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**Data visualization and graphic design**

Special topics R can be downloaded from and is available free for Windows, Mac OS X, and Linux. Allan Just and Andrew Rundle EPIC Short Course June 24, 2011 Wickham 2008

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**Agenda Wrap up Quick hits Layer order in Deducer Bubble charts**

ggplot2 quasi-beanplot Being on your own with ggplot2 and R – getting unstuck Small datasets revisited Large datasets Displaying uncertainty Automated generation of many plots Extending ggplot2 – direct labels and scatterplot matrices New geoms More practice exercises! Wrap up

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**A theory about practice…**

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**Getting unstuck… Check the str() of your data**

Check the console for error messages Look at the call for your plot – is that what you wanted? Easier to start with something that works but is too simple Simplify the plot until it works Add back components one-by-one to isolate the problem

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**Reproducible examples and the ggplot2 listserve**

Compose your question well and you might figure out the answer in the process!

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Data + summary Loss of information

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**Better than bar charts…**

data(airquality) # open the plot builder and add geom_point # with x = Month and y = Ozone Data + summary – building this ourselves…

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Pseudo beanplots g_violin_bean <- ggplot(sleep, aes(x = extra)) + geom_ribbon(aes(ymax = ..density.., ymin = -..density..), stat = "density", fill = "black") + geom_segment(aes(y = -.05, yend = .05, xend = extra), color = "grey90") + facet_grid(. ~ group, as.table = FALSE, scales = "free_y") + opts(panel.margin = unit(0 , "lines")) + xlab(NULL) + theme_bw(base_size = 20) + coord_flip() + opts(axis.text.x = theme_blank()) + expand_limits(x = c(-5, 9)) g_violin_bean

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**What about large datasets?**

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**Playing with diamonds…**

data(diamonds) str(diamonds) With your neighbor: how do we show the data on the caret – price relationship…

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**Strategies for large datasets**

Use smaller points - use circles Use partial transparency Jitter (small random noise) if data take discrete values Overlay a smoother to show the trend Display a random sample from your data

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**How do you show 54,000 diamonds?**

Partial transparency Alpha = 0.01 Contours for density Alpha = 0.1 Hexagonal bins with legend

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**Displaying uncertainty**

Confidence intervals (uniformly shaded or bounded) Pointwise errorbars Bayesian simulations Resampling based estimates

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**Model shouldn’t extend beyond the range of your data**

xkcd.com/605/

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**Graph your uncertainty**

Informal Bayesian Simulation Run regression Draw random numbers based on uncertainty of your regression Plot some lines! Uses the sim() function in package “arm” Gelman and Hill 2007

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**Informal bayesian simulation**

Figure 3. Association between DEP concentrations in personal air and the urinary metabolite MEP concentrations (adjusted for specific gravity) stratified by perfume use using linear regression of log transformed values. Lighter lines represent predictive uncertainty in regression parameters from informal Bayesian simulations (20 simulation draws with uniform priors). Boxplots show the distribution of MEP with means (“X”). Just et al 2010

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**Resampling - Spline after bootstrap**

Cosma Shalizi 2010

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**How random is random - the qq-plot**

qqreference from package DAAG

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**a Q-Q envelope – show range from 19 draws of random normal**

Venables and Ripley

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**Generating many graphs**

Example: suppose we wanted to save a separate plot of mileage for each car manufacturer in "mpg" Start with data formatted so that it is long… manufacturer cty hwy audi audi chevrolet chevrolet honda honda Use the magic of R and ggplot2…

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**Generating many graphs**

Example: suppose we wanted to save a separate plot of mileage for each car manufacturer in "mpg" Start with data formatted so that it is long… manufacturer cty hwy audi audi chevrolet chevrolet honda honda Use d_ply (from the plyr package – also by Hadley Wickham) to split up the dataframe by our subsetting variable Define a function to run on subsets; we name these smaller dataframes "dat" Call ggplot() and ggsave() within this function to generate and save our plot

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**Generating many graphs**

Example: suppose we wanted to save a separate plot of mileage for each car manufacturer in "mpg" # d_ply takes a dataframe, splits it apart, applies a function d_ply(mpg, .(manufacturer), function(dat) { # create a ggplot2 object named figure using 'dat' figure <- ggplot(dat, aes(cty, hwy)) + geom_smooth(method = "lm") + geom_point(alpha = 0.7, size = 2.5, position = position_jitter(height = 0.1, width = 0.1)) + annotate("text", x = -Inf, y = Inf, hjust = -.1, vjust = 1.2, label = paste("n =", nrow(dat))) + opts(title = dat$manufacturer[1]) # unique title can help # create a unique filename for each subset (e.g. "MPG_Audi.png") filename <- paste("MPG_", dat$manufacturer[1], ".png", sep = "") # by default this saves to your working directory; see ?getwd ggsave(filename, figure, height = 6.5, width = 10) })

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**Extending ggplot2 Let's get some more packages: directlabels GGally**

install.packages() directlabels GGally

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**Extending ggplot2: directlabels**

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**A fully polished plot probably took a lot of coding**

