Visualizing process data
From Statistics for Engineering
- 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.
- Class notes 2015
- Class notes 2014
- Textbook, chapter 1
- Check your knowledge with these quizzes:
- Complete steps 1, 2 ... 9 of the software tutorial
- 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
Class videos from prior years
Videos from 2015
Videos from 2014
Videos from 2013
Software codes for this section
Code to show how to superimpose plots
# 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")