Data Modification and Analysis for Communication Research

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Documentation for package ‘tidycomm’ version 0.4.1

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add_index Add index
categorize_scale Categorize numeric variables into categories
center_scale Center numeric, continuous variables
correlate Compute correlation coefficients
crosstab Crosstab variables
describe Describe numeric variables
describe_cat Describe categorical variables
design_gray Gray design
design_grey Grey design
design_lmu Colorbrewer-inspired design with focus on LMU (lmu.de) green
dummify_scale Convert categorical variables to dummy variables
fbposts Facebook posts reliability test
get_reliability Get reliability estimates of index variables
incvlcomments Incivil Comments Data
is_tdcmm 'tdcmm' output constructor
minmax_scale Rescale numeric continuous variables to new minimum/maximum boundaries
model Access model(s) used to estimate output
new_tdcmm 'tdcmm' output constructor
recode_cat_scale Recode one or more categorical variables into new categories
regress Compute linear regression
reverse_scale Reverse numeric, logical, or date/time continuous variables
setna_scale Set specified values to NA in selected variables or entire data frame
snscomments SNS Comments data
tab_frequencies Tabulate frequencies
tab_percentiles Tabulate percentiles for numeric variables
tdcmm 'tdcmm' class
tdcmm-class 'tdcmm' class
test_icr Perform an intercoder reliability test
to_correlation_matrix Create correlation matrix
t_test Compute t-tests
unianova Compute one-way ANOVAs
visualize Visualize tidycomm output
visualize.tdcmm_crrltn Visualize tidycomm output
visualize.tdcmm_ctgrcl Visualize tidycomm output
visualize.tdcmm_dscrb Visualize tidycomm output
visualize.tdcmm_nnv Visualize tidycomm output
visualize.tdcmm_prcntl Visualize tidycomm output
visualize.tdcmm_rgrssn Visualize tidycomm output
visualize.tdcmm_ttst Visualize tidycomm output
WoJ Worlds of Journalism sample data
z_scale Z-standardize numeric, continuous variables