The Mysterious Case of dplyr’s Summarise Function
Introduction
As a data analyst and technical blogger, I have encountered numerous issues while working with the popular R package dplyr. In this article, we will delve into one such conundrum involving the summarise function. Our goal is to understand why dplyr fails to summarize in certain scenarios.
Background
The dplyr package provides a flexible and efficient way to manipulate and analyze data in R. One of its most powerful functions is summarise, which allows us to perform various aggregation operations, such as calculating means, medians, or sums. In this article, we will explore why dplyr fails to summarize certain columns of our data.
The Problem
The provided code snippet showcases an issue with the summarise function in dplyr. We start by reading a CSV file into R using read.csv(), then perform various operations on the data, including grouping and summarizing. However, when we try to summarize columns using mk.test(ts(prcpmm))$pvalue[1] and mk.test(ts(prcpmm))$Sg[1], dplyr throws an error.
Understanding the Error
The error message indicates that the column pvalMK is of unsupported type NULL. To comprehend this, we must delve deeper into the workings of the mk.test() function and its output. The mk.test() function performs a Mann-Kendall trend test, which returns several objects:
$data.name: The name of the time series.$p.value: The p-value associated with the test.$statistic: The value of the statistic used in the test.$null.value: The null hypothesis value for the test.$parameter: The parameter used in the test (in this case, 13).$estimates: A named numeric vector containing estimates from the test.$alternative: The alternative hypothesis for the test (“two.sided” in this case).$method: The method used in the test (“Mann-Kendall trend test” in this case).$pvalg: The p-value of the test (the same as$p.value).
The Issue
The crucial point here is that mk.test(ts(prcpmm))$pvalue[1] does not exist. What exists is mk.test(ts(prcpmm))$p.value[1], which refers to the first element of the p-value vector.
str(mk.test(ts(Adrian$prcpmm)))
The Solution
To resolve this issue, we need to understand how to access the relevant columns of the mk.test() output. We can do this by changing our code to:
Observed_everyseason_pVal <- Adrian %>% group_by(yearNew, season) %>%
summarise(pvalMK = mk.test(ts(prcpmm))$p.value[1], SMK =
mk.test(ts(prcpmm))$Sg[1])
This modification ensures that we are accessing the correct columns of the mk.test() output.
Additional Considerations
Another important aspect to consider is the order of operations. In our original code, we first performed a group_by operation on yearNew and season, but then we tried to summarize pvalMK without completing the grouping process. By changing the order of operations, we can avoid this issue.
Observed_everyseason_pVal <- Adrian %>%
summarise(pvalMK = mk.test(ts(prcpmm))$p.value[1], SMK =
mk.test(ts(prcpmm))$Sg[1]) %>% group_by(yearNew, season)
Best Practices
To avoid similar issues in the future, it is essential to thoroughly understand how to access and manipulate objects returned by statistical functions. This includes being aware of the order of operations, data types, and possible null values.
In addition, always check the output of str() or summary() functions when working with statistical outputs to ensure that you are accessing the correct columns.
Conclusion
dplyr’s summarise function is a powerful tool for performing aggregation operations on data. However, it can be finicky and require careful attention to detail. By understanding how to access and manipulate the output of statistical functions, we can avoid common pitfalls and write more efficient code.
In conclusion, this article has provided an in-depth exploration of why dplyr fails to summarize certain columns. We hope that the information presented here has been informative and helpful. If you have any questions or comments, please feel free to reach out to us.
Last modified on 2024-04-10