How many times did you wish to dive into a three-dimensional biplot, enjoying the dispersion of your data sample like in a starring sky? Now, the BiPlotteR R-package makes it possible! BiPlotteR is based on the Plotly interactive graphical interface and allows you to create captivating 2D and 3D biplots after performing the Principal Component Analysis on your dataset using the prcomp function provided by the stats R package.
Install BiPlotteR
You can install the development version of BiPlotteR from GitHub. For this purpose, you have to formerly install the devtools package.
install.packages("devtools")
devtools::install_github("AnCaste/BiPlotteR")Uninstall BiPlotteR
You can remove the BiPlotteR package in any moment using the following code line:
remove.packages("BiPlotteR")Load BiPlotteR
Prior to further operations, the content of the BiPlotteR package can be loaded into the R environment using the library function, as shown below.
Organize your Data
All BiPlotteR functions require a precise input database structure. Specifically, the dataset has to be generated in R-mode, i.e. showing variables (features) in columns and samples (objects) in rows. Additionally, the first column should contain all the sample annotations (e.g. sample name, sample number, etc.), which will be displayed next to each point in the biplot. Furthermore, the second column has to be filled with parameters (e.g., types, species, treatments, etc.) useful for samples classification into groups. In this way, all the points referring to samples sharing the same properties will have the same color in the biplot. Variable values are listed starting from the third column onwards. Here, the column headers should be filled with the name of the related feature. These names will be displayed next to the corresponding loading vector in the biplot. As an example, the BiPlotteR package offers you the Iris_4BPLTR data frame as a template. This is an adaptation of the “iris” dataset (embedded in the “datasets” R-package) to the needs of the BiPlotteR functions. You can load it into the R environment using the following code line:
data("Iris_4BPLTR")Here you can explore the content of the Iris_4BPLTR dataset:
| Sample Number | Species | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width |
|---|---|---|---|---|---|
| 1 | setosa | 5.1 | 3.5 | 1.4 | 0.2 |
| 2 | setosa | 4.9 | 3.0 | 1.4 | 0.2 |
| 3 | setosa | 4.7 | 3.2 | 1.3 | 0.2 |
| 4 | setosa | 4.6 | 3.1 | 1.5 | 0.2 |
| 5 | setosa | 5.0 | 3.6 | 1.4 | 0.2 |
| 6 | setosa | 5.4 | 3.9 | 1.7 | 0.4 |
| 7 | setosa | 4.6 | 3.4 | 1.4 | 0.3 |
| 8 | setosa | 5.0 | 3.4 | 1.5 | 0.2 |
| 9 | setosa | 4.4 | 2.9 | 1.4 | 0.2 |
| 10 | setosa | 4.9 | 3.1 | 1.5 | 0.1 |
| 11 | setosa | 5.4 | 3.7 | 1.5 | 0.2 |
| 12 | setosa | 4.8 | 3.4 | 1.6 | 0.2 |
| 13 | setosa | 4.8 | 3.0 | 1.4 | 0.1 |
| 14 | setosa | 4.3 | 3.0 | 1.1 | 0.1 |
| 15 | setosa | 5.8 | 4.0 | 1.2 | 0.2 |
| 16 | setosa | 5.7 | 4.4 | 1.5 | 0.4 |
| 17 | setosa | 5.4 | 3.9 | 1.3 | 0.4 |
| 18 | setosa | 5.1 | 3.5 | 1.4 | 0.3 |
| 19 | setosa | 5.7 | 3.8 | 1.7 | 0.3 |
| 20 | setosa | 5.1 | 3.8 | 1.5 | 0.3 |
