R Programming/Graphics

R includes at least three graphical systems, the standard graphics package, the lattice package for Trellis graphsD. Sarkar. Lattice: Multivariate Data Visualization with R. Springer, 2008. 9780387759685. and the grammar-of-graphics ggplot2 packageggplot2: Elegant Graphics for Data Analysis (Use R) by Hadley Wickham and a list of examples on his own website : http://had.co.nz/ggplot2/. R has good graphical capabilities but there are some alternatives like gnuplot. Interactive Graphics This section discuss some ways to draw graphics without using R scripts. The playwith package provides a graphical user interface to customize the graphs, add a title, a grid, some text, etc and it exports the R code you need if you want to replicate the analysisplaywith : http://code.google.com/p/playwith/. If you want to know more, you can have a look at the screenshots on the website (link). See also the example on "R you Ready" . This package require GTK+ libraries. library("playwith") playwith(plot(x1)) There is also a graphical user interface GrapheR which makes it very easy to draw graphs for beginnersHervé Maxime 2011 GrapheR: a Multiplatform GUI for Drawing Customizable Graphs in R The R Journal 3 2 http://journal.r-project.org/archive/2011-2/RJournal_2011-2_Herve.pdf. This solution is cross-platform. > library(GrapheR) latticist (link) is another similar project. Note also that some graphical user interface such as RKward and R Commander makes it easy to draw graphs. Standard R graphs In this section we present what you need to know if you want to customize your graphs in the default graph system. plot() is the main function for graphics. The arguments can be a single point such as 0 or c(.3,.7), a single vector, a pair of vectors or many other R objects. par() is another important function which defines the default settings for plots. There are many other plot functions which are specific to some tasks such as hist(), boxplot(), etc. Most of them take the same arguments as the plot() function. > N <- 10^2 > x1 <- rnorm(N) > x2 <- 1 + x1 + rnorm(N) > plot(0) > plot(0,1) > plot(x1) > plot(x1,x2) # scatter plot x1 on the horizontal axis and x2 on the vertical axis > plot(x2 ~ x1) # the same but using a formula (x2 as a function of x1) > methods(plot) # show all the available methods for plot (depending on the number of loaded packages). Titles, legends and annotations Titles main gives the main title, sub the subtitle. They can be passed as argument of the plot() function or using the title() function. xlab the name of the x axis and ylab the name of the y axis. plot(x1,x2, main = "Main title", sub = "sub title" , ylab = "Y axis", xlab = "X axis") plot(x1,x2 , ylab = "Y axis", xlab = "X axis") title(main = "Main title", sub = "sub title" ) The size of the text can be modified using the parameters cex.main, cex.lab, cex.sub, cex.axis. Those parameters define a scaling factor, ie the value of the parameter multiply the size of the text. If you choose cex.main=2 the main title will be twice as big as usual. Legend legend(). The position can be "bottomleft", "bottomright", "topleft", "topright" or exact coordinates. plot(x1, type = "l", col = 1, lty = 1) lines(x2, col = 2, lty = 2) legend("bottomleft", legend = c("x1","x2"), col = 1:2, lty = 1:2) Text in the margin mtext() puts some texts in the margin. The margin can be at the bottom (1), the left (2), the top (3) or the right (4). plot(x1, type = "l", col = 1, lty = 1) ; mtext("some text", side = 1) # the bottom plot(x1, type = "l", col = 1, lty = 1) ; mtext("some text", side = 2) # the left plot(x1, type = "l", col = 1, lty = 1) ; mtext("some text", side = 3) # the top plot(x1, type = "l", col = 1, lty = 1) ; mtext("some text", side = 4) # the right margin Text in the graph text() Mathematical annotations We can add mathematical symbols using expression() and makes some substitution in a formula using substitute(). ?plotmath # gives help for mathematical annotations Types The type of a plot can be : n for none (nothing is printed), p for points, l for lines, b for both, o for both overlayed, h for histogram-like and s/S for steps. R code Output x1 <- rnorm(50) png("plottype.png") par(mfrow = c(2,2)) plot(x1, type = "p", main = "points", ylab = "", xlab = "") plot(x1, type = "l", main = "lines", ylab = "", xlab = "") plot(x1, type = "b", main = "both", ylab = "", xlab = "") plot(x1, type = "o", main = "both overplot", ylab = "", xlab = "") dev.off() thumb|left|click on the graph to zoom Axes The default output print the axes. We can remove them with axes=FALSE. We can also change them using the axis() function. > plot(x1,x2,axes=FALSE) > > plot(x1,x2,axes=FALSE) > axis(1,col="red",col.axis="blue",font.axis=3) > axis(2,col="red",col.axis="blue",font.axis=2,las=2) las specifies the style of axis labels. It can be 0, 1, 2 or 3. 