Appendix C — Errors and Debugging

Even experienced programmers encounter errors daily. This chapter will help you understand common error types in R and develop systematic debugging strategies for data journalism projects.

C.1 Understanding R Messages

R communicates through three types of feedback that appear in your console:

Message Types at a Glance

Type	Icon	Meaning	Example
Message	ℹ	Informational note	`library()` loading messages
Warning	⚠️	Potential issue (code still runs)	`NA` in calculations
Error	✖	Critical problem (code stops)	Missing function arguments

1. Messages

These blue notes provide non-critical information. Example:

message("This is an FYI – check your data source if unexpected")

2. Warnings

Yellow alerts indicate potential issues. Your code runs, but results might be suspect:

c(1, 2, NA) |> mean(na.rm = FALSE)
# Warning: NA detected – did you want na.rm = TRUE?

3. Errors

Red stops mean critical failures. Code execution halts immediately:

read_csv("nonexistent_file.csv")
# Error: 'nonexistent_file.csv' does not exist

C.2 Common Errors & Solutions

1. Object Not Found (`object 'x' not found`)

What Happened?
R can’t locate a variable or dataset you referenced.

Check: - Spelling mistakes: sumarry() vs summary() - Scope issues: Created in different environment/chunk? - Execution order: Did you run the creation code?

# Common mistake
population_density <- pop / area
print(population_dentsy)  # Typo in variable name

2. Missing Functions (`could not find function`)

What Happened?
R doesn’t recognize the function name.

Fix: 1. Check spelling: str_detect() vs str_detect 2. Load required package: r library(stringr) # For string manipulation functions 3. Install missing package: r install.packages("janitor") # First-time installation

3. Syntax Issues (`unexpected symbol`)

What Happened?
Invalid characters or structure in your code.

Common Culprits: - Reserved words as variables: if <- 5 - Special characters: data$2023 (use backticks: data$`2023`) - Missing operators: sqrt(16 # Missing closing parenthesis

Prohibited Names

Avoid these reserved words for variables: if, else, function, for, while, repeat, break, next

4. Incomplete Code (`unexpected end of input`)

What Happened?
R reached the end of your code while expecting more.

Check For: - Unclosed parentheses: mean(c(1, 5, 10) - Missing commas: tribble(~Name, ~Age ("Alice", 25)) - Unfinished pipes: r mtcars |> filter(mpg > 20) |> select(mpg, cyl # Missing closing )

C.3 Debugging Workflow

Follow this systematic approach when encountering errors:

Debugging Checklist

Read the error message carefully
Isolate the problem (reproduce in new chunk)
Check variable states with str() or glimpse()
Test components individually
Search error message (Google/R community forums)
Ask peers or AI with reproducible example

Helpful Techniques

1. Documented Help

?filter        # Base R help
??"regex"      # Search documentation
vignette("dplyr")  # Package overview

2. Contextual Checks

mtcars |> 
  filter(mpg > 20) |>  # Error here?
  select(mpg, cyl)

# Test filter separately
mtcars |> filter(mpg > 20)  # Reveals missing package?

3. AI Prompt Template
When seeking help, provide:

1. Your goal
2. Error message
3. Relevant code snippet
4. Sample data (use `dput(head(your_data))`)
5. What you've tried

C.4 Prevention Strategies

Write Modular Code
Break scripts into small, testable chunks
Use Consistent Style
Follow tidyverse style guide
Implement Version Control
For big projects, use Git to track changes and revert mistakes

Remember: Debugging is a normal part of programming. Each error resolved makes you a better analyst. In data journalism, thorough debugging ensures the accuracy of your stories and maintains public trust in your work.

Key Takeaways

Always read error messages completely
Reproduce errors in minimal examples
Use systematic elimination to find causes
Document solutions for future reference