Ggplot2 QMMA. Emanuele Taufer. 2/19/2018 Ggplot2 (1)

Transcription

1 Ggplot2 QMMA Emanuele Taufer file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 1/27

2 Ggplot2 ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics. ( Basic idea: develope a grammar of graphics to have a language to build a graph like one builds a sentence. file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 2/27

3 Components of the layered grammar Components that make up a plot: data and aesthetic mappings, geometric objects, scales, and facet specification. Also one can consider statistical transformations, and the coordinate system See Wickham, Hadley, A layered grammar of graphics. J. Comput. Graph. Statist. 19 (2010), no. 1, Wilkinson, Leland, The grammar of graphics. Handbook of computational statistics-concepts and methods. 1, 2, , Springer Handb. Comput. Stat., Springer, Heidelberg, file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 3/27

4 The package ggplot2 Installing install.packages("ggplot2") library("ggplot2") Documentation Examples Aesthetic specifications file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 4/27

5 Data Executive salaries salaries<-read.table(" header=t,sep="") salaries$gender<-factor(salaries$gender,c(0,1),c("f","m")) head(salaries) ## Salary Experience Education Gender Employees Assets ## M ## M ## F ## M ## M ## F file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 5/27

6 Function ggplot() An aesthetic is a dimension of a graph that we can perceive visually: the simplest example being the x and y axes. When we make a scatterplot, we choose one attribute to assign to the x axis, and one attribute to assign to the y axis. Other aesthetics we can use in a scatter plot are the color, size, and shape of the points in the graph: each of these aesthetics lets us communicate some dimension of the data, and understand complex relationships between them. gg1<-ggplot(salaries, aes(x=experience, y=salary)) gg1 file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 6/27

7 Adding a geometry gg1 + geom_point() file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 7/27

8 Changing size and color - globally gg1+geom_point(size=3,color="blue") file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 8/27

9 Using size, shape and color as aesthetics gg3<-ggplot(salaries, aes(x=experience, y=salary, shape=gender, size=assets, color=employees)) + geom_point( gg3 file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 9/27

10 gg3 + scale_colour_gradient(low = "blue",high="red") file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 10/27

11 Adding a fit Note that color here is an aesthetic of geom_point() gg4<-ggplot(salaries, aes(x=experience, y=salary)) gg4+geom_point(aes(color=gender)) + geom_smooth() ## `geom_smooth()` using method = 'loess' file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 11/27

12 Adding a linear fit gg4+geom_point(size=2,aes(color=gender)) + geom_smooth(se=f, method=lm) + scale_colour_brewer(palette = "Set file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 12/27

13 Separate fit Note that color here is an aesthetic of ggplot gg5<-ggplot(salaries, aes(x=experience, y=salary, color=gender)) gg5+geom_point(size=2) + geom_smooth(se=f, method=lm) + scale_colour_brewer(palette = "Set1") file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 13/27

14 Data Diamonds data("diamonds") head(diamonds) ## # A tibble: 6 x 10 ## carat cut color clarity depth table price x y z ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## Ideal E SI ## Premium E SI ## Good E VS ## Premium I VS ## Good J SI ## Very Good J VVS file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 14/27

15 Scatter plot gg6<-ggplot(diamonds, aes(x=carat,y=price))+geom_point(aes(color=cut))+geom_smooth(method=lm,se=f) gg6 file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 15/27

16 Zooming in Note the functions xlim() and ylim() will delete data outside the limits, while coord_cartesian() do not gg6 + coord_cartesian(xlim = c(0,3), ylim = c(0,20000), expand = TRUE) file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 16/27

17 Adding title and change labels ggplot(diamonds, aes(x=carat,y=price))+geom_point(aes(color=cut)) + ggtitle("diamonds data") + xlab("weight file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 17/27

18 Using a log scale ggplot(diamonds, aes(x=carat,y=price))+geom_point(aes(color=cut))+geom_smooth(method=lm,se=f)+scale_x_log10( scale_y_log10() file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 18/27

19 Faceting - one attribute Use facet_wrap(~ attribute) ggplot(diamonds, aes(x=carat, y=price, color=cut)) + geom_point() + facet_wrap(~ clarity) file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 19/27

20 Faceting - two attributes Use facet_grid(attribute 1 ~ attribute 2) ggplot(diamonds, aes(x=carat, y=price, color=cut)) + geom_point() + facet_grid(color ~ clarity) file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 20/27

21 Other geometries geom_histogram() geom_density geom_boxplot file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 21/27

22 The function qplot() The function ggplot() requires that the data be in a data.frame If this is not the case the function qplot() needs to be used x=rnorm(1000) y=rnorm(1000) file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 22/27

23 Histogram qplot(x)+geom_histogram(binwidth=0.5,color="black",fill="white") ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 23/27

24 Scatter plot - short form qplot(x,y) file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 24/27

25 Plotting data from cross tables data("worldphones") WorldPhones ## N.Amer Europe Asia S.Amer Oceania Africa Mid.Amer ## ## ## ## ## ## ## To put the data in a standard form we can use the package reshape2 library(reshape2) WorldPhones.m = melt(worldphones) colnames(worldphones.m) = c("year", "Area", "Phones") WorldPhones.m ## Year Area Phones ## N.Amer ## N.Amer ## N.Amer ## N.Amer ## N.Amer ## N.Amer ## N.Amer ## Europe ## Europe ## Europe ## Europe ## Europe ## Europe ## Europe ## Asia 2876 ## Asia 4708 ## Asia 5230 ## Asia 6662 ## Asia 6856 ## Asia 8220 ## Asia 9053 ## S.Amer 1815 ## S.Amer 2568 ## S.Amer 2695 ## S.Amer 2845 ## S.Amer 3000 ## S.Amer 3145 ## S.Amer 3338 ## Oceania 1646 ## Oceania 2366 ## Oceania 2526 ## Oceania 2691 file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 25/27

26 ## Oceania 2868 ## Oceania 3054 ## Oceania 3224 ## Africa 89 ## Africa 1411 ## Africa 1546 ## Africa 1663 ## Africa 1769 ## Africa 1905 ## Africa 2005 ## Mid.Amer 555 ## Mid.Amer 733 ## Mid.Amer 773 ## Mid.Amer 836 ## Mid.Amer 911 ## Mid.Amer 1008 ## Mid.Amer 1076 file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 26/27

27 Plotting trends ggplot(worldphones.m, aes(x=year, y=phones, color=area)) + geom_line() + ggtitle("number of phones by area") file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-4_ggplot2.html#(1) 27/27