sjmisc package - RDocumentation

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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 or basic statistical tests, performing common recode and data transformation tasks or working with labelled data (especially intended for people coming from 'SPSS' and/or who are new to R).

Basically, this package covers four 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 used statistical tests and computation of statistical coefficients, or at least convenient wrappers for such test functions
frequently applied recoding and variable transformation tasks

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

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

32,088

Version

1.8

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/sjPlot/sjmisc

Maintainer

Daniel Lüdecke

Last Published

May 19th, 2016

Functions in sjmisc (1.8)

Drop labels of zero-count values

Check whether two factors are nested

Import SAS dataset as data frame into R

Design effects for two-level mixed models

Standardized Beta coefficients and CI of lm and mixed models

Coefficient of Variation

Sample dataset from the EUROFAMCARE project

Add value labels to variables

Cramer's V for a contingency table

Check whether value is even

Expected and relative table values

Retrieve variable label(s) of labelled data

Merge labelled data frames

Create labels for recoded groups

Random effect variances

Retrieve notes (annotations) from labelled variables

Convert factors to numeric variables

Retrieve values of labelled variables

Check whether value is odd

Convert variable into factor and keep value labels

Recode variable categories into new values

Intraclass-Correlation Coefficient

Retrieve missing value flags of labelled variables

Chi-square goodness-of-fit-test

Sample size for linear mixed models

Tjur's Coefficient of Discrimination

Check if string contains pattern

Add variable label(s) to variables

Root Mean Squared Error (RMSE)

Check whether a factor has numeric levels only

Cronbach's Alpha for a matrix or data frame

Retrieve missing values of labelled variables

Group near elements of string vectors

Split (categorical) vectors into dummy variables

Recode numeric variables into equal-ranged groups

Phi value for contingency tables

Convergence test for mixed effects models

Weight a variable

Retrieve value labels of labelled data

Row means with min amount of valid values

Check whether string or vector is empty

Convert labelled values into NA

Create recode pattern for 'rec' function

Add notes (annotations) to (labelled) variables

Check whether two factors are crossed

Import STATA dataset as data frame into R

Convert vector to labelled class

Check whether object is of class "labelled"

Weighted standard deviation for variables

Write content of data frame to SPSS sav-file

Dichotomize variables

Convert labelled vectors into normal classes

Insert line breaks in long labels

Compute R-squared of (generalized) linear (mixed) models

Plot Levene-Test for One-Way-Anova

Import SPSS dataset as data frame into R

Set NA for specific variable values

Summary of labelled vectors

Change reference level of (numeric) factors

Write content of data frame to STATA dta-file

Copy value and variable labels to (subsetted) data frames

Remove value labels from variables

Data Transformation and Labelled Data Utility Functions

Standard Error for variables

Weight a variable

Check overdispersion of GL(M)M's

Convert non-labelled values into NA

Convert wide data to long format

remove_all_labels

Remove value and variable labels from vector or data frame

Recode variables

Split numeric variables into smaller groups

Replace NA with specific values

Eta-squared of fitted anova

Random effect variances

Add missing value labels to partially labelled vector

Find partial matching and close distance elements in strings

Get summary of labelled vectors

Hosmer-Lemeshow Goodness-of-fit-test

Weighted standard error for variables

Mean Inter-Item-Correlation

Mann-Whitney-U-Test

Add value labels to variables

Performs a reliability test on an item scale

Convert variable into factor and replaces values with associated value labels

Trim leading and trailing whitespaces from strings

Create a labelled vector

Create a labelled data frame

Convert missing values of labelled variables into NA