%nin%                   Value matching
add_columns             Add or replace data frame columns
add_rows                Merge labelled data frames
add_variables           Add variables or cases to data frames
all_na                  Check if vector only has NA values
big_mark                Format numbers
count_na                Frequency table of tagged NA values
de_mean                 Compute group-meaned and de-meaned variables
descr                   Basic descriptive statistics
dicho                   Dichotomize variables
efc                     Sample dataset from the EUROFAMCARE project
empty_cols              Return or remove variables or observations that
                        are completely missing
find_var                Find variable by name or label
flat_table              Flat (proportional) tables
frq                     Frequency table of labelled variables
group_str               Group near elements of string vectors
group_var               Recode numeric variables into equal-ranged
                        groups
has_na                  Check if variables or cases have missing /
                        infinite values
is_crossed              Check whether two factors are crossed or nested
is_empty                Check whether string, list or vector is empty
is_even                 Check whether value is even or odd
is_float                Check if a variable is of (non-integer) double
                        type or a whole number
is_num_fac              Check whether a factor has numeric levels only
merge_imputations       Merges multiple imputed data frames into a
                        single data frame
move_columns            Move columns to other positions in a data frame
numeric_to_factor       Convert numeric vectors into factors associated
                        value labels
rec                     Recode variables
rec_pattern             Create recode pattern for 'rec' function
recode_to               Recode variable categories into new values
ref_lvl                 Change reference level of (numeric) factors
remove_var              Remove variables from a data frame
replace_na              Replace NA with specific values
rotate_df               Rotate a data frame
round_num               Round numeric variables in a data frame
row_count               Count row or column indices
row_sums                Row sums and means for data frames
seq_col                 Sequence generation for column or row counts of
                        data frames
set_na                  Replace specific values in vector with NA
shorten_string          Shorten character strings
sjmisc-package          Data and Variable Transformation Functions
split_var               Split numeric variables into smaller groups
spread_coef             Spread model coefficients of list-variables
                        into columns
std                     Standardize and center variables
str_contains            Check if string contains pattern
str_pos                 Find partial matching and close distance
                        elements in strings
str_start               Find start and end index of pattern in string
to_character            Convert variable into character vector and
                        replace values with associated value labels
to_dummy                Split (categorical) vectors into dummy
                        variables
to_factor               Convert variable into factor and keep value
                        labels
to_label                Convert variable into factor with associated
                        value labels
to_long                 Convert wide data to long format
to_value                Convert factors to numeric variables
trim                    Trim leading and trailing whitespaces from
                        strings
var_rename              Rename variables
var_type                Determine variable type
word_wrap               Insert line breaks in long labels
zap_inf                 Convert infiite or NaN values into regular NA
