Advanced Data Visualization in R (2024)

UC Berkeley Biostatistics seminar

Sara E. Moore
24 February 2015

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Intro

You have:

a working knowledge of R,
some familiarity with the usage of ggplot2 (such as what was presented during the 2013 or 2014 UC Berkeley SCF/D-Lab R Bootcamp),
an interest in creating data visualizations in R, both static (mostly using ggplot2) and interactive (using a variety of packages).

Why `ggplot`?

It's pretty.
Its commands are intuitive and "human-readable."
Nearly any graphic can be created, so you can use it for everything and maintain a consistent style.
It has (sort of) built-in support for maps.

Why not `ggplot`?

It's slow.
It won't do some things.
There's a steep learning curve.
~~lattice is better at trellis graphs?~~ Faceting is just as powerful.

Tidy data ^[1]

Each variable forms a column.
Each observation forms a row.
Each type of observational unit forms a table.

The (Layered) Grammar of Graphics ^{[2; 3; 4]}

Move away from using "names" and "chart typologies."
Instead, use "statements" constructed via grammar
Why?
- An infinite number of unique graphics can be created.
- The implementation is DRY ("don't repeat yourself") not WET ("write everything twice" or "we enjoy typing").

"Good grammar is just the first step in creating a good sentence."
^{Wickham, 2010}

Components of the Grammar

Specify a statistical graphic using components of statements:

Data (data),
Statistical transformations (stat: identity, count, mean, etc.),
Geometric elements/objects (geom: points, lines, etc.),
Aesthetic mappings (aes: color, shape, size, transparency, etc.),
Coordinate systems (coord: cartesian, polar, map, etc.),
Guides/Scales and transformations thereof (scale, guide, and others: axes, log-transformed scales, legends, etc.),
Faceting/conditioning/latticing/trellising (facet),
Tweaking graphical positioning and visual elements (position, theme, etc.), and
Layering.

The anatomy of a `ggplot` command

All arguments to the main, initial function call, ggplot, set graph defaults.
These defaults can be changed for an individual element (even data).

ggplot(data=, aes(x=,y=,...)) + geom_????(...) + ...

ggplot() + geom_????(data=, aes(x=,y=,...),...) + ...

The data

Kaggle March Machine Learning "Mania" 2015

source(paste("assets","load_marchmania2015.R", sep=.Platform[["file.sep"]]))# change this path to the location of your CSVs downloaded from kaggle# note that required packages will be installed automaticallyncaa.bball = load.clean.summ.ncaa("~/Dropbox/kaggle/marchmania2015")

Alternatively, use data that ships with ggplot2:
- diamonds,
- mpg,
- movies, etc.

A simple scatterplot

library(ggplot2)ggplot(data=ncaa.bball[["games"]], aes(x=lscore, y=wscore)) + geom_point() + xlab("Points scored by losing team") + ylab("Points scored by winning team") + ggtitle("Final scores of NCAA basketball games\nNovember 1984 - April 2014") + theme(text = element_text(size = 16))

Improving the scatterplot

library(munsell) # color system used by ggplot2ggplot(data=ncaa.bball[["games"]], aes(x=lscore, y=wscore)) + # default is bins=c(30,30) # can use scalar when no. of bins for x and y are same. # here alternatively specifying binwidth: stat_binhex(binwidth=c(4, 4)) + # mnsl converts [hue lightness/color purity] to hex color codes. # scale_fill_gradient == scale_fill_continuous. scale_fill_gradient("Number of games", trans = "sqrt", low=mnsl("7.5G 2/4"), high=mnsl("7.5G 9/6")) + xlab("Points scored by losing team") + ylab("Points scored by winning team") + ggtitle("Final scores of NCAA basketball games\nNovember 1984 - April 2014") + theme_classic(base_size = 16)

Improving the scatterplot

ggplot(data=ncaa.bball[["games"]], aes(x=lscore, y=wscore)) + # default is bins=c(30,30) # can use scalar when no. of bins for x and y are same. # here alternatively specifying binwidth: geom_hex(stat = "binhex", binwidth=c(4,4)) + # scale_fill_gradient == scale_fill_continuous. # the mnsl fxn converts [hue lightness/color purity] to hex color codes. scale_fill_gradient("Number of games", trans = "sqrt", low=mnsl("7.5G 2/4"), high=mnsl("7.5G 9/6")) + xlab("Points scored by losing team") + ylab("Points scored by winning team") + ggtitle("Final scores of NCAA basketball games\nNovember 1984 - April 2014") + theme_classic(base_size = 16)

