Principal component analysis (PCA) is a technique used to reduce the dimensionality of data and emphasize variation to make data easier to explore and visualize. It works by finding the eigenvectors of the covariance matrix of the data, which point in directions that maximize variance. The covariance of two variables measures how their variances are related. PCA can be performed in R using the prcomp() function, which takes a data frame and returns a list of principal components.

1. Data Transformation
Summer Data Jam
Chris Orwa
14th July 2015

2. Principal Component Analysis
Principal component analysis (PCA) is a technique used
to emphasize variation and bring out strong patterns in a
dataset. It's often used to make data easy to explore and
visualize.
Statistically, PCA is the eigenvectors of a covariance
matrix.

3. Let us Look at Some Concepts
Covariance
The covariance of two variables x and y in a data sample
measures how the variance of two attributes are related.
R code
duration = faithful$eruptions
waiting = faithful$waiting
cov(duration, waiting)

4. Covariance Matrix

5. Eigen Vectors
Eigenvector is a vector of a square matrix that points in a
direction invariant under the associated linear
transformation.
R code
B <- matrix(1:9, 3)
eigen(B)

6. Principal Component Analysis
R Code
#load data
a = read.csv(‘my_data.csv')
#perform PCA
c = prcomp(a)