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sjmisc (version 1.2)

Data Transformation and Labelled Data Utility Functions

Description

Collection of miscellaneous utility functions (especially intended for people coming from other statistical software packages like 'SPSS', and/or who are new to R), supporting following common tasks: 1) Reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata' and working with labelled data; this includes easy ways to get and set label attributes, to convert labelled vectors into factors (and vice versa), or to deal with multiple declared missing values etc. 2) Data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. 3) Convenient functions to perform frequently used statistical tests, or to calculate various commonly used statistical coefficients.

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Version

Install

install.packages('sjmisc')

Monthly Downloads

32,088

Version

1.2

License

GPL-3

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Repository

https://github.com/sjPlot/sjmisc

Maintainer

Daniel Lüdecke

Last Published

October 12th, 2015

Functions in sjmisc (1.2)

Cramer's V for a contingency table

Check whether value is even

Convergence test for mixed effects models

Check whether a factor has numeric levels only

Retrieve missing value flags of labelled variables

Recode count variables into grouped factors

Cronbach's Alpha for a matrix or data frame

Retrieve missing values of labelled variables

Retrieve values of labelled variables

Create a labelled vector

Weight a variable

Group near elements of string vectors

Retrieve value labels of labelled data

Set NA for specific variable values

Import SAS dataset as data frame into R

Weight a variable

Convert labelled values into NA

Tjur's Coefficient of Discrimination

Performs a reliability test on an item scale

Sample dataset from the EUROFAMCARE project

Remove value and variable labels from vector or data frame

Coefficient of Variation

Check whether string is empty

Dichotomize variables

Mann-Whitney-U-Test

Row means with min amount of valid values

Create labels for recoded groups

Retrieve variable label(s) of labelled data

Add value labels to variables

Add variable label(s) to variables

Trim leading and trailing whitespaces from strings

Expected and relative table values

Check whether two factors are crossed

Check whether object is of class "labelled"

Recode numeric variables

Add value labels to variables

Data Transformation and Labelled Data Utility Functions

Chi-square goodness-of-fit-test

Check whether value is odd

Replace NA with specific values

Convert labelled data (frames) into normal classes

Convert non-labelled values into NA

Summary of labelled vectors

Convert missing values of labelled variables into NA

Convert factors to numeric variables

Copy value and variable labels to (subsetted) data frames

Import STATA dataset as data frame into R

Intra-Class-Correlation Coefficient

Hosmer-Lemeshow Goodness-of-fit-test

Standard Error for variables

Root Mean Squared Error (RMSE)

Import SPSS dataset as data frame into R

Convert variable into factor and keep value labels

Standardized Beta coefficients and CI of lm and mixed models

Plot Levene-Test for One-Way-Anova

Nagelkerke's and Cox-Snell's Pseudo R-squared

Phi value for contingency tables

Convert vector to labelled class

Find partial matching and close distance elements in strings

Recode variable categories into new values

Convert variable into factor and replaces values with associated value labels

Write content of data frame to SPSS sav-file

Write content of data frame to STATA dta-file

Eta-squared of fitted anova

Insert line breaks in long labels

Add missing value labels to partially labelled vector

Mean Inter-Item-Correlation

Check whether two factors are nested