Hello, I’m Katie! I made this website using R Markdown!

About this page:

I made this R Markdown website to demonstrate and teach R skills
It looks basic right now, because it is a work in progress
My first project was a PCA one, see below
I plan to add more advanced models as time goes by
Check back regularly to join me on my data science journey

Disclaimer: Code and stat tips originate from my experience in academic research. Always remember to use reliable literature (language documentation, published text books, academic research papers, your own class notes) and fact check when using a website as a resource. I will try to keep everything as accurate as possible, but feel free to contact me if you have any questions on facts, figures and references. I hope you find this page useful! Enjoy!

Let’s get started!

knitr::opts_chunk$set(echo = TRUE) #Setting this equal to true allows the code to show up on the page

library(tidyverse) #Includes packages such as; ggplot2, dplyr, tibble and more

getwd()

## [1] "/Users/katiedunne/Documents/April2021/R_April2021/RMarkdown1"

Demo of basic R skills 1

# Vector
a_vector <- c(3, 4)
a_vector

## [1] 3 4

Demo of basic R skills 2

# Matrix
a_matrix <- matrix(c(1, 2, 3, 4),nrow =2, ncol=2)
a_matrix

##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4

Demo of PCA on iris data

PCA reference: Statistics class notes combined with my own opinions and interpretation

PCA is useful for dimention reduction and identifying drivers

data(iris)
# View(iris)

summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

fit = prcomp(iris[,1:4])
fit

## Standard deviations (1, .., p=4):
## [1] 2.0562689 0.4926162 0.2796596 0.1543862
## 
## Rotation (n x k) = (4 x 4):
##                      PC1         PC2         PC3        PC4
## Sepal.Length  0.36138659 -0.65658877  0.58202985  0.3154872
## Sepal.Width  -0.08452251 -0.73016143 -0.59791083 -0.3197231
## Petal.Length  0.85667061  0.17337266 -0.07623608 -0.4798390
## Petal.Width   0.35828920  0.07548102 -0.54583143  0.7536574

round(fit$rotation, 2)

##                PC1   PC2   PC3   PC4
## Sepal.Length  0.36 -0.66  0.58  0.32
## Sepal.Width  -0.08 -0.73 -0.60 -0.32
## Petal.Length  0.86  0.17 -0.08 -0.48
## Petal.Width   0.36  0.08 -0.55  0.75

The magnitude of the loadings are important, however the signs on the loadings are arbitrary. It matters if the loadings have opposite signs, but not which is positive and which is negative.

In PC1, Sepal.length, Petal.Length and Petal.Width all have large positive loadings, whereas Sepal.Width has a negative loading which is close to zero.

We could interpret the first PC as an overall measure of the ‘size’ of the flower. The sepal width variable has close to zero loading.

PC2 is contrasting flowers that have large sepals against flowers that have long petals.

summary(fit)

## Importance of components:
##                           PC1     PC2    PC3     PC4
## Standard deviation     2.0563 0.49262 0.2797 0.15439
## Proportion of Variance 0.9246 0.05307 0.0171 0.00521
## Cumulative Proportion  0.9246 0.97769 0.9948 1.00000

With regard to the code chunk above, it is important to note that the value of a particular eigenvalue divided by the sum of the eigenvalues is called the proportion of variance explained by that principal component (REF: A past statistics class)

# We can plot the proportion of variance explained by each PC using:
plot(fit, main = "Scree plot", xlab = "Comps 1:4")

newiris = predict(fit)
newiris

To get a ‘picture’ of the reduced dimensional data we often produce a scatter plot of the scores on one PC against the scores on another PC. As we have decided that only PC1 (and maybe PC2) need to be considered we can use the plot function to plot the values of PC1 against PC2 (REF: A past statistics class)

plot(newiris[,1], newiris[,2], type="n", xlab="PC1", ylab="PC2")
text(newiris[,1], newiris[,2], labels=substr(iris[,5],1,2), col=as.integer(iris[,5]))