| 21 | setosa | 5.4 | 3.4 | 1.7 | 0.2 |
| 22 | setosa | 5.1 | 3.7 | 1.5 | 0.4 |
| 23 | setosa | 4.6 | 3.6 | 1.0 | 0.2 |
| 24 | setosa | 5.1 | 3.3 | 1.7 | 0.5 |
| 25 | setosa | 4.8 | 3.4 | 1.9 | 0.2 |
| 26 | setosa | 5.0 | 3.0 | 1.6 | 0.2 |
| 27 | setosa | 5.0 | 3.4 | 1.6 | 0.4 |
| 28 | setosa | 5.2 | 3.5 | 1.5 | 0.2 |
| 29 | setosa | 5.2 | 3.4 | 1.4 | 0.2 |
| 30 | setosa | 4.7 | 3.2 | 1.6 | 0.2 |
| 31 | setosa | 4.8 | 3.1 | 1.6 | 0.2 |
| 32 | setosa | 5.4 | 3.4 | 1.5 | 0.4 |
| 33 | setosa | 5.2 | 4.1 | 1.5 | 0.1 |
| 34 | setosa | 5.5 | 4.2 | 1.4 | 0.2 |
| 35 | setosa | 4.9 | 3.1 | 1.5 | 0.2 |
| 36 | setosa | 5.0 | 3.2 | 1.2 | 0.2 |
| 37 | setosa | 5.5 | 3.5 | 1.3 | 0.2 |
| 38 | setosa | 4.9 | 3.6 | 1.4 | 0.1 |
| 39 | setosa | 4.4 | 3.0 | 1.3 | 0.2 |
| 40 | setosa | 5.1 | 3.4 | 1.5 | 0.2 |
| 41 | setosa | 5.0 | 3.5 | 1.3 | 0.3 |
| 42 | setosa | 4.5 | 2.3 | 1.3 | 0.3 |
| 43 | setosa | 4.4 | 3.2 | 1.3 | 0.2 |
| 44 | setosa | 5.0 | 3.5 | 1.6 | 0.6 |
| 45 | setosa | 5.1 | 3.8 | 1.9 | 0.4 |
| 46 | setosa | 4.8 | 3.0 | 1.4 | 0.3 |
| 47 | setosa | 5.1 | 3.8 | 1.6 | 0.2 |
| 48 | setosa | 4.6 | 3.2 | 1.4 | 0.2 |
| 49 | setosa | 5.3 | 3.7 | 1.5 | 0.2 |
| 50 | setosa | 5.0 | 3.3 | 1.4 | 0.2 |
| 51 | versicolor | 7.0 | 3.2 | 4.7 | 1.4 |
| 52 | versicolor | 6.4 | 3.2 | 4.5 | 1.5 |
| 53 | versicolor | 6.9 | 3.1 | 4.9 | 1.5 |
| 54 | versicolor | 5.5 | 2.3 | 4.0 | 1.3 |
| 55 | versicolor | 6.5 | 2.8 | 4.6 | 1.5 |
| 56 | versicolor | 5.7 | 2.8 | 4.5 | 1.3 |
| 57 | versicolor | 6.3 | 3.3 | 4.7 | 1.6 |
| 58 | versicolor | 4.9 | 2.4 | 3.3 | 1.0 |
| 59 | versicolor | 6.6 | 2.9 | 4.6 | 1.3 |
| 60 | versicolor | 5.2 | 2.7 | 3.9 | 1.4 |
| 61 | versicolor | 5.0 | 2.0 | 3.5 | 1.0 |
| 62 | versicolor | 5.9 | 3.0 | 4.2 | 1.5 |
| 63 | versicolor | 6.0 | 2.2 | 4.0 | 1.0 |
| 64 | versicolor | 6.1 | 2.9 | 4.7 | 1.4 |
| 65 | versicolor | 5.6 | 2.9 | 3.6 | 1.3 |
| 66 | versicolor | 6.7 | 3.1 | 4.4 | 1.4 |
| 67 | versicolor | 5.6 | 3.0 | 4.5 | 1.5 |
| 68 | versicolor | 5.8 | 2.7 | 4.1 | 1.0 |
| 69 | versicolor | 6.2 | 2.2 | 4.5 | 1.5 |
| 70 | versicolor | 5.6 | 2.5 | 3.9 | 1.1 |
| 71 | versicolor | 5.9 | 3.2 | 4.8 | 1.8 |
| 72 | versicolor | 6.1 | 2.8 | 4.0 | 1.3 |
| 73 | versicolor | 6.3 | 2.5 | 4.9 | 1.5 |
| 74 | versicolor | 6.1 | 2.8 | 4.7 | 1.2 |
| 75 | versicolor | 6.4 | 2.9 | 4.3 | 1.3 |
| 76 | versicolor | 6.6 | 3.0 | 4.4 | 1.4 |
| 77 | versicolor | 6.8 | 2.8 | 4.8 | 1.4 |
| 78 | versicolor | 6.7 | 3.0 | 5.0 | 1.7 |
| 79 | versicolor | 6.0 | 2.9 | 4.5 | 1.5 |
| 80 | versicolor | 5.7 | 2.6 | 3.5 | 1.0 |
| 81 | versicolor | 5.5 | 2.4 | 3.8 | 1.1 |
| 82 | versicolor | 5.5 | 2.4 | 3.7 | 1.0 |
| 83 | versicolor | 5.8 | 2.7 | 3.9 | 1.2 |
| 84 | versicolor | 6.0 | 2.7 | 5.1 | 1.6 |
| 85 | versicolor | 5.4 | 3.0 | 4.5 | 1.5 |
| 86 | versicolor | 6.0 | 3.4 | 4.5 | 1.6 |
| 87 | versicolor | 6.7 | 3.1 | 4.7 | 1.5 |
| 88 | versicolor | 6.3 | 2.3 | 4.4 | 1.3 |
| 89 | versicolor | 5.6 | 3.0 | 4.1 | 1.3 |
| 90 | versicolor | 5.5 | 2.5 | 4.0 | 1.3 |
| 91 | versicolor | 5.5 | 2.6 | 4.4 | 1.2 |
| 92 | versicolor | 6.1 | 3.0 | 4.6 | 1.4 |
| 93 | versicolor | 5.8 | 2.6 | 4.0 | 1.2 |
