library(readr)
mydata <-read_csv('survey_results.csv')
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Airbnb = col_double(),
##   Vrbo = col_double(),
##   Hotels.com = col_double(),
##   Booking.com = col_double()
## )
pr.out=prcomp(mydata, scale=TRUE)
names(pr.out)
## [1] "sdev"     "rotation" "center"   "scale"    "x"
pr.out$center
##      Airbnb        Vrbo  Hotels.com Booking.com 
##    4.727273    1.545455    3.772727    3.772727
pr.out$scale
##      Airbnb        Vrbo  Hotels.com Booking.com 
##    2.529480    1.595448    2.599117    2.389099
pr.out$rotation
##                     PC1         PC2         PC3         PC4
## Airbnb       0.49418163 -0.46340086  0.64757555 -0.34884104
## Vrbo        -0.02826569 -0.81589927 -0.57686767 -0.02707639
## Hotels.com   0.57077718  0.34552972 -0.49035396 -0.56069209
## Booking.com  0.65513280  0.01331296 -0.08615452  0.75046731
dim(pr.out$x)
## [1] 22  4
biplot(pr.out, scale=0)

pr.out$rotation=-pr.out$rotation
pr.out$x=-pr.out$x
biplot(pr.out, scale=0)

pr.out$sdev
## [1] 1.3946308 1.1090774 0.7342365 0.5346486
pr.var=pr.out$sdev^2
pr.var
## [1] 1.9449950 1.2300526 0.5391033 0.2858491
pve=pr.var/sum(pr.var)
pve
## [1] 0.48624876 0.30751314 0.13477582 0.07146227
plot(pve, xlab="Principal Component", ylab="Proportion of Variance Explained", ylim=c(0,1),type='b')

plot(cumsum(pve), xlab="Principal Component", ylab="Cumulative Proportion of Variance Explained", ylim=c(0,1),type='b')

#save your cluster solutions in the working directory
#We want to examine the cluster memberships for each observation - see last column of pca_data

References

Principal component analysis - reading (p.404-p.405) https://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf

Principal Component Methods in R: Practical Guide http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/118-principal-component-analysis-in-r-prcomp-vs-princomp/

Interpretation of the Principal Components https://online.stat.psu.edu/stat505/lesson/11/11.4