Data Modification and Analysis for Communication Research

Provides convenience functions for common data modification and analysis tasks in communication research. This includes functions for univariate and bivariate data analysis, index generation and reliability computation, and intercoder reliability tests. All functions follow the style and syntax of the tidyverse, and are construed to perform their computations on multiple variables at once. Functions for univariate and bivariate data analysis comprise summary statistics for continuous and categorical variables, as well as several tests of bivariate association including effect sizes. Functions for data modification comprise index generation and automated reliability analysis of index variables. Functions for intercoder reliability comprise tests of several intercoder reliability estimates, including simple and mean pairwise percent agreement, Krippendorff's Alpha (Krippendorff 2004, ISBN: 9780761915454), and various Kappa coefficients (Brennan & Prediger 1981 ; Cohen 1960 ; Fleiss 1971 ).

tidycomm

Tidycomm provides convenience functions for common tasks in communication research. All functions follow the style and syntax of the tidyverse.

Currently, tidycomm includes functions for various methods of univariate and bivariate data description and analysis, data modification, visualization, and intercoder reliability tests.

Installation

Install tidycomm from CRAN:

install.packages("tidycomm")

Or install the most recent development version of tidycomm with:

# install.packages("pak")
pak::pak("tidycomm/tidycomm")

Usage

library(tidycomm)

tidycomm functions follow the style and syntax of the tidyverse functions:

they always assume a tibble as their first argument
they will always return a tibble as well, so they can be easily integrated into pipes
data variables (tibble columns) are passed to function calls directly as symbols

WoJ %>% # Worlds of Journalism sample data
  describe(autonomy_selection, autonomy_emphasis)
#> # A tibble: 2 × 15
#>   Variable             N Missing     M    SD   Min   Q25   Mdn   Q75   Max Range
#> * <chr>            <int>   <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 autonomy_select…  1197       3  3.88 0.803     1     4     4     4     5     4
#> 2 autonomy_emphas…  1195       5  4.08 0.793     1     4     4     5     5     4
#> # ℹ 4 more variables: CI_95_LL <dbl>, CI_95_UL <dbl>, Skewness <dbl>,
#> #   Kurtosis <dbl>

Most functions will automatically use all relevant variables in the data if no variables are specified in the function call. For example, to compute descriptive statistics for all numeric variables in the data, just call describe() without further arguments:

WoJ %>%
  describe()
#> # A tibble: 11 × 15
#>    Variable           N Missing     M     SD   Min   Q25   Mdn   Q75   Max Range
#>  * <chr>          <int>   <int> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 autonomy_sele…  1197       3  3.88  0.803     1  4        4     4     5     4
#>  2 autonomy_emph…  1195       5  4.08  0.793     1  4        4     5     5     4
#>  3 ethics_1        1200       0  1.63  0.892     1  1        1     2     5     4
#>  4 ethics_2        1200       0  3.21  1.26      1  2        4     4     5     4
#>  5 ethics_3        1200       0  2.39  1.13      1  2        2     3     5     4
#>  6 ethics_4        1200       0  2.58  1.25      1  1.75     2     4     5     4
#>  7 work_experien…  1187      13 17.8  10.9       1  8       17    25    53    52
#>  8 trust_parliam…  1200       0  3.05  0.811     1  3        3     4     5     4
#>  9 trust_governm…  1200       0  2.82  0.854     1  2        3     3     5     4
#> 10 trust_parties   1200       0  2.42  0.736     1  2        2     3     4     3
#> 11 trust_politic…  1200       0  2.52  0.712     1  2        3     3     4     3
#> # ℹ 4 more variables: CI_95_LL <dbl>, CI_95_UL <dbl>, Skewness <dbl>,
#> #   Kurtosis <dbl>

Likewise, compute intercoder reliability tests for all variables by only specifying the post and coder ID variables:

fbposts %>% # Facebook post codings sample data
  test_icr(post_id, coder_id)
#> # A tibble: 5 × 8
#>   Variable     n_Units n_Coders n_Categories Level   Agreement Holstis_CR
#> * <chr>          <int>    <int>        <int> <chr>       <dbl>      <dbl>
#> 1 type              45        6            4 nominal     1          1    
#> 2 n_pictures        45        6            7 nominal     0.822      0.930
#> 3 pop_elite         45        6            6 nominal     0.733      0.861
#> 4 pop_people        45        6            2 nominal     0.778      0.916
#> 5 pop_othering      45        6            4 nominal     0.867      0.945
#> # ℹ 1 more variable: Krippendorffs_Alpha <dbl>

Almost all functions also come with integrated visualizations. So to visualize your results you can easily just append visualize() to the end of your commands:

snscomments %>% # experimental social-media-post perception sample data
  dplyr::filter(group == 3) %>%
  t_test(
    gender,
    medium_evaluation,
    article_evaluation,
    comments_quality,
    comments_valence
  ) %>%
  visualize()