Package index • tidycomm

Univariate analysis

describe(): Describe numeric variables

tab_percentiles(): Tabulate percentiles for numeric variables

describe_cat(): Describe categorical variables

tab_frequencies(): Tabulate frequencies

Bivariate analysis

crosstab(): Crosstab variables

t_test(): Compute t-tests

unianova(): Compute one-way ANOVAs

correlate(): Compute correlation coefficients

to_correlation_matrix(): Create correlation matrix

regress(): Compute linear regression

Data modification

add_index(): Add index

reverse_scale(): Reverse numeric, logical, or date/time continuous variables

minmax_scale(): Rescale numeric continuous variables to new minimum/maximum boundaries

center_scale(): Center numeric, continuous variables

z_scale(): Z-standardize numeric, continuous variables

categorize_scale(): Categorize numeric variables into categories

dummify_scale(): Convert categorical variables to dummy variables

setna_scale(): Set specified values to NA in selected variables or entire data frame

recode_cat_scale(): Recode one or more categorical variables into new categories

Reliability tests

get_reliability(): Get reliability estimates of index variables

Intercoder reliability

test_icr(): Perform an intercoder reliability test

Visualization

visualize(): Visualize tidycomm output

design_lmu(): Colorbrewer-inspired design with focus on LMU (lmu.de) green

design_gray(): Gray design

design_grey(): Grey design

Sample data

WoJ: Worlds of Journalism sample data

fbposts: Facebook posts reliability test

snscomments: SNS Comments data

incvlcomments: Incivil Comments Data