| 94 | versicolor | 5.0 | 2.3 | 3.3 | 1.0 |
| 95 | versicolor | 5.6 | 2.7 | 4.2 | 1.3 |
| 96 | versicolor | 5.7 | 3.0 | 4.2 | 1.2 |
| 97 | versicolor | 5.7 | 2.9 | 4.2 | 1.3 |
| 98 | versicolor | 6.2 | 2.9 | 4.3 | 1.3 |
| 99 | versicolor | 5.1 | 2.5 | 3.0 | 1.1 |
| 100 | versicolor | 5.7 | 2.8 | 4.1 | 1.3 |
| 101 | virginica | 6.3 | 3.3 | 6.0 | 2.5 |
| 102 | virginica | 5.8 | 2.7 | 5.1 | 1.9 |
| 103 | virginica | 7.1 | 3.0 | 5.9 | 2.1 |
| 104 | virginica | 6.3 | 2.9 | 5.6 | 1.8 |
| 105 | virginica | 6.5 | 3.0 | 5.8 | 2.2 |
| 106 | virginica | 7.6 | 3.0 | 6.6 | 2.1 |
| 107 | virginica | 4.9 | 2.5 | 4.5 | 1.7 |
| 108 | virginica | 7.3 | 2.9 | 6.3 | 1.8 |
| 109 | virginica | 6.7 | 2.5 | 5.8 | 1.8 |
| 110 | virginica | 7.2 | 3.6 | 6.1 | 2.5 |
| 111 | virginica | 6.5 | 3.2 | 5.1 | 2.0 |
| 112 | virginica | 6.4 | 2.7 | 5.3 | 1.9 |
| 113 | virginica | 6.8 | 3.0 | 5.5 | 2.1 |
| 114 | virginica | 5.7 | 2.5 | 5.0 | 2.0 |
| 115 | virginica | 5.8 | 2.8 | 5.1 | 2.4 |
| 116 | virginica | 6.4 | 3.2 | 5.3 | 2.3 |
| 117 | virginica | 6.5 | 3.0 | 5.5 | 1.8 |
| 118 | virginica | 7.7 | 3.8 | 6.7 | 2.2 |
| 119 | virginica | 7.7 | 2.6 | 6.9 | 2.3 |
| 120 | virginica | 6.0 | 2.2 | 5.0 | 1.5 |
| 121 | virginica | 6.9 | 3.2 | 5.7 | 2.3 |
| 122 | virginica | 5.6 | 2.8 | 4.9 | 2.0 |
| 123 | virginica | 7.7 | 2.8 | 6.7 | 2.0 |
| 124 | virginica | 6.3 | 2.7 | 4.9 | 1.8 |
| 125 | virginica | 6.7 | 3.3 | 5.7 | 2.1 |
| 126 | virginica | 7.2 | 3.2 | 6.0 | 1.8 |
| 127 | virginica | 6.2 | 2.8 | 4.8 | 1.8 |
| 128 | virginica | 6.1 | 3.0 | 4.9 | 1.8 |
| 129 | virginica | 6.4 | 2.8 | 5.6 | 2.1 |
| 130 | virginica | 7.2 | 3.0 | 5.8 | 1.6 |
| 131 | virginica | 7.4 | 2.8 | 6.1 | 1.9 |
| 132 | virginica | 7.9 | 3.8 | 6.4 | 2.0 |
| 133 | virginica | 6.4 | 2.8 | 5.6 | 2.2 |
| 134 | virginica | 6.3 | 2.8 | 5.1 | 1.5 |
| 135 | virginica | 6.1 | 2.6 | 5.6 | 1.4 |
| 136 | virginica | 7.7 | 3.0 | 6.1 | 2.3 |
| 137 | virginica | 6.3 | 3.4 | 5.6 | 2.4 |
| 138 | virginica | 6.4 | 3.1 | 5.5 | 1.8 |
| 139 | virginica | 6.0 | 3.0 | 4.8 | 1.8 |
| 140 | virginica | 6.9 | 3.1 | 5.4 | 2.1 |
| 141 | virginica | 6.7 | 3.1 | 5.6 | 2.4 |
| 142 | virginica | 6.9 | 3.1 | 5.1 | 2.3 |
| 143 | virginica | 5.8 | 2.7 | 5.1 | 1.9 |
| 144 | virginica | 6.8 | 3.2 | 5.9 | 2.3 |
| 145 | virginica | 6.7 | 3.3 | 5.7 | 2.5 |
| 146 | virginica | 6.7 | 3.0 | 5.2 | 2.3 |
| 147 | virginica | 6.3 | 2.5 | 5.0 | 1.9 |
| 148 | virginica | 6.5 | 3.0 | 5.2 | 2.0 |
| 149 | virginica | 6.2 | 3.4 | 5.4 | 2.3 |
| 150 | virginica | 5.9 | 3.0 | 5.1 | 1.8 |
As you can see, the first column contains a numerical codification of the iris samples; the elements in the first column will appear as point annotations in the biplot when using the Biplot 3D and Biplot 2D functions. The second column is dedicated to a priori information about your samples; in the “iris” dtaset, iris species (i.e. setosa, versicolor, virginica) are reported. When using the Biplot 3D and Biplot 2D functions, points referred to samples that have the same value in the second column of the dataset (the name of the species in this case) will be displayed with the same color in the biplot. This might help you to interpret the PCA output in light to your a priori information. As previously mentioned, features values are listed from the third column onwards.
