userfriendlyscience-package

Userfriendlyscience

This package contains a number of functions that serve two goals: first, make R more accessible to people migrating from SPSS by adding a number of functions that behave roughly like their SPSS equivalents; and second, make a number of slightly more advanced functions more user friendly to relatively novice users.

Keywords
package
Details

Package:
userfriendlyscience
Type:
Package
Version:
0.5-2
Date:
December 2016
License:
GPL (>= 2)
The package contains a variety of functions designed to make life easier. These functions are geared towards researchers in psychology.

The package implements many solutions provided by people all over the world, most from Stack Exchange (both from Cross Validated and Stack Overflow). I credit these authors in the help pages of those functions and in the Author(s) section of this page. If you wrote a function included here, and you want me to take it out, feel free to contact me of course (also, see http://meta.stackoverflow.com/questions/319171/i-would-like-to-use-a-function-written-by-a-stack-overflow-member-in-an-r-packag).

References

Peters, G.-J. Y. (2014). The alpha and the omega of scale reliability and validity: why and how to abandon Cronbach's alpha and the route towards more comprehensive assessment of scale quality. European Health Psychologist, 16(2), 56-69.

See Also

psych and MBESS contain many useful functions for researchers in psychology.

Aliases
  • userfriendlyscience-package
  • userfriendlyscience
Examples
### Create simple dataset
dat <- PlantGrowth[1:20,];
### Remove third level from group factor
dat$group <- factor(dat$group);

### Examine normality
normalityAssessment(dat$weight);

### Compute mean difference and show it
meanDiff(dat$weight ~ dat$group, plot=TRUE);

### Show the t-test
didacticPlot(meanDiff(dat$weight ~ dat$group)$t,
             statistic='t',
             df1=meanDiff(dat$weight ~ dat$group)$df);

### Load data from simulated dataset testRetestSimData (which
### satisfies essential tau-equivalence).
data(testRetestSimData);

### Select some items in the first measurement
exampleData <- testRetestSimData[2:6];

### Show reliabilities
scaleStructure(dat=exampleData);

### Create a dichotomous variable
exampleData$group <- cut(exampleData$t0_item2, 2);

### Show a dlvPlot
dlvPlot(exampleData, x="group", y="t0_item1");

### show a dlvPlot with less participants, showing the confidence
### interval and standard error bars better
dlvPlot(exampleData[1:30, ], x="group", y="t0_item1");

Documentation reproduced from package userfriendlyscience, version 0.5-2, License: GPL (>= 2)

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