Often, you will not be the first person to encounter an error or to have a particular question. (Some examples: "How do I remove NAs from my data?" "How do I make R understand this date information?" "How do I combine a boxplot and a scatterplot so I can show multiple aspects of the data?")
When searching for answers to your code-related questions, include the name of the package that you are using and a few words about what you are trying to do (or the error text). If the code in your search results doesn't make sense to you, do not despair - there are many ways of accomplishing the same task in R.
Two good places to look for already-answered questions are the RStudio Community and Stack Overflow. Reading forum posts can be useful because you can see how other people have solved a similar problem.
Often, it can be helpful to work through problems with another person in real time. For work related to Reed classes, theses, and student/faculty research, you can get help from someone at the mLab, and drop-in help hours can be found on our calendar.
For all data questions, coding and otherwise, you can reach out to the Data@Reed team by emailing data@reed.edu .
When asking for help, the first step is creating a "reprex", short for reproducible example. Reproducible examples help others help you by ensuring that they can replicate the problem that you're experiencing in order to troubleshoot more easily. There are a couple of key steps to creating a reprex:
Include any package loading calls (library(package)) and objects (i.e. datasets) that are relevant to your problem.
Only include whatever is relevant or necessary to your problem. In reprex jargon, this is called making your example "minimal".
By taking the time to create a reprex, you are asking a more understandable question - meaning that you are much more likely to get a useful answer. For more on reproducible examples and asking questions, check out the tidyverse "Get help!" page.
Once you have prepared a reprex, two possible courses of action: