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Chapter 6 DSUR Correlations.R
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Chapter 6 DSUR Correlations.R
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#------------------------------------------------------------------------------#
#R Code for Chapter 6 of:
#
#Discovering Statistics Using R: and Sex and Drugs and Rock 'N' Roll.
#Field, A. P., Miles, J. N. V., & Field, Z. C. (2012).
#
#London Sage
#
#(c) 2011 Andy P. Field, Jeremy N. V. Miles & Zoe C. Field
#------------------------------------------------------------------------------#
#-----Set and create the directories-----
imgDir <- file.path(getwd(), 'images')
if (!dir.exists(imgDir)) {
dir.create(imgDir)
}
#-----Install and Load Packages------
# install.packages("Hmisc")
# install.packages("polycor")
# install.packages("ggm")
library(Hmisc)
library(ggplot2)
library(boot)
library(polycor)
library(ggm)
#library(Rcmdr)
#-----Entering data-----
adverts <- c(5,4,4,6,8)
packets <- c(8,9,10,13,15)
advertData <- data.frame(adverts, packets)
cov(advertData)
var(adverts)
#-----Self Help Task----
scatter <- ggplot(advertData, aes(adverts, packets))
scatter + geom_point()
imgFile <- paste(imgDir,"06 Advert Scatter Basic.png", sep="/")
ggsave(file = imgFile)
scatter +
geom_point(size = 3) +
labs(x = "Adverts", y = "Packets") +
scale_y_continuous(limits=c(0, 15), breaks=0:15) +
scale_x_continuous(limits=c(0, 9), breaks=0:9)
imgFile <- paste(imgDir,"06 Advert Scatter Nice.png", sep="/")
ggsave(file = imgFile)
#----Dealing with misisng cases----
adverts <- c(5,4,4,6,8)
packetsNA <- c(8,9,10,NA,15)
age <- c(5, 12, 16, 9, 14)
advertNA <- data.frame(adverts, packetsNA, age)
cor(advertNA, use = "everything", method = "pearson")
cor(advertNA, use = "complete.obs", method = "kendall")
#----Pearson r----
#cor(x, y, use = "everything", method = "correlation type")
#cor.test(x, y, alternative = "string", method = "correlation type", conf.level = 0.95)
examData <- read.delim("data/Exam Anxiety.dat", header = TRUE)
examData
cor(examData[, c(3, 4)], use = "complete.obs", method = "pearson")
cor(examData$Exam, examData$Anxiety, use = "complete.obs", method = 'pearson')
examData2 <- examData[, c("Exam", "Anxiety", "Revise")]
cor(examData2)
cor(examData[, c("Exam", "Anxiety", "Revise")])
cor(examData2)^2 * 100
examMatrix <- as.matrix(examData[, c("Exam", "Anxiety", "Revise")])
Hmisc::rcorr(examMatrix)
Hmisc::rcorr(as.matrix(examData[, c("Exam", "Anxiety", "Revise")]))
cor.test(examData$Anxiety, examData$Exam)
cor.test(examData$Revise, examData$Exam)
cor.test(examData$Anxiety, examData$Revise)
#----Spearman's Rho----
liarData <- read.delim("data/The Biggest Liar.dat", header = TRUE)
liarData
#liarData = read.delim(file.choose(), header = TRUE)
cor(liarData$Position, liarData$Creativity, method = "spearman")
cor.test(liarData$Position, liarData$Creativity, alternative = "less", method = "spearman")
liarMatrix <- as.matrix(liarData[, c("Position", "Creativity")])
rcorr(liarMatrix)
#--------Kendall's Tau----------
cor(liarData$Position, liarData$Creativity, method = "kendall")
cor.test(liarData$Position, liarData$Creativity, alternative = "less", method = "pearson")
cor(advertData$adverts, advertData$packets, method = "pearson")
cor.test(advertData$adverts, advertData$packets, alternative = "greater", method = "kendall")
#--------Self Test----------
adverts <- c(5,4,4,6,8)
packets <- c(8,9,10,13,15)
cor.test(adverts, packets)
#--------Bootstrapping----------
bootTau<-function(liarData,i)cor(liarData$Position[i], liarData$Creativity[i], use = "complete.obs", method = "kendall")
boot_kendall<-boot(liarData, bootTau, 2000)
boot_kendall
boot.ci(boot_kendall)
boot.ci(boot_kendall, 0.99)
#--------Self Test----------
bootR<-function(examData2,i) cor(examData2$Exam[i], examData2$Anxiety[i], use = "complete.obs")
boot_pearson<-boot(examData2, bootR, 2000)
boot_pearson
boot.ci(boot_pearson)
bootR<-function(examData2,i) cor(examData2$Revise[i], examData2$Anxiety[i], use = "complete.obs")
boot_pearson<-boot(examData2, bootR, 2000)
boot_pearson
boot.ci(boot_pearson)
bootR<-function(examData2,i) cor(examData2$Revise[i], examData2$Exam[i], use = "complete.obs")
boot_pearson<-boot(examData2, bootR, 2000)
boot_pearson
boot.ci(boot_pearson)
bootRho<-function(examData2,i) cor(examData2$Exam[i], examData2$Anxiety[i], use = "complete.obs", method = "spearman")
