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sjmisc (version 1.1)

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

34,037

Version

1.1

License

GPL-3

Issues

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Repository

https://github.com/sjPlot/sjmisc

Maintainer

Daniel Lüdecke

Last Published

August 26th, 2015

Functions in sjmisc (1.1)

Add value labels to variables

Chi-square goodness-of-fit-test

Recode variable categories into new values

Dichotomize variables

Import SAS dataset as data frame into R

Mean Inter-Item-Correlation

Retrieve variable label(s) of labelled data

Check whether string is empty

Row means with min amount of valid values

Retrieve value labels of labelled data

Convert vector to labelled class

Tjur's Coefficient of Discrimination

Check whether value is even

Check whether object is of class "labelled"

Eta-squared of fitted anova

Retrieve missing values of labelled variables

Copy value and variable labels to (subsetted) data frames

Check whether two factors are crossed

Check whether two factors are nested

Import STATA dataset as data frame into R

Data Transformation and Labelled Data Utility Functions

Check whether a factor has numeric levels only

Performs a reliability test on an item scale

Replace NA with specific values

Trim leading and trailing whitespaces from strings

Convert variable into factor and keep value labels

Convert variable into factor and replaces values with associated value labels

Find partial matching and close distance elements in strings

Expected and relative table values

Retrieve values of labelled variables

Convert non-labelled values into NA

Cronbach's Alpha for a matrix or data frame

Create labels for recoded groups

Cramer's V for a contingency table

Create a labelled vector

Group near elements of string vectors

Summary of labelled vectors

Weight a variable

Intra-Class-Correlation Coefficient

Plot Levene-Test for One-Way-Anova

Standardized Beta coefficients and CI of lm and mixed models

Weight a variable

Sample dataset from the EUROFAMCARE project

Retrieve missing value flags of labelled variables

Convert labelled data (frames) into normal classes

Insert line breaks in long labels

Phi value for contingency tables

Mann-Whitney-U-Test

Check whether value is odd

Root Mean Squared Error (RMSE)

Add missing value labels to partially labelled vector

Hosmer-Lemeshow Goodness-of-fit-test

Convert labelled values into NA

Convert missing values of labelled variables into NA

Import SPSS dataset as data frame into R

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

Write content of data frame to SPSS sav-file

Convert factors to numeric variables

Recode numeric variables

Remove value and variable labels from vector or data frame

Add variable label(s) to variables

Recode count variables into grouped factors

Set NA for specific variable values

Add value labels to variables

Write content of data frame to STATA dta-file

Coefficient of Variation

Standard Error for variables