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sjmisc (version 1.7)

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

1.7

License

GPL-3

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Repository

https://github.com/sjPlot/sjmisc

Maintainer

Daniel Lüdecke

Last Published

April 19th, 2016

Functions in sjmisc (1.7)

Get summary of labelled vectors

Chi-square goodness-of-fit-test

Recode numeric variables into equal-ranged groups

Retrieve value labels of labelled data

Dichotomize variables

Replace NA with specific values

Set NA for specific variable values

Eta-squared of fitted anova

Sample dataset from the EUROFAMCARE project

Check whether two factors are crossed

Create labels for recoded groups

Add missing value labels to partially labelled vector

Cronbach's Alpha for a matrix or data frame

Summary of labelled vectors

Import SPSS dataset as data frame into R

Check whether value is odd

Convert vector to labelled class

Mann-Whitney-U-Test

Find partial matching and close distance elements in strings

Convert variable into factor and keep value labels

Cramer's V for a contingency table

Add value labels to variables

Retrieve values of labelled variables

Drop labels of zero-count values

Check whether a factor has numeric levels only

Check whether value is even

Trim leading and trailing whitespaces from strings

Check whether two factors are nested

Convergence test for mixed effects models

Convert missing values of labelled variables into NA

Check whether string or vector is empty

Import SAS dataset as data frame into R

Intraclass-Correlation Coefficient

Import STATA dataset as data frame into R

Convert wide data to long format

Check if string contains pattern

Convert non-labelled values into NA

Merge labelled data frames

Standard Error for variables

Retrieve notes (annotations) from labelled variables

Copy value and variable labels to (subsetted) data frames

Weighted standard error for variables

Check whether object is of class "labelled"

Create recode pattern for 'rec' function

Mean Inter-Item-Correlation

Group near elements of string vectors

Data Transformation and Labelled Data Utility Functions

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

Remove value labels from variables

Performs a reliability test on an item scale

remove_all_labels

Remove value and variable labels from vector or data frame

Retrieve variable label(s) of labelled data

Split numeric variables into smaller groups

Add variable label(s) to variables

Recode numeric variables

Create a labelled data frame

Convert factors to numeric variables

Hosmer-Lemeshow Goodness-of-fit-test

Weight a variable

Standardized Beta coefficients and CI of lm and mixed models

Add notes (annotations) to (labelled) variables

Random effect variances

Change reference level of (numeric) factors

Convert labelled values into NA

Plot Levene-Test for One-Way-Anova

Root Mean Squared Error (RMSE)

Phi value for contingency tables

Convert variable into factor and replaces values with associated value labels

Convert labelled vectors into normal classes

Expected and relative table values

Insert line breaks in long labels

Retrieve missing values of labelled variables

Add value labels to variables

Recode variable categories into new values

Weight a variable

Write content of data frame to STATA dta-file

Write content of data frame to SPSS sav-file

Create a labelled vector

Check overdispersion of GL(M)M's

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

Retrieve missing value flags of labelled variables

Weighted standard deviation for variables

Coefficient of Variation

Tjur's Coefficient of Discrimination

Row means with min amount of valid values

Split (categorical) vectors into dummy variables