π 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")Conduct an Ordinary Least Squares (OLS) regression analysis.
polcom %>%
tidy_regression(pp_ideology ~ news_1 + ambiv_sexism_1 + pie_1) %>%
tidy_summary()
#> Model type : ols regression
#> Model formula: pp_ideology ~ news_1 + ambiv_sexism_1 + pie_1
#> $fit
#> # A tibble: 6 x 6
#> fit_stat n df estimate p.value stars
#> <chr> <int> <int> <dbl> <dbl> <chr>
#> 1 F 243 3 17.2 3.60e-10 ***
#> 2 R^2 243 NA 0.178 NA ""
#> 3 Adj R^2 243 NA 0.168 NA ""
#> 4 RMSE 243 NA 1.70 NA ""
#> 5 AIC 243 NA 955. NA ""
#> 6 BIC 243 NA 972. NA ""
#>
#> $coef
#> # A tibble: 4 x 6
#> term estimate s.e. est.se p.value stars
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 (Intercept) 2.40 0.475 5.04 0.00000100 ***
#> 2 news_1 -0.0219 0.0524 -0.417 0.677 ""
#> 3 ambiv_sexism_1 0.594 0.0859 6.92 0. ***
#> 4 pie_1 -0.0843 0.0960 -0.878 0.381 ""Conduct a logistic regression analysis for binary (dichotomous) outcomes.
polcom %>%
tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1 + pie_1,
type = "logistic") %>%
tidy_summary()
#> Model type : logistic regression
#> Model formula: follow_trump ~ news_1 + ambiv_sexism_1 + pie_1
#> $fit
#> # A tibble: 7 x 6
#> fit_stat n df estimate p.value stars
#> <chr> <int> <int> <dbl> <dbl> <chr>
#> 1 Ο2 243 239 245. 0.383 ""
#> 2 ΞΟ2 243 3 9.99 0.0186 *
#> 3 Nagelkerke R^2 243 NA 0.0403 NA ""
#> 4 McFadden R^2 243 NA 0.0392 NA ""
#> 5 RMSE 243 NA 2.68 NA ""
#> 6 AIC 243 NA 253. NA ""
#> 7 BIC 243 NA 267. NA ""
#>
#> $coef
#> # A tibble: 4 x 6
#> term estimate s.e. est.se p.value stars
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 (Intercept) 1.72 0.672 2.56 0.0106 *
#> 2 news_1 0.160 0.0739 2.17 0.0300 *
#> 3 ambiv_sexism_1 -0.238 0.122 -1.96 0.0498 *
#> 4 pie_1 -0.224 0.143 -1.57 0.117 ""Conduct a poisson regression analysis for count data.
polcom %>%
tidy_regression(news_1 ~ pp_ideology + ambiv_sexism_1 + pie_1,
type = "poisson") %>%
tidy_summary()
#> Model type : poisson regression
#> Model formula: news_1 ~ pp_ideology + ambiv_sexism_1 + pie_1
#> $fit
#> # A tibble: 7 x 6
#> fit_stat n df estimate p.value stars
#> <chr> <int> <int> <dbl> <dbl> <chr>
#> 1 Ο2 243 239 209. 0.919 ""
#> 2 ΞΟ2 243 3 15.3 0.00161 **
#> 3 Nagelkerke R^2 243 NA 0.0608 NA ""
#> 4 McFadden R^2 243 NA 0.0680 NA ""
#> 5 RMSE 243 NA 0.364 NA ""
#> 6 AIC 243 NA 1082. NA ""
#> 7 BIC 243 NA 1096. NA ""
#>
#> $coef
#> # A tibble: 4 x 6
#> term estimate s.e. est.se p.value stars
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 (Intercept) 1.59 0.105 15.1 0. ***
#> 2 pp_ideology -0.00584 0.0155 -0.377 0.706 ""
#> 3 ambiv_sexism_1 -0.0224 0.0225 -0.996 0.319 ""
#> 4 pie_1 0.0795 0.0228 3.49 0.000482 ***Conduct a negative binomial regression analysis for overdispersed count data.
polcom %>%
mutate(polarize = abs(therm_1 - therm_2)) %>%
tidy_regression(polarize ~ pp_ideology + news_4 + ambiv_sexism_1,
type = "negbinom") %>%
tidy_summary()
#> Model type : negbinom regression
#> Model formula: polarize ~ pp_ideology + news_4 + ambiv_sexism_1
#> $fit
#> # A tibble: 7 x 6
#> fit_stat n df estimate p.value stars
#> <chr> <int> <int> <dbl> <dbl> <chr>
#> 1 Ο2 242 238 294. 0.00819 **
#> 2 ΞΟ2 242 3 13.1 0.00450 **
#> 3 Nagelkerke R^2 242 NA 0.0525 NA ""
#> 4 McFadden R^2 242 NA 0.0426 NA ""
#> 5 RMSE 242 NA 0.775 NA ""
#> 6 AIC 242 NA 2310. NA ""
#> 7 BIC 242 NA 2327. NA ""
#>
#> $coef
#> # A tibble: 4 x 6
#> term estimate s.e. est.se p.value stars
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 (Intercept) 3.99 0.196 20.4 0. ***
#> 2 pp_ideology -0.0687 0.0402 -1.71 0.0874 +
#> 3 news_4 0.0518 0.0275 1.88 0.0599 +
#> 4 ambiv_sexism_1 -0.0775 0.0584 -1.33 0.185 ""Conduct an analysis of variance.
