• Like

Loading…

Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

20100929 ggplot - triangle useRs group presentation

  • 1,424 views
Uploaded on

 

More in: Career , Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
1,424
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
44
Comments
0
Likes
2

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. ggplot2 Elaine McVey BD Technologies Triangle Area R useRs Group September 29, 2010
  • 2. What is ggplot2?
    • ggplot2 is an R package written by Hadley Wickham that provides a comprehensive framework for statistical graphics based on The Grammar of Graphics (LeLand Wilkinson, 2005)
  • 3. Goal
    • To show you enough about ggplot2 that you can understand its capabilities, get started using it, and know where to learn more.
  • 4. Benefits
    • Since ggplot2 has an underlying conceptual framework, it becomes very powerful once you understand it.
    • ggplot2 code is concise and readable
    • Writing statistical functions (i.e. panel functions) for ggplot2 is relatively easy.
    • ggplot2 enforces the link between the data and the legend, so labeling errors are minimized.
    • The layering concept makes it easy to combine data from different dataframes in a single plot.
    • The active user community provides a good place to get help and drives ongoing development.
  • 5. Jumping In - Dataframe
  • 6. Jumping In – First Plot
  • 7. Layers
  • 8. More Layers - oops
  • 9. More Layers - corrected
  • 10. Non-Mapped Attributes
  • 11. Accidental Aesthetic Mapping
  • 12. Aesthetic Mapping by geom
  • 13. Bigger Dataframe
  • 14. Facets and Colors
  • 15. Statistical Summaries
  • 16. Creating errBars
  • 17. Lattice Comparison
  • 18. Expanding errBars
  • 19. Layering Dataframes plyr aside:
  • 20. Layering Dataframes in geoms
  • 21. Updating Plots
  • 22. Where to go next
    • ggplot2: Elegant Graphics for Data Analysis
    • (skip qplot)
    • 2) had.co.nz/ggplot2 (& plyr & reshape . . . )
    • 3) Google group listserv
  • 23.  
  • 24.  
  • 25.  
  • 26. Default statistics and aesthetics for each geom
  • 27.