The goal of UnsupLearnR is to assist with unsupervised learning in R - particularly with visualisation.
You can install the development version of UnsupLearnR like so:
# install pacman
if (!require("pacman")) install.packages("pacman")
# install dependencies from CRAN
pacman::p_install(devtools, ggplot2, ggpubr)
# install graphicsPLr from GitHub
if (!require("graphicsPLr")) devtools::install_github('Phuong-Le/graphicsPLr')
# the package
devtools::install_github('Phuong-Le/UnsupLearnR')I will use the iris data as a demonstration
PCA
library(UnsupLearnR)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(stats)
library(factoextra)
#> Loading required package: ggplot2
#> Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
data(iris)
# let's briefly have a look at the iris data
head(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
# let's do a PCA on Sepal.Length, Sepal.Width and Petal.Length (ie leave out species)
iris_numeric = select(iris, -c(Species))
## performing PCA,
## the `scale. = T` option scales your data to standard normal distribution (mean=0, var=1) - this is generally recommended as the categories on PCA are not on the same units/scales
pca = prcomp(iris_numeric, scale. = T)
pca_ed = as.data.frame(pca$x)
# let's colour the PCA plot by iris species
# first create a param dataframe - the column to be coloured has to be named "param"
params = data.frame(param=iris$Species)
plot_pca(pca_ed, params = params)
# it's worth looking at the loadings plot, let's use fviz_pca_var from factoextra package (I think it does a really good job so I don't think I need to write a similar function in UnsupLearnR)
library(ggpubr)
fviz_pca_var(pca) +
theme_pubr()
# you can also choose the dimensions (principle components) on the x and y axis
plot_pca(pca_ed, param = params, x = 2, y= 3)
# now PCA preserves the number of dimensions, as we start with 4 variables (columns), we should now have 4 principle components
# how do you know how many dimensions to look at? let's check the scree plot (we should have done this before even looking at the pca plots themselves)
# The scree plot shows that the first 2 dimensions already captured almost 100% of the variation (ie information) of the data -
# so looking at the last 2 dimensions won't add much ore values
scree_plot(pca_ed)
UnsupLearnR also helps with clustering - note that this is designed for
the function in the stats package
# let's do kmeans clustering
# let's set K=3 (because it's quite obvious on the pca plot), # let's use 30 initialisations because the first step of kmeans is a randomisation step
# and set a limit to the number of iterations at 20
k = 3
set.seed(2510)
cluster = make_cluster(iris_numeric, clusterFUN = kmeans, centers = k, nstart = 30, iter.max = 20)
set.seed(NULL)
# now that we have the cluster object, let's see how species are assigned to clusters
plot_labelled_cluster_bars(cluster = cluster, param = params)
# and map the clustering result to the PCA plot
plot_pca(pca_ed, params, cluster = cluster)
What if it’s unclear how many K clusters to choose? Let’s examine a range
library(ggplot2)
set.seed(2510)
n_clusters = 15
clusters = list()
for (i in 1:n_clusters) {
clusters[[i]] = make_cluster(iris_numeric, kmeans, centers = i, nstart = 30, iter.max = 20)
}
set.seed(NULL)
# let's look at the total variation within clusters plots
# total variation within clusters measures how far away datapoints are from their centers
# as K increases, it always decreases
# A good K is one where you see a kink
# here the decrease from 3 to 4 is tiny compared to 2 to 3, suggesting that 3 is a good K value
tot_within_var_plot(clusters = clusters) +
scale_x_continuous(n.breaks = n_clusters)