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)
```
# tidyversity <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 tidyversity from [CRAN](https://CRAN.R-project.org) with:

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

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

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

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

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

## load tidyversity
library(tidyversity)
```

## 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()
```

## Mean comparison models

### ANOVA

Conduct an analysis of variance (ANOVA).

```{r anova}
polcom %>%
  mutate(sex = ifelse(sex == 1, "Male", "Female"),
  vote_choice = case_when(
    vote_2016_choice == 1 ~ "Clinton",
    vote_2016_choice == 2 ~ "Trump",
    TRUE ~ "Other")) %>%
  tidy_anova(pp_party ~ sex * vote_choice) %>%
  tidy_summary()
```

### t-tests

```{r ttest}
polcom %>%
  tidy_ttest(pp_ideology ~ follow_trump) %>%
  tidy_summary()
```

## Latent variable models

### Structural equation modeling (SEM)

Conduct latent variable analysis using structural equation modeling.

```{r sem}
## mutate data and then specify and estimate model
sem1 <- polcom %>%
  mutate(therm_2 = therm_2 / 10, 
    therm_1 = 10 - therm_1 / 10) %>%
  tidy_sem_model(news =~ news_1 + news_2 + news_3 + news_4 + news_5 + news_6,
    ambiv_sexism =~ ambiv_sexism_1 + ambiv_sexism_2 + ambiv_sexism_3 + 
      ambiv_sexism_4 + ambiv_sexism_5 + ambiv_sexism_6,
    partisan =~ a*therm_1 + a*therm_2,
    ambiv_sexism ~ age + sex + hhinc + edu + news + partisan) %>%
  tidy_sem()

## print model summary
sem1 %>%
  tidy_summary()
```

### Multilevel modeling (MLM)

Estimate multilevel (mixed effects) models.

```{r mlm}
lme4::sleepstudy %>%
  tidy_mlm(Reaction ~ Days + (Days | Subject)) %>%
  summary()
```

# Data sets

Comes with one data set. 

### `polcom` 

Consists of survey responses to demographic, background, and likert-type attitudinal items about political communication.

```{r polcom}
print(tibble::as_tibble(polcom), n = 5)
```

## Descriptive statistics

Return summary statistics in the form of a data frame ***(not yet added)***.

```{r summarize, eval=FALSE}
## summary stats for social media use (numeric) variables
summarize_numeric(polcom_survey, smuse1:smuse3)

## summary stats for respondent sex and race (categorical) variables
summarize_categorical(polcom_survey, sex, race)
```

Estimate Cronbach's alpha for a set of variables.

```{r reliability}
## reliability of social media use items
cronbachs_alpha(polcom, ambiv_sexism_1:ambiv_sexism_6)
```