tidyversity

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

Installation

## install devtools if not alreadyif (!requireNamespace("devtools",quietly=TRUE)) {  install.packages("devtools")}## install tidyversity from Githubdevtools::install_github("mkearney/tidyversity")

## load tidyverselibrary(tidyverse)#> ── Attaching packages ─────────────────────────────────────────────────── tidyverse 1.2.1 ──#> ✔ ggplot2 2.2.1     ✔ purrr   0.2.4#> ✔ tibble  1.4.2     ✔ dplyr   0.7.4#> ✔ tidyr   0.8.0     ✔ stringr 1.3.0#> ✔ readr   1.1.1     ✔ forcats 0.3.0#> ── Conflicts ────────────────────────────────────────────────────── tidyverse_conflicts() ──#> ✖ dplyr::filter() masks stats::filter()#> ✖ dplyr::lag()    masks stats::lag()## load tidyversitylibrary(tidyversity)

Regression models

Ordinary Least Squares (OLS)

polcom %>%  tidy_regression(follow_trump~news_1+ambiv_sexism_1) %>%  tidy_summary()#> # A tidy model#> Model formula  : follow_trump ~ news_1 + ambiv_sexism_1#> Model type     : Ordinary Least Squares (OLS) regression#> Model pkg::fun : stats::lm()#> Model data     : 243 (observations) X 3 (variables)#> $fit#> fit_stat     n     df    estimate    p.value  stars#> F          243      2      3.831      0.023   *#> R^2        243      -      0.031       -#> Adj R^2    243      -      0.023       -#> RMSE       243      -      0.409       -#> AIC        243      -    260.148       -#> BIC        243      -    274.121       -#>#> $coef#> term               est     s.e.    est.se    p.value  stars   std.est#> (Intercept)      0.745    0.097     7.692      <.001   ***      <.001#> news_1           0.022    0.012     1.811      0.071   +        0.048#> ambiv_sexism_1  -0.038    0.021    -1.870      0.063   +       -0.050

Logistic (dichotomous)

polcom %>%  tidy_regression(follow_trump~news_1+ambiv_sexism_1,type="logistic") %>%  tidy_summary()#> # A tidy model#> Model formula  : follow_trump ~ news_1 + ambiv_sexism_1#> Model type     : Logistic regression#> Model pkg::fun : stats::glm()#> Model data     : 243 (observations) X 3 (variables)#> $fit#> fit_stat           n     df    estimate    p.value  stars#> χ2               243    240    247.442      0.357#> Δχ2              243      2      7.466      0.024   *#> Nagelkerke R^2   243      -      0.030       -#> McFadden R^2     243      -      0.029       -#> RMSE             243      -      2.540       -#> AIC              243      -    253.442       -#> BIC              243      -    263.921       -#>#> $coef#> term               est     s.e.    est.se    p.value  stars   std.est#> (Intercept)      1.133    0.553     2.049      0.040   *        <.001#> news_1           0.127    0.070     1.808      0.071   +        0.195#> ambiv_sexism_1  -0.229    0.122    -1.872      0.061   +       -0.201

Poisson (count)

polcom %>%  mutate(polarize= abs(therm_1-therm_2)) %>%  tidy_regression(polarize~news_1+ambiv_sexism_1,type="poisson") %>%  tidy_summary()#> # A tidy model#> Model formula  : polarize ~ news_1 + ambiv_sexism_1#> Model type     : Poisson regression#> Model pkg::fun : stats::glm()#> Model data     : 242 (observations) X 3 (variables)#> $fit#> fit_stat           n     df    estimate    p.value  stars#> χ2               242    239   6549.419      <.001   ***#> Δχ2              242      2    399.077      <.001   ***#> Nagelkerke R^2   242      -      0.808       -#> McFadden R^2     242      -      0.057       -#> RMSE             242      -      0.760       -#> AIC              242      -   7725.222       -#> BIC              242      -   7735.689       -#>#> $coef#> term               est     s.e.     est.se    p.value  stars   std.est#> (Intercept)      3.798    0.038     99.448      <.001   ***      <.001#> news_1           0.045    0.005      9.358      <.001   ***      0.881#> ambiv_sexism_1  -0.126    0.008    -15.852      <.001   ***     -2.230

