Ggplot2 cheatsheet-2.1
- 1. Geoms - Use a geom function to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer. Three Variables l + geom_contour(aes(z = z)) x, y, z, alpha, colour, group, linetype, size, weight seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2)) l <- ggplot(seals, aes(long, lat)) 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 Two Variables Discrete X, Discrete Y g <- ggplot(diamonds, aes(cut, color)) g + geom_count() x, y, alpha, color, fill, shape, size, stroke Discrete X, Continuous Y f <- ggplot(mpg, aes(class, hwy)) f + geom_col() x, y, alpha, color, fill, group, linetype, size f + geom_boxplot() x, y, lower, middle, upper, ymax, ymin, alpha, color, fill, group, linetype, shape, size, weight f + geom_dotplot(binaxis = "y", stackdir = "center") x, y, alpha, color, fill, group f + geom_violin(scale = "area") x, y, alpha, color, fill, group, linetype, size, weight Continuous X, Continuous Y 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 e + geom_jitter(height = 2, width = 2) x, y, alpha, color, fill, shape, size e + geom_point() x, y, alpha, color, fill, shape, size, stroke e + geom_quantile() x, y, alpha, color, group, linetype, size, weight e + geom_rug(sides = "bl") x, y, alpha, color, linetype, size e + geom_smooth(method = lm) x, y, alpha, color, fill, group, linetype, size, weight 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 AB C A B C Continuous Function i <- ggplot(economics, aes(date, unemploy)) i + geom_area() x, y, alpha, color, fill, linetype, size i + geom_line() x, y, alpha, color, group, linetype, size i + geom_step(direction = "hv") x, y, alpha, color, group, linetype, size Continuous Bivariate Distribution h <- ggplot(diamonds, aes(carat, price)) j + geom_crossbar(fatten = 2) x, y, ymax, ymin, alpha, color, fill, group, linetype, size j + geom_errorbar() x, ymax, ymin, alpha, color, group, linetype, size, width (also geom_errorbarh()) j + geom_linerange() x, ymin, ymax, alpha, color, group, linetype, size j + geom_pointrange() x, y, ymin, ymax, alpha, color, fill, group, linetype, shape, size Visualizing error 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)) 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 Maps 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 h + geom_hex() x, y, alpha, colour, fill, size Data Visualization with ggplot2 Cheat Sheet RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org and www.ggplot2-exts.org • ggplot2 2.1.0 • Updated: 11/16 ggplot(data = mpg, aes(x = cty, y = hwy)) Begins a plot that you finish by adding layers to. Add one geom function per layer. Basics Complete the template below to build a graph. 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. To display values, map variables in the data to visual properties of the geom (aesthetics) like size, color, and x and y locations. Graphical Primitives Data Visualization with ggplot2 Cheat Sheet RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15 Geoms - Use a geom to represent data points, use the geom’s aesthetic properties to represent variables Basics One Variable a + geom_area(stat = "bin") x, y, alpha, color, fill, linetype, size b + geom_area(aes(y = ..density..), stat = "bin") a + geom_density(kernal = "gaussian") x, y, alpha, color, fill, linetype, size, weight b + geom_density(aes(y = ..county..)) a+ geom_dotplot() x, y, alpha, color, fill a + geom_freqpoly() x, y, alpha, color, linetype, size b + geom_freqpoly(aes(y = ..density..)) a + geom_histogram(binwidth = 5) x, y, alpha, color, fill, linetype, size, weight b + geom_histogram(aes(y = ..density..)) Discrete a <- ggplot(mpg, aes(fl)) b + geom_bar() x, alpha, color, fill, linetype, size, weight Continuous a <- ggplot(mpg, aes(hwy)) Two Variables Discrete X, Discrete Y h <- ggplot(diamonds, aes(cut, color)) h + geom_jitter() x, y, alpha, color, fill, shape, size Discrete X, Continuous Y g <- ggplot(mpg, aes(class, hwy)) g + geom_bar(stat = "identity") x, y, alpha, color, fill, linetype, size, weight g + geom_boxplot() lower, middle, upper, x, ymax, ymin, alpha, color, fill, linetype, shape, size, weight g + geom_dotplot(binaxis = "y", stackdir = "center") x, y, alpha, color, fill g + geom_violin(scale = "area") x, y, alpha, color, fill, linetype, size, weight Continuous X, Continuous Y f <- ggplot(mpg, aes(cty, hwy)) f + geom_blank() f + geom_jitter() x, y, alpha, color, fill, shape, size f + geom_point() x, y, alpha, color, fill, shape, size f + geom_quantile() x, y, alpha, color, linetype, size, weight f + geom_rug(sides = "bl") alpha, color, linetype, size f + geom_smooth(model = lm) x, y, alpha, color, fill, linetype, size, weight f + geom_text(aes(label = cty)) x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust Three Variables i + geom_contour(aes(z = z)) x, y, z, alpha, colour, linetype, size, weight seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2)) i <- ggplot(seals, aes(long, lat)) g <- ggplot(economics, aes(date, unemploy)) Continuous Function g + geom_area() x, y, alpha, color, fill, linetype, size g + geom_line() x, y, alpha, color, linetype, size g + geom_step(direction = "hv") x, y, alpha, color, linetype, size Continuous Bivariate Distribution h <- ggplot(movies, aes(year, rating)) h + geom_bin2d(binwidth = c(5, 0.5)) xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size, weight h + geom_density2d() x, y, alpha, colour, linetype, size h + geom_hex() x, y, alpha, colour, fill size d + geom_segment(aes( xend = long + delta_long, yend = lat + delta_lat)) x, xend, y, yend, alpha, color, linetype, size d + geom_rect(aes(xmin = long, ymin = lat, xmax= long + delta_long, ymax = lat + delta_lat)) xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size c + geom_polygon(aes(group = group)) x, y, alpha, color, fill, linetype, size d<- ggplot(seals, aes(x = long, y = lat)) i + geom_raster(aes(fill = z), hjust=0.5, vjust=0.5, interpolate=FALSE) x, y, alpha, fill i + geom_tile(aes(fill = z)) x, y, alpha, color, fill, linetype, size e + geom_crossbar(fatten = 2) x, y, ymax, ymin, alpha, color, fill, linetype, size e + geom_errorbar() x, ymax, ymin, alpha, color, linetype, size, width (also geom_errorbarh()) e + geom_linerange() x, ