We, the authors of the tmle3mediate R package, use the same guide as is used for contributing to the development of the popular ggplot2 R package. This document is simply a formal re-statement of that fact.
The goal of this guide is to help you get up and contributing to tmle3mediate as quickly as possible. The guide is divided into two main pieces:
When filing an issue, the most important thing is to include a minimal reproducible example so that we can quickly verify the problem, and then figure out how to fix it. There are three things you need to include to make your example reproducible: required packages, data, code.
Packages should be loaded at the top of the script, so it’s easy to see which ones the example needs.
The easiest way to include data is to use dput() to generate the R code to recreate it. For example, to recreate the mtcars dataset in R, I’d perform the following steps:
dput(mtcars) in Rmtcars <- then paste.But even better is if you can create a data.frame() with just a handful of rows and columns that still illustrates the problem.
Spend a little bit of time ensuring that your code is easy for others to read:
make sure you’ve used spaces and your variable names are concise, but informative
use comments to indicate where your problem lies
do your best to remove everything that is not related to the problem. The shorter your code is, the easier it is to understand.
You can check you have actually made a reproducible example by starting up a fresh R session and pasting your script in.
(Unless you’ve been specifically asked for it, please don’t include the output of sessionInfo().)
To contribute a change to tmle3mediate, you follow these steps:
tmle3mediate.Each of these steps are described in more detail below. This might feel overwhelming the first time you get set up, but it gets easier with practice.
If you’re not familiar with git or GitHub, please start by reading http://r-pkgs.had.co.nz/git.html
Pull requests will be evaluated against the a checklist:
Also include this motivation in NEWS so that when a new release of ggplot2 comes out it’s easy for users to see what’s changed. Add your item at the top of the file and use markdown for formatting. The news item should end with (@yourGithubUsername, #the_issue_number).
Only related changes. Before you submit your pull request, please check to make sure that you haven’t accidentally included any unrelated changes. These make it harder to see exactly what’s changed, and to evaluate any unexpected side effects.
Each PR corresponds to a git branch, so if you expect to submit multiple changes make sure to create multiple branches. If you have multiple changes that depend on each other, start with the first one and don’t submit any others until the first one has been processed.
Use tmle3mediate coding style. Please follow the official ggplot2 style. Maintaing a consistent style across the whole code base makes it much easier to jump into the code. If you’re modifying existing ggplot2 code that doesn’t follow the style guide, a separate pull request to fix the style would be greatly appreciated.
If you’re adding new parameters or a new function, you’ll also need to document them with roxygen. Make sure to re-run devtools::document() on the code before submitting.
This seems like a lot of work but don’t worry if your pull request isn’t perfect. It’s a learning process. A pull request is a process, and unless you’ve submitted a few in the past it’s unlikely that your pull request will be accepted as is. Please don’t submit pull requests that change existing behaviour. Instead, think about how you can add a new feature in a minimally invasive way.