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sjmisc (version 1.5)

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

32,088

Version

1.5

License

GPL-3

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Repository

https://github.com/sjPlot/sjmisc

Maintainer

Daniel Lüdecke

Last Published

February 11th, 2016

Functions in sjmisc (1.5)

Coefficient of Variation

Convert vector to labelled class

Copy value and variable labels to (subsetted) data frames

Check whether value is odd

Standardized Beta coefficients and CI of lm and mixed models

Add missing value labels to partially labelled vector

Import STATA dataset as data frame into R

Weight a variable

Chi-square goodness-of-fit-test

Retrieve variable label(s) of labelled data

Data Transformation and Labelled Data Utility Functions

Create a labelled vector

Recode variable categories into new values

Convert non-labelled values into NA

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

Convergence test for mixed effects models

Add value labels to variables

Cronbach's Alpha for a matrix or data frame

Set NA for specific variable values

Mean Inter-Item-Correlation

Retrieve missing values of labelled variables

Import SAS dataset as data frame into R

Expected and relative table values

Check whether value is even

Plot Levene-Test for One-Way-Anova

Tjur's Coefficient of Discrimination

Split numeric variables into smaller groups

Retrieve missing value flags of labelled variables

Convert labelled data (frames) into normal classes

Summary of labelled vectors

Check overdispersion of GLMM's

Sample dataset from the EUROFAMCARE project

Group near elements of string vectors

Convert factors to numeric variables

Dichotomize variables

Cramer's V for a contingency table

Retrieve value labels of labelled data

Check whether a factor has numeric levels only

Check whether two factors are crossed

Check whether string or vector is empty

Convert missing values of labelled variables into NA

Retrieve values of labelled variables

Mann-Whitney-U-Test

Recode numeric variables into equal-ranged groups

Remove value labels from variables

Eta-squared of fitted anova

Convert wide data to long format

Check whether object is of class "labelled"

Convert variable into factor and keep value labels

Add value labels to variables

Replace NA with specific values

Write content of data frame to SPSS sav-file

Change reference level of (numeric) factors

Trim leading and trailing whitespaces from strings

Recode numeric variables

Intra-Class-Correlation Coefficient

Find partial matching and close distance elements in strings

remove_all_labels

Remove value and variable labels from vector or data frame

Check whether two factors are nested

Add variable label(s) to variables

Standard Error for variables

Convert labelled values into NA

Create labels for recoded groups

Performs a reliability test on an item scale

Weight a variable

Write content of data frame to STATA dta-file

Import SPSS dataset as data frame into R

Convert variable into factor and replaces values with associated value labels

Split (categorical) vectors into dummy variables

Root Mean Squared Error (RMSE)

Hosmer-Lemeshow Goodness-of-fit-test

Row means with min amount of valid values

Phi value for contingency tables

Insert line breaks in long labels