# original code adapted from library(ggplot2) # define the dataset df <- structure(list(City = structure(c(2L, 3L, 1L), .Label = c("Minneapolis", "Phoenix", "Raleigh"), class = "factor"), January = c(52.1, 40.5, 12.2), February = c(55.1, 42.2, 16.5), March = c(59.7, 49.2, 28.3), April = c(67.7, 59.5, 45.1), May = c(76.3, 67.4, 57.1), June = c(84.6, 74.4, 66.9), July = c(91.2, 77.5, 71.9), August = c(89.1, 76.5, 70.2), September = c(83.8, 70.6, 60), October = c(72.2, 60.2, 50), November = c(59.8, 50, 32.4), December = c(52.5, 41.2, 18.6)), .Names = c("City", "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"), class = "data.frame", row.names = c(NA, -3L)) #and season labels seasons <- data.frame(month = c(1.5, 4.5, 7.5, 10.5), value = 97, season = c("Winter", "Spring", "Summer", "Autumn")) # melt the dataset to a long format dfm <- melt(df, variable_name = "month") levels(dfm$month) <- month.abb #build the basic plot p <- ggplot(dfm, aes(month, value, group = City, colour = City)) p1 <- p + geom_line(size = 1) dgr_fmt <- function(x, ...) { parse(text = paste(x, "*degree", sep = "")) } none <- theme_blank() p2 <- p1 + theme_bw() + scale_y_continuous(formatter = dgr_fmt, limits = c(0, 100), expand = c(0, 0)) + xlab(NULL) + ylab(NULL) + opts(title = expression("Average Monthly Temperatures (" * degree * "F)"), panel.grid.major = none, panel.grid.minor = none, legend.position = "none", panel.background = none, panel.border = none, axis.line = theme_segment(colour = "grey50")) (p3 <- p2 + geom_vline(xintercept = c(2.9, 5.9, 8.9, 11.9), colour = "grey85", alpha = 0.5) + geom_hline(yintercept = 32, colour = "grey80", alpha = 0.5) + annotate("text", x = 1.2, y = 35, label = "Freezing", colour = "grey80", size = 4) + geom_text(data = seasons, aes(label = season, group = NULL), colour = "grey70", size = 4)) (p4 <- p3 + geom_text(data = dfm[dfm$month == "Dec", ], aes(label = City), hjust = 0.7, vjust = 1)) data_table <- ggplot(dfm, aes(x = month, y = factor(City), label = format(value, nsmall = 1), colour = City)) + geom_text(size = 3.5) + theme_bw() + scale_y_discrete(formatter = abbreviate, limits = c("Minneapolis", "Raleigh", "Phoenix")) + opts(panel.grid.major = none, axis.text.x = none, axis.ticks = none, plot.margin = unit(c(-0.5, 1, 0, 0.5), "lines")) Layout <- grid.layout(nrow = 2, ncol = 1, heights = unit(c(2, 0.25), c("null", "null"))) grid.show.layout(Layout) vplayout <- function(...) { grid.newpage() pushViewport(viewport(layout = Layout)) } subplot <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y) mmplot <- function(a, b) { vplayout() print(a, vp = subplot(1, 1)) print(b, vp = subplot(2, 1)) } mmplot(p4, data_table) # to save - run the following code - see ?png ##### # png("temperature_plot.png") # mmplot(p4, data_table) # dev.off() #note that when we were at the p3 stage we didn't yet have labels for the data p3 library(directlabels) # code to put labels into your ggplot2 objects p3.labelled <- direct.label(p3, list(last.points, hjust = 0.7, vjust = 1)) p3.labelled ############################# A fully polished plot probably took a lot of coding

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Extending ggplot2: GGally Scatterplot matrix: 36 plots showing ~9K measures bivariate densities and correlations Each plot shows 2D density fit to 249 points (36 plots with a total of almost 9000 points)

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**Making a scatterplot matrix**

library(GGally) data(iris) head(iris[, 3:5]) #iris columns 3 to 5 # example 1 - defaults ggpairs(iris[, 3:5]) # example 2 – more customized by data type ggpairs(iris[,3:5], upper = list(continuous = "density", combo = "box"), lower = list(continuous = "points", combo = "dot"), diag = list(continuous = "bar", discrete = "bar")) # example 3 – some new stuff!!! dat <- data.frame(x = rnorm(100), y = rnorm(100), z = rnorm(100)) plotmatrix <- GGally::ggpairs(dat, lower = list(continuous = "density", aes_string = aes_string(fill = "..level..")), upper = "blank") plotmatrix #EOF

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**Thinking about some new geoms**

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**Showing density surfaces from stat_density2d**

Let's make a plot of x and y from data.frame dat with stat_density2d What is the default geom? In the previous plot, which aesthetic was showing those colors? What geom would we need to make that plot?

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**geom_rug to show marginal distribution**

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geom_polygon after computing the convex outer hull, labels at the centroids, moved the legend to the top

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**“Hey, what did you learn in that EPIC class you took?”**

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Recap: Why we did this Visualization is important for communicating information and promoting your ideas Effective designs will be noticed We make many graphs quickly for discovery and choose the best ones to polish for communication With a theory of visualization we can create sophisticated graphics using basic components

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**Recap: Designing a good scientific figure**

Answer a question – usually a comparison Use an appropriate design (emphasize comparisons of position before length, angle, area or color) Make it self-sufficient (annotation & figure legend) Show your data – tell its story

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Recap: ggplot2 and R R is a powerful language for statistics and data analysis ggplot2 implements a “grammar of graphics” ggplot2: Builds plots using data, and layers of geometric objects, mapping variables to aesthetic features, which have been transformed by scales, summarized with statistics, projected into a coordinate system, and subset into adjacent plots with facets

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Recap: JGR and Deducer JGR: a graphic interface system for R programming Deducer: adds menu driven analysis and plotting

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**Deducer: Plot Builder Save or import View call to see R code .ggp file**

Send R code to Console ggsave("plot.png", height = 6.5, width = 10)

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**Deducer: Plot Builder Right-click to Get info Right-click to edit,**

toggle, remove Adjust position Geom Stat Data More options by component Switch to map to a var Mapped vars Set to a constant value Order of drawing layers

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