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sjmisc - Data Transformation and Labelled Data Utility Functions

This package contains utility functions that are useful when carrying out data analysis, performing common recode and data transformation tasks or working with labelled data (especially intended for people coming from 'SPSS', 'SAS' or 'Stata' and/or who are new to R).

Basically, this package covers three domains of functionality:

  • reading and writing data between other statistical packages (like 'SPSS') and R, based on the haven and foreign packages
  • hence, this package also includes functions to make working with labelled data easier
  • frequently applied recoding and variable transformation tasks, also with support for labelled data

Installation

Latest development build

To install the latest development snapshot (see latest changes below), type following commands into the R console:

library(devtools)
devtools::install_github("sjPlot/sjmisc")

Officiale, stable release

     

To install the latest stable release from CRAN, type following command into the R console:

install.packages("sjmisc")

References, documentation and examples

Citation

In case you want / have to cite my package, please use citation('sjmisc') for citation information.

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Version

Install

install.packages('sjmisc')

Monthly Downloads

34,037

Version

2.2.0

License

GPL-3

Issues

Pull Requests

Stars

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Maintainer

Daniel Lüdecke

Last Published

December 21st, 2016

Functions in sjmisc (2.2.0)

empty_cols

Return or remove variables or observations that are completely missing
descr

Basic descriptive statistics
efc

Sample dataset from the EUROFAMCARE project
flat_table

Flat (proportional) tables
count_na

Frequency table of tagged NA values
as_labelled

Convert vector to labelled class
find_var

Find variable by name or label
big_mark

Formats large numbers with big marks
dicho

Dichotomize variables
copy_labels

Copy value and variable labels to (subsetted) data frames
is_crossed

Check whether two factors are crossed or nested
get_values

Retrieve values of labelled variables
is_empty

Check whether string, list or vector is empty
get_note

Retrieve notes (annotations) from labelled variables
get_label

Retrieve variable label(s) of labelled data
frq

Frequencies of labelled variables
group_str

Group near elements of string vectors
get_labels

Retrieve value labels of labelled data
group_var

Recode numeric variables into equal-ranged groups
get_na

Retrieve tagged NA values of labelled variables
is_even

Check whether value is even or odd
set_labels

Add value labels to variables
str_pos

Find partial matching and close distance elements in strings
set_na

Replace specific values in vector with NA
is_labelled

Check whether object is of class "labelled"
set_note

Add notes (annotations) to (labelled) variables
str_contains

Check if string contains pattern
to_factor

Convert variable into factor and keep value labels
sjmisc-package

Data Transformation and Labelled Data Utility Functions
to_label

Convert variable into factor and replace values with associated value labels
merge_imputations

Merges multiple imputed data frames into a single data frame
merge_df

Merge labelled data frames
drop_labels

Drop, add or convert (non-)labelled values to vectors
zap_inf

Convert infiite or NaN values into regular NA
var_labels

Add variable label(s) to variables
remove_all_labels

Remove value and variable labels from vector or data frame
ref_lvl

Change reference level of (numeric) factors
rec_pattern

Create recode pattern for 'rec' function
var_rename

Rename variables
read_spss

Import data from other statistical software packages
replace_na

Replace NA with specific values
set_label

Add variable label(s) to variables
trim

Trim leading and trailing whitespaces from strings
zap_na_tags

Convert tagged NA values into regular NA
unlabel

Convert labelled vectors into normal classes
to_character

Convert variable into character vector and replace values with associated value labels
lbl_df

Create a labelled data frame
is_num_fac

Check whether a factor has numeric levels only
to_dummy

Split (categorical) vectors into dummy variables
rec

Recode variables
split_var

Split numeric variables into smaller groups
recode_to

Recode variable categories into new values
spread_coef

Spread model coefficients of list-variables into columns
to_value

Convert factors to numeric variables
to_long

Convert wide data to long format
word_wrap

Insert line breaks in long labels
write_spss

Write data to other statistical software packages