Data and Variable Transformation Functions

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Documentation for package ‘sjmisc’ version 2.8.10

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

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