sjmisc complements dplyr with functions for data transformation and variable recoding, designed to work with pipes and labelled data. Functions follow the tidyverse convention of taking the data as the first argument and returning the same type as the input.
What’s covered#
- Design philosophy – vector and data frame input, ellipsis argument for variable selection
- Frequency tables –
frq,flat_table,count_na - Descriptive summary –
descr - Finding variables in a data frame –
find_var - Recode and transform variables –
rec,dicho,split_var,group_var,std,recode_to - Summarise variables and cases –
row_sums,row_means,row_count - Other useful functions –
add_columns,set_na,remove_var,group_str,to_long,merge_df