Comparing Groups with Different Lengths in a Tibble

As data analysts, we often encounter situations where we need to compare groups with varying lengths. In this article, we will explore how to perform such comparisons using R and the tibble library.

Introduction

The tibble library provides a convenient way to store and manipulate data in R. However, when dealing with groups of different lengths, traditional methods can become cumbersome. In this article, we will discuss a promising solution for comparing groups with different lengths in a tibble.

The Problem

Let’s consider an example where we have a tibble myData containing daily resolution data for several years. We want to compare the means of Amp (amplitude) for each drought period with the mean of Amp for the normal periods before and after the drought. However, the length of days varies between groups.

# Create a simplified example of my data
myData <- tibble(
  day = c(1:16),
  TWD = c(0,0,0,0.444,0.234,0.653,0,0,0.789,0.734,0.543,0.843,0,0,0,0),
  Amp = c(0.6644333,0.4990167,0.3846500,0.5285000,0.4525833,0.4143667,0.3193333,0.5690167,0.2614667,0.2646333,0.7775167,3.5411667,0.4515333,2.3781333,2.4140667,2.6979333)
)

# Identify drought periods
myData <- myData %>% mutate(status = case_when(TWD > 0 ~ "drought", TWD == 0 ~ "normal")) %>%

  # Create a new column for group indices
  mutate(group = rep(seq_along(z - rle(myData$status)$lengths), z))

# Print the first few rows of myData
print(head(myData))

Solution

To compare groups with different lengths, we can use the pairwise Wilcoxon test. This non-parametric test is suitable for comparing two independent samples and can handle groups of varying sizes.

# Perform pairwise Wilcoxon tests for Amp across all drought groups
pairwise.wilcox.test(myData$Amp, myData$group, p.adjust.method = 'none', alternative = 'greater')

In this code:

• We use myData$Amp as the data variable to compare.
• myData$group is used as the grouping variable. Since we have groups of different lengths, we need to specify an appropriate method for handling ties and differences in group sizes.

The pairwise Wilcoxon test returns a table showing the results of each pairwise comparison between groups. In this instance, you know that the even-numbered groups are the drought periods, so we use myData$group with its original indices to correctly compare these groups.

Correcting for Multiple Comparisons

When performing multiple comparisons, it’s essential to adjust for the increased type I error rate. The p.adjust.method parameter in the pairwise.wilcox.test() function allows us to correct for this issue.

In our example, we set p.adjust.method = 'none', which means that no adjustment is made. This may not be suitable for all situations and should be investigated further if necessary.

To adjust for multiple comparisons, you can use the Bonferroni or Holm-Bonferroni methods, depending on your desired level of significance.

# Perform pairwise Wilcoxon tests with Bonferroni adjustment
pairwise.wilcox.test(myData$Amp, myData$group, p.adjust.method = 'bonferroni', alternative = 'greater')

In this example, we use the Bonferroni method to adjust for multiple comparisons.

Conclusion

Comparing groups with different lengths in a tibble can be challenging. The pairwise Wilcoxon test provides an effective solution for comparing means across these groups. By correctly adjusting for multiple comparisons and considering your data’s distribution, you can make informed decisions about your results.

In the future, consider using more advanced methods to analyze your data, such as using mixed effects models or generalized linear mixed models (GLMMs), which can provide more powerful and flexible analysis options.

For now, remember that comparing groups with different lengths in a tibble requires careful consideration of multiple comparisons.

Last modified on 2023-10-06