Visualizing process data

Learning outcomes

• Understand when it is appropriate to use scatter plots, bar plots, pie charts (hint: almost never), and even tables.
• Learn an interesting, potentially new plot: the box plot, to summarize and compare data.
• How to effectively visualize up to 5 dimensions on a 2-D plot, as shown in a video by Hans Rosling.
• Know the meaning of words like sparklines, data density, and chart junk.

Resources

• Class notes 2015
• Class notes 2014
• Textbook, chapter 1
• Check your knowledge with these quizzes:
  • Quiz 1 and Solution 1
  • Quiz 2 and Solution 2
  • Quiz 3 and Solution 3
• Complete steps 1, 2 ... 9 of the software tutorial

Extended readings

• Sankey diagrams for example, would make a great way to show energy utilization in your company, or even a mass balance superimposed on a flowsheet. Here's a great example applied to the UK energy supply and demand.
• 40 years of boxplots
• Why you should never have to use pie charts, an article by Stephen Few.
• This is one video you must watch for the course: Hans Rosling shows an incredible data visualization

Software codes for this section

Code to show how to superimpose plots

Run this code in a web-browser

# Run this code line-by-line (copy & paste) to understand the demonstration f <- 'http://openmv.net/file/raw-material-properties.csv' data <- read.csv(f) summary(data) # Single plot plot(data$density1) # Connect the dots plot(data$density1, type='b') # Another variable plot(data$density2, type='b', col="red") # Superimpose them? plot(data$density1, type='b', col="blue") # Where's density2 ? lines(data$density2, type='b', col="red") # Superimpose them: limits plot(data$density1, type='b', col="blue", ylim=c(10, 45)) # Now density2 shows up lines(data$density2, type='b', col="red")