On Nov 28 2017, Brian Connolly came and visited and taught us how to do basic data analysis in R.

He in particular showed us how to do a Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA).

Attached are three of the data files Carl Pfendner prepared for us to analyze (ARA data, including simulation, force triggers, and RF triggers).

Also attached is some R code that shows how to set up the LDA and the PCA and how to plot their result. You are meant to run each line of the code in r by hand (this is not a functioning R script I don't think).

Go here (https://www.r-project.org/) to learn how to download R. You will also probably have to download GGFortify. To do that, open an r session, and type "install.packages('ggfortify')".

Notes

#First we need to read in the data
dataSim <- read.table("output.txt.Simulation",sep=" ",header=TRUE)
dataRF <- read.table("output.txt.RFTriggers",sep=" ",header=TRUE)
dataPulser <- read.table("output.txt.CalPulsers",sep=" ",header=TRUE)

#Now we combine them into one data object
data <- rbind(dataRF,dataSim)

#Now we actually have to specify that we
#want to use the first 10 columns
data <- data[,1:10]

#make a principal component analysis object
pca <- prcomp(data,center=TRUE,scale=TRUE,retx=TRUE)

#Load a plotting library
library(ggfortify)

#then can plot
autplot(prcomp(data))

#or, we can plot like this
#to get some color data points
labels<-c(rep(0,nrow(dataSim)),rep(1,nrow(dataRF)))
plot(pca$x[,1],pca$x[,2],col=labels+1)

#we can also do an LDA analysis

#we need to import the MASS library
library(MASS)
#and now we make the lda object
lda(data,grouping=labels)

#didn't write down any plotting unfortunately.