Question
Answer and Explanation
Using a case statement with decimals in Tidyverse, specifically with dplyr::case_when(), requires careful handling due to the nature of floating-point numbers. Direct equality comparisons (e.g., x == 1.5) can be unreliable because of how decimals are stored in memory. Instead, you should use a range or a tolerance-based approach.
Here's how you can effectively use case_when() with decimals:
1. Using Ranges:
- Define ranges for your decimal values. This is the most robust method for handling decimals in case_when().
- Example:
library(dplyr)
data <- data.frame(values = c(1.2, 1.5, 1.8, 2.1, 2.5))
data_modified <- data %>%
mutate(
category = case_when(
values >= 1.0 & values < 1.5 ~ "Low",
values >= 1.5 & values < 2.0 ~ "Medium",
values >= 2.0 & values < 2.5 ~ "High",
TRUE ~ "Other"
)
)
print(data_modified)
- In this example, we categorize values based on ranges, which avoids direct equality comparisons.
2. Using a Tolerance:
- If you need to check for values "close to" a specific decimal, use a tolerance value.
- Example:
library(dplyr)
data <- data.frame(values = c(1.499, 1.5, 1.501, 2.0, 2.5))
tolerance <- 0.01
data_modified <- data %>%
mutate(
category = case_when(
abs(values - 1.5) <= tolerance ~ "Around 1.5",
abs(values - 2.0) <= tolerance ~ "Around 2.0",
TRUE ~ "Other"
)
)
print(data_modified)
- Here, we check if a value is within a certain tolerance of 1.5 or 2.0.
3. Avoid Direct Equality:
- Avoid using direct equality (==) with decimals in case_when(). It can lead to unexpected results due to floating-point representation issues.
4. Consider Rounding:
- If your decimals have a specific precision, you can round them before using case_when(). This can simplify your conditions.
- Example:
library(dplyr)
data <- data.frame(values = c(1.234, 1.567, 1.890, 2.123, 2.567))
data_modified <- data %>%
mutate(
rounded_values = round(values, 1),
category = case_when(
rounded_values == 1.2 ~ "1.2",
rounded_values == 1.6 ~ "1.6",
rounded_values == 1.9 ~ "1.9",
TRUE ~ "Other"
)
)
print(data_modified)
- In this case, we round the values to one decimal place before using case_when().
By using ranges, tolerances, or rounding, you can effectively use case_when() with decimal values in Tidyverse, avoiding common pitfalls associated with floating-point comparisons.