ggplot Iain Hume 10 November 2015

Transcription

1 ggplot Iain Hume 1 November 215

2 Today s workshop ggplot grammar of graphics basic plot types subsetting saving plots prettying things up

3 Why use ggplot Takes the best of basic & latice graphics Progressive and build from simple to complex Uses grammar (Lee Wilkinson)

4 Packages and data you need Put these in a folder and start a new project ggplot2 ggthemes climate.csv

5 Grammar Data (data) Aesthetic mappings (aes) Geometric objects (geoms) Statistical transformations (stats) Sub setting (faceting)

6 Grammar does not Suggest what graphics will answer your questions Define the style of a graphic (themes)

7 First ggplot Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2 5. measurements in centimeters of four variables 5 flowers Iris setosa, versicolor, and virginica. Sepal.Length Sepal.Width Petal.Length Petal.Width Species setosa setosa setosa setosa setosa setosa

8 Scatter plot library(ggplot2) p1 <- ggplot(data=iris, aes(x=sepal.length, y = Sepal.Width)) p1 +geom_point() Sepal.Width Sepal.Length

9 Layers p1 <- ggplot(data=iris, aes(x=sepal.length, y = Sepal.Width)) p1 +geom_point() ggplot layer Data Variables Global settings Other objects geometric statistical models etc

10 Change symbol size p1 <- ggplot(data=iris, aes(x=sepal.length, y = Sepal.Width)) p1 + geom_point(size = 3) Sepal.Width Sepal.Length

11 Add colour p1 <- ggplot(data=iris, aes(x=sepal.length, y = Sepal.Width, colour = Species)) p1 + geom_point(size = 3) Sepal.Width Species setosa versicolor virginica Sepal.Length

12 Different shape for species p1 <- ggplot(data=iris, aes(x=sepal.length, y = Sepal.Width, colour = Species)) p1 + geom_point(aes(shape = Species), size = 3) Sepal.Width Species setosa versicolor virginica Sepal.Length

13 Exercise 1 - scatter plot Re sample the diamonds data set # Load the ggplot library library(ggplot2) # Resample the diamonds data set d2 <- diamonds[sample(1:dim(diamonds)[1], 1),] kable(head(d2[,1:7],2)) carat cut color clarity depth table price Fair J SI Premium J SI

14 Draw this plot 125 price/carat color D E F G H I J carat

15 Box Plots p3 <- ggplot(data=d2, aes(x= color, y = price/carat)) p3 + geom_boxplot() price/carat D E F G H I J color

16 Histograms Azzalini, A. and Bowman, A. W. (199). A look at some data on the Old Faithful geyser. Applied Statistics 39, observations of eruption time (minutes), and waiting time (minutes) for next eruption head(faithful) ## eruptions waiting ## ## ## ## ## ##

17 Basic histogram p4 <- ggplot(data=faithful, aes(x= waiting)) p4 + geom_histogram(binwidth=3, colour="black") 15 count waiting

18 Change stats and appearance p4 + geom_histogram(binwidth=8, fill="steelblue", colour="black") 6 count waiting?geom_histogram gives more options

19 Line plots obs Decadal land surface temperature anomaly 95% uncertainty # Get the data climate <- read.csv("climate.csv", header=t) list <- c(1,2,3,6,7) kable(head(climate[list],5)) X Source Year Anomaly1y Unc1y 12 Berkeley Berkeley Berkeley Berkeley Berkeley

20 Simple line plot p5 <- ggplot(climate, aes(x=year, y=anomaly1y)) p5 + geom_line().5 Anomaly1y Year

21 Line plot with confidence regions p5 <- ggplot(climate, aes(x=year, y=anomaly1y)) p5 + geom_ribbon(aes(ymin=anomaly1y - Unc1y, ymax = Anomaly1y + Unc1y), fill="blue", alpha=.1) + geom_line(colour="steelblue").5 Anomaly1y Year

22 Exercise 2 Modify the previous plot to show confidence limits as lines instead of a ribbon.5 Anomaly1y Year

23 Bar Plots p6 <- ggplot(diamonds, aes(clarity)) p6 + geom_bar() 1 count 5 I1 SI2 SI1 VS2 VS1 VVS2 VVS1 IF clarity

24 Stacked Bar Plots p7 <- ggplot(diamonds, aes(clarity, fill=cut)) p7 + geom_bar() 1 count 5 cut Fair Good Very Good Premium Ideal I1 SI2 SI1 VS2 VS1 VVS2 VVS1 IF clarity

