https://github.com/mkearney/tidyversity

🎓 Tidy tools for academics
https://github.com/mkearney/tidyversity

academic analysis general-linear-model latent-variables linear-models logistic-regression mkearney-r-package negative-binomial-regression poisson-regression r regression research robust-regression rstats science statistics structural-equation-modeling tidy tidyverse tidyversity

Last synced: 3 months ago
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🎓 Tidy tools for academics

• Host: GitHub
• URL: https://github.com/mkearney/tidyversity
• Owner: mkearney
• License: other
• Created: 2018-04-26T19:40:57.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-08-02T21:02:20.000Z (over 6 years ago)
• Last Synced: 2024-07-31T19:28:52.815Z (6 months ago)
• Topics: academic, analysis, general-linear-model, latent-variables, linear-models, logistic-regression, mkearney-r-package, negative-binomial-regression, poisson-regression, r, regression, research, robust-regression, rstats, science, statistics, structural-equation-modeling, tidy, tidyverse, tidyversity
• Language: R
• Homepage: https://github.com/mkearney/tidyversity
• Size: 480 KB
• Stars: 171
• Watchers: 15
• Forks: 16
• Open Issues: 3
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

---

```{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  

[![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

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)

```