polcom %>%
dplyr::mutate(sex = ifelse(sex == 1, "Male", "Female"),
vote_choice = dplyr::case_when(
vote_2016_choice == 1 ~ "Clinton",
vote_2016_choice == 2 ~ "Trump",
TRUE ~ "Other")) %>%
tidy_anova(pp_ideology ~ sex * vote_choice) %>%
tidy_summary()
#> $fit
#> # A tibble: 6 x 6
#> fit_stat n df estimate p.value stars
#> <chr> <int> <int> <dbl> <dbl> <chr>
#> 1 F 243 5 30.8 3.98e-24 ***
#> 2 R^2 243 NA 0.394 NA ""
#> 3 Adj R^2 243 NA 0.381 NA ""
#> 4 RMSE 243 NA 1.47 NA ""
#> 5 AIC 243 NA 884. NA ""
#> 6 BIC 243 NA 909. NA ""
#>
#> $coef
#> # A tibble: 4 x 7
#> term estimate s.e. est.se statistic p.value stars
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 sex 1. 8.64 8.64 4.00 0.0466 *
#> 2 vote_choice 2. 321. 160. 74.3 0. ***
#> 3 sex:vote_choice 2. 3.60 1.80 0.834 0.436 ""
#> 4 Residuals 237. 511. 2.16 NA NA ""Comes with one data set.
Consists of survey responses to demographic, background, and likert-type attitudinal items about political communication.
print(tibble::as_tibble(polcom), n = 5)
#> # A tibble: 244 x 63
#> follow_trump news_1 news_2 news_3 news_4 news_5 news_6 ambiv_sexism_1
#> * <lgl> <int> <int> <int> <int> <int> <int> <int>
#> 1 TRUE 8 1 1 1 1 6 3
#> 2 TRUE 1 1 1 1 1 1 5
#> 3 TRUE 8 1 1 1 8 1 5
#> 4 TRUE 8 1 1 1 1 6 2
#> 5 TRUE 6 1 2 1 1 3 4
#> # ... with 239 more rows, and 55 more variables: ambiv_sexism_2 <int>,
#> # ambiv_sexism_3 <int>, ambiv_sexism_4 <int>, ambiv_sexism_5 <int>,
#> # ambiv_sexism_6 <int>, img1_hrc_1 <int>, img1_hrc_2 <dbl>,
#> # img1_hrc_3 <int>, img1_hrc_4 <dbl>, img1_hrc_5 <int>,
#> # img1_hrc_6 <int>, img1_hrc_7 <int>, img1_hrc_8 <int>,
#> # img1_hrc_9 <int>, img2_hrc_10 <int>, img2_hrc_11 <int>,
#> # img2_hrc_12 <dbl>, img2_hrc_13 <int>, img2_hrc_14 <int>,
#> # img2_hrc_15 <dbl>, img1_djt_1 <int>, img1_djt_2 <dbl>,
#> # img1_djt_3 <int>, img1_djt_4 <dbl>, img1_djt_5 <int>,
#> # img1_djt_6 <int>, img1_djt_7 <int>, img1_djt_8 <int>,
#> # img1_djt_9 <int>, img2_djt_10 <int>, img2_djt_11 <int>,
#> # img2_djt_12 <dbl>, img2_djt_13 <int>, img2_djt_14 <int>,
#> # img2_djt_15 <dbl>, pie_1 <int>, pie_2 <int>, pie_3 <int>, pie_4 <int>,
#> # vote_2016 <int>, vote_2016_choice <int>, pp_ideology <int>,
#> # pp_party <int>, pp_party_lean <int>, therm_1 <int>, therm_2 <int>,
#> # therm_3 <int>, therm_4 <int>, therm_5 <int>, age <int>, sex <int>,
#> # gender <int>, race <int>, edu <int>, hhinc <int>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 Cronbachβs alpha for a set of variables.
## reliability of social media use items
cronbachs_alpha(polcom, ambiv_sexism_1:ambiv_sexism_6)
#> items alpha alpha.std
#> 1 ambiv_sexism_1:ambiv_sexism_6 0.904609 0.904600
#> 2 -ambiv_sexism_1 0.882322 0.882225
#> 3 -ambiv_sexism_2 0.884272 0.884121
#> 4 -ambiv_sexism_3 0.896061 0.896218
#> 5 -ambiv_sexism_4 0.897127 0.897411
#> 6 -ambiv_sexism_5 0.883554 0.883420
#> 7 -ambiv_sexism_6 0.881595 0.881855
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