knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%", eval = TRUE ) options(width = 100)
🎓 Tidy tools for academics
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)
Conduct an Ordinary Least Squares (OLS) regression analysis.
polcom %>% tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1) %>% tidy_summary()
Conduct a logistic regression analysis for binary (dichotomous) outcomes.
polcom %>% tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1, type = "logistic") %>% tidy_summary()
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()
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()
polcom %>% mutate(polarize = abs(therm_1 - therm_2)) %>% tidy_regression(polarize ~ news_1 + ambiv_sexism_1, type = "quasipoisson", robust = TRUE) %>% tidy_summary()
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()
polcom %>% tidy_ttest(pp_ideology ~ follow_trump) %>% tidy_summary()
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()
Estimate multilevel (mixed effects) models.
lme4::sleepstudy %>% tidy_mlm(Reaction ~ Days + (Days | Subject)) %>% summary()
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)
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)
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