README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  eval = TRUE
)
options(width = 100)
polcom <- tidyversity::polcom
```
# tidyreg <img src="man/figures/logo.png" width="160px" align="right" /> 

[![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)

🎓 Tidy tools for academics


## \*\*\* This package is in very early development. Feedback is encouraged!!! \*\*\*

## Installation

<!-- You can install the released version of tidyreg from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("tidyreg")
```
-->

Install the development version from [Github](https://github.com/mkearney/tidyreg) with:

```{r install, eval=FALSE}
## install devtools if not already
if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}
## install tidyreg from Github
devtools::install_github("mkearney/tidyreg")
```

Load the package (it, of course, plays nicely with tidyverse).

```{r library}
## load tidyverse
library(tidyverse)

## load tidyreg
library(tidyreg)
```

## Regression models

### Ordinary Least Squares (OLS)

Conduct an Ordinary Least Squares (OLS) regression analysis.

```{r ols}
polcom %>%
  tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1) %>%
  tidy_summary()
```

### Logistic (dichotomous)

Conduct a logistic regression analysis for binary (dichotomous) outcomes.

```{r logistic}
polcom %>%
  tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1, type = "logistic") %>%
  tidy_summary()
```

### Poisson (count)

Conduct a poisson regression analysis for count data.

```{r poisson}
polcom %>%
  mutate(polarize = abs(therm_1 - therm_2)) %>%
  tidy_regression(polarize ~ news_1 + ambiv_sexism_1, type = "poisson") %>%
  tidy_summary()
```

### Negative binomial (overdispersed)

Conduct a negative binomial regression analysis for overdispersed count data.

```{r, negbinom}
polcom %>%
  mutate(polarize = abs(therm_1 - therm_2)) %>%
  tidy_regression(polarize ~ news_1 + ambiv_sexism_1, type = "negbinom") %>%
  tidy_summary()
```

### Robust and quasi- models

```{r, robust_glm}
polcom %>%
  mutate(polarize = abs(therm_1 - therm_2)) %>%
  tidy_regression(polarize ~ news_1 + ambiv_sexism_1, 
    type = "quasipoisson", robust = TRUE) %>%
  tidy_summary()
```