When geoms transform

geom	stat	modifiable defaults
`geom_boxplot()`	`stat_boxplot()`	max length of whiskers (beyond hinges) = 1.5*IQR
`geom_bar()`	`stat_bin()`	30 bins: binwidth = [range of x]/30
`geom_histogram()`	`stat_bin()`	30 bins: binwidth = [range of x]/30
`geom_freqpoly()`	`stat_bin()`	30 bins: binwidth = [range of x]/30
`geom_dotplot()`	`stat_bindot()`	30 bins: binwidth = [range of x]/30; "dotdensity" method
`geom_bin2d()`	`stat_bin2d()`	30 bins for each of x and y
`geom_hex()`	`stat_binhex()`	30 bins for each of x and y (calls `hexbin::hexBin()`)
`geom_density2d()`	`stat_density2d()`	Gaussian kernel; bandwidths (x and y) set by Silverman's "rule of thumb"; 100 grid points for x and y (calls `MASS::kde2d()`)
`geom_density()`	`stat_density()`	Gaussian kernel; bandwidth set by Silverman's "rule of thumb" (calls `stats::density()`)
`geom_violin()`	`stat_ydensity()`	Gaussian kernel; bandwidth set by Silverman's "rule of thumb" (calls `stats::density()`); all violins have same area before trimming tails, tails are trimmed to [range of y]
`geom_smooth()`	`stat_smooth()`	if n<1000, `stats::loess()` with polynomial degree 2, \(\alpha=0.75\), etc.; else, `gam::gam()` with penalized cubic regression splines, etc.; 80 evaluation points
`geom_quantile()`	`stat_quantile()`	3 quartiles; "br" method (modified Barrodale & Roberts method; calls `quantreg::rq()`)
`geom_contour()`	`stat_contour()`	10 `pretty` breakpoints covering [range of z]

Other `ggplot` transformations

stat_ecdf: Empirical Cumulative Density Function
stat_function: Superimpose a function.
stat_qq: Calculation for quantile-quantile plot.
stat_spoke: Convert angle and radius to xend and yend.
stat_sum: Sum unique values. Useful for overplotting on scatterplots.
stat_summary: Summarise y values at every unique x.
stat_summary_hex: Apply funciton for 2D hexagonal bins.
stat_summary2d: Apply function for 2D rectangular bins.
stat_unique: Remove duplicates.
stat_identity

Dates, `tidyr`, and summaries with `ggplot2`

library(tidyr) # gatherduke.2014 = subset(ncaa.bball[["teams"]], (season==2014)&(team.name=="Duke"))[, c("date","fg.pct","fg3.pct","ft.pct","result")] %>% tidyr::gather(shot.type, pct, -c(date, result))library(scales) # date_formatggplot(duke.2014, aes(x=date, y=pct, color=shot.type)) + geom_line() + geom_line(stat = "hline", yintercept = "mean", linetype="dashed", alpha=0.65) + geom_rug(data=subset(duke.2014,(result=="Loss")&(shot.type=="fg.pct")), sides="b", color="grey20") + scale_x_datetime("Game date", labels = date_format("%b %Y")) + ylab("Proportion of shots made") + scale_color_discrete("Type of shot", labels=c("two point field goal","three point field goal","free throw")) + ggtitle("Duke's per-game shot percentages, 2013-14 season:\nbottom ticks indicate losses; dashed lines are season averages") + theme_classic(base_size = 16) + theme(legend.position = "bottom")

A heatmap with `ggplot2::geom_tile`

library(RColorBrewer) # brewer.pallibrary(grid) # unittourney.teams.2014 = as.character( unique(subset(ncaa.bball[["team.season.summ"]], (season==2014)&(max.tourney.round>="Sweet Sixteen"))$team.name))hist.perf.teams.2014 = subset(ncaa.bball[["team.season.summ"]], team.name%in%tourney.teams.2014)p1 = ggplot(hist.perf.teams.2014, aes(x=season, y=team.name, fill=win.pct)) + geom_tile() + scale_fill_gradientn( "Proportion of regular season games won", colours = brewer.pal(9,"GnBu"), na.value="grey80", breaks=seq(0,1,0.25), guide = guide_colorbar(barwidth = 15, barheight = 1)) + scale_x_continuous("Season", expand = c(0, 0)) + scale_y_discrete("Team", expand = c(0, 0)) + ggtitle("Historical regular season performance of\n2014 NCAA tournament 'Sweet 16' teams") + theme_classic(base_size=16) + theme(legend.position = "bottom", axis.text.y = element_text(size = 11), plot.margin = unit(c(0,0.1,-0.4,0.1), "cm"))p1

Creating a dendrogram with `ggdendro`

library(ggdendro)team.season.df = subset(ncaa.bball[["team.season.summ"]], team.name%in%tourney.teams.2014)[, c("season", "team.name","win.pct")]# use tidyr, but this time go long --> wide (spread)team.season.mat = as.matrix(team.season.df %>% spread(team.name, win.pct))rownames(team.season.mat) = team.season.mat[,"season"]team.season.mat = team.season.mat[ ,-which(colnames(team.season.mat)=="season")]teams.hc = hclust(dist(t(team.season.mat)), "ave")ggdendrogram(teams.hc, rotate = TRUE)

