Package index • surveytoolbox

All functions
`CAGR()`	Calculate CAGR
`any_x()`	Function that returns TRUE/FALSE if value exists in x, but returns NA if x consists entirely of NAs
`append_to_list()`	Append an item to a list dynamically
`apply_row()`	Apply a function rowwise, selecting variables with dplyr::select() syntax
`as_nps()`	Convert numeric variable to NPS variable
`as_nps_cat()`	Convert numeric variable to NPS categorical variable
`as_percent()`	Convert as percent (string)
`box_it()`	Convert ordinal variables into binary variables by "boxing"
`calc_pc_loglin()`	Calculate percentage impact from coefficients of a log-linear model
`calc_weights()`	Calculate (simple) weights based on the proportion of another variable.
`categorise()`	Convert numeric variable into categorical variable
`char_to_lab()`	Convert character variable to labelled integer variable
`chr_to_var()`	Convert a string / character variable into a labelled double variable, using a pre-specified set of variable and value label mappings.
`clean_strings()`	Clean strings so that they can be used as variable names or column names
`copy_df()`	Copy a data frame to clipboard for pasting in Excel
`cor_to_df()`	Convert correlation matrix into a tidy data frame
`create_named_list()`	Create a named list object with two vectors
`data_dict()`	Create a Data Dictionary from a data frame with Variable and Value Labels.
`extract_fa_loads()`	Function to create a loadings file from the factanal() output
`extract_vallab()`	Return value labels as tibble
`lab_to_char()`	Convert labelled double variable to character variable
`labelled_quantile()`	Convert a numeric into a quantile categorical variable, labelled by lower and upper bounds of quantiles (string)
`likert_convert()`	Convert a Likert scale from one scale to another
`likert_reverse()`	Reverse a Likert scale
`look_up()`	Replace x values with corresponding values using a key
`maxmin()`	Max-Min Scaling Function
`read_df()`	Read in a data frame in the clipboard, copied from Excel
`recode_vallab()`	Recode value labels based on numeric code
`remove_na_only()`	Remove all columns which contain only NAs Optimised for magrittr / dplyr pipes
`remove_zero_only()`	Remove all columns which contain only zeros Optimised for magrittr / dplyr pipes
`replace_na_range()`	Replace NAs randomly from a selected range with replacement
`run_hclust()`	A wrapper function to run hierarchical clustering
`sav_to_rds()`	Serialise a SPSS file to RDS
`set_vall()`	Set value labels
`set_varl()`	Set variable labels
`split_tt()`	Split the data into a simple training and testing set
`squish()`	Returns a single-length vector if all values in vector are identical.
`superspread()`	Creates dummy variables from multiple categorical variables. Uses data.table() for speed (enhanced from the previous version) Uses dplyr select() special functions to select categorical variables.
`superspread_count()`	Convert single-code column(s) into "multiple choice" formats, filling data with sum of counts.
`superspread_fill()`	Convert single-code column(s) into "multiple choice" formats, filling data from a target column
`timed_fn()`	Create file name with time stamp
`ttest_nps()`	Performs a t-test on NPS
`varl_tb()`	Create tidy data frame with variable and variable labels
`wrap_text()`	Wrap text based on character threshold

Reference

All functions