Principal Component Analysis with R Programming

• Last Updated : 16 Dec, 2021

Principal component analysis(PCA) in R programming is an analysis of the linear components of all existing attributes. Principal components are linear combinations (orthogonal transformation) of the original predictor in the dataset. It is a useful technique for EDA(Exploratory data analysis) and allows you to better visualize the variations present in a dataset with many variables.

R – Principal Component Analysis

First principal component captures the maximum variance in dataset. It determines the direction of higher variability. Second principal component captures the remaining variance in data and is uncorrelated with PC1. The correlation between PC1 and PC2 should be zero. So, all succeeding principal components follow the same concept. They capture the remaining variance without being correlated to the previous principal component.

The Dataset

The dataset mtcars(motor trend car road test) comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles. It comes pre-installed with dplyr package in R.

R

 # Installing required packageinstall.packages("dplyr") # Loading the packagelibrary(dplyr) # Importing excel filestr(mtcars)

Output: Principal Component Analysis with R language using dataset

We perform Principal component analysis on mtcars which consists of 32 car brands and 10 variables.

R

 # Loading Datadata(mtcars) # Apply PCA using prcomp function# Need to scale / Normalize as# PCA depends on distance measuremy_pca <- prcomp(mtcars, scale = TRUE,                center = TRUE, retx = T)names(my_pca) # Summarysummary(my_pca)my_pca # View the principal component loading# my_pca\$rotation[1:5, 1:4]my_pca\$rotation # See the principal componentsdim(my_pca\$x)my_pca\$x # Plotting the resultant principal components# The parameter scale = 0 ensures that arrows# are scaled to represent the loadingsbiplot(my_pca, main = "Biplot", scale = 0) # Compute standard deviationmy_pca\$sdev # Compute variancemy_pca.var <- my_pca\$sdev ^ 2my_pca.var # Proportion of variance for a scree plotpropve <- my_pca.var / sum(my_pca.var)propve # Plot variance explained for each principal componentplot(propve, xlab = "principal component",            ylab = "Proportion of Variance Explained",            ylim = c(0, 1), type = "b",            main = "Scree Plot") # Plot the cumulative proportion of variance explainedplot(cumsum(propve),    xlab = "Principal Component",    ylab = "Cumulative Proportion of Variance Explained",    ylim = c(0, 1), type = "b") # Find Top n principal component# which will atleast cover 90 % variance of dimensionwhich(cumsum(propve) >= 0.9) # Predict mpg using first 4 new Principal Components# Add a training set with principal componentstrain.data <- data.frame(disp = mtcars\$disp, my_pca\$x[, 1:4]) # Running a Decision tree algporithm## Installing and loading packagesinstall.packages("rpart")install.packages("rpart.plot")library(rpart)library(rpart.plot) rpart.model <- rpart(disp ~ .,                    data = train.data, method = "anova") rpart.plot(rpart.model)

Output:

• Bi plot • The resultant principal components are plotted as Biplot. Scale value 0 represents that arrows are scaled representing loadings.
• Variance explained for each principal component • Scree Plot represents the proportion of variance and a principal component. Below 2 principal components, there is a maximum proportion of variance as clearly seen in the plot.
• Cumulative proportion of variance • Scree Plot represents the Cumulative proportion of variance and a principal component. Above 2 principal components, there is a maximum cumulative proportion of variance as clearly seen in the plot.
• Decision tree model • Decision tree model was built to predict disp using other variables in the dataset and using ANOVA method. The decision tree plot is plotted and displays the information.

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