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# making a Likert plot
# load the data
data <- read.csv("teachingeval.csv",
# rename the response columns from "Q1" to the actual questions
data <- rename(data, c(
Q1 = "In-class work in groups has improved my understanding of the material",
Q2 = "Collaborative work with fellow students has made the class more enjoyable",
Q3 = "Being able to watch screen casts ahead of time has helped me prepare for class",
Q4 = "Having lecture slides available electronically is helpful",
Q5 = "I learned best when I watched a screencast ahead of material covered in class",
Q6 = "I learned best when I simply followed lectures without a screencast before",
Q7 = "I learned best studying material on my own in the textbook",
Q8 = "This course made me more likely to take another logic course",
Q9 = "This course made me more likely to take another philosophy course"))
# the likert responses are in columns 5-13
responses <- data[c(5:13)]
# get the factors right: Strongly Disagree is low, Strongly Agree is high
mylevels <- c('Strongly Disagree', 'Disagree', 'Neutral', 'Agree', 'Strongly Agree')
for(i in seq_along(responses)) {
responses[,i] <- factor(responses[,i], levels=mylevels)
# compute Likert stuff and put it in lresponses
lresponses <- likert(responses)
# now plot it
colors=c('darkred','darkorange','palegoldenrod','greenyellow','darkgreen')) + ggtitle("Teaching Evaluations")
# now let's plot it by major, we need to omit the lines with NA (no answer) in the Major column
data <- na.omit(data)
respmaj <- data[c(5:13)]
for(i in seq_along(respmaj)) {
respmaj[,i] <- factor(respmaj[,i], levels=mylevels)
# compute the Likert stuff
lrespmaj <- likert(respmaj,grouping=data$Major)
# plot result by major
# plot a density map
plot(lrespmaj,type='density') + scale_color_manual(values=c('darkred','darkorange','palegoldenrod','greenyellow','darkgreen','purple','darkblue')) + ggtitle("Teaching Evaluations by Major")