**Chuck Powell**, and kindly contributed to R-bloggers)

## Overview

This package includes functions that I find useful for teaching

statistics as well as actually practicing the art. They typically are

not “new” methods but rather wrappers around either base R or other

packages and concepts I’m trying to master. Currently contains:

`Plot2WayANOVA`

which as the name implies conducts a 2 way ANOVA and

plots the results using`ggplot2`

`neweta`

which is a helper function that appends the results of a

Type II eta squared calculation onto a classic ANOVA table`Mode`

which finds the modal value in a vector of data`SeeDist`

which wraps around ggplot2 to provide visualizations of

univariate data.`OurConf`

is a simulation function that helps you learn about

confidence intervals

## Installation

```
# Install from CRAN
install.packages("CGPfunctions")
# Highly recommended since it is under rapid development right now
# Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("ibecav/CGPfunctions")
```

## Usage

`library(CGPfunctions)`

will load the package which contains 5

functions:

`SeeDist`

will give you some plots of the distribution of a variable

using `ggplot2`

```
library(CGPfunctions)
SeeDist(mtcars$hp,whatvar="Horsepower",whatplots="d")
```

```
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 52.0 96.5 123.0 146.7 180.0 335.0
```

`Mode`

is a helper function that simply returns one or more modal values

```
Mode(mtcars$hp)
#> [1] 110 175 180
```

`neweta`

is a helper function which returns a tibble containing AOV

output similar to summary(aov(MyAOV)) but with eta squared computed and

appended as an additional column

```
MyAOV <- aov(mpg~am*cyl, mtcars)
neweta(MyAOV)
#> # A tibble: 4 x 8
#> Source Df `Sum Sq` `Mean Sq` `F value` p sigstars `eta sq`
#>
```
#> 1 am 1 37.0 37.0 4.30 0.0480 * 0.0330
#> 2 cyl 1 450. 450. 52.0 0. *** 0.399
#> 3 am:cyl 1 29.4 29.4 3.40 0.0760 . 0.0260
#> 4 Residuals 28 242. 8.64 NA NA 0.215

The `Plot2WayANOVA`

function conducts a classic analysis using existing

R functions and packages in a sane and defensible manner not necessarily

in the one and only manner.

```
Plot2WayANOVA(mpg~am*cyl, mtcars)
#>
#> Converting am to a factor --- check your results
#>
#> Converting cyl to a factor --- check your results
#>
#> You have an unbalanced design. Using Type II sum of squares, eta squared may not sum to 1.0
#> # A tibble: 4 x 8
#> Source Df `Sum Sq` `Mean Sq` `F value` p sigstars `eta sq`
#>
```
#> 1 am 1 36.8 36.8 4.00 0.0560 . 0.0330
#> 2 cyl 2 456. 228. 24.8 0. *** 0.405
#> 3 am:cyl 2 25.4 12.7 1.40 0.269 "" 0.0230
#> 4 Residuals 26 239. 9.19 NA NA 0.212
#>
#> Table of group means
#> # A tibble: 6 x 9
#> # Groups: am [2]
#> am cyl TheMean TheSD TheSEM CIMuliplier LowerBound UpperBound N
#>
#> 1 0 4 22.9 1.45 0.839 4.30 19.3 26.5 3
#> 2 0 6 19.1 1.63 0.816 3.18 16.5 21.7 4
#> 3 0 8 15.0 2.77 0.801 2.20 13.3 16.8 12
#> 4 1 4 28.1 4.48 1.59 2.36 24.3 31.8 8
#> 5 1 6 20.6 0.751 0.433 4.30 18.7 22.4 3
#> 6 1 8 15.4 0.566 0.400 12.7 10.3 20.5 2
#>
#> Testing Homogeneity of Variance with Brown-Forsythe
#> *** Possible violation of the assumption ***
#> Levene's Test for Homogeneity of Variance (center = median)
#> Df F value Pr(>F)
#> group 5 2.736 0.04086 *
#> 26
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Testing Normality Assumption with Shapiro-Wilk
#>
#> Shapiro-Wilk normality test
#>
#> data: MyAOV_residuals
#> W = 0.96277, p-value = 0.3263
#>
#> Interaction graph plotted...

`OurConf`

is a simulation function that helps you learn about confidence

intervals

```
OurConf(samples = 20, n = 15, mu = 100, sigma = 20, conf.level = 0.90)
```

```
#> 100 % of the confidence intervals contain Mu = 100 .
```

## Credits

Many thanks to Dani Navarro and the book > (Learning Statistics with

R)

whose etaSquared function was the genesis of `neweta`

.

“He who gives up safety for speed deserves neither.”

(via)

#### A shoutout to some other packages I find essential.

- stringr, for strings.
- lubridate, for date/times.
- forcats, for factors.
- haven, for SPSS, SAS and Stata

files. - readxl, for
`.xls`

and`.xlsx`

files. - modelr, for modelling within a

pipeline - broom, for turning models into

tidy data - ggplot2, for data visualisation.
- dplyr, for data manipulation.
- tidyr, for data tidying.
- readr, for data import.
- purrr, for functional programming.
- tibble, for tibbles, a modern

re-imagining of data frames.

## Leaving Feedback

If you like **CGPfunctions**, please consider leaving feedback

here.

## Contributing

Contributions in the form of feedback, comments, code, and bug reports

are most welcome. How to contribute:

- Issues, bug reports, and wish lists: File a GitHub

issue. - Contact the maintainer ibecav at gmail.com by email.

### License

This work (blogpost) is licensed under a

Creative

Commons Attribution-ShareAlike 4.0 International License.

**leave a comment**for the author, please follow the link and comment on their blog:

**Chuck Powell**.

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