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sjmisc (version 1.6)

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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Install

install.packages('sjmisc')

Monthly Downloads

34,037

Version

1.6

License

GPL-3

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Repository

https://github.com/sjPlot/sjmisc

Maintainer

Daniel Lüdecke

Last Published

March 14th, 2016

Functions in sjmisc (1.6)

Chi-square goodness-of-fit-test

Sample dataset from the EUROFAMCARE project

Replace NA with specific values

Retrieve missing values of labelled variables

Row means with min amount of valid values

Add value labels to variables

Hosmer-Lemeshow Goodness-of-fit-test

Retrieve missing value flags of labelled variables

Standardized Beta coefficients and CI of lm and mixed models

Find partial matching and close distance elements in strings

Import SAS dataset as data frame into R

Check whether a factor has numeric levels only

Dichotomize variables

Mann-Whitney-U-Test

Check whether object is of class "labelled"

Convert vector to labelled class

Copy value and variable labels to (subsetted) data frames

Recode numeric variables

Convert factors to numeric variables

Recode numeric variables into equal-ranged groups

Convergence test for mixed effects models

Retrieve value labels of labelled data

Group near elements of string vectors

Tjur's Coefficient of Discrimination

Performs a reliability test on an item scale

Set NA for specific variable values

Check overdispersion of GL(M)M's

Check whether two factors are crossed

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

Check whether value is even

Mean Inter-Item-Correlation

Remove value labels from variables

Weight a variable

Check whether string or vector is empty

Convert missing values of labelled variables into NA

Coefficient of Variation

Get summary of labelled vectors

Summary of labelled vectors

Convert variable into factor and keep value labels

Create a labelled vector

Convert non-labelled values into NA

Split (categorical) vectors into dummy variables

Retrieve variable label(s) of labelled data

Root Mean Squared Error (RMSE)

Plot Levene-Test for One-Way-Anova

Create a labelled data frame

Create labels for recoded groups

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

Check whether value is odd

Data Transformation and Labelled Data Utility Functions

Write content of data frame to STATA dta-file

Check whether two factors are nested

remove_all_labels

Remove value and variable labels from vector or data frame

Phi value for contingency tables

Cramer's V for a contingency table

Expected and relative table values

Convert labelled values into NA

Import SPSS dataset as data frame into R

Eta-squared of fitted anova

Drop labels of zero-count values

Add missing value labels to partially labelled vector

Retrieve values of labelled variables

Insert line breaks in long labels

Intraclass-Correlation Coefficient

Recode variable categories into new values

Convert labelled vectors into normal classes

Split numeric variables into smaller groups

Convert wide data to long format

Standard Error for variables

Add value labels to variables

Create recode pattern for 'rec' function

Add variable label(s) to variables

Cronbach's Alpha for a matrix or data frame

Weight a variable

Write content of data frame to SPSS sav-file

Change reference level of (numeric) factors

Check if string contains pattern

Trim leading and trailing whitespaces from strings

Convert variable into factor and replaces values with associated value labels

Import STATA dataset as data frame into R