Creating a Minimal Reproducible Example (MRE) is essential for effective troubleshooting and collaboration in R programming.

An MRE in R includes:

• The minimal amount of R code necessary to replicate an issue.
• Required data structures or inputs. Clear description of expected vs. actual outcomes.

• Efficiency: Quickly conveys the issue to others.
• Clarity: Reduces noise, focusing on the problem. Community Support: Essential for getting help on forums like Stack Overflow.

1. Isolate the Issue: Remove unrelated R code.
2. Simplify Your Code: Minimize the code while still reproducing the problem.
3. Include Data and Context: Use dput() for data and specify package versions if relevant.
4. Test Your MRE: Ensure it independently reproduces the issue.

Original Complex Script

# A complex script with multiple operations leading to an
unexpected output
library(dplyr)

# Lots of data manipulation here...

library(dplyr)

# Simplified dataset
data <- data.frame(x = 1:5, y = c("a", "b", "c", "d", "e"))

# Simplified operation causing the unexpected outcome
result <- data %>% mutate(z = x * 2)
print(result)

• Includes only the essentials: library, data, and problematic code.

library(ggplot2)

# Plotting without specifying a necessary aesthetic
ggplot(mtcars, aes(x = wt, y = mpg)) + geom_line()

Answer 1

# Attempting to merge two data frames with a common key
a <- data.frame(id = 1:3, value = c("A", "B", "C"))
b <- data.frame(id = 2:4, value2 = c("X", "Y", "Z"))

merged <- merge(a, b, by = "id")
print(merged)

Answer 2

# Function to calculate mean; missing na.rm = TRUE
calculate_mean <- function(x) mean(x)
nums <- c(1, 2, 3, NA)

result <- calculate_mean(nums)
print(result)

Answer 3

• Use dput() for data to ensure reproducibility.
• Clearly state the problem, including any error messages.
• Include library versions if they are relevant to the issue.