Elegant Graphics for Data Analysis with ggplot2
- 1. Elegant Graphics for Data Analysis with ggplot2 Yann Abraham BaselR 28.04.2010
- 2. Who is Yann Abraham • Biochemist by training • Bioinformatician by trade • Pharma/Biotech – Cellzome AG – Novartis Pharma AG • http://ch.linkedin.com/in/yannabraham
- 3. How to Represent Data
- 4. “A Picture is Worth a Thousand Words” • Visualization is a critical component of data analysis • Graphics are the most efficient way to digest large volumes of data & identify trends • Graphical design is a mixture of mathematical and perceptual science
- 5. A Straightforward Way to Create Visualizations • Grammar of Graphics provides a framework to streamline the description and creation of graphics • For a given dataset to be displayed: – Map variables to aesthetics – Define Layers • A representation (a ‘geom’) ie line, boxplot, histogram,… • Associated statistical transformation ie counts, model,… – Define Scales • Color, Shape, axes,… – Define Coordinates – Define Facets
- 6. Why Use ggplot2? • Simple yet powerful syntax • Provides a framework for creating any type of graphics • Implements basic graphical design rules by default
- 7. An Example • 4 cell lines where treated with a compound active against a class of enzymes • Proteins where extracted and quantified using mass spectrometry • Is there anything interesting?!?
- 8. Excel…
- 9. R…
- 10. R…
- 11. R…
- 12. R… (this could go on for hours)
- 13. …ggplot2!
- 14. ggplot2!
- 15. ggplot2!
- 16. ggplot2!
- 17. ggplot2! qplot(Experiment_1,Experiment_2,data=comp,color=CELLLINE,facets=ISTARGET~CELLLINE)+ coord_equal()+ scale_x_log10()+ scale_y_log10()
- 18. When Visualization Alone is Not Enough • Some datasets are large multidimensional data structures • Representing data from such structure requires data transformation • R is good at handling large sets • R functions for handling multidimensional sets are complex to use
- 19. Easy Data Transformation With plyr • plyr provides wrappers around typical R operations – Split – Apply – Combine • plyr functions are similar to the by() function
- 20. Why use plyr? • Simple syntax • Predictable output • Tightly integrated into ggplot2 • This comes at a price – somewhat slower than apply
- 21. An Example • Given a set of raw data from a High Throughput Screen, compute the plate- normalized effect
- 22. The standard R way plate.mean <- aggregate(hts.data$RAW, list(hts.data$PLATE_ID),mean) names(plate.mean) <- c(‘PLATE_ID’,’PLATE_MEAN’) hts.data <- merge(hts.data,plate.mean) hts.data$NORM<- hts.data$RAW/hts.data$PLATE_MEAN
- 23. The plyr way hts.data <- ddply(hts.data,.(PLATE_ID),function(df) { df$NORM<-df$RAW/mean(df$RAW) return(df) } )
- 24. Benefits of Using plyr & ggplot2 • Compact, straightforward syntax – Good basic output, complex options only required for polishing • Shifts focus from plotting to exploring – Presentation graphics can be created from there at minimal cost – Data transformation is intuitive • Powerful statistics available – It’s R!
- 25. Some links… • The Grammar of Graphics book by Leland Wilkinson • The ggplot2 book by Hadley Wickham – And the corresponding website • A presentation about plyr by JD Long – And his initial blog post
- 26. THANK YOU FOR YOUR ATTENTION!