# 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 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.

##### 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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