0 : always parallel to the axis [default], 1 : always horizontal, 2 : always perpendicular to the axis, 3 : always vertical. R code Output x1 <- rnorm(100) par(mfrow = c(2,2)) plot(x1, las = 0, main = "las = 0", sub = "always parallel to the axis", xlab = "", ylab = "") plot(x1, las = 1, main = "las = 1", sub = "always horizontal", xlab = "", ylab = "") plot(x1, las = 2, main = "las = 2", sub = "always perpendicular to the axis", xlab = "", ylab = "") plot(x1, las = 3, main = "las = 3", sub = "always vertical", xlab = "", ylab = "") thumb|left|click on the graph It is also possible to add another y axis on the right by adding axis(4,). Margins Margins can be computed in inches or in lines. The default is par(mar = c(5,4,4,2)) which means that there are 5 lines at the bottom, 4 lines on the left, 4 lines in the top and 2 lines on the right. This can be modified using the par() function. If you want to specify margins in inches, use par(mai = c(bottom, left, top, right). If you want to modify margins in lines, use par(mar = c(bottom, left, top, right). See ?par to learn more about the topic. Colors The color of the points or lines can be changed using the col argument, fg for foreground colors (boxes and axes) and bg for background colors. show.col(object=NULL) (Hmisc) package plots the main R colors with their numeric code. The list of all colors in R ( pdf) colors() # list the r colors show.col(object=NULL) # graphs the main R colors plot(x1, col = "blue") plot(x1, col = "red") plot(x1, col = "red", col.axis = "dodgerblue", col.lab = "firebrick", col.main = "darkgreen", col.sub = "cyan4", main = "Testing colors", sub = "sub titles", ylab = "y axis", xlab = "x axis") We can also generate new colors using the rgb() function. The first argument is the intensity of red, the second, the intensity of green and the third, the intensity of blue. They vary between 0 and 1 by default but this can be modified with the option max = 255. col2rgb() returns the RGB code of R colors. col2hex() (gplots) gives the hexadecimal code. col2grey() and col2gray() (TeachingDemos) converts colors to grey scale. > mycolor <- rgb(.2,.4,.6) > plot(x1, col = mycolor) > col2rgb("pink") [,1] red 255 green 192 blue 203 > library("gplots") > col2hex("pink") [1] "#FFC0CB" Points For points the symbols can be changed using the pch option which takes integer values between 0 and 25 or a single character. pch can also takes a vector as argument. In that case the first points will use the first element of the vector as symbol, and so on. plot(x1, type = "p", pch = 0) plot(x1, type = "p", pch = 10) plot(x1, type = "p", pch = 25) plot(x1, type = "p", pch = "a") plot(x1, type = "p", pch = "*") plot(x1[1:26], type = "p", pch = 0:25) plot(x1[1:26], type = "p", pch = letters) The following code displays all the symbols on the same plot : x <- rep(1,25) plot(x, pch = 1:25, axes = F, xlab = "", ylab = "") text(1:25,.95,labels = 1:25) points() adds points to an existing plot. > plot(x1, pch = 0) # plot x1 > points(x2, pch = 1, col = "red") # add x2 to the existing plot Lines We can change the line type with lty. The argument is a string ("blank", "solid", "dashed", "dotted", "dotdash", "longdash", or "twodash") or an integer (0=blank, 1=solid (default), 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash). The line width can be changed with lwd. The default is lwd=1. lwd=2 means that the width is twice the normal width. plot(x1, type = "l", lty = "blank") plot(x1, type = "l", lty = "solid") plot(x1, type = "l", lty = "dashed") plot(x1, type = "l", lty = "dotted") plot(x1, type = "l", lty = "dotdash") plot(x1, type = "l", lty = "longdash") plot(x1, type = "l", lty = "twodash") lines() adds an additional lines on a graph. plot(x1, type = "l", lty = "solid") lines(x2, type = "l", lty = "dashed", col = "red") abline() adds an horizontal line (h=), a vertical line (v=) or a linear function to the current plot (a= for the constant and b= for the slope). abline() can also plot the regression line. > plot(x1, type = "l", lty = "solid") > abline(h= -3, lty = "dashed", col = "gray") > abline(v = 0, lty = "dashed", col = "gray") > abline(a = -3 , b = .06, lty = "dotted", col = "red") Boxes Each graph is framed by a box. bty specifies the box type. plot(x1, bty = "o") # the default plot(x1, bty = "n") # no box plot(x1, bty = "l") plot(x1, bty = "7") plot(x1, bty = "u") plot(x1, bty = "c") plot(x1, bty = "]") See also box() to add a box to an existing plot. Grid grid() adds a grid to the current graph. > plot(x1) > grid() Although grid has an optional argument nx for setting the number of grid lines, it is not possible to tell it