Use the following command to install the package from CRAN:
This section demonstrates the usage of the package and its underlying functions. Factorial experiments are ubiquitous in all science and technology fields, and as an example, a factorial AB-design will be used. While some parameters are specifically relevant to agriculture, most others are beneficial for every user.
Use the following command to load the package after installation. The two packages below ‘agricolaeplotr’ are only needed for the examples.
To create a design, we first utilize the agricolae package. All examples provided are directly sourced from agricolae.
After creating the object, everything is set to plot a basic graph. It is assumed that the height and width of each plot are both set to 1. In agricultural designs, it is recommended to input the measures from a plot to estimate the dimensions needed for implementing such an experiment in the field. Knowing the required dimensions in meters or other units is crucial for machinery and experiment management.
Complete randomized designs lack a factor like blocks, requiring the user to input suitable numbers for columns and rows. The product of these numbers must be greater than the size of the experiment, allowing the program to place all plots.
The following figure illustrates the output of a factorial design with two factors. The first factor has three levels, and the second one has two. The output is a standard ggplot2 design. This implies that users can apply all operations that ggplot2 and other packages using ggplot2 functions can offer. There are no layer restrictions or overly specialized layers preventing other transformations. Additionally, users may leverage ‘plotly’ to create interactive visualizations of the designs. This is particularly useful for field demonstrations involving various project stakeholders such as scientists, farmers, and funding agencies.
library(agricolae) # origin of the needed design object
trt <- c(3, 2) # factorial 3x2
outdesign <- design.ab(trt, r = 3, serie = 2, design = 'crd')
head(outdesign$book, 10)
plot_design.factorial_crd(outdesign, ncols = 6, nrows = 3, width = 1, height = 1)