Learning Outcomes

By the end of this Activity, you will be able to:

Part of this tutorial was adapted from:

Since we want to use the plots from the library ggplot2, the first thing we need to do is to import this library

#install.packages('ggplot2')   # install it first if it is not installed yet

library(ggplot2)

Loading the Data

We first want to load some data, so we need to tell the computer where to get it from

Set the working directory to the folder where you have your files and read the file into a data frame

setwd('C:/Users/cvieira/Dropbox/Phd/Administrative/Mimi/FIE/2019/ScriptsAndData/data/')
litReview <- read.csv2("litReviewData.csv",header=TRUE,sep=";")

Let’s see the first rows our data set.

A quick reminder of our columns:

  • Connection to Visualization Background (CVG)

  • Connection to Educational Theories (CET)

  • Sophistication of the Visualization (SoV)

  • Venue - Journal or conference where the paper was published

  • Author - First author of the paper

head(litReview)
##   CVG CET SoV              Venue       Author
## 1   3   3   4               ABSc  Verbert, K.
## 2   6   8   2               ABSc  Lockyer, L.
## 3   7   3   3               ABSc Thompson, K.
## 4   1   7   4               ABSc  de Laat, M.
## 5   9   1  10 IEEE Transactions        Fu, S.
## 6   7   3  10 IEEE Transactions    Schwab, M.

Plotting

The function ggplot will only create the blank plot specifying our axes

For example, in the following instruction, we set up the CET as the x axis, and the SoV as the y-axis.

Check out what happens when you run it.

ggplot(litReview, aes(x=CET, y=SoV))

Why did we include that function ‘aes()’ in the code?
?aes() # Describes how variables in the data are mapped to visual properties of geoms.
## starting httpd help server ... done
       # Everything we want to include into the visualization should be included there.

We now need to tell ggplot what kind of plot we want.

ggplot(litReview, aes(x=CET, y=SoV))+
  geom_point()  # Adding this line we are saying we want a scatter plot

###### Here are some other ideas:

  • geom_point() ==> Scatter Plot

  • geom_bar() ==> Bar Plot

  • geom_line() ==> Line Plot

  • geom_histogram() ==> Histogram

Each plot would require specific data and columns within the aes function. For instance, to plot a histogram, we only need x variable:

ggplot(litReview, aes(x=CET))+
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Here is a useful cheatsheet for different plots:

https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

Activity

Change the plot to a line plot

# write your code here.

Let’s now get back to our scatter plot, which seems to be best for our purpose: identifying gaps in this literature

ggplot(litReview, aes(x=CET, y=SoV))+
      geom_point()  # Adding this line we are saying we want a scatter plot

These are still many points, and they are coming from differnt venues. So, let’s use the state to differentiate them with colors

ggplot(litReview, aes(x=CET, y=SoV))+
  geom_point(aes(color=Venue), size=3)  # Do you remember the aes() function? Here it is again

Aesthetics

You can change the color palette, edit the legend on the right, and many other things, but we will leave these aesthetics functions for some other time. If you want to explore, here are a few functions you could use:

  • theme(legend.position=“None”) # remove legend

  • scale_colour_brewer(palette = “Set1”) # change color palette

This is a nice simple scatter plot, but the scales in each axis seem odd. Let’s adjust the axes to go on the whole range of our scale: 0-10

ggplot(litReview, aes(x=CET, y=SoV))+
  geom_point(aes(color=Venue), size=3)+
  ylim(c(0, 10))

Did you notice that we just added up these new lines of code to the plot?

That’s how ggplot works, we can just continue adding new lines to modify our plot

Activity

Set the limits 0 to 10 to the x-axis.

#Write your code here.

If we don’t want to write the whole thing again, we can just store it in a variable

myPlot <- ggplot(litReview, aes(x=CET, y=SoV))+
  geom_point(aes(color=Venue), size=3)+
  ylim(c(0, 10))

And then, use that variable to add new things to our plot

myPlot+
  ggtitle("Two Dimensional Comparison", subtitle="Gap between Education and Visualization Researchers") + # Add a title and subtitle
  xlab("Connection to Edu. Theories") # Set the axis names

#### Activity Set the name for the y-axis

#Write your code here.

We can also change the breaks in each axis. Here are a couple of parameters to explain

myPlot+
scale_y_continuous(breaks=seq(0, 10, 1), # Set a break in the y axis every unit
                   limits=c(0, 10)) # Set the limits
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Also note that we used scale_y_continuous because y is a continuous variable, but there are other types:

  • scale_x_continuous, scale_y_continuous ==> For numbers

  • scale_x_discrete, scale_y_discrete ==> For categories

  • scale_x_date, scale_y_date ==> For dates

Activity

Change the x-axis limit so have breaks every 1

#Write your code here.

Now, there is an issue with our plot, there may be more than one point overlapping with each other at a single position.

We can use the size as an indicaton of how many papers are at each point. To do that, we use geom_count instead of geom_point

myPlot <- ggplot(litReview, aes(x=CET, y=SoV))+
  geom_count(aes(color=Venue))+
  scale_x_continuous(breaks=seq(0, 10, 1), limits=c(0, 10))+
  scale_y_continuous(breaks=seq(0, 10, 1), limits=c(0, 10))+ 
  scale_size_continuous(breaks = seq(0, 5, 1))+
  labs(title="Two Dimensional Comparison", 
       subtitle="Gap between Education and Visualization Researchers",
       y="Sophistication of the Visualization", 
       x="Connection to Educational Theories", 
       size="# of Studies")
myPlot

Activity

Create a scatter plot for Connection to Educational Theories (CET) vs. Connectino to Visualizaiton Background (CET vs CVG)

#Write your code here.