The following is a compendium of all R packages used in this course (in order of appearance), their main uses, and what we use them for^{1}. Each package name below is a **link** to the package’s website and/or documentation.

You can install all of these packages at once with the following command:

```
install.packages(c("tidyverse", "ggrepel", "broom", "car", "estimatr", "lmtest",
"huxtable", "infer", "dagitty", "ggdag", "modelsummary", "fixest"))
```

† Indicates package is part of the `tidyverse`

Name | Type | Description/Reason(s) for Use | Classes Used |
---|---|---|---|

`ggplot2` † |
Plotting | For nice plots | [1.3] |

`gganimate` |
Plotting | For animating plots | [1.3] |

`haven` † |
Data Wrangling | For importing nonstandard data files | [1.4] |

`dplyr` † |
Data Wrangling | For manipulating data (part of tidyverse) | [1.4] |

`readr` † |
Data Wrangling | For importing most data files | [1.4] |

`tidyr` † |
Data Wrangling | For reshaping data (wide and long) | [1.4] |

`magrittr` † |
Data Wrangling | For the pipe | [1.4] |

`tibble` † |
Data Wrangling | For a friendlier data.frame | [1.4] |

`ggrepel` |
Plotting | For annotating text that doesn’t cover observations | [1.4] |

`broom` |
Models | For tidying regression output | [2.3] |

`car` |
Models | For testing for outliers | [2.5] |

`estimatr` |
Models | For calculating heteroskedasticity-robust standard errors | [2.5] |

`lmtest` |
Models | For testing for heteroskedasticity | [2.5] |

`huxtable` |
Output | For making nice regression tables | [2.5] |

`infer` |
Models | For simulation and statistical inference | [2.6] |

`dagitty` |
Models | For working with DAGs in R | [3.2] |

`ggdag` |
Plotting | For plotting DAGs in ggplot | [3.2] |

`modelsummary` |
Output | For making nice regression tables | [3.5] |

`fixest` |
Models | For working with panel data | [4.1] |

Note, many of these packages have multiple uses beyond our purposes!↩︎