boot_spearman<-boot(examData2, bootRho, 2000)
boot_spearman
boot.ci(boot_spearman)
bootRho<-function(examData2,i) cor(examData2$Revise[i], examData2$Anxiety[i], use = "complete.obs", method = "spearman")
boot_spearman <-boot(examData2, bootRho, 2000)
boot_spearman
boot.ci(boot_spearman)
bootRho<-function(examData2, i) {
cor(examData2$Revise[i], examData2$Exam[i], use = "complete.obs", method = "spearman")
}
boot_spearman <-boot(examData2, bootRho, 2000)
boot_spearman
boot.ci(boot_spearman)
#----Point Biserial----
catData <- read.csv("data/pbcorr.csv", header = TRUE)
cor.test(catData$time, catData$gender)
cor.test(catData$time, catData$recode)
catFrequencies <- table(catData$gender)
prop.table(catFrequencies)
polyserial(catData$time, catData$gender)
#----Partial----
maleExam <- subset(examData, Gender == "Male", select= c("Exam", "Anxiety"))
femaleExam <- subset(examData, Gender == "Female", select= c("Exam", "Anxiety"))
cor(maleExam)
cor(femaleExam)
pc <- pcor(c("Exam", "Anxiety", "Revise"), var(examData2))
pc
pc^2
pcor.test(pc, 1, 103)
#----Differences between independent rs----
zdifference<-function(r1, r2, n1, n2) {
zd <- (atanh(r1)-atanh(r2))/sqrt(1/(n1-3)+1/(n2-3))
p <- 1 - pnorm(abs(zd))
print(paste("Z Difference: ", zd))
print(paste("One-Tailed P-Value: ", p))
}
zdifference(-0.506, -0.381, 52, 51)
#----Differences between dependent rs----
tdifference<-function(rxy, rxz, rzy, n) {
df<-n-3
td<-(rxy-rzy)*sqrt((df*(1 + rxz))/(2*(1-rxy^2-rxz^2-rzy^2+(2*rxy*rxz*rzy))))
p <-pt(td, df)
print(paste("t Difference: ", td))
print(paste("One-Tailed P-Value: ", p))
}
tdifference(-0.441, -0.709, 0.397, 103)
#-------labcoat leni-----
#Load the data
personalityData <- read.delim("data/Chamorro-Premuzic.dat", header = TRUE)
#Create a matrix from the personalityData dataframe:
personalityMatrix<-as.matrix(personalityData[, c("studentN", "studentE", "studentO", "studentA", "studentC", "lectureN", "lecturE", "lecturO", "lecturA", "lecturC")])
#run the correlation analysis:
rcorr(personalityMatrix)
#or convert the dataframe into a matrix and run the correlation analysis in one:
rcorr(as.matrix(personalityData[, c("studentN", "studentE", "studentO", "studentA", "studentC", "lectureN", "lecturE", "lecturO", "lecturA", "lecturC")]))
#-------------------------Smart Alex Task 1
#Load the data:
essayData <- read.delim("data/EssayMarks.dat", header = TRUE)
#Create a plot object called scatter:
scatter<-ggplot(essayData, aes(hours, essay))
#Add labels and with to your scatterplot:
scatter + geom_point(size = 3) + labs(x = "Hours Spent on Essay", y = "Essay Mark (%)")
#Do a Shapiro-Wilks test to see whether the data are normal:
shapiro.test(essayData$essay)
shapiro.test(essayData$hours)
#Because the shapiro-Wilks tests were both non-sig we can use pearsons correltation:
cor.test(essayData$essay, essayData$hours, alternative = "greater", method = "pearson")
#correlation of hours spent on essay and grade:
essayData$grade<-factor(essayData$grade, levels = c("First Class","Upper Second Class", "Lower Second Class", "Third Class"))
cor.test(essayData$hours, as.numeric(essayData$grade), alternative = "less", method = "kendall")
cor.test(essayData$hours, as.numeric(essayData$grade), alternative = "less", method = "spearman")
#Smart Alex Task 2-------------
#load the data
chickFlick <- read.delim("data/ChickFlick.dat", header = TRUE)
#conduct two point-biserial correlations
cor.test(as.numeric(chickFlick$gender), chickFlick$arousal)
cor.test(as.numeric(chickFlick$film), chickFlick$arousal)
#----Smart Alex Task 3----
#load in the data
gradesData <- read.csv("data/grades.csv", header = TRUE)
#Conduct a Spearman correlation:
cor.test(gradesData$gcse, gradesData$stats, alternative = "greater", method = "spearman")
#conduct a Kendall correlation:
cor.test(gradesData$gcse, gradesData$stats, alternative = "greater", method = "kendall")
#----R Souls Tip Writing Functions----
# nameofFunction<-function(inputObject1, inputObject2, etc.) {
# a set of commands that do things to the input objects
# a set of commands that specify the output of the function
# }
meanOfVariable<-function(variable) {
mean<-sum(variable)/length(variable)
cat("Mean = ", mean)
}
meanOfVariable<-function(HarryTheHungyHippo) {
mean<-sum(HarryTheHungyHippo)/length(HarryTheHungyHippo)
cat("Mean = ", mean)
}
lecturerFriends = c(1,2,3,3,4)
meanOfVariable(lecturerFriends)