The following presentation is based on the ggplot2 tutotial written by Prof. Jennifer Bryan.

Transcription

1 Graphics Agenda Grammer of Graphics Using ggplot2 The following presentation is based on the ggplot2 tutotial written by Prof. Jennifer Bryan. ggplot2 (wiki) ggplot2 is a data visualization package Created by Hadley Wickham in 2005 ggplot2 is an implementation of Leland Wilkinson s Grammar of Graphics scheme ggplot2 has grown in use to become one of the most popular R packages. Grammar of Graphics Elments data: The data that you want to visualise aes: Aesthetic mappings describing how variables in the data are mapped to aesthetic attributes horizontalposition (x) vertical position (y) colour size Grammar of Graphics Elments geoms: Geometric objects thatrepresent what you actually see on the plot points lines polygons bars 1

2 Grammar of Graphics Elments stats: Statistics transformations binning and counting observations to create a histogram, summarising a 2d relationship with a linear model stats are optional Grammar of Graphics Elments scales: related the data to the aesthetic coord: A coordinate systemthat describes how data coordinates are mapped to the plane of the graphic. facet: A faceting specification describes how to break up the data into subsets. Grammar of Graphics Layers A layer is composed of four parts: - data and aesthetic mapping, - a statistical transformation (stat), - a geometric object (geom) - a position adjustment. A plot is constructed by adding layers to each other ggplot2 The slides are baded on the tutorial... Load the library library(ggplot2) ## Warning: package 'ggplot2' was built under R version Read data gdurl <- " gdat <- read.table(file = gdurl,header=t,sep = "\t") str(gdat) ## 'data.frame': 1698 obs. of 6 variables: ## $ country : Factor w/ 147 levels "Afghanistan",..: ## $ year : int ## $ pop : num ## $ continent: Factor w/ 5 levels "Africa","Americas",..: ## $ : num ## $ gdppercap: num

3 Scatterplot Creating a plot object p <- ggplot(gdat, aes(x = gdppercap, y = )) # just initializes Scatterplot p + geom_point() gdppercap #p + layer(geom = "point") Data transformation Log transformation... quick and dirty ggplot(gdat, aes(x = log10(gdppercap), y = )) + geom_point() 3

4 3 4 5 log10(gdppercap) Data transformation A better way to log transform p + geom_point() + scale_x_log10() 4

5 1e+03 1e+04 1e+05 gdppercap === Let s make that stick p <- p + scale_x_log10() common workflow: gradually build up the plot you want Re-define the object p as you develop keeper commands === Convey continent by color: MAP continent variable to aesthetic color p + geom_point(aes(color = continent)) 5

6 continent Africa Americas Asia Europe Oceania 1e+03 1e+04 1e+05 gdppercap === In full detail, up to now: ggplot(gdat, aes(x = gdppercap, y =, color = continent)) + geom_point() + scale_x_log10() === Address overplotting: SET alpha transparency and size to a value p + geom_point(alpha = (1/3), size = 3) 6

7 1e+03 1e+04 1e+05 gdppercap Curves Add a fitted curve or line p + geom_point() + geom_smooth() ## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, b 7

8 1e+03 1e+04 1e+05 gdppercap Curves p + geom_point() + geom_smooth(lwd = 3, se = FALSE) ## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, b 8

9 1e+03 1e+04 1e+05 gdppercap Curves p + geom_point() + geom_smooth(lwd = 3, se = FALSE, method = "lm") 9

10 1e+03 1e+04 1e+05 gdppercap Curves Return to continents: p + aes(color = continent) + geom_point() + geom_smooth(lwd = 3, se = FALSE) 10

11 continent Africa Americas Asia Europe Oceania 1e+03 1e+04 1e+05 gdppercap Facets Facetting: another way to exploit a factor p + geom_point(alpha = (1/3), size = 3) + facet_wrap(~ continent) 11

12 Africa Americas Asia 80 Europe Oceania 1e+03 1e+04 1e+05 1e+03 1e+04 1e+05 gdppercap Facets p + geom_point(alpha = (1/3), size = 3) + facet_wrap(~ continent) + geom_smooth(lwd = 2, se = FALSE) 12

