Graphing Survey Responses

As I reported last year, we’ve been running surveys in our classes that use open logic textbooks. We now have another year of data, and I’ve figured out R well enough to plot the results. Perhaps someone else is in a similar situation, so I’ve written down all the steps. Results aren’t perfect yet. All the data and code is on Github, and any new discoveries I make will be updated there.

What follows is the content of the HOWTO:

As part of two Taylor Institute Teaching & Learning Grants, we developed course materials for use in Calgary’s Logic I and Logic II courses. In the case of Logic I, we also experimented with partially flipping the course. One of the requirements of the grants was to evaluate the effectiveness of the materials and interventions. To evaluate the textbooks, we ran a survey in the courses using the textbooks, and in a number of other courses that used commercial textbooks. These surveys were administered through SurveyMonkey. To evaluate the teaching interventions, we designed a special course evaluation instrument that included a number of general questions with Likert responses. The evaluation was done on paper, and the responses to the Likert questions were entered into a spreadsheet.

In order to generate nice plots of the results, we use R. This documents the steps taken to do this.

Installing R, RStudio, and likert

We’re running RStudio, a free GUI frontend to R. In order to install R on Ubuntu Linux, we followed the instructions here, updated for zesty:

  • Start “Software & Updates”, select add a source, enter the line zesty/

    Then in the command line:

    $ sudo apt-get install r-base r-base-dev
  • We then installed RStudio using the package provided here. The R packages for analyzing Likert data and plotting them require devtools, which we installed following the instructions here:
    $ sudo apt-get install build-essential libcurl4-gnutls-dev libxml2-dev libssl-dev
    $ R
    > install.packages('devtools')
  • Now you can install the likert package from Github:
    > install_github('likert', 'jbryer')

Preparing the data

The source data comes in CSV files, teachingevals.csv for the teaching evaluation responses, and textbooksurvey.csv for the textbook survey responses.

Since we entered the teaching evaluation responses manually, it was relatively simple to provide them in a format usable by R. Columns are Respondent ID for a unique identifier, Gender (M for male, F for female, O for other), Major, Year, Q1 through Q9 for the nine Likert questions. For each question, a response of one of Strongly Agree, Agree, Neutral, Disagree, or Strongly Disagree is recorded.

For the textbook survey we collected a whole lot of responses more, and the data SurveyMonkey provided came in a format not directly usable by R. We converted it to a more suitable format by hand.

  • SurveyMonkey results have two header lines, the first being the question, the second being the possible responses in multiple-response questions. We have to delete the second line. For instance, a question may have five different possible responses, which correspond to five columns. If a box was checked, the corresponding cell in a response will contain the answer text, otherwise it will be empty. In single-choice and Likert responses, SurveyMonkey reports the text of the chosen answer. For analysis, we wanted a simple 1 for checked and 0 for unchecked, and a number from 1 to 5 for the Likert answers. This was done easily enough with some formulas and search-and-replacing.
  • Since the question texts in the SurveyMonkey spreadsheet don’t make for good labels for importing from CSV, we replaced them all by generic labels such as Q5 (or Q6R2, for Question 6, Response 2, for multiple-choice questions).
  • We deleted data columns we don’t need such as timestamps and empty colums for data we didn’t collect such as names and IP addresses.
  • We added columns so we can collate data more easily: Section to identify the individual course the data is from, Course for which course it is (PHIL279 for Logic I, PHIL379 for Logic II), Term for Fall or Winter term, Open to distinguish responses from sections using an open or a commercial text, and Text for the textbook used. Text is one of SLC (for Sets, Logic, Computation, BBJ (for Boolos, Burgess, and Jeffrey, Computability and Logic), ForallX (for forall x: Calgary Remix, Chellas (for Chellas, Elementary Formal Logic), or Goldfarb (for Goldfarb, Deductive Logic). This was done by combining multiple individual spreadsheets provided by SurveyMonkey into one. (One spreadsheet contained responses from three different “Email Collectors”, one for each section surveyed.) Q27GPA contains the answer to Question 27, “What grade do you expect to get?”, converted to a 4-point grade scale.
  • Question 23, “Is the price of the textbook too high for the amount of learning support it provides?”, had the same answer scale as other questions (“Not at all” to “Very much so”), but the “Not at all” is now the positive answer, and “Very much so” the negative answer. To make it easier to produce a graph in line with the others, I added a Q23Rev column, where the values are reversed (i.e., Q23Rev = 6 – Q23).
  • Q26 is the 4-letter code of the major reported in the multiple-choice question 26, and Q26R1 to Q26R8 are responses to the checkboxes corresponding to options “Mathematics”, “Computer Science”, “Physics”, “Philosophy”, “Engineering”, “Neuroscience”, “Other”, and the write-in answer for Other. These responses don’t correspond to the questions asked: we offered “Lingustics” as an answer but noone selected it. A number of “Other” respondents indicated a Neuroscience major. So Q26R6 is NEUR in Q26. Question 26 allowed multiple answers, Q26 is the first answer only.

Loading data into R

In order to analyze the Likert data, we have to tell R which cells contain what, set the levels in the right order, and rename the columns so they are labelled with the question text instead of the generic Q1 etc. We’ll begin with the teaching evaluation data. The code is in teachingevals.R. Open that file in RStudio. You can run individual lines from that file, or selections, by highlighting the commands you want to run and then clicking on the “run” button.

First we load the required packages. likert is needed for all the Likert stuff; plyr just so we have the rename function used later; and reshape2 for the melt function.