explicitly where to place those lines (it will usually not place them at integer values). A more precise and manageable alternative is to use abline(). > abline(v=(seq(0,100,5)), col="lightgray", lty="dotted") > abline(h=(seq(0,100,5)), col="lightgray", lty="dotted") Arrows and segments Polygons Other figures We can also add a circle to a plot with the circle() function in the calibrate package. Background You can choose the background of your plot. For instance, you can change the background color with par(bg=). par(bg="whitesmoke") par(bg="transparent") Overlaying plots matplot() can plot several plots at the same time. N <- 100 x1 <- rnorm(N) x2 <- rnorm(N) + x1 + 1 y <- 1 + x1 + x2 + rnorm(N) mydat <- data.frame(y,x1,x2) matplot(mydat[,1],mydat[,2:3], pch = 1:2) Multiple plots With par() we can display multiple figures on the same plot. mfrow = c(3,2) prints 6 figures on the same plot with 3 rows and 2 columns. mfcol = c(3,2) does the same but the order is not the same. par(mfrow = c(3,2)) plot(x1, type = "n") plot(x1, type = "p") plot(x1, type = "l") plot(x1, type = "h") plot(x1, type = "s") plot(x1, type = "S") par(mfcol = c(3,2)) plot(x1, type = "n") plot(x1, type = "p") plot(x1, type = "l") plot(x1, type = "h") plot(x1, type = "s") plot(x1, type = "S") Plotting a function curve() plots a function. This can be added to an existing plot with the option add = TRUE. plot() can also plots functions. curve(x^2, from = -1 , to = 1, main = "Quadratic function", ylab = "f(x)=x^2") plot(rnorm(100)) curve((x/100)^2, add = TRUE, col = "red") Exporting graphs How can you export a graph ? First you can plot the graph and use the context menu (right click on Windows and Linux or control + click on Mac) to copy or save the graphs. The available options depend on your operating system. On Windows, you can also use copy the current graph to the clipboard as a Bitmap file (raster graphics) using CTRL + C or as a Windows Metafile (vector graphics) using CTRL + W. You can then paste it into another application. You can export a plot to pdf, png, jpeg, bmp or tiff by adding pdf("filename.pdf"), png("filename.png"), jpeg("filename.jpg"), bmp("filename.bmp") or tiff("filename.tiff") prior to the plotting, and dev.off() after the plotting. You can also use the savePlot() function to save existing graphs. Sweave also produce ps and pdf graphics (See the Sweave section). It is better to use vectorial devices such as pdf, ps or svg. How can you know the list of all available devices ? ?Devices Use the capabilities() function to see the list of available devices on your computer. ?Devices > capabilities() jpeg png tiff tcltk X11 aqua http/ftp sockets TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE libxml fifo cledit iconv NLS profmem cairo TRUE FALSE TRUE TRUE TRUE TRUE FALSE png("r_plot.png", width = 420, height = 340) plot(x1, main = " Example") dev.off() pdf("r_plot.pdf", width = 420, height = 340) plot(x1, main = " Example") dev.off() postscript(file="graph1.ps",horizontal=F,pagecentre=F,paper="special",width=8.33,height=5.56) plot(x1, main = "Example") dev.off() plot(x1, main = "Example") savePlot("W:/Bureau/plot.pdf", type = "pdf") savePlot("W:/Bureau/plot.png", type = "png") We can also export to SVG using the svg() function. svg("scatterplot.svg", width = 7, height = 7) plot(x, y) dev.off() The RSvgDevice library which was used in earlier versions of R seems now outdated. Advanced topics Animated plots The animation package provides dynamic graphics capabilities. It is possible to export the animation in flash, mpeg or gif format. There are more example on the aniwiki website : http://animation.yihui.name/. You can also create motion charts using the googleVis packageTutorial for the googleVis package : http://stackoverflow.com/questions/4646779/embedding-googlevis-charts-into-a-web-site/4649753#4649753. Examples Interactive Graphics The iplots package provides a way to have interactive data visualization in Rhttp://www.r-bloggers.com/interactive-graphics-with-the-iplots-package-from-%E2%80%9Cr-in-action%E2%80%9D/http://www.r-statistics.com/2012/01/interactive-graphics-with-the-iplots-package-from-r-in-action/ Interactive Graphics with the iplots Package] - a chapter from the R in action book. R GUI now offers interactive graphics – Deducer 0.4-2 connects with iplots To create an interactive, animated plot viewable in a web browser, the animint package can be used. The main idea is to define an interactive animation as a list of ggplots with two new aesthetics: showSelected=variable means that only the subset of the data that corresponds to the selected value of variable will be shown. clickSelects=variable means that clicking a plot element will change the currently selected value of