25 Exercise 3 Using the d2 diamonds data set create this plot 5 4 count 3 2 cut Fair Good Very Good Premium Ideal 1 I1 SI2 SI1 VS2 VS1 VVS2 VVS1 IF clarity Hint?geom_bar is useful

26 Density Plots p9 <- ggplot(faithful, aes(waiting)) p9 + geom_density().3 density waiting

27 Density Plots (filled) p9 + geom_density(fill="blue", alpha =.1).3 density waiting

28 Density Plots using stat p9 + geom_line(stat="density").3 density waiting

29 Grouping Data Recall the iris data p1 <- ggplot(iris,aes(x=sepal.length, y=sepal.width, colour = Species)) p1 + geom_point(size=2) Sepal.Width Species setosa versicolor virginica Sepal.Length

30 Faceting along rows p1 <- ggplot(iris,aes(x=sepal.length, y=sepal.width, colour = Species)) p1 + geom_point(size=2) + facet_grid(species ~. ) Sepal.Width Sepal.Length setosa versicolor virginica Species setosa versicolor virginica

31 Faceting down columns p1 <- ggplot(iris,aes(x=sepal.length, y=sepal.width, colour = Species)) p1 + geom_point(size=2) + facet_grid(. ~ Species) 4.5 setosa versicolor virginica 4. Sepal.Width Species setosa versicolor virginica Sepal.Length

32 Simplifying the diamond data p11 <- ggplot(d2, aes(x=carat, y = price)) + geom_point() p11 15 price carat

33 Add an extra dimension p11 <- ggplot(d2, aes(x=carat, y = price)) + geom_point() + facet_grid(cut~.) p11 price carat Fair Good Very Good Premium Ideal

34 Add another p11 <- ggplot(d2, aes(x=carat, y = price)) + geom_point() + facet_grid(cut~color) p11 price D E F G H I J carat Fair Good Very Good Premium Ideal

35 Add another P11 <- ggplot(d2, aes(x=carat, y = price, colour=)) + geom_point() + facet_grid(cut~color) p11 price D E F G H I J carat Fair Good Very Good Premium Ideal Any more?

36 One final dimension p11 <- ggplot(d2, aes(x=carat, y = price, colour=clarity)) + geom_point() + facet_grid(cut~color) p11 D E F G H I J price carat Fair Good Very Good Premium Ideal clarity I1 SI2 SI1 VS2 VS1 VVS2 VVS1 IF

37 Adding Smoothers p12 <- ggplot(iris, aes(x=sepal.length, y= Sepal.Width))+ geom_point(size=2)+ geom_smooth(method= "lm")+ facet_grid(.~species) p setosa versicolor virginica 4. Sepal.Width Sepal.Length

38 Can specify the model p12a <- ggplot(iris, aes(x=sepal.length, y= Sepal.Width))+ geom_point(size=2)+ geom_smooth(method= "lm", formula = y~ poly(x,2)) + facet_grid(.~species) p12a 4.5 setosa versicolor virginica 4. Sepal.Width Sepal.Length

39 Alternative themes theme_bw p13 <- p12 +theme_bw() p setosa versicolor virginica 4. Sepal.Width Sepal.Length

40 Minimalist presentation theme_classic p14 <- p13 + theme_classic() p setosa versicolor virginica 4. Sepal.Width Sepal.Length

41 My favourate theme_tufte library(ggthemes) p15 <- p14 + geom_rangeframe() + theme_tufte() p setosa versicolor virginica 4. Sepal.Width Sepal.Length

42 Saving your work As.png,.eps,.jpg or.pdf files Into a document with knitr Easy if you work in projects i.e no paths # If the plot is on the screen i.e last one plotted ggsave(file = "Figure 15.jpg") ## Saving 1 x 5 in image # If the plot has been asigned to an object ggsave(p14, file = "Figure 14.jpg") ## Saving 1 x 5 in image

43 Tidying things up scales etc p16 <- ggplot(iris, aes(x=sepal.length, y= Sepal.Width))+ geom_point(size=2)+ stat_smooth(method= "lm", se=false) + facet_grid(.~species) + labs(x="sepal length (cm)", y="sepal Width (cm)") + geom_rangeframe() + theme_tufte() p setosa versicolor virginica 4. Sepal Width (cm) Sepal length (cm)

44 Thanks and happy plotting