Loading the data from a CSV value file is easy:

data <- read.csv("teachingeval.csv",

Now the table data contains everything in our CSV file, with empty cells having the NA value rather than an empty string. We want the responses to be labelled by the text of the question rather than just Q1 etc.

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 colums 5-13, so let’s make a table with just those:

responses <- data[c(5:13)]

The responses table still contains just the answer strings; we want to tell R that these are levels, and have the labels in the right order (“Strongly Disagree” = 1, etc.)

mylevels <- c('Strongly Disagree', 'Disagree', 'Neutral', 'Agree', 'Strongly Agree')

for(i in seq_along(responses)) {
  responses[,i] <- factor(responses[,i], levels=mylevels)

Analyzing and Plotting

Now we can analyze the likert data.

lresponses <- likert(responses)

You can print the analyzed Likert data:

> lresponses
1          In-class work in groups has improved my understanding of the material
2      Collaborative work with fellow students has made the class more enjoyable
3 Being able to watch screen casts ahead of time has helped me prepare for class
4                      Having lecture slides available electronically is helpful
5  I learned best when I watched a screencast ahead of material covered in class
6     I learned best when I simply followed lectures without a screencast before
7                     I learned best studying material on my own in the textbook
8                   This course made me more likely to take another logic course
9              This course made me more likely to take another philosophy course
  Strongly Disagree  Disagree   Neutral    Agree Strongly Agree
1          1.785714  5.357143 10.714286 37.50000      44.642857
2          1.785714  0.000000 10.714286 37.50000      50.000000
3          8.928571 14.285714 26.785714 28.57143      21.428571
4          1.785714  1.785714  5.357143 37.50000      53.571429
5          7.142857 10.714286 37.500000 33.92857      10.714286
6          3.571429 19.642857 51.785714 21.42857       3.571429
7          3.571429 12.500000 23.214286 33.92857      26.785714
8         20.000000 10.909091 32.727273 27.27273       9.090909
9         16.363636 18.181818 38.181818 18.18182       9.090909

And now we plot it:

  colors=c('darkred','darkorange','palegoldenrod','greenyellow','darkgreen')) +
  ggtitle("Teaching Evaluations")

The group.order=names(responses) makes the lines of the plot sorted in the order of the questions, you need ordered=FALSE or else it’ll be ordered alphabetically. Leave those out and you get it sorted by level of agreement. You can of course change the colors to suit.

In textbooksurvey.R we do much of the same stuff, except for the results of the textbook survey. Some sample differences:

Here’s how to group charts for multiple questions by textbook used:

lUseByText <- likert(items=survey[,27:31,drop=FALSE],
  colors=c('darkred', 'darkorange', 'palegoldenrod','greenyellow','darkgreen')
  ) + 
  ggtitle("Textbook Use Patterns")

To plot a bar chart for a scaled question, but without centering the bars, use centered=FALSE:

lQ5byText <- likert(items=survey[,26,drop=FALSE],
  centered= FALSE,
  colors=c('darkred','darkorange', 'gold', 'palegoldenrod','greenyellow','darkgreen')
  ) +
  ggtitle("Textbook Use Frequency")

Plotting Bar Charts for Multiple-Answer Questions

Some of the questions in the textbook survey allowed students to check multiple answers. We want those plotted with a simple bar chart, grouped by, say, the textbook used. To do this, we first have to the data for that. First, we extract the responses into a new table.

Q1 <- survey[,c(6,7:13)]

Now Q1 is just the column Text and Q1R1 through Q1R7. Next, we sum the answers (a checkmark is a 1, unchecked is 0, so number of mentions is the sum).

Q1 <- ddply(Q1,.(Text),numcolwise(sum))

Next, we convert this to “long form”:

Q1 <- melt(sumQ1,id.var="Text")

Now Q1 has three columns: Text, variable, and value. Now we can plot it:

ggplot() + 
    stat="identity") + 
  coord_flip() +
  ggtitle("01. How do you access the textbook?") +
  theme(legend.position = "bottom",
        axis.title.x = element_blank()) +

This makes a bar chart with Text on the x-axis, stacking variable, and using values for the value of each bar. stat="identity" means to just use value and not count. coord_flip() makes it into a horizontal chart. ggtitle(...) adds a title, theme(...) puts the legend on the bottom and removes the x axis label, and guides(...) formats the legend in one column.

UPDATE: Better Visualization of Multiple-Answer Responses

I figured out a better way to visualize multiple-answer responses (thanks to Norbert Preining for the help!). You don’t want the number of respondents which checked a box, but the percentage of all respondents (in a category) who did, so instead of adding up a column you compute the mean for it. Also, aggregate is an easier way to do this, and it doesn’t make sense to stack the responses, so I’m going to graph them side-by-side.

Here’s the code:

# load responses for question 4 into df Q4
Q4 <- survey[,c(6,20:25)]

# aggregate by Text, computing means = percent respondents who checked box
Q4 <- aggregate( . ~ Text, data=Q4, mean)

# make table long form for ggplot
Q4 <- melt(Q4,id.var="Text")

ggplot() + 
    stat="identity", position="dodge") + 
  coord_flip() +
  ggtitle("04. When using the text in electronic form, do you....") +
  theme(legend.position = "bottom",
        axis.title.x = element_blank()) +
  guides(fill=guide_legend(title=NULL,ncol=1)) +
  scale_fill_brewer(palette="Dark2") +
  scale_y_continuous(labels = scales::percent)

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