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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "80%"
)
```
# openintro <a href="https://www.openintro.org/"><img src="man/figures/openintro-hex.png" align="right" height="170"></a>
<!-- badges: start -->
[](https://cran.r-project.org/package=openintro)
[](https://github.com/OpenIntroStat/openintro/actions)
<!-- badges: end -->
Supplemental functions and data for 'OpenIntro' resources, which includes open-source textbooks and resources for introductory statistics at [openintro.org](https://www.openintro.org/).
The package contains data sets used in our open-source textbooks along with custom plotting functions for reproducing book figures.
The package also contains the datasets used in OpenIntro labs.
Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.
## Installation
You can install the released version of openintro from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("openintro")
```
You can install the development version of openintro from GitHub with:
``` r
# install.packages("devtools")
library(devtools)
install_github("OpenIntroStat/openintro")
```
This package was produced as part of the OpenIntro project.
For the accompanying textbook, visit [openintro.org](https://openintro.org). A PDF of the textbook is free and paperbacks can be purchased online (royalty-free).
## Questions, bugs, feature requests
You can file an issue to get help, report a bug, or make a feature request.
When filing an issue to get help or report a bug, please make a minimal **repr**oducible **ex**ample using the [reprex](https://reprex.tidyverse.org/) package.
If you haven't heard of or used reprex before, you're in for a treat!
Seriously, reprex will make all of your R-question-asking endeavors easier (which is a pretty insane ROI for the five to ten minutes it'll take you to learn what it's all about).
For additional reprex pointers, check out the [Get help!](https://www.tidyverse.org/help/) section of the tidyverse site.
Before opening a new issue, be sure to [search issues and pull requests](https://github.com/openintrostat/openintro/issues) to make sure the bug hasn't been reported and/or already fixed in the development version.
By default, the search will be pre-populated with `is:issue is:open`.
You can [edit the qualifiers](https://help.github.com/articles/searching-issues-and-pull-requests/) (e.g. `is:pr`, `is:closed`) as needed.
For example, you'd simply remove `is:open` to search _all_ issues in the repo, open or closed.
## Contributing
Process for adding new data to the package
The following steps use the **devtools** and **usethis** packages for various steps.
We recommend using this process when suggesting new datasets to be added to the package.
If the dataset is large (>500MB) or you'd like to add a function, please open an issue first for discussion before making the pull request.
1. Fork and clone the repo with `usethis::create_from_github("OpenIntroStat/openintro")`
- Note: If you have write access to the repo, you can skip this step.
1. Start a new pull request with `usethis::pr_init("BRANCH-NAME")`, where `BRANCH-NAME` is an informative branch name.
1. If adding a file that is not an .rda file to begin with (Excel, csv, etc.), create a folder in the `data-raw` folder with the name of the dataset (how you'd like it to show up in the package). Please use `snake_case` for naming, e.g. `name_of_dataset`.
1. Place your dataset in its raw form in the folder.
1. Also in the `data-raw` folder, create a new R script called `name_of_dataset-dataprep.R` and write the code needed to read in the file, make any modifications to the data that are needed (if any), and end with `usethis::use_data()` to save the data in the package as an .rda file with the ideal compression. See examples from other folders in `data-raw` for sample code. The contents of this folder do not end up in the package (the entire folder is ignored in the `.Rbuildignore`) so you don't need to worry about adding package dependencies etc.
1. In the `R` folder, create an R script called `data-name_of_dataset` and add documentation using Roxygen style. See other documentation files for help with style. In the examples, use tidyverse syntax but do not use `library(tidyverse)` and only use the relevant packages, e.g. `library(dplyr)`, `library(ggplot2)`.
1. Restart R and run `devtools::load_all()` to make sure the data loads and run your examples to confirm they all work.
1. Run `devtools::docment()`, restart R, and then `devtools::load_all()`. Then, check out `?name_of_dataset` to make sure the documentation looks as expected.
1. Run `devtools::check()`. The only NOTE you should see as a result of the check should be about the package size. If any other ERRORs, NOTEs, or WARNINGs are generated, resolve them or open an issue for help.
1. In the `pkgdown.yml` file, add the name of the dataset under `reference`, in the correct alphabetical order.
1. Add a note in the `NEWS.md` with the new dataset you've added with a link to your GitHub username so we can acknowledge your contribution, e.g. "added by [\@mine-cetinkaya-rundel](https://github.com/mine-cetinkaya-rundel)".
## Code of Conduct
Please note that the openintro project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.