First and foremost, please ask!
A core goal of community is to be a friendly place to chat about topics related to data science, R, and RStudio.
We know that posting to technical forums can be intimidating. But know that many here would love to see you overcome your inhibition and engage with us.
Here are a few tips some folks here think might be helpful.
Before you post
Check Out R Documentation - R has built in documentation on packages and functions.
For example typing
?lm into your R console will open the documentation on the
Search - Be sure to search for the basic keywords of your question with your favorite search engine, Stack Overflow, and R-Help. Stack Overflow is a popular Q&A website with hundreds of thousands of R-related questions and answers.
Writing your post
- Be specific - A title like "Having Issues with My R" just isn't specific enough. Tell us as much useful information as possible in as few words as possible.
- Include distinct keywords like key parts from any error messages, packages or functions or methods key to your qurey. This helps make your issue more searchable.
Format your code
For inline code, uses
``` r Code chunk goes here ```
How to make your code look nice? Markdown Formatting
reprex package will do this automatically for you. See below.
Example - a
The goal of a reprex is to package your problematic code in such a way that other people can run it and feel your pain. Others should be able to copy your code-snippet and paste it into an R script to get as close to replicating your issue as possible.
If you’re still new to this idea, check out So You’ve Been Asked to Make a
Screenshots can be useful when showing plots and complex tables. They can more clearly communicate where certain features may be found (e.g. where's the environment pane) or what some kind of behavior should look like (e.g. what should RStudio's Import Dataset gui look like).
But screenshots are not searchable and don't copy-and-paste as code. Avoid using them when text will do.
Data for my reprex?
Providing example data is often important for a good reprex. There are a few good ways to do this;
- Use R’s built in example datasets. For example, simply calling
mtcarsin R produces the mtcars dataset.
- Here’s a nice discussion on creating simple data frames for your reprex with
- And many link to their data via a URL.
Putting your query into the right category and with good tags can be important to making it visible to the right people. Many folks only regularly follow certain categories and tags.
Goals of this guide
- Pose your question so that it is more likely to get a useful response.
- Help potential responders grasp your issue.
- Keep our community tidy so folks in the future can easily find how people dealt with similar issues.
If you feel this guide or community.rstudio.com's processes can be improved, please let us know.