FAQ: Tips for writing R-related questions

faq

#1

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 lm function.

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

Title

Put extra effort into your title.

  • 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 makes your issue more likely to be discovered by people best able to help.
    • This also helps make your issue more discoverable by people in the future seeking help with the same issue.

Format your code

For inline code, uses `back-ticks`

``` r
    Code chunk goes here
```

How to make your code look nice? Markdown Formatting
The reprex package will do this automatically for you. See below.


Include a Reproducible Example - a reprex

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 reprex.

Avoid Screenshots

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 or reprex 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;


After Your Initial Post, Be a Good Thread-Moderator

With coding questions, it is important to keep your thread focused on your initial issue. This makes your thread more useful to people who find it in the future. And if done well, this can save you a lot of time finding your solution.

  • Avoid back-and-forths - Be as efficient as possible in any reply-posts - for example including a new reprex, debugging info, and extra clarifications to ensure other can help solve your issue. Keep in mind that people helping here are being incredibly kind volunteering to assist you. They may only have a small amount of time each week to help out.

  • Mark Solutions - If someone solved your headline problem, mark it as a solution. FAQ: How do I mark a solution?

  • Tangential questions - If you have an unrelated question, or question only loosely related to the headline topic, open a new thread and link back to your first one.


Category and tags
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.


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