Visualizing process data

From Statistics for Engineering
Jump to: navigation, search

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.


Extended readings

Class videos from prior years

Videos from 2015

07:31 | Download video | Download captions | Script
03:16 | Download video | Download captions | Script
04:51 | Download video | Download captions | Script
07:23 | Download video | Download captions | Script

Videos from 2014

Videos from 2013

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 <- '' 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")