The functions in the BiPlotteR package: Biplot3D
Biplot 3D performs PCA on your input dataset and generates an interactive three-dimensional biplot referred to the first three principal components (PC). This function will automatically center your input data matrix. However, data can be scaled to have unit variance before performing the Principal Component Analysis (PCA). If you want to scale your data, you have to specify it in the input as follows:
Biplot3D(Iris_4BPLTR,scaling="YES")You can also use a loading filter to limit the number of the loading vectors which will be displayed in the biplot. If you want to, you have to define a threshold value so that only vectors having modules greater than such value will be shown in the biplot. For example, only vectors having modules greater than 0.2 will be displayed when the following code line is used.
Biplot3D(Iris_4BPLTR,load.filter=0.2)By definition, the modules of loading vectors cannot exceed 1. However, their length appears to be too small if compared to scatter point coordinates on the principal component axis. If needed, you can magnify the loading vector modules. More properly, you can do it either by specifying a proper magnification factor, e.g. 2 as in the example below:
Biplot3D(Iris_4BPLTR,mag.fact=2)or asking for an automatic magnification as follows:
Biplot3D(Iris_4BPLTR,mag.fact="AUTO")Here you can explore an example of the interactive output of the Biplot 3D function.
The functions in the BiPlotteR package: Biplot2D
Biplot 2D generates an interactive three-dimensional biplot referred to the two principal components (PC) indicated by the user. Similarly to Biplot 3D, the Biplot 2D function automatically centers the input data matrix. Analogously to Biplot 3D, you can decide to scale your data, to limit the number of the loading vectors in the biplot and to ask for loading vector modules amplification to increase the biplot intelligibility. (see above or check the Biplot 2D help file for further details). Furthermore, the Biplot 2D allows you to decide which principal components will be displayed on the x and y axis. For example, if you want to put the PC2 on the x axis and the PC3 on the y axis, you have to specify the corresponding PCx and PCy values in the Biplot 2D, as shown in the example below:
Here you can explore an example of the interactive output of the Biplot 2D function .
The functions in the BiPlotteR package: ScreePlot
ScreePlot generates an interactive two-dimensional bar chart. The height of each bar shows the variance explained by the corresponding principal component. The screeplot returned by the ScreePlot function refers to the Principal Component Analysis performed on centered data. If scaling is needed, you have to specify it by setting scaling="YES" in the function input, as shown below:
Here you can explore an example of the interactive output of the ScreePlot function.