Survey your audience and visualise the results with R and Google forms

I wanted to make my presentation on dataviz at the UQ School of Bioinformatics more interactive.

A quiz is a good way to engage your audience. Given I was giving a talk about R datavisuals I thought it would be fun to visualise the quiz results using R live with the audience. To top it off, we posted the results to Twitter.

This blog describes is how I did that.

• Our next R course is at University of Queensland (Australia) from Feburary 5th 2019. Beginner, intermediate and advanced classes available.

You could also use this system to survey our audience and share the results live. Just prepare you R code and set it to run at a certain time during your talk with a task scheduling algorithm.

Setting up the survey

I used Google Forms to do my quiz. You can take it here. I posed a few questions that challenged the audience to think about the best way to visualise data.

It is pretty easy to set up a survey if you have a gmail account. A few tips:

• You can add images, which is great posing questions about results.

• I used the ‘short answer’ input for numeric answers. If you click the validation tab at the bottom of each ‘short answer’ question you can require users enter certain types of numbers (e.g. within a range).

• Think carefully about limiting required inputs if you want to avoid bugs that might arise from unexpected answers.

• There is a green button at the top of the form that let’s you link it to a google sheet. Do this.

• You can make the sheet public, so other people can use it, but changing the sharing settings.

Connecting to your survey answers in R

I used the googlesheets package to read my survey answers from the (public) spreadsheet. You will need to authenticate yourself first:

library(googlesheets)
gs_ls()

This will prompt you to login to your google account and authenticate an app that allows the connection to happen.

Now we can load our data:

sheet_url <- "https://docs.google.com/spreadsheets/d/10i3v3NIVpgmURyLVzsiadPAMGeqa7dLFcDb9sqFe8KA/edit#gid=1513779153"
dataviz <- gs_url(sheet_url) #creates connection

If you want to keep your sheet private you can use gs_ls() to list all your sheets, and then pick a name to read it in. e.g. like this:

dataviz <- gs_title("Dataviz quiz 2018 v2 (Responses)")
dat <- gs_read(dataviz)

Analysing your data

The file dat we just read in is a dataframe like object (actually a tibble) where each column is a question and each row is a response. The first column is a time stamp.

All other columns are titled with your questions.

It will make life easier if we rename the columns to shorter (but still descriptive) names.

newnames <- c("timestamp", "shopping",
              "bar_percent",
              "pie_percent",
              "room",
              "cb_age")
names(dat) <- newnames

Now let’s create some dataviz

library(ggplot2)
datplot <- na.omit(dat)
ggplot(datplot, aes(x = room, y = cb_age)) +
  geom_boxplot() +
  xlab("Position in room") +
  ylab("Guess at CB's age") +
  ylim(0, 75) +
  theme_bw()

A boxplot of the audience’s guesses at my age by their position in the room. I limited the y-axis because there were some outrageously large numbers!

Share the results

We could show the audience the results on our screen. But why not let Twitter know too!

For this, I used the rtweet package. rtweet is pretty simple to use once you’ve set up an app on Twitter’s API and authorised R to access it. So get rtweet then look at the vignette vignette("auth"). Follow the instructions to the letter and you shouldn’t have any problems.

Once authorisation is done, its a simple matter to save our plot as a png to use in a tweet:

myplot <- ggplot(datplot, aes(x = room, y = cb_age)) +
  geom_boxplot()
ggsave(filename = "myplot.png", myplot)

Now just write your tweet and send it off to twitter.

library(rtweet)
newstatus = "Chris age as surveyed at  #UQwinterSchool
@DoktrNick @UQwinterSchool"

post_tweet(status = newstatus,
           media = "myplot.png")

Next steps?

So I tried this as a way of doing a live R tutorial. Next step would be to try and integrate it into a talk without showing the R coding. For that you would either need to get a friend to run the code or use a scheduler (like the taskscheduleR R package).

Be careful though! You never know what answers people may give if allowed. So design you code to be robust to strange answers (like that I am 100 years old).