Function reference • sjmisc

Introduction
`sjmisc-package`	Data and Variable Transformation Functions
Recode and Data Transformation Functions
`rec()` `rec_if()`	Recode variables
`recode_to()` `recode_to_if()`	Recode variable categories into new values
`dicho()` `dicho_if()`	Dichotomize variables
`group_var()` `group_var_if()` `group_labels()` `group_labels_if()`	Recode numeric variables into equal-ranged groups
`group_str()`	Group near elements of string vectors
`split_var()` `split_var_if()`	Split numeric variables into smaller groups
`std()` `std_if()` `center()` `center_if()`	Standardize and center variables
Data Frame Transformation Functions
`merge_imputations()`	Merges multiple imputed data frames into a single data frame
`rotate_df()`	Rotate a data frame
Adding, Replacing and Removing Variables or Cases from Data Frames
`add_columns()` `replace_columns()` `add_id()`	Add or replace data frame columns
`add_rows()` `merge_df()`	Merge labelled data frames
`add_variables()` `add_case()`	Add variables or cases to data frames
`move_columns()`	Move columns to other positions in a data frame
`empty_cols()` `empty_rows()` `remove_empty_cols()` `remove_empty_rows()`	Return or remove variables or observations that are completely missing
`remove_var()` `remove_cols()`	Remove variables from a data frame
`var_rename()` `rename_variables()` `rename_columns()`	Rename variables
Variable Transformation
`numeric_to_factor()`	Convert numeric vectors into factors associated value labels
`reexports`	Objects exported from other packages
`to_value()`	Convert factors to numeric variables
Creating New Variables
`de_mean()`	Compute group-meaned and de-meaned variables
`to_dummy()`	Split (categorical) vectors into dummy variables
Variable Utility Functions
`ref_lvl()`	Change reference level of (numeric) factors
`round_num()`	Round numeric variables in a data frame
`shorten_string()`	Shorten character strings
`zap_inf()`	Convert infiite or NaN values into regular NA
Missing Values
`replace_na()`	Replace NA with specific values
`set_na_if()`	Replace specific values in vector with NA
Finding Variables and Values
`find_var()` `find_in_data()`	Find variable by name or label
`str_contains()`	Check if string contains pattern
`str_start()` `str_end()`	Find start and end index of pattern in string
`str_find()`	Find partial matching and close distance elements in strings
Descriptive and Summary Statistics
`count_na()`	Frequency table of tagged NA values
`descr()`	Basic descriptive statistics
`flat_table()`	Flat (proportional) tables
`frq()`	Frequency table of labelled variables
`row_count()` `col_count()`	Count row or column indices
`row_sums()` `row_means()` `total_mean()`	Row sums and means for data frames
Properties of Data Frames or Variables
`all_na()`	Check if vector only has NA values
`empty_cols()` `empty_rows()` `remove_empty_cols()` `remove_empty_rows()`	Return or remove variables or observations that are completely missing
`has_na()` `incomplete_cases()` `complete_cases()` `complete_vars()` `incomplete_vars()`	Check if variables or cases have missing / infinite values
Check Variables
`is_crossed()` `is_nested()` `is_cross_classified()`	Check whether two factors are crossed or nested
`is_empty()`	Check whether string, list or vector is empty
`is_even()` `is_odd()`	Check whether value is even or odd
`is_float()` `is_whole()`	Check if a variable is of (non-integer) double type or a whole number
`is_num_fac()` `is_num_chr()`	Check whether a factor has numeric levels only
Utility Functions
`%nin%`	Value matching
`big_mark()` `prcn()`	Format numbers
`rec_pattern()`	Create recode pattern for 'rec' function
`reshape_longer()`	Reshape data into long format
`seq_col()` `seq_row()`	Sequence generation for column or row counts of data frames
`spread_coef()`	Spread model coefficients of list-variables into columns
`tidy_values()` `clean_values()`	Clean values of character vectors.
`to_long()`	Convert wide data to long format
`trim()`	Trim leading and trailing whitespaces from strings
`typical_value()`	Return the typical value of a vector
`var_type()`	Determine variable type
`word_wrap()`	Insert line breaks in long labels
Sample Data
`efc`	Sample dataset from the EUROFAMCARE project

Reference

Introduction

Recode and Data Transformation Functions

Data Frame Transformation Functions

Adding, Replacing and Removing Variables or Cases from Data Frames

Variable Transformation

Creating New Variables

Variable Utility Functions

Missing Values

Finding Variables and Values

Descriptive and Summary Statistics

Properties of Data Frames or Variables

Check Variables

Utility Functions

Sample Data