In mkearney/tidyacademic: Tidy Tools for Academics

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tidyversity

🎓 Tidy tools for academics

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

Installation

Install the development version from Github with:

## 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).

## load tidyverse
library(tidyverse)

## load tidyversity
library(tidyversity)

Regression models

Ordinary Least Squares (OLS)

Conduct an Ordinary Least Squares (OLS) regression analysis.

polcom %>%
  tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1) %>%
  tidy_summary()

Logistic (dichotomous)

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

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

Poisson (count)

Conduct a poisson regression analysis for count data.

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.

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

Robust and quasi- models

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).

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

polcom %>%
  tidy_ttest(pp_ideology ~ follow_trump) %>%
  tidy_summary()

Latent variable models

Structural equation modeling (SEM)

Conduct latent variable analysis using structural equation modeling.

## 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.

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.

print(tibble::as_tibble(polcom), n = 5)

Descriptive statistics

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

## 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.

## reliability of social media use items
cronbachs_alpha(polcom, ambiv_sexism_1:ambiv_sexism_6)

mkearney/tidyacademic documentation built on May 14, 2019, 6:12 a.m.