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README.Rmd
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README.Rmd
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---
output: github_document
---
# ggDoE <img src="man/figures/logo.png" align="right" height="139" alt="" />
[](https://cran.r-project.org/package=ggDoE)
[](https://ggdoe.netlify.app/)
[](https://github.com/toledo60/ggDoE/actions/workflows/R-CMD-check.yaml)
```{r,echo=FALSE}
knitr::opts_chunk$set(eval = FALSE, fig.width = 6,out.width = '100%')
```
## Installation
You can install:
* the latest release from CRAN with
```{r,eval=FALSE}
install.packages('ggDoE')
```
* the development version from GitHub with
```{r,eval=FALSE}
if (!require("pak")) install.packages("pak")
pak::pak("toledo60/ggDoE")
```
## Overview
With ggDoE you'll be able to generate common plots used in Design of Experiments with ggplot2.
```{r,eval=TRUE}
library(ggDoE)
```
The following plots are currently available:
1. [Alias Matrix](#alias-matrix)
2. [Box-Cox Transformation](#box-cox-transformation)
3. [Lambda Plot](#lambda-plot)
4. [Boxplots](#boxplots)
5. [Regression Diagnostic Plots](#regression-diagnostic-plots)
6. [Half-Normal Plot](#half-normal-plot)
7. [Interaction Effects Plot for a Factorial Design](#interaction-effects-plot)
8. [Main Effects Plot for a Factorial Design](#main-effects-plot)
9. [Contour Plots for Response Surface Methodology](#contour-plots)
10. [Pareto Plot](#pareto-plot)
11. [Two Dimensional Projections of a Latin Hypercube Design](#two-dimensional-projections)
The following datasets/designs are included in ggDoE as tibbles:
- **adapted_epitaxial**: Adapted epitaxial layer experiment obtain from the book <br>
*"Experiments: Planning, Analysis, and Optimization, 2nd Edition"*
- **original_epitaxial**: Original epitaxial layer experiment obtain from the book <br>
*"Experiments: Planning, Analysis, and Optimization, 2nd Edition"*
- **pulp_experiment**: Reflectance Data, Pulp Experiment obtain from the book <br>
*"Experiments: Planning, Analysis, and Optimization, 2nd Edition"*
- **girder_experiment**: Girder experiment obtain from the book <br>
*"Experiments: Planning, Analysis, and Optimization, 2nd Edition"*
- **aliased_design**: D-efficient minimal aliasing design obtained from the article <br>
*"Efficient Designs With Minimal Aliasing by Bradley Jones and Christopher J. Nachtsheim"*
## 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
```{r,eval=TRUE,comment=NA}
citation('ggDoE')
```
## Contributing to the package
I welcome feedback, suggestions, issues, and contributions!
Check out the [CONTRIBUTING](https://github.com/toledo60/ggDoE/blob/main/.github/CONTRIBUTING.md) file for more details.
## Examples of Plots
### Alias Matrix
Correlation matrix plot to visualize the Alias matrix
```{r}
alias_matrix(design=aliased_design)
```
### Box-Cox Transformation
```{r}
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
```{r}
model <- lm(s2 ~ (A+B+C)^2,data=original_epitaxial)
lambda_plot(model)
```
```{r}
lambda_plot(model, lambda = seq(0,2,by=0.1))
```
### Boxplots
```{r}
data <- ToothGrowth
data$dose <- factor(data$dose,levels = c(0.5, 1, 2),
labels = c("D0.5", "D1", "D2"))
gg_boxplots(data,y = 'len',x = 'dose')
```
```{r}
gg_boxplots(data,y = 'len',x = '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
```{r,fig.height=10,fig.width=8}
model <- lm(mpg ~ wt + am + gear + vs * cyl, data = mtcars)
gg_lm(model,which_plots=1:6)
```
### Half-Normal Plot
```{r}
model <- lm(ybar ~ (A+B+C+D)^4,data=adapted_epitaxial)
half_normal(model)
```
```{r}
half_normal(model,method='Zahn',alpha=0.1,
ref_line=TRUE,label_active=TRUE,
margin_errors=TRUE)
```
### Interaction Effects Plot
Interaction effects plot between two factors in a *factorial design*
```{r,fig.width=8,fig.height=6}
interaction_effects(adapted_epitaxial,response = 'ybar',
exclude_vars = c('s2','lns2'))
```
```{r,fig.width=9,fig.height=4}
interaction_effects(adapted_epitaxial,response = 'ybar',
exclude_vars = c('A','s2','lns2'),
n_columns=3)
```
### Main Effects Plots
Main effect plots for each factor in a *factorial design*
```{r}
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
```{r}
heli.rsm <- rsm::rsm(ave ~ SO(x1, x2, x3, x4),
data = rsm::heli)
```
```{r}
gg_rsm(heli.rsm,formula = ~x1+x2+x3+x4,
at = rsm::xs(heli.rsm))
```
```{r,echo=FALSE}
gg_rsm(heli.rsm,formula = ~x1+x2+x3+x4,
at = rsm::xs(heli.rsm))
```
### Pareto Plot
Pareto plot of effects with cutoff values for the margin of error (ME) and simultaneous margin of error (SME)
```{r}
model <- lm(lns2 ~ (A+B+C+D)^4,data=original_epitaxial)
pareto_plot(model)
```
```{r}
pareto_plot(model,method='Zahn',alpha=0.1)
```
### Two Dimensional Projections
This function will output all two dimensional projections from a Latin hypercube design
```{r}
set.seed(10)
X <- lhs::randomLHS(n=10, k=4)
pair_plots(X,n_columns=3,grid = TRUE)
```
