Data Visualization Using R

An invited workshop at Science Online 2013 on data visualization using R.

Transcript

1. Data Visualization using R
   How to get, manage, and present
   data to tell a compelling science
   story
   William Gunn
   @mrgunn
   Head of Academic Outreach, Mendeley
   Access point: NRC Visitor
   

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2. 1. A short history of graphical
   presentation of data
   2. Introduction to R
   3. Finding, cleaning, and presenting
   data
   4. Reproducibility and data sharing
   

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3. Data viz has a long history
   John Snow’s
   cholera map
   helped
   communicate
   the idea that
   cholera was a
   water-borne
   disease.
   

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4. Florence Nightingale used dataviz
   

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5. Modernization of dataviz
   

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6. Chart junk: good, bad, and ugly
   Which presentation is better?
   

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8. It can be elegant…
   

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10. Tufte
    

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11. Tufte
    

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12. How our eyes and brain perceive
    It takes 200 ms to initiate an eye movement, but the red dot
    can be found in 100 ms or less. This is due to pre-attentive
    processing.
    

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13. Shape is a little slower than color!
    

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14. Pre-attentive processing fails!
    

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15. There are many “primitive”
    properties which we perceive
    • Length
    • Width
    • Size
    • Density
    • Hue
    • Color intensity
    • Depth
    • 3-D orientation
    

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16. Length
    

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17. Width
    

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18. Density
    

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19. Hue
    

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20. Color Intensity
    

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21. Depth
    

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22. 3D orientation
    

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24. Types of color schemes
    • Sequential – suited for ordered data that
    progress from low to high. Use light colors for
    low values and dark colors for higher.
    • Diverging – uses hue to show the breakpoint
    and intensity to show divergent extremes.
    • Qualitative – uses different colors to represent
    different categories. Beware of using
    hue/saturation to highlight unimportant
    categories.
    

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25. Sequential
    http://colorbrewer2.org/
    

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26. Diverging
    

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27. Qualitative
    

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28. Tips for maps
    • Keep it to 5-7 data classes
    • ~8% of men are red-green colorblind
    • Diverging schemes don’t do well when
    printed or photocopied
    • Colors will often render differently on
    different screens, especially low-end LCD
    screens
    • http://colorbrewer2.org
    

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29. Part 2
    Introduction to R
    

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30. Why R?
    • Open source tool
    • Huge variety of packages for any kind of
    analysis
    • Saves time repeating data processing steps
    • Allows working with more diverse types of
    data and much larger datasets than Excel
    • Processing is much faster than Excel
    • Scripts are easily shareable, promoting
    reproducible work
    

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31. .csv and .xls / xlsx
    • Excel files are designed to hold the
    appearance of the spreadsheet in addition
    to the data.
    • R just wants the data, so always save as
    .csv if you have tabular data
    

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32. data structures
    • x• x
    • length(x)
    • x[1]
    • x[2]
    • x• x
    

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33. types of data
    • y• y
    • class(y)
    • y[2]
    • length(y)
    • data can be integer (1,2,3,…), numeric (1.0,
    2.3, …), character (a, b, c,…), logical
    (TRUE, FALSE) or other things
    

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34. Vectors
    • R can hold data organized a few different
    ways
    • vectors (1,2,3,4) but not (1,2,3,x,y,z)
    • lists – can hold heterogeneous data
    – 1
    – 2
    – a
    • x
    • arrays – multi-dimensional
    • dataframes – lists of vectors - like
    spreadsheets
    

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35. Vector operations
    • x + 1
    • x
    • sum(x)
    • mean(x)
    • mean(x+1)
    • x[2]• x
    • x+c(2:3)
    • x[2:10] + c(2:3)
    

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36. working with lists
    • y• y
    • y$name
    • y[1]
    • y[[1]]
    • class(y[1])
    • class(y[[1]])
    • y• y$name
    • y$age[2]

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37. Loading data
    • dataGunn/Desktop/Dropbox/Scripting/Data/t
    raffic_accidents/accidents2010_all.csv",
    header = TRUE, stringsAsFactors =
    FALSE)
    

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38. Selecting subsets of data
    • “[“
    • “$”
    • which
    • grep and grepl
    • subset
    

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39. PLOTS
    • ggplot2 – an implementation of the
    “grammar of graphics” in R
    • a set of graph types and a way of mapping
    variables to graph features
    • graph types are called “geoms”
    • mappings are “aesthetics”
    • graphs are built up by layering geoms
    

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40. Types of geoms
    • point – dotplot – takes x,y coords of points
    • abline – line layer – takes slope, intercept
    • line – connect points with a line
    • smooth – fit a curve
    • bar – aka histogram – takes vector of data
    • boxplot – box and whiskers
    • density – to show relative distributions
    • errorbar – what it says on the tin
    

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