13 Africa Americas Asia 80 Europe Oceania 1e+03 1e+04 1e+05 1e+03 1e+04 1e+05 gdppercap === Exercises: Plot against year Make mini-plots, split out by continent Add a fitted smooth and/or linear regression, with or without facetting === Plot against year (p1 <- ggplot(gdat, aes(x = year, y = )) + geom_point()) 13

14 year === Make mini-plots, split out by continent p1 + facet_wrap(~ continent) 14

15 Africa Americas Asia 80 Europe Oceania year === Add a fitted smooth and/or linear regression, without facetting p1 + geom_smooth(se = FALSE, lwd = 2) + geom_smooth(se = FALSE, method ="lm", color = "orange", lwd = 2) ## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, b 15

16 year === Add a fitted smooth and/or linear regression, with facetting p1+ geom_smooth(se = FALSE, lwd = 2) + facet_wrap(~ continent) 16

17 Africa Americas Asia 80 Europe Oceania year === Last bit on scatterplots How can we connect the dots for one country? make a spaghetti plot? p1 + facet_wrap(~ continent) + geom_line() # uh, no 17

18 Africa Americas Asia 80 Europe Oceania year === p1 + facet_wrap(~ continent) + geom_line(aes(group = country)) # yes! 18

19 Africa Americas Asia 80 Europe Oceania year === p1 + facet_wrap(~ continent) + geom_line(aes(group = country)) + geom_smooth(se = FALSE, lwd = 2) 19

20 Africa Americas Asia 80 Europe Oceania year Subsetting data ggplot() does not have a subset = argument Do that on the fly with subset(..., subset =...) ggplot(subset(gdat, country == "Zimbabwe"), aes(x = year, y = )) + geom_line() + geom_point() 20

21 year === Let just look at four countries jcountries <- c("canada", "Rwanda", "Cambodia", "Mexico") ggplot(subset(gdat, country %in% jcountries), aes(x = year, y =, color = country)) + geom_line() + geom_point() 21

22 country Cambodia Canada Mexico Rwanda year === When you really care, make your legend easy to navigate This means visual order = data order = factor level order ggplot(subset(gdat, country %in% jcountries), aes(x = year, y =, color = reorder(country, -1 *, max))) + geom_line() + geom_poi 22

23 reorder(country, 1 *, max) Canada Mexico Cambodia Rwanda year === Another approach to overplotting ggplot(gdat, aes(x = gdppercap, y = )) + scale_x_log10() + geom_bin2d() 23

24 count e+03 1e+04 1e+05 gdppercap Stripplots Stripplots: univariate scatterplots (but w/ ways to also convey 1+ factors) ggplot(gdat, aes(x = continent, y = )) + geom_point() 24

25 Africa Americas Asia Europe Oceania continent Stripplots We have an overplotting problem; need to spread things out ggplot(gdat, aes(x = continent, y = )) + geom_jitter() 25

26 Africa Americas Asia Europe Oceania continent === We can have less jitter in x, no jitter in y, more alpha transparency ggplot(gdat, aes(x = continent, y = )) + geom_jitter(position = position_jitter(width = 0.1, height = 0), alpha = 1/4) 26

27 Africa Americas Asia Europe Oceania continent Boxplots === Boxplots covered properly elsewhere ggplot(gdat, aes(x = continent, y = )) + geom_boxplot() 27

28 Africa Americas Asia Europe Oceania continent Boxplots === Raw data AND boxplots ggplot(gdat, aes(x = continent, y = )) + geom_boxplot(outlier.colour = "hotpink") + geom_jitter(position = position_jitter(width = 0.1, height = 0), alpha = 1/4) 28

29 Africa Americas Asia Europe Oceania continent === Superpose a statistical summary ggplot(gdat, aes(x = continent, y = )) + geom_jitter(position = position_jitter(width = 0.1), alpha = 1/4) + stat_summary(fun.y = median, colour = "red", geom = "point", size = 5) 29

30 Africa Americas Asia Europe Oceania continent === Let s reorder the continent factor based on ggplot(gdat, aes(reorder(x = continent, ), y = )) + geom_jitter(position = position_jitter(width = 0.1), alpha = 1/4) + stat_summary(fun.y = median, colour = "red", geom = "point", size = 5) 30

31 Africa Asia Americas Europe Oceania reorder(x = continent, ) 31