6  Understanding Error Messages in R - Part 3

6.1 Example of a Novel Error Message

In this chapter we will be taking a crack at a brand-new error message produced by routine code when trying to determine the number of cases in each district of Eastern Province in 2021. This time, there will not be an answer provided in this document. Please use the steps from the last two chapters to figure out what went wrong and how to fix it.

#load packages
library(tidyverse)
library(lubridate)

case_data <- readRDS("data/district-cases-long-scrambled.rds")

#generate summary
case_data %>%
  filter(province == "Eastern") %>%
  filter(between(period, as.Date("2021-01-01"), as.Date("2021-12-01"))) %>%
  group_by(district) %>%
  sum(count, na.rm = TRUE)

Error in FUN(X[[i]], ...): only defined on a data frame with all numeric-alike variables

What went wrong? Use the error message above to determine the source of the error, and follow the steps from the previous lesson to fix it. Tips and tricks from previous lessons below.

6.2 Overall Process for Dealing with Unfamiliar Error Messages in R

This is a quick rundown of my recommended “process” for dealing with error messages in R. The first four steps are a miniature version of the lessons we have already covered in this unit. Steps 5 through 7 are more broad strategies, with commentary from Y. Wendy Huynh’s R for Graduate Students that I find to be particularly helpful.

1. First, I usually like to look for misspellings or improper calls to the wrong data objects.

   • This tends to account for well over 90% of the errors in my own code (Because I copy and paste a lot from one code section to another, I am particularly vulnerable to hanging parentheses, + signs, and dplyr %>% pipes)

2. Second, I look at the dataset itself to see if I can find any clues as to what went wrong in the dataset structure.

   • Has a column gotten filled with NA values somehow?

   • Did a variable get misspelt or removed by mistake?

     • Try using the table() command

   • Is the variable type incompatible with my code?

     • Try using the class() command

3. Third, I will run the code line by line until I find exactly where the error occurs.

   • This is particularly useful when using dplyr pipes %>% over multiple lines of code

4. Fourth, I will google the problem.

   • Use the formula: “key word” “error message” “R”; and look for stack overflow responses that match your situation to some degree.

   • No answer is likely to give you exactly the solution to your situation, find the piece that is useful to you.

5. Fifth, I will restart R.

   • “In rare (but real) circumstances, I have found that RStudio can run into some issues that have nothing to do with your code. If your code is running strangely, the age-old trick of turning it off and on again (i.e., RStudio or your computer) occasionally does work. Make sure you save the information you need to save before you close RStudio or restart your computer. Upon reopening RStudio, make sure the Global Environment is clean before attempting to execute code.” (Huynh, 2019)

6. Sixth, I will take a break.

   • “Another option is to take a break. During that period, little elves come and fix your computer for you. If you don’t leave, they’ll never show up. In all seriousness, error messages can be anger-inducing and in your cloud of frustration, you may not be able to think clearly. Step away for a bit and look at it from a different emotional state.” (Huynh, 2019)

7. Seventh, I will ask someone for help.

   • “Finally, ask someone for help. An experienced R user should be able to figure out what went wrong pretty quickly. This is because experts have made (and hopefully fixed) 100X more errors than beginners; many people struggle with the same problems at the beginning. Problem solving is (in my opinion) the best way to learn. However, it’s also the most time consuming. Ask for help when you need it. Time is an invaluable, nonrenewable resource.” (Huynh, 2019)