Introducing the CGPfunctions package

March 21, 2018
By

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CRAN
Version
RBloggers

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

Creative Commons License
This work (blogpost) is licensed under a
Creative
Commons Attribution-ShareAlike 4.0 International License
.

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