Simplifying the dendrogram

teams.dendro = as.dendrogram(teams.hc)teams.ddata = dendro_data(teams.dendro)p2 = ggplot(segment(teams.ddata)) + geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) + coord_flip() + theme_dendro() + # tweak these if the dendrogram doesn't line up: theme(plot.margin = unit(c(-7,0,-15,-20), "points"))p2

Heatmap, reordered

# need to remove the extra factor levels# and rorder according to the clusteringhist.perf.teams.2014$team.name = as.character( hist.perf.teams.2014$team.name)# can do sort(unique(x)) here because they were originally # in alphabetical order. just be sure the order of the # dendrogram matches up with the new order of the heatmap.hist.perf.teams.2014$team.name = factor( hist.perf.teams.2014$team.name, sort(unique(hist.perf.teams.2014$team.name))[ order.dendrogram(teams.dendro)], ordered=TRUE)p1 = ggplot(hist.perf.teams.2014, aes(x=season, y=team.name, fill=win.pct)) + geom_tile() + scale_fill_gradientn( "Proportion of regular season games won", colours = brewer.pal(9,"GnBu")[3:9], na.value="grey80", breaks=seq(0,1,0.25), guide = guide_colorbar(barwidth = 15, barheight = 1)) + scale_x_continuous("Season", expand = c(0, 0)) + scale_y_discrete("Team", expand = c(0, 0)) + ggtitle("Historical regular season performance of\n2014 NCAA tournament 'Sweet 16' teams") + theme_classic(base_size=16) + theme(legend.position = "bottom", axis.text.y = element_text(size = 11), plot.margin = unit(c(0,0.1,-0.4,0.1), "cm"))p1

Putting it all together

library(gtable)g1 = gtable_add_cols(ggplotGrob(p1), unit(4,"cm"))# may need to adjust "t" and "b" if you don't add a ggtitle:g = gtable_add_grob(g1, ggplotGrob(p2), t=3, l=ncol(g1), b=4, r=ncol(g1))grid.newpage()grid.draw(g)

Packages that pair well with `ggplot2`

grid
gridExtra: additional functions to tweak/manipulate grid graphics
scales: additional functions to deal with the scale portions of the grammar of graphics
gtable: use to dismantle/hack underlying table of Grid Graphical Objects (grobs) that make up a ggplot
ggsubplot: embed smaller subplots within larger plots, all using ggplot2 graphics
dplyr (or plyr): manipulate data
tidyr (or reshape2): restructure data (esp. wide \(\leftrightarrow\) long)
lubridate: "makes working with dates fun instead of frustrating"
devtools: R package development tools (esp. ability to install packages from github rather than CRAN)

ggthemes, xkcd: for when you don't want your ggplot to look so much like a ggplot
extrafont, RColorBrewer, munsell, https://github.com/karthik/wesanderson : custom fonts and color schemes
ggdendro: dendrograms and trees with ggplot2
GGally: plot matrices (as in graphics::pairs), parallel coordinate plots, and network plots with ggplot2
ggtern: ternary plots (as in vcd::ternaryplot) and other additional *geom*s for ggplot2.
ggmap, maps, maptools, sp, rgdal, RgoogleMaps: maps with ggplot2
ggHorizon horizon graphs with ggplot2 (example 1, example 2)
\(\ldots\)

Where to go for help with `ggplot2`

Why interactive?

They're pretty, fun, and people love them (see Hans Rosling's TED talks).
They allow you to engage with, explore, and discover more about your data -- visually.
Static graphics are "dead" (according to The Economist).

SVG `ggplot` with `plotly`

# start with a static ggplotp = ggplot(subset(ncaa.bball[["team.season.summ"]], max.tourney.round>="Sweet Sixteen"), aes(x=season, y=win.pct, color=max.tourney.round)) + # ideally we would jitter here, but plotly has trouble with this # geom_point(position = position_jitter(w = 0.4, h = 0.002)) + geom_point(size=3, alpha=0.7) + # instead use transparency xlab("Year") + ylab("Proportion of regular season games won") + scale_color_discrete("Highest tournament round achieved") + ggtitle("Regular season performance of 'Sweet Sixteen' teams 1985-2014, by season") + theme_classic(base_size = 16) + theme(legend.position = "bottom")p

SVG `ggplot` with `plotly`

# library(devtools)# install_github("ropensci/plotly")library(plotly)# get a plot.ly account and get your api key here:# https://plot.ly/settings/api# plug it in with your username in the statement below.# set_credentials_file("username", "xxxxxxxxxx")py <- plotly()# recall p is the object returned by ggplot# plot.info = py$ggplotly(p) # in an R session, opens in browserplot.info = py$ggplotly(p, session="knitr") # embed in knitr document# if you're embedding in a knitr document, # be sure to also set the code chunk# plotly=TRUE

