Data Visualization with ggplot2 : : CHEAT SHEET Basics Geoms ggplot2is based on thegrammar of graphics, the idea that you can build every graph from the same components: adataset, acoordinate system, andgeoms—visual marks that represent data points. F M A + data geom x=F·y=A plot coordinate system a + geom_path(lineend="butt", linejoin="round", linemitre=1)( x, y, alpha, color, group, linetype, size To display values, map variables in the data to visual properties of the geom (aesthetics) like size, color, and x and y locations. F M A data geom x=F·y=A color = F size = A GRAPHICAL PRIMITIVES a <- ggplot(economics, aes(date, unemploy)) b <- ggplot(seals, aes(x = long, y = lat)) a + geom_blank()( (Useful for expanding limits) b + geom_curve(aes(yend = lat + 1,( xend=long+1,curvature=z)) - x, xend, y, yend, alpha, angle, color, curvature, linetype, size = + Use a geom function to represent data points, use the geom’s aesthetic properties to represent variables. ( Each function returns a layer. = required stat = , position = ) + + + + Not ( required, sensible defaults supplied aesthetic mappings data geom qplot(x = cty, y = hwy, data = mpg, geom = “point") Creates a complete plot with given data, geom, and mappings. Supplies many useful defaults. last_plot() Returns the last plot ggsave("plot.png", width = 5, height = 5) Saves last plot as 5’ x 5’ file named "plot.png" in working directory. Matches file type to file extension. h + geom_bin2d(binwidth = c(0.25, 500))( x, y, alpha, color, fill, linetype, size, weight h + geom_density2d()( x, y, alpha, colour, group, linetype, size e + geom_jitter(height = 2, width = 2) ( x, y, alpha, color, fill, shape, size h + geom_hex()( x, y, alpha, colour, fill, size e + geom_point(), x, y, alpha, color, fill, shape, size, stroke ( e + geom_rug(sides = "bl"), x, y, alpha, color, linetype, size i + geom_area()( x, y, alpha, color, fill, linetype, size e + geom_smooth(method = lm), x, y, alpha, color, fill, group, linetype, size, weight i + geom_line()( x, y, alpha, color, group, linetype, size b + geom_abline(aes(intercept=0, slope=1)) b + geom_hline(aes(yintercept = lat)) b + geom_vline(aes(xintercept = long)) b + geom_segment(aes(yend=lat+1, xend=long+1)) b + geom_spoke(aes(angle = 1:1155, radius = 1)) ONE VARIABLE continuous c <- ggplot(mpg, aes(hwy)); c2 <- ggplot(mpg) c + geom_area(stat = "bin")( x, y, alpha, color, fill, linetype, size ggplot(data = mpg, aes(x = cty, y = hwy)) Begins a plot that you finish by adding layers to. Add one geom function per layer. ( h <- ggplot(diamonds, aes(carat, price)) b + geom_rect(aes(xmin = long, ymin=lat, xmax= long + 1, ymax = lat + 1)) - xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size common aesthetics: x, y, alpha, color, linetype, size ggplot (data = ) + (mapping = aes( ), continuous bivariate distribution e + geom_quantile(), x, y, alpha, color, group, linetype, size, weight( LINE SEGMENTS Complete the template below to build a graph. e <- ggplot(mpg, aes(cty, hwy)) e + geom_label(aes(label = cty), nudge_x = 1, nudge_y = 1, check_overlap = TRUE) x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust ( a + geom_polygon(aes(group = group))( x, y, alpha, color, fill, group, linetype, size a + geom_ribbon(aes(ymin=unemploy - 900, ymax=unemploy + 900)) - x, ymax, ymin, alpha, color, fill, group, linetype, size plot coordinate system TWO VARIABLES continuous x , continuous y c + geom_density(kernel = "gaussian")( x, y, alpha, color, fill, group, linetype, size, weight