Finding a Solution: Converting Breakpoints to DataFrames

The issue here lies in how we’re presenting our breakpoints to R. Since they are represented as a list, the cut() function can’t directly handle them.

To get around this limitation, we need to convert our breakpoints into dataframes and then use these dataframes to define our cuts.

# Convert breakpoints into dataframes
breakpoints_df <- tibble::tibble(
  group = names(cutpoints),
  value = sapply(cutpoints, function(x) c(-Inf, sort(na.omit(x)), Inf))
)

# Join the dataframes with our original dataset
df <- df %>%
  left_join(breakpoints_df, by = "group")

Creating Separate Groups Based on Ranges

Now that we have our breakpoints defined in a dataframe format, we can use them to create separate groups based on age ranges.

# Use cut() with the breakpoints dataframe
df <- df %>%
  group_by(group) %>%
  mutate(grp_int = cut(age, c(-Inf, sort(na.omit(first(value))), Inf)))

Using Grouping Variables for Further Analysis

We can use both our original grouping variable (group) and the newly created grp_int to calculate further information about each age range within a group.

# Select only the desired columns
df <- df %>%
  select(group, grp_int)

# Now we can perform analysis on these groups

Conclusion

By converting our breakpoints into dataframes and then using them with the cut() function, we’ve successfully grouped our dataset based on age ranges defined by external sources.

This process not only resolves common issues related to error messages but also opens up new avenues for further analysis within each group. Whether you’re comparing the age breakdowns of your original dataset with those from another source or performing more in-depth analyses, these steps will provide a solid foundation for your next project.

Last modified on 2024-09-02