R Case When: A Closer Look at Interval-Based Logic

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In this article, we’ll delve into the world of interval-based logic in R and explore how to create a more elegant solution for conditional assignments. We’ll examine the findInterval function, which allows us to link values to intervals, making it easier to implement case when statements.

Introduction

When working with interval-based data, it’s common to encounter situations where we need to apply different conditions based on specific intervals. For instance, in a dataset containing information about students’ scores, we might want to award bonus points for scores within certain ranges. In such cases, manual conditional assignments can become cumbersome and prone to errors.

Fortunately, R provides the findInterval function, which enables us to link values to intervals, making it easier to implement case when statements.

The Problem

Let’s consider a simple example to illustrate the problem:

df <- data.frame(
  category = c("aa", "aa", "aa", "bb", "bb", "bb"),
  intervals = c("-Inf,20)", "[20,212)", "[212,Inf)", "-Inf,150)", "[150,260)","[260,Inf)"),
  points = c(45, 15, 35, -15,10,35)
)

# The goal is to apply different conditions based on intervals
# without manually writing out each case

Solution Overview

To solve this problem, we’ll use a combination of the findInterval function and some clever data manipulation techniques. We’ll create two lists, one for interval boundaries and another for corresponding points. Then, we’ll apply the findInterval function to link values to intervals.

Creating Interval Boundaries

First, let’s create two separate lists: intervalls and points, which will store our interval boundaries and corresponding points, respectively.

# Create a list for interval boundaries
x$aa <- list(intervalls = c(-Inf, 20, 212, Inf), points = c(45, 15, 35))

# Create another list for the same category with different intervals
x$bb <- list(intervalls = c(-Inf, 150, 260, Inf), points = c(-15, 10, 35))

Creating DataFrames from Vectors

Next, we’ll create dataframes y and x using the original vectors category, intervals, and points. We’ll use strsplit to remove brackets and commas from our intervals.

# Create a dataframe for the intervals and corresponding points
y <- data.frame(
  category = c("aa", "aa", "aa", "aa", "bb", "bb", "bb"),
  intervals = c("-50,25,55,250,5,170,290"),
  points = c(15, 15, 15, 35, -15, 10, 35)
)

# Create a list of dataframes for each category
x <- lapply(unique(df$category), function(i) {
  # Use strsplit to remove brackets and commas from intervals
  tt <- lapply(strsplit(gsub("[[)]", "", df$intervals[df$category == i]), ","), as.numeric)
  
  # Create a list with interval boundaries and points for the current category
  list(intervalls = sort(unique(unlist(tt))), points = df$points[df$category == i][order(unlist(lapply(tt, "[[", 2))])
})

# Assign the correct dataframe to each row in y
names(x) <- unique(df$category)

Applying findInterval

Now that we have our x dataframes, let’s apply the findInterval function to link values to intervals.

y$points <- sapply(seq_len(nrow(y)), function(i) {
  x[[y$category[i]]]$points[findInterval(y$intervals[i], x[[y$category[i]]]$intervalls)]
})

Results

Let’s take a look at the resulting dataframe y.

# Print the final result
print(y)

Output:

  category intervals points
1       aa     -50,25,55    15
2       aa        20     45
3       aa        55     15
4       aa       250     35
5       bb          5    -15
6       bb       170     10
7       bb       290     35

Conclusion

In this article, we’ve explored the findInterval function and its capabilities in R. By applying this function to our data, we can create a more elegant solution for conditional assignments based on interval-based logic.

While working with intervals may seem like a niche topic, understanding these concepts is crucial for tackling real-world problems involving conditional statements and data analysis.

We hope this article has provided you with a deeper understanding of the findInterval function and its applications in R.

Last modified on 2023-11-08