Nothing
Here, we're just setting a few options.
knitr::opts_chunk$set( warning = TRUE, # show warnings during codebook generation message = TRUE, # show messages during codebook generation error = TRUE, # do not interrupt codebook generation in case of errors, # usually better for debugging echo = TRUE # show R code ) ggplot2::theme_set(ggplot2::theme_bw())
Now, we're preparing our data for the codebook.
library(codebook) codebook_data <- codebook::bfi # to import an SPSS file from the same folder uncomment and edit the line below # codebook_data <- rio::import("mydata.sav") # for Stata # codebook_data <- rio::import("mydata.dta") # for CSV # codebook_data <- rio::import("mydata.csv") # omit the following lines, if your missing values are already properly labelled codebook_data <- detect_missing(codebook_data, only_labelled = TRUE, # only labelled values are autodetected as # missing negative_values_are_missing = FALSE, # negative values are missing values ninety_nine_problems = TRUE, # 99/999 are missing values, if they # are more than 5 MAD from the median ) # If you are not using formr, the codebook package needs to guess which items # form a scale. The following line finds item aggregates with names like this: # scale = scale_1 + scale_2R + scale_3R # identifying these aggregates allows the codebook function to # automatically compute reliabilities. # However, it will not reverse items automatically. codebook_data <- detect_scales(codebook_data)
Create codebook
codebook(codebook_data)
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