Error handling and debugging

Sometimes rather than doing what you expect it to R will return an Error message to you via the Console prefaced with Error in… followed by text that will try to explain what went wrong. This, generally, means something has gone wrong, so what do you do?

Read it! THE MESSAGES ARE WRITTEN IN PLAIN ENGLISH (MOSTLY)
DO NOT continue running bits of code hoping the issue will go away. IT WILL NOT.
Try and work out what it means and fix it (some tips below).

Interpreting R errors is an invaluable skill! The error messages are designed to be clear and informative. They aim to tell might be going wrong in the code. These messages typically contain information about the type of error hinting at how you might fix it. For example, if there is a typo in a function or variable name, R will produce an error like "Error: object 'variable_name' not found." This suggests that R couldn’t find the specified object variable_name.

Understanding error messages involves paying attention to the details, such as the error type (e.g., syntax error, object not found, etc.), and the specific line number where the error occurred! You should always run your code line-by-line, especially if you are new to programming. This means that the execution of each line of code is done in in isolation, providing immediate feedback and pinpointing the exact location where an error occurs. If the meaning or solution to an error isn’t immediately obvious then make use of the documentation (RTFM) and even technology. Online platforms like Stack Overflow or RStudio Community and tools like ChatGPT can often be your friend! It is very unlikely that you are the first one to have encountered the error and some kind soul with have posted a solution online (which the AI bot will have scraped…). However, it’s crucial to approach online answers carefully. You need to first understand the context of your error and use your knowledge about your issue to figure out if the solution is applicable (this gets easier with experience).

It is always best to try debugging yourself before blindly following a stranger’s advice. Debugging is a crucial aspect of R programming and the process itself helps solidify your understanding. There are several widely used methods that help identify and resolve issues in your code, two are outlined below.

Print debugging involves actively printing out objects and asking the software to display variable values or check the flow of execution. For example, if you get this error message "Error: object 'variable_name' not found. I might ask R to print out what objects do exist in my workspace, it could be that a previous typo means that the object variable_nam exists.
Rubber duck debugging is a useful debugging strategy where you explain your code or problem out loud (as if explaining it to a rubber duck). Honestly it works! The process helps clarify your thoughts and identify issues even before seeking external help.

The table below summaries some common R issues and errors and their solutions.

Error/Issue	Description	Solution
Syntax Error	Incorrect use of `R` syntax, such as missing or mismatched parentheses, braces, or quotes.	Carefully review the code, check for missing or mismatched symbols, and ensure proper syntax is used.
Object Not Found	Attempting to use a variable or function that hasn’t been defined or loaded into the workspace.	Check for typos in variable or function names, ensure the object is created or loaded, and use correct names.
Package Not Installed/Loaded	Trying to use a function from a package that is not installed or loaded into the environment.	Install the required package using `install.packages("package_name")`, and load it using `library(package_name)`.
Undefined Function/Variable	Using a function or variable that hasn’t been defined or is out of scope.	Define the function or variable, or check its scope within the code.
Data Type Mismatch	Operating on data of incompatible types, such as performing arithmetic on character data.	Ensure that data types are compatible, and consider using functions like `as.numeric()` or `as.character()` for conversions.
Misuse of Assignment Operator	Using the wrong assignment operator (`=` instead of `<-` or vice versa).	Be consistent with the assignment operator, and use `<-` for assignment in most cases.
Missing Data	Dealing with missing values in the data, leading to errors in calculations or visualizations.	Handle missing data appropriately, using functions like `na.omit()` or `complete.cases()`.
Index Out of Bounds	Attempting to access an element in a vector or data frame using an index that is out of range.	Check the length of the vector or data frame and ensure the index is within bounds.
Failure to Load a File	Issues with loading a data file using functions like `read.csv()` or `read.table()`.	Check the file path, file format, and encoding. Confirm that the file exists in the specified location.
Incorrect Function Arguments	Providing incorrect arguments or parameters to a function, leading to errors.	Refer to the function’s documentation to understand the correct parameters and ensure proper usage.

Sometimes your computer will return a warning messages to you prefaced “Warning:”. These can sometimes be ignored as they may not affect us. However, READ THE MESSAGE and decide for yourself. Occasionally, also your computer will write you a friendly message, just keeping you up-to date with what it’s doing, again don’t ignore these they might be telling you something useful!

TASK Keep a bug diary! Each time you get an error message, see if you can solve it and keep a diary of your attempts and solution.