Automatic Codebooks from Metadata Encoded in Dataset Attributes
Append R to string, if it doesn't end in R already.
Aggregate variables and remember which variables this were
Browse and search codebook
Codebook component for scales
Codebook component for single items
Codebook data info
Tabular codebook
Codebook missingness
Codebook survey overview
Codebook metadata table
Generate rmarkdown codebook
Compact Codebook
Add metadata to a dataset
How many surveys were modified?
Create a codebook rmarkdown document
Plot labelled vector
Briefly summarise available reliability results
Objects exported from other packages
Rescue lost attributes
Reverse labelled values reverse the underlying values for a numeric `h...
Skim codebook
To factor
Zap attributes
Zap labelled class
Define skimmers for haven_labelled_spss variables
Define skimmers for haven_labelled variables
Has label
Has labels
Pretty-print a Cronbach's alpha object
Print a stats::cor.test() object for knitr
Print a psych::multilevel.reliability() object for knitr
Browse and search variable and value labels
Browse and search variable and value labels
Derive a likert object from items
Go from a named list to a key-value data frame or data dictionary and ...
Submit a data file and an rmarkdown template as a file to generate a c...
Missing data patterns
Metadata as JSON-LD
Metadata from dataframe
Compute reliabilities
Data description default
Detect missing values
Detect item scales
How many surveys were ended?
How many surveys were expired?
Easily automate the following tasks to describe data frames: Summarise the distributions, and labelled missings of variables graphically and using descriptive statistics. For surveys, compute and summarise reliabilities (internal consistencies, retest, multilevel) for psychological scales. Combine this information with metadata (such as item labels and labelled values) that is derived from R attributes. To do so, the package relies on 'rmarkdown' partials, so you can generate HTML, PDF, and Word documents. Codebooks are also available as tables (CSV, Excel, etc.) and in JSON-LD, so that search engines can find your data and index the metadata. The metadata are also available at your fingertips via RStudio Addins.
Useful links