In joon-e/tidycomm: Data Modification and Analysis for Communication Research

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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:

remotes::install_github("joon-e/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)

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()

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)

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()

For detailed examples, see the following articles: