202502202 - troubleshooting guide

Author: jncaskey

R.Rmd

@@ -316,8 +316,42 @@ However, do make sure to save your `R` scripts before exiting Rstudio.
 To troubleshoot `R` issues, be resourceful.
 Try googling the error message or issue you are experiencing.
 See [here](#questions) for a list of places you can pose `R`-related questions for help.
-
-In addition, particular errors/warnings/issues are below:
+In addition, particular errors/warnings/issues are included below:
+
+## General Troubleshooting Tips
+
+1. **Run code line-by-line to identify the source of the error**
+   - If you are using RStudio, a red line will often appear on the lefthand side of the screen to indicate the particular line(s) of code that result in an error
+   - It may be useful to clear your working environment in R before trying to diagnose an error to be sure that you are not experiencing any interference from existing objects in the environment
+   - Running code line-by-line is particularly useful because it allows you to inspect the data at each step of its processing
+      - Data issues are a commonly the culprit of code errors (see below)
+1. **Check for typos**
+   - Are all brackets/parentheses/etc. closed? 
+   - Does the object/variable that you are referring to exist? (*If the object does not exisit, you will likely get an error message indicating `[object name] not found`*)
+      - Has it been defined somewhere prior in the code? 
+      - Does it depend on something else to be created? 
+      - Is its name spelled correctly? 
+      - Highlighting the object or variable in question and using `CTRL-F` to search for it is a good way to check these things
+    - Did you spell the function you want to use correctly?
+    - Do you have commas separating items within a list/function/etc.?
+1. **Does your working environment have what it needs?**
+    - Are all necessary packages/libraries installed? 
+    Do any require an update?
+    - Are all expected objects (dataframes, lists, environment variables, etc) present? 
+    Are they correct? 
+1. **Investigate the structure of your data**
+   - Errors such as `non-numeric argument to binary operator` indicate that the function you are trying to use is expecting to recieve numeric data as an input but one or more of the inputs are not numeric
+      - The function `class(data$variable)` (replace "data" and "variable" with whatever you are trying to invesitgate) is useful for determining how R is interpreting your data.
+      Often, data that look like numbers can end up being stored as a character (text) variable.
+      This can be due to import/export processes or due to an issue with data entry. 
+         - For example, when `00` is entered instead of `0` in a numeric field R is likely to interpret the variable as a character instead of a number when importing the data
+    - Check to make sure that all expected rows/columns are present and that they look the way you expect them to
+       - If your dataset has been created by joining/merging multiple dataframes, any duplicate columns not accounted for when joining may have a suffix appended to distinguish them (i.e., `variable.x` or `variable.y`)
+       - To resolve this, either call the appended variable in subsequent manipulations (`variable.x` instead of `variable`) or deal with the duplicated columns before/during the joining process (e.g., include duplicate columns in the `by` argument of joining function or remove the redundant column from one of the datasets to be joined)
+    - If you have been creating/computing variables in your dataset, ensure that they are being computed as expected
+       - Check for `NA` (missing) or `NaN` (not a number) values.
+       Such values may not be an issue or error in all cases, but if you are not expecting that as a result of your computations, this might suggest an issue with the code and/or data structure
+       - Check whether the computed values are reasonable 
 
 ## Warning: `PACKAGENAME` package in `FILEPATH` library will not be updated