Data Modification and Analysis for Communication Research
Add index
Categorize numeric variables into categories
Center numeric, continuous variables
Compute correlation coefficients
Crosstab variables
Describe numeric variables
Describe categorical variables
Gray design
Grey design
Colorbrewer-inspired design with focus on LMU (lmu.de) green
Convert categorical variables to dummy variables
Expand a range with a multiplicative or additive constant
Get reliability estimates of index variables
Rescale numeric continuous variables to new minimum/maximum boundaries
Access model(s) used to estimate output
tdcmm output constructor
Out of bounds handling
Helper function for labelling purposes
Pipe operator
Recode one or more categorical variables into new categories
Compute linear regression
Rescale continuous vector to have specified minimum and maximum
Rescale numeric vector to have specified maximum
Rescale vector to have specified minimum, midpoint, and maximum
Don't perform rescaling
Reverse numeric, logical, or date/time continuous variables
Set specified values to NA in selected variables or entire data frame
Compute t-tests
Tabulate frequencies
Tabulate percentiles for numeric variables
tdcmm class
Perform an intercoder reliability test
Create correlation matrix
Compute one-way ANOVAs
Visualize tidycomm output
Z-standardize numeric, continuous variables
Determine if range of vector is close to zero, with a specified tolera...
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 <doi: 10.1177/001316448104100307>; Cohen 1960 <doi: 10.1177/001316446002000104>; Fleiss 1971 <doi: 10.1037/h0031619>).