SVG graphic with `clickme`

# library(devtools)# install_github("nachocab/clickme")library(clickme)cmplot = with(subset(ncaa.bball[["team.season.summ"]], max.tourney.round>="Sweet Sixteen"), clickme("points", x = season, y = win.pct, names = team.name, color_groups = as.character(max.tourney.round), x_title = "Year", x_format = "", y_title = "Proportion of regular season games won", color_title = "Highest tournament round achieved", color_group_order = levels(max.tourney.round)[4:8], title = "Regular season performance of 'Sweet 16' teams", subtitle = "1985-2014, by season", file_path = paste(getwd(),"clickme0.html", sep=.Platform$file.sep), height = 600, width = 700))# cmplot # in an R session, open in browser# embed in knitr document:cmplot$iframe()$hide()

MotionChart with `googleVis` (Flash)

library(googleVis)# data.frame with >=4 cols: x, y, id, time. color and size optional,# but if you don't provide them, # it will choose them for you (if there are columns left to use)mc = gvisMotionChart( subset(ncaa.bball[["team.season.summ"]], !is.na(tourney.seed))[,c("team.name", "season", "win.pct", "points.avg", "mov.avg", "tourney.seed")], idvar="team.name", timevar="season", xvar="win.pct", yvar="tourney.seed", sizevar="mov.avg", colorvar="points.avg", options=list(width=750, height=650))# plot(mc) # in an R session, opens in browserprint(mc, 'chart') # embed in knitr document

Many interactive options in R

plotly: ggplot2 graphics \(\rightarrow\) SVG via plot.ly's R API
googleVis: R interface to Google Charts API; SVG and Flash
rCharts: SVG graphics with popular JS libs, directly from R
ggvis: SVG and HTML5 Canvas graphics, rendered using vega, declared in a "grammar of graphics" style similar to ggplot2
gridSVG: ggplot2 and lattice graphics \(\rightarrow\) SVG image

clickme: interactive SVG graphics from R
rMaps: interactive maps with popular JS libs, directly from R
networkD3: d3.js network graphs from R (SVG)
rgl: interactive 3D visualizations using OpenGL and other frameworks/formats (example)
rggobi: R interface to GGobi, a "data visualization system" separate from R
SVGAnnotation: used for "post-processing SVG plots created in R"

References

[1] L. Wilkinson. The Grammar of Graphics. Springer-Verlag NewYork, 2005. DOI: 10.1007/0-387-28695-0. .

[2] H. Wickham. ggplot2. Springer Science + Business Media,2009. DOI: 10.1007/978-0-387-98141-3. .

[3] H. Wickham. "A Layered Grammar of Graphics". In: Journal ofComputational and Graphical Statistics 19.1 (2010), pp. 3-28.DOI: 10.1198/jcgs.2009.07098. .

[4] H. Wickham. "Tidy Data". In: Journal of Statistical Software59.10 (2014). .

Credits

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knitr,
the deck.js framework,
the RefManageR bibliography manager, and
the prettify syntax highlighter.

Advanced Data Visualization in R (2024)

UC Berkeley Biostatistics seminar

Intro

Why ggplot?

Why not ggplot?

Tidy data [1]

The (Layered) Grammar of Graphics [2; 3; 4]

Components of the Grammar

The anatomy of a ggplot command

The data

A simple scatterplot

Improving the scatterplot

Improving the scatterplot

When geoms transform

Other ggplot transformations

Dates, tidyr, and summaries with ggplot2

A heatmap with ggplot2::geom_tile

Creating a dendrogram with ggdendro

Simplifying the dendrogram

Heatmap, reordered

Putting it all together

Packages that pair well with ggplot2

Where to go for help with ggplot2

Why interactive?

SVG ggplot with plotly

SVG ggplot with plotly

SVG graphic with clickme

MotionChart with googleVis (Flash)

Many interactive options in R

References

Credits

Built using slidify with

References

Why `ggplot`?

Why not `ggplot`?

Tidy data ^[1]

The (Layered) Grammar of Graphics ^{[2; 3; 4]}

The anatomy of a `ggplot` command

Other `ggplot` transformations

Dates, `tidyr`, and summaries with `ggplot2`

A heatmap with `ggplot2::geom_tile`

Creating a dendrogram with `ggdendro`

Packages that pair well with `ggplot2`

Where to go for help with `ggplot2`

SVG `ggplot` with `plotly`

SVG `ggplot` with `plotly`

SVG graphic with `clickme`

MotionChart with `googleVis` (Flash)