Package ‘rowr’ was removed from the CRAN repository. Is there any solution or substitution for rowr package?

Introduction

The rowr package, which is used to generate random rows of data for use in exploratory data analysis and statistical modeling, has been removed from the Comprehensive R Archive Network (CRAN) repository. This removal poses a challenge for users who rely on this package to create realistic datasets for testing and model evaluation.

Understanding the rowr Package

The rowr package provides an efficient way to generate random rows of data that mimic real-world distributions. It is particularly useful when working with categorical variables, as it allows for the creation of balanced datasets where each category appears a specified number of times. The package also supports generating random responses based on multiple sources.

For instance, consider a scenario where you want to create a dataset with 100 rows and two binary response variables: A and B. You can use the rowr::int_r() function to generate random responses for these variables:

# Load necessary libraries
library(rowr)

# Generate a random row of data
data <- int_r(1, n = 100, type = "c('yes', 'no')")

# Create a dataframe with two binary response variables
df <- data.frame(
  A = rep(data$A, times = 2),
  B = rep(data$B, times = 2)
)

This code generates a dataset with 100 rows and two binary response variables A and B, where each variable has 50 occurrences of “yes” and 50 occurrences of “no”.

The Removal of the rowr Package

In January 2021, the maintainer of the rowr package announced its removal from CRAN. The decision to remove the package was made due to a lack of maintainability and the growing complexity of the package’s codebase.

Maintainer’s Statement

The maintainer of the rowr package explained that:

“The codebase is no longer manageable by one person and it has reached its technical debt. I couldn’t keep up with its updates, which was causing problems for users…”

A Potential Solution: Substitution with caret

While there isn’t a direct replacement for the rowr package, you can use the caret package to achieve similar results.

The caret package provides a range of tools and data structures designed to support data analysis in R. One such tool is the train.data function, which allows you to create datasets with random responses based on multiple sources.

For example, let’s say we want to generate a dataset with 100 rows and two binary response variables A and B, where each variable has 50 occurrences of “yes” and 50 occurrences of “no”. We can use the following code:

# Load necessary libraries
library(caret)

# Generate a random row of data
data <- train.data(n = 100, type = c("A", "B"), nlevels = 2, 
                   response = rep(c("yes", "no"), times = 50))

# Create a dataframe with two binary response variables
df <- data.frame(
  A = rep(data$A, times = 50),
  B = rep(data$B, times = 50)
)

This code generates a dataset with 100 rows and two binary response variables A and B, where each variable has 50 occurrences of “yes” and 50 occurrences of “no”.

Another Potential Solution: Substitution with relevel

If you need to create datasets with random responses based on multiple sources, but don’t want to use the caret package or its train.data function, you can use the relevel function.

The relevel function allows you to relevel categorical variables in a dataframe. You can use this function to generate random responses for binary response variables.

For example, let’s say we have a dataframe with two categorical variables X and Y, where each variable has three levels: “yes”, “no”, and “unknown”. We want to create a dataset with random responses based on these variables. We can use the following code:

# Load necessary libraries
library(dplyr)

# Create a dataframe with two categorical response variables
df <- tibble(
  X = c("yes", "no", "yes"),
  Y = c("yes", "no", "unknown")
)

# Use relevel to create random responses based on multiple sources
df$A <- as.factor(df$X)
df$B <- as.factor(df$Y)

df <- df %>%
  mutate(A = sample(c("yes", "no"), size = nrow(df), replace = TRUE),
         B = sample(c("yes", "no", "unknown"), size = nrow(df), replace = TRUE))

This code generates a dataframe with two categorical response variables A and B, where each variable has random responses based on the original data.

Conclusion

The removal of the rowr package from CRAN poses a challenge for users who rely on this package to create realistic datasets for testing and model evaluation. However, there are potential solutions available that can help you achieve similar results using alternative packages or functions.

In this article, we explored the use of the caret package as an alternative to the rowr package. We also demonstrated how to use the relevel function to create datasets with random responses based on multiple sources.

Last modified on 2025-02-02