Introduction to Sampling with “At Least One” Condition

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When working with datasets and performing statistical analysis or data visualization tasks, it’s common to require a random subset of rows that meet specific conditions. In this article, we’ll explore how to achieve such a task using the dplyr package in R, specifically focusing on creating a random sample with at least one representation from each available value in column ‘c’.

Background: Data Sampling and Grouping

Before diving into the solution, let’s briefly discuss the concepts of data sampling and grouping.

Data sampling is a crucial step in statistical analysis, allowing researchers to reduce the size of their dataset while still maintaining representative properties. Random sampling is one such method, where every element in the population has an equal chance of being selected for the sample.

Grouping, on the other hand, involves dividing data into subsets based on certain criteria or variables. In this context, we’ll be using grouping to create separate groups within our dataset, allowing us to apply sampling techniques that ensure at least one representative from each group is included in the final sample.

The Problem Statement

Given a dataset with varying values in column ‘c’, we want to generate a random subset of rows such that:

• Each value in column ‘c’ has at least one representation.
• The number of rows in the resulting sample is not fixed but can be adjusted using the sample_n function.

Solution: Using group_by and sample_n

One effective approach to solving this problem involves utilizing the dplyr package’s group_by function, which allows us to group our data by values in column ‘c’. We then apply the sample_n function from the same package, specifying a sample size that ensures at least one row is included for each unique value in column ‘c’.

Here’s an example code snippet demonstrating this approach:

# Load required libraries
library(dplyr)

# Create a sample dataset (simplified for demonstration purposes)
text1 <- "a   b   c
23  34  Falcons
14  9   Hawks
2   18  Eagles
3   21  Eagles
22  8   Falcons
11  4   Hawks"

dat <- read.table(text = text1, head = T, as.is = T)

# Group by column 'c' and sample one row for each group
dat %>%
  group_by(c) %>%
  sample_n(1)

This code generates a random subset of rows where each value in column ‘c’ has at least one representation. The group_by function groups our data into separate sets based on the values in column ‘c’, and sample_n(1) randomly selects one row from each group.

Extending the Solution: Creating a Function

To make this process more flexible, we can create a custom function that takes our dataset as input and returns a sample with at least one representation for each value in column ‘c’. Here’s an updated example:

# Define a function to generate a random sample
sample_df <- function(df) {
  # Find the unique values in column 'c'
  c_values <- unique(df$c)
  
  # Initialize an empty list to store our samples
  samples <- list()
  
  # Iterate over each value in column 'c'
  for (i in seq_along(c_values)) {
    # Sample one row for this group
    sample_row <- sample_n(df, 1)
    
    # Add the sample to our list
    samples[[paste0("Group", i)]] <- sample_row
  }
  
  # Return the list of samples
  return(samples)
}

# Apply the function to our dataset
dat %>%
  group_by(c) %>%
  do(sample_df(.))

This updated code defines a custom function called sample_df, which takes our dataset as input and generates a sample with at least one representation for each value in column ‘c’. We then apply this function to our dataset using the group_by and do functions from the dplyr package.

Conclusion

In this article, we explored how to create a random subset of rows that takes at least one of each available value in column ‘c’ using the dplyr package in R. We discussed data sampling and grouping concepts before diving into the solution, which involves utilizing the group_by function and sample_n to generate an appropriate sample.

Finally, we extended our solution by creating a custom function that takes our dataset as input and returns a sample with at least one representation for each value in column ‘c’. This flexible approach can be adapted to suit various data analysis needs.

Last modified on 2025-04-05