variable. Graphics gallery In this section, we review all kind of statistical plots and review all alternatives to draw them using R. This include code for the standard graphics package, the lattice package and the ggplot2 package. Also, we add some examples from the commons repository. We only add examples which are provided with the R code. You can click on any graph and find the R code. Category:Created with R Line plot To draw a line plot, use the generic plot() function by setting type="l". > x <- seq(0, 2*pi, pi/10) > plot(x, sin(x), type="l") Then, you can add further lines on the same plot using the lines() function. > lines(x, cos(x)) Examples Scatter plot Scatter plot plot(x,y) plot(y ~ x) xyplot(y ~ x) (lattice) qplot(x,y) (ggplot2) Log scale Sometimes it is useful to plot the log of a variable and to have a log scale on the axis. It is possible to plot the log of a variable using the log option in the plot() function. For a log log plot, use log = "xy" For a log in the x axis only, use log = "x" For a log in the x axis only, use log = "y" plot(x, y , log = "xy") Label points in a plot It is possible to add labels with the text() function. textxy() (calibrate) makes it easy to add labels. N <- 10 u <-rnorm(N) x <- 1 + rnorm(N) y <- 1 + x + u plot(x, y) textxy(x, y,labs = signif(x,3), cx=0.7) Examples Histogram histogram hist() histogram() (lattice) You can learn more about histograms in the Non parametric methods page. Examples Box plot Box plot : boxplot() Examples See also How to label all the outliers in a boxplot Bar charts See Bar charts on wikipedia. barplot() takes a table as argument and returns a bar chart. qlot() (ggplot2) with the option geom = "bar" takes a variable as argument and returns a bar chartHadley Wickham ggplot2: Elegant Graphics for Data Analysis, Springer Verlag, 2009. barchart() takes a variable as argument and returns a bar chart. Examples Dot plot See also Dot plot on Wikipedia. dotchart() Examples Pie charts Pie chart pie() Examples Treemap The tmPlot() function in the treemap package makes it easy to draw a treemap. Confidence interval plot Standard error bar chart are very useful to plot several estimates with confidence intervals. The Hmisc package has an errbar() function. This function takes the upper and lower bounds of the confidence intervals as argumentThe default output in errbar() changed between R version 2.8.1 and R version 2.9.2. Axis are not displayed by default anymore. coefplot() function in Gelman and Hill's arm package. This functions is designed to display estimation results. It takes point estimates and standard errors as arguments. coefs <- c(0.2, 1.4, 2.3, 0.5,.3) # vector of point estimates se <- c(0.12, 0.24, 0.23, 0.15,.2) # standard errors of point estimates variable <- 1:5 # variable names library("arm") # we use CI = qnorm(.975) to have 95% confidence interval coefplot(coefs, se, variable, vertical = T, CI = qnorm(.975)) coefplot(coefs, se, variable, vertical = F, CI = qnorm(.975)) library("Hmisc") errbar(variable, coefs, coefs - qnorm(.975) * se, coefs + qnorm(.975) * se) See also There is another errbar() function in the sfsmisc package. plotCI() (gplots) also plot error bars. plotmeans() (gplots) ciplot() (hacks) See also Error bar on Wikipedia 3D plots contour(), image(), persp() plot3d() (rgl) wireframe() (lattice) Examples Diagrams grid package by Paul MurrellPaul Murrell Drawing Diagrams with R, The R Journal, 2009 http://journal.r-project.org/2009-1/RJournal_2009-1_Murrell.pdf diagram package (example: Using a binary tree diagram for describing a Bernoulli process) Rgraphviz package igraph package Arc Diagrams It is also possible to draw Arc Diagramshttp://gastonsanchez.wordpress.com/2013/02/03/arc-diagrams-in-r-les-miserables/ Arc Diagrams in R: Les Miserables February 5th, 2013 February 3rd, 2013 Gaston Sanchez. Dendrograms It is possible to plot dendrograms in Rhttp://gastonsanchez.wordpress.com/2012/10/03/7-ways-to-plot-dendrograms-in-r/ 7+ ways to plot dendrograms in R Gaston Sanchez February 5th, 2013 October 3, 2012. Treemap It is possible to draw a treemap using the treemap() function in the treemap packagehttp://cran.r-project.org/web/packages/treemap/treemap.pdf. Wordcloud There is : the wordcloud() function in the wordcloud package the tagcloud() function in the tagcloud package Timeline timeline() in the timeline package Maps See also Geograpic Maps in R (Revolution Computing Blog) Global Administrative Areas Andy Egger's googlemap.r function Resources Tables 2 Graphs R Graphics by Paul Murrellhttp://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html ggplot2 http://had.co.nz/ggplot2/ Graphical Parameters References 2 Data Management Descriptive Statistics pt:R (linguagem de programação)/Gráficos