Negative binomial (overdispersed)

polcom %>%  mutate(polarize= abs(therm_1-therm_2)) %>%  tidy_regression(polarize~news_1+ambiv_sexism_1,type="negbinom") %>%  tidy_summary()#> # A tidy model#> Model formula  : polarize ~ news_1 + ambiv_sexism_1#> Model type     : Negative binomial regression#> Model pkg::fun : MASS::glm.nb()#> Model data     : 242 (observations) X 3 (variables)#> $fit#> fit_stat           n     df    estimate    p.value  stars#> χ2               242    239    293.328      0.009   **#> Δχ2              242      2      8.440      0.015   *#> Nagelkerke R^2   242      -      0.034       -#> McFadden R^2     242      -      0.028       -#> RMSE             242      -      0.761       -#> AIC              242      -   2312.391       -#> BIC              242      -   2326.347       -#>#> $coef#> term               est     s.e.    est.se    p.value  stars   std.est#> (Intercept)      3.741    0.258    14.510      <.001   ***      3.752#> news_1           0.053    0.032     1.632      0.103            0.113#> ambiv_sexism_1  -0.123    0.054    -2.273      0.023   *       -0.158

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()#> # A tidy model#> Model formula  : polarize ~ news_1 + ambiv_sexism_1#> Model type     : [Robust] Poisson regression#> Model pkg::fun : robust::glmRob()#> Model data     : 242 (observations) X 3 (variables)#> $fit#> fit_stat           n     df     estimate    p.value  stars#> χ2               242    239    6989.543      <.001   ***#> Δχ2              242      2   58782.937      <.001   ***#> Nagelkerke R^2   242      -       1.000       -#> McFadden R^2     242      -       0.894       -#> RMSE             242      -      31.865       -#> AIC              242      -    2245.147       -#> BIC              242      -    2259.103       -#>#> $coef#> term               est     s.e.     est.se    p.value  stars   std.est#> (Intercept)      3.705    0.071     51.968      <.001   ***      <.001#> news_1           0.079    0.010      8.325      <.001   ***      1.279#> ambiv_sexism_1  -0.241    0.022    -11.179      <.001   ***     -2.086

Mean comparison models

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()#> # A tidy model#> Model formula  : pp_party ~ sex * vote_choice#> Model type     : Analysis of variance (ANOVA)#> Model pkg::fun : stats::aov()#> Model data     : 243 (observations) X 3 (variables)#> $fit#> fit_stat     n     df    estimate    p.value  stars#> F          243      5     53.327      <.001   ***#> R^2        243      -      0.529       -#> Adj R^2    243      -      0.519       -#> RMSE       243      -      1.238       -#> AIC        243      -    801.115       -#> BIC        243      -    825.567       -#>#> $coef#> term                 est       s.e.     est.se    statistic    p.value  stars   std.est#> sex                1.000     19.238     19.238       12.561      <.001   ***      2.000#> vote_choice        2.000    388.606    194.303      126.867      <.001   ***      2.000#> sex:vote_choice    2.000      0.519      0.259        0.169      0.844            2.000#> Residuals        237.000    362.978      1.532         -          -             237.000

t-tests

polcom %>%  tidy_ttest(pp_ideology~follow_trump) %>%  tidy_summary()#> # A tidy model#> Model formula  : pp_ideology ~ follow_trump#> Model type     : T-test#> Model pkg::fun : stats::t.test()#> Model data     : 244 (observations)#> $fit#> group       df     mean      diff     lo.95     hi.05#> FALSE  76.911    4.185     0.922     0.308     1.536#> TRUE   76.911    3.263    -0.922    -0.308    -1.536#>#> $coef#>     est        t    p.value  stars#>  0.922    2.992      0.004   **

Latent variable models

Structural equation modeling (SEM)