c + geom_dotplot() ( x, y, alpha, color, fill c + geom_freqpoly() x, y, alpha, color, group, linetype, size c + geom_histogram(binwidth = 5) x, y, alpha, color, fill, linetype, size, weight continuous function i <- ggplot(economics, aes(date, unemploy)) e + geom_text(aes(label = cty), nudge_x = 1, nudge_y = 1, check_overlap = TRUE), x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust i + geom_step(direction = "hv")( x, y, alpha, color, group, linetype, size( ( ( ( visualizing error discrete x , continuous y f <- ggplot(mpg, aes(class, hwy)) df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2) j <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se)) j + geom_crossbar(fatten = 2)( x, y, ymax, ymin, alpha, color, fill, group, linetype, size f + geom_col(), x, y, alpha, color, fill, group, linetype, size j + geom_errorbar(), x, ymax, ymin, alpha, color, group, linetype, size, width (also geom_errorbarh()) f + geom_boxplot(), x, y, lower, middle, upper, ymax, ymin, alpha, color, fill, group, linetype, shape, size, weight j + geom_linerange()( x, ymin, ymax, alpha, color, group, linetype, size f + geom_dotplot(binaxis = "y", stackdir = "center"), x, y, alpha, color, fill, group j + geom_pointrange()( x, y, ymin, ymax, alpha, color, fill, group, linetype, shape, size f + geom_violin(scale = "area"), x, y, alpha, color, fill, group, linetype, size, weight maps discrete x , discrete y g <- ggplot(diamonds, aes(cut, color)) g + geom_count(), x, y, alpha, color, fill, shape, size, stroke data <- data.frame(murder = USArrests$Murder,( state = tolower(rownames(USArrests)))( map <- map_data("state")( k <- ggplot(data, aes(fill = murder)) k + geom_map(aes(map_id = state), map = map) + expand_limits(x = map$long, y = map$lat), map_id, alpha, color, fill, linetype, size c2 + geom_qq(aes(sample = hwy)) x, y, alpha, color, fill, linetype, size, weight THREE VARIABLES seals$z <- with(seals, sqrt(delta long^2 + delta lat^2))l <- ggplot(seals, aes(long, lat)) ( discrete d <- ggplot(mpg, aes(fl)) d + geom_bar() ( x, alpha, color, fill, linetype, size, weight , q ( _ g l + geom_contour(aes(z = z))( x, y, z, alpha, colour, group, linetype, ( size, weight _ )) ggp ( , ( g, )) l + geom_raster(aes(fill = z), hjust=0.5, vjust=0.5, interpolate=FALSE)( x, y, alpha, fill l + geom_tile(aes(fill = z)), x, y, alpha, color, fill, linetype, size, width Stats An alternative way to build a layer A stat builds new variables to plot (e.g., count, prop). fl cty cyl x ..count.. data stat geom + = coordinate plot x = x ·( system y = ..count.. Visualize a stat by changing the default stat of a geom function, geom_bar(stat="count") or by using a stat function, stat_count(geom="bar"), which calls a default geom to make a layer (equivalent to a geom function). Use ..name.. syntax to map stat variables to aesthetics. geom to use stat function geommappings i + stat_density2d(aes(fill = ..level..), geom = "polygon") variable created by stat c + stat_bin(binwidth = 1, origin = 10)( x, y | ..count.., ..ncount.., ..density.., ..ndensity.. c + stat_count(width = 1) x, y, | ..count.., ..prop.. c + stat_density(adjust = 1, kernel = “gaussian") ( x, y, | ..count.., ..density.., ..scaled.. e + stat_bin_2d(bins = 30, drop = T)( x, y, fill | ..count.., ..density.. e + stat_bin_hex(bins=30) x, y, fill | ..count.., ..density.. e + stat_density_2d(contour = TRUE, n = 100)( x, y, color, size | ..level.. e + stat_ellipse(level = 0.95, segments = 51, type = "t") l + stat_contour(aes(z = z)) x, y, z, order | ..level.. l + stat_summary_hex(aes(z = z), bins = 30, fun = max)( x, y, z, fill | ..value.. l + stat_summary_2d(aes(z = z), bins = 30, fun = mean)( x, y, z, fill | ..value.. f + stat_boxplot(coef = 1.5) x, y | ..lower.., ( ..middle.., ..upper.., ..width.. , ..ymin.., ..ymax.. f + stat_ydensity(kernel = "gaussian", scale = “area") x, y | ..density.., ..scaled.., ..count.., ..n.., ..violinwidth.., ..width.. Scales map data values to the visual values of an aesthetic. To change a mapping, add a new scale. (n <- d + geom_bar(aes(fill = fl))) aesthetic prepackaged scale-specific scale_ to adjust scale to use arguments n + scale_fill_manual( values = c("skyblue", "royalblue", "blue", “navy"), limits = c("d", "e", "p", "r"), breaks =c("d", "e", "p", “r"), name = "fuel", labels = c("D", "E", "P", "R")) range of title to use in values to include legend/axis in mapping labels to use in legend/axis breaks to use in legend/axis GENERAL PURPOSE SCALES Use with most aesthetics scale_*_continuous() - map cont’ values to visual ones scale_*_discrete() - map discrete values to visual ones scale_*_identity() - use data values as visual ones scale_*_manual(values = c()) - map discrete values to manually chosen visual ones scale_*_date(date_labels = "%m/%d"), date_breaks = "2 weeks") - treat data values as dates. scale_*_datetime() - treat data x values as date times. Use same arguments as scale_x_date(). See ?strptime for label formats. X & Y LOCATION SCALES Coordinate Systems Faceting r <- d + geom_bar() r + coord_cartesian(xlim = c(0, 5)) ( xlim, ylim( The default cartesian coordinate system r + coord_fixed(ratio = 1/2) ( ratio, xlim, ylim( Cartesian coordinates with fixed aspect ratio between x and y units r + coord_flip() ( xlim, ylim( Flipped Cartesian coordinates r + coord_polar(theta = "x", direction=1 ) ( theta, start, direction( Polar coordinates r + coord_trans(ytrans = “sqrt") ( xtrans, ytrans, limx, limy( Transformed cartesian coordinates. Set xtrans and ytrans to the name of a window function. Facets divide a plot into ( subplots based on the ( values of one or more ( discrete variables. π + coord_quickmap() π + coord_map(projection = "ortho", orientation=c(41, -74, 0))projection, orienztation, xlim, ylim Map projections from the mapproj package (mercator (default), azequalarea, lagrange, etc.) Position Adjustments Position adjustments determine how to arrange geoms that would otherwise occupy the same space. COLOR AND FILL SCALES (DISCRETE) A B s <- ggplot(mpg, aes(fl, fill = drv)) s + geom_bar(position = "dodge")( Arrange elements side by side s + geom_bar(position = "fill")( Stack elements on top of one another, ( normalize height e + geom_point(position = "jitter")( Add random noise to X and Y position of each element to avoid overplotting e + geom_label(position = "nudge")( Nudge labels away from points( s + geom_bar(position = "stack")( Stack elements on top of one another COLOR AND FILL SCALES (CONTINUOUS) e + stat_ecdf(n = 40) x, y | ..x.., ..y.. e + stat_quantile(quantiles = c(0.1, 0.9), formula = y ~ log(x), method = "rq") x, y | ..quantile.. e + stat_smooth(method = "lm", formula = y ~ x, se=T, level=0.95) x, y | ..se.., ..x.., ..y.., ..ymin.., ..ymax.. o <- c + geom_dotplot(aes(fill = ..x..)) ggplot() + stat_function(aes(x = -3:3), n = 99, fun = dnorm, args = list(sd=0.5)) x | ..x.., ..y.. e + stat_identity(na.rm = TRUE) ggplot() + stat_qq(aes(sample=1:100), dist = qt, dparam=list(df=5)) sample, x, y | ..sample.., ..theoretical.. e + stat_sum() x, y, size | ..n.., ..prop.. e + stat_summary(fun.data = "mean_cl_boot") h + stat_summary_bin(fun.y = "mean", geom = "bar") e + stat unique() o + scale_fill_gradient2(low="red", high=“blue", mid = "white", midpoint = 25) o + scale_fill_distiller(palette = "Blues") Each position adjustment can be recast as a function with manual width and height arguments s + geom_bar(position = position_dodge(width = 1)) ( o + scale_fill_gradient(low="red", high="yellow") ( ( o + scale_fill_gradientn(colours=topo.colors(6)) Also: rainbow(), heat.colors(), terrain.colors(), cm.colors(), RColorBrewer::brewer.pal() SHAPE AND SIZE SCALES p <- e + geom_point(aes(shape = fl, size = cyl)) p + scale_shape() + scale_size() t + facet_grid(. ~ fl)( facet into columns based on fl t + facet_grid(year ~ .)( facet into rows based on year t + facet_grid(year ~ fl)( facet into both rows and columns t + facet_wrap(~ fl)( wrap facets into a rectangular layout Set scales to let axis limits vary across facets t + facet_grid(drv ~ fl, scales = "free")( x and y axis limits adjust to individual facets( "free_x" - x axis limits adjust( "free_y" - y axis limits adjust Set labeller to adjust facet labels Use with x or y aesthetics (x shown here) scale_x_log10() - Plot x on log10 scale scale_x_reverse() - Reverse direction of x axis scale_x_sqrt() - Plot x on square root scale n <- d + geom_bar(aes(fill = fl)) n + scale_fill_brewer(palette = "Blues") ( For palette choices: RColorBrewer::display.brewer.all() n + scale_fill_grey(start = 0.2, end = 0.8, ( na.value = "red") t <- ggplot(mpg, aes(cty, hwy)) + geom_point() Themes r + theme_bw()( White background( with grid lines r + theme_gray()( Grey background ( (default theme) r + theme_dark()( dark for contrast r + theme_classic() r + theme_light() r + theme_linedraw() r + theme_minimal()( Minimal themes r + theme_void()( Empty theme t + facet_grid(. ~ fl, labeller = label_both) fl: c fl: d fl: e fl: p fl: r t + facet_grid(fl ~ ., labeller = label_bquote(alpha ^ .(fl))) αp αc αe αr αd t + facet_grid(. ~ fl, labeller = label_parsed) c d e p r Labels t + labs( x = "New x axis label", y = "New y axis label",( title ="Add a title above the plot", ( Use scale functions subtitle = "Add a subtitle below title",( to update legend caption = "Add a caption below plot", labels = "New legend title") t + annotate(geom = "text", x = 8, y = 9, label = "A") geom to place manual values for geom’s aesthetics Legends n + theme(legend.position = "bottom")( Place legend at "bottom", "top", "left", or "right" n + guides(fill = "none")( Set legend type for each aesthetic: colorbar, legend, or none (no legend) n + scale_fill_discrete(name = "Title", ( labels = c("A", "B", "C", "D", "E"))( Set legend title and labels with a scale function. Zooming Without clipping (preferred) t + coord_cartesian(( _ q () p + scale_shape_manual(values = c(3:7)) p + scale_radius(range = c(1,6)) p + scale_size_area(max_size = 6) xlim = c(0, 100), ylim = c(10, 20)) With clipping (removes unseen data points) t + xlim(0, 100) + ylim(10, 20) t + scale_x_continuous(limits = c(0, 100)) + scale_y_continuous(limits = c(0, 100)) RStudio® is a trademark of RStudio, Inc. • CC BY SA RStudio • info@rstudio.com • 844-448-1212 • rstudio.com • Learn more at http://ggplot2.tidyverse.org • ggplot2 2.1.0 • Updated: 2016-11

Cheat Sheet ggplot 2 Data Visualization

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