# ggDoE

## Installation

You can get the development version from GitHub:

``````if (!require("remotes")) install.packages("remotes")
remotes::install_github("toledo60/ggDoE")``````

## Overview

With ggDoE you’ll be able to generate common plots used in Design of Experiments with ggplot2.

``library(ggDoE)``

The following plots are currently available:

Alias Matrix

Correlation matrix plot to visualize the Alias matrix

``alias_matrix(design=aliased_design)`` Box-Cox Transformation

``````model <- lm(s2 ~ (A+B+C+D),data = adapted_epitaxial)
boxcox_transform(model,lambda = seq(-5,5,0.2))`````` Lambda Plot

Obtain the trace plot of the t-statistics after applying Boxcox transformation across a specified sequence of lambda values

``````model <-  lm(s2 ~ (A+B+C)^2,data=original_epitaxial)
lambda_plot(model)`````` ``lambda_plot(model, lambda = seq(0,2,by=0.1))`` Boxplots

``````data <- ToothGrowth
data\$dose <- factor(data\$dose,levels = c(0.5, 1, 2),
labels = c("D0.5", "D1", "D2"))

gg_boxplots(data,response = 'len',
factor = 'dose')`````` ``````gg_boxplots(data,response = 'len',
factor = 'dose',
group_var = 'supp',
color_palette = 'viridis',
jitter_points = TRUE)`````` Regression Diagnostic Plots

1. Residual vs. Fitted Values
2. Normal-QQ plot
3. Scale-Location plot
4. Residual vs. Leverage
5. Cook’s Distance
6. Collinearity

The default plots are 1-4

``````model <- lm(mpg ~ wt + am + gear + vs * cyl, data = mtcars)
diagnostic_plots(model,which_plots=1:6)`````` Half-Normal Plot

``````model <- lm(ybar ~ (A+B+C+D)^4,data=adapted_epitaxial)
half_normal(model)`````` ``````half_normal(model,method='Zahn',alpha=0.1,
ref_line=TRUE,label_active=TRUE,
margin_errors=TRUE)`````` Interaction Effects Plot (Factorial Design)

Interaction effects plot between two factors in a factorial design

``````interaction_effects(adapted_epitaxial,response = 'ybar',
exclude_vars = c('s2','lns2'))`````` ``````interaction_effects(adapted_epitaxial,response = 'ybar',
exclude_vars = c('A','s2','lns2'),
n_columns=3)`````` Main Effects Plots (Factorial Design)

Main effect plots for each factor in a factorial design

``````main_effects(original_epitaxial,
response='s2',
exclude_vars = c('ybar','lns2'))`````` ``````main_effects(original_epitaxial,
response='s2',
exclude_vars = c('A','ybar','lns2'),
color_palette = 'viridis',
n_columns=3)`````` Contour Plots

contour plot(s) that display the fitted surface for an rsm object involving two or more numerical predictors

``````heli.rsm <- rsm::rsm(ave ~ SO(x1, x2, x3, x4),
data = rsm::heli)``````
``````gg_rsm(heli.rsm,form = ~x1+x2+x3+x4,
at = rsm::xs(heli.rsm))`````` ``````gg_rsm(heli.rsm,form = ~x1+x2+x3+x4,
at = rsm::xs(heli.rsm),
filled = TRUE)`````` Pareto Plot

Pareto plot of effects with cutoff values for the margin of error (ME) and simultaneous margin of error (SME)

``````model <- lm(lns2 ~ (A+B+C+D)^4,data=original_epitaxial)
pareto_plot(model)`````` ``pareto_plot(model,method='Zahn',alpha=0.1)`` Two Dimensional Projections

This function will output all two dimensional projections from a Latin hypercube design

``````set.seed(10)
X <- lhs::randomLHS(n=10, k=4)
twoD_projections(X,n_columns=3,grid = TRUE)`````` Lastly, the following datasets/designs are included in ggDoE as tibbles:

“Experiments: Planning, Analysis, and Optimization, 2nd Edition”

• original_epitaxial: Original epitaxial layer experiment obtain from the book
“Experiments: Planning, Analysis, and Optimization, 2nd Edition”

• aliased_design: D-efficient minimal aliasing design obtained from the article
“Efficient Designs With Minimal Aliasing by Bradley Jones and Christopher J. Nachtsheim”
Source: https://www.tandfonline.com/doi/abs/10.1198/TECH.2010.09113

## Citation

If you want to cite this package in a scientific journal or in any other context, run the following code in your `R` console

``citation('ggDoE')``
``````Warning in citation("ggDoE"): no date field in DESCRIPTION file of package
'ggDoE'

Warning in citation("ggDoE"): could not determine year for 'ggDoE' from package
DESCRIPTION file

To cite package 'ggDoE' in publications use:

Toledo Luna J (????). _ggDoE: Modern Graphs for Design of Experiments
with 'ggplot2'_. R package version 0.7.8,
<https://ggdoe.netlify.app>.

A BibTeX entry for LaTeX users is

@Manual{,
title = {ggDoE: Modern Graphs for Design of Experiments with 'ggplot2'},
author = {Jose {Toledo Luna}},
note = {R package version 0.7.8},
url = {https://ggdoe.netlify.app},
}``````

Note: Once this package is submitted to CRAN the date warning will disappear. Simply change (????) to (2022)

## Contributing to the package

I welcome feedback, suggestions, issues, and contributions! Check out the CONTRIBUTING file for more details.