## mutate data and then specify and estimate modelsem1<-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 summarysem1 %>%  tidy_summary()#> # A tidy model#> Model formula  : 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#> Model type     : Structural Equation Model (SEM)#> Model pkg::fun : lavaan::sem()#> Model data     : 235 (observations) X 18 (variables)#> $fit#> fit_stat             n     df     estimate    p.value  stars#> chisq              235    127     239.579      <.001   ***#> aic                235      -       0.907       -#> bic                235      -       0.892       -#> cfi                235      -   16138.684       -#> tli                235      -   16256.310       -#> rmsea              235      -       0.061       -#> srmr               235      -       0.073       -#> R^2:ambiv_sexism   235      -       0.379       -#>#> $coef#> term                               est       se    est.se    p.value  stars   std.est#> news =~ news_1                   1.000    <.001      -          -               0.173#> news =~ news_2                   1.592    0.722     2.204      0.028   *        0.340#> news =~ news_3                   5.069    2.095     2.419      0.016   *        0.781#> news =~ news_4                   5.587    2.312     2.417      0.016   *        0.851#> news =~ news_5                   3.493    1.485     2.353      0.019   *        0.520#> news =~ news_6                   1.255    0.683     1.838      0.066   +        0.196#> ambiv_sexism =~ ambiv_sexism_1   1.000    <.001      -          -               0.825#> ambiv_sexism =~ ambiv_sexism_2   0.942    0.067    14.043      <.001   ***      0.801#> ambiv_sexism =~ ambiv_sexism_3   0.795    0.067    11.844      <.001   ***      0.706#> ambiv_sexism =~ ambiv_sexism_4   0.743    0.064    11.647      <.001   ***      0.697#> ambiv_sexism =~ ambiv_sexism_5   0.902    0.062    14.644      <.001   ***      0.825#> ambiv_sexism =~ ambiv_sexism_6   0.904    0.064    14.185      <.001   ***      0.807#> partisan =~ therm_1              1.000    <.001      -          -               0.577#> partisan =~ therm_2              1.000    <.001      -          -               0.592#> ambiv_sexism ~ age              -0.004    0.005    -0.824      0.410           -0.051#> ambiv_sexism ~ sex              -0.271    0.130    -2.089      0.037   *       -0.130#> ambiv_sexism ~ hhinc            -0.021    0.023    -0.878      0.380           -0.057#> ambiv_sexism ~ edu              -0.088    0.069    -1.279      0.201           -0.083#> ambiv_sexism ~ news              0.130    0.215     0.607      0.544            0.047#> ambiv_sexism ~ partisan          0.347    0.069     5.032      <.001   ***      0.592

Multilevel modeling (MLM)

lme4::sleepstudy %>%  tidy_mlm(Reaction~Days+ (Days|Subject)) %>%  summary()#> Linear mixed model fit by REML ['lmerMod']#> Formula: Reaction ~ Days + (Days | Subject)#>    Data: .data#>#> REML criterion at convergence: 1743.6#>#> Scaled residuals:#>    Min     1Q Median     3Q    Max#> -3.954 -0.463  0.023  0.463  5.179#>#> Random effects:#>  Groups   Name        Variance Std.Dev. Corr#>  Subject  (Intercept) 612.1    24.74#>           Days         35.1     5.92    0.07#>  Residual             654.9    25.59#> Number of obs: 180, groups:  Subject, 18#>#> Fixed effects:#>             Estimate Std. Error t value#> (Intercept)   251.41       6.82   36.84#> Days           10.47       1.55    6.77#>#> Correlation of Fixed Effects:#>      (Intr)#> Days -0.138

Data sets

polcom

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 ambiv_sexism_2#> * <lgl>         <int>  <int>  <int>  <int>  <int>  <int>          <int>          <int>#> 1 TRUE              8      1      1      1      1      6              3              3#> 2 TRUE              1      1      1      1      1      1              5              5#> 3 TRUE              8      1      1      1      8      1              5              4#> 4 TRUE              8      1      1      1      1      6              2              2#> 5 TRUE              6      1      2      1      1      3              4              4#> # ... with 239 more rows, and 54 more variables: 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>

Descriptive statistics

## summary stats for social media use (numeric) variablessummarize_numeric(polcom_survey,smuse1:smuse3)## summary stats for respondent sex and race (categorical) variablessummarize_categorical(polcom_survey,sex,race)

## reliability of social media use itemscronbachs_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