MSS PS: Introduction to R, R

MSS PS: Introduction to R

#MSS PS: Introduction to R!
# this is the code wall editor (not live), you'll have to edit along with me-
#you may need to click "Run it" in the bottom left corner to see the output & pretty graphs!

#let's make up some data!

set.seed(42)

mydata<-rnorm(1:100, mean=50, sd=10)

mydata_shifted<-mydata+20

mydata2<-rnorm(1:100, mean=50, sd=10)

mydata_shifted_random<-mydata+sample(15:30, 100, replace=TRUE)

randomdata<-sample(1:100, 100, replace=TRUE)

#Descriptives

#what is the mean of the data?

#mean(mydata)

#what is the standard deviation of the data?

#sd(mydata)

#what are the z scores of the data?

#mydata_zscored<-scale(mydata)

#Plotting

#histogram/bar chart

#hist(mydata)

#box plots of the mydata and mydata_shifted

#boxplot(mydata, mydata_shifted)

#density plots of mydata (smoothed histogram)

#plot(density(mydata))

#lines(density(mydata2), col="red")

#density plot of shifted data

#plot(density(mydata))

#lines(density(mydata_shifted), col="green")

#desnity plot of randomly shifted data

#plot(density(mydata))

#lines(density(mydata_shifted_random), col="orange")

#Inferential Analyses

#t test, paired, assuming equal variances

#t.test(mydata, mydata_shifted_random, paired=TRUE, var.equal=FALSE)

#ANOVA ("Analysis of Variance") test of mydata and mydata_shifted_random

#aov(mydata~mydata_shifted_random)

#plot of my data vs mydata_shifted_random

#plot(mydata, mydata_shifted_random)

#draw the line of best fit from the linear model/random

#lines((c(0:100)*beta-intercept),c(0:100), col="purple")

#Non-inferential Analyses

#correlation between mydata & mydata_shifted_random

#cor(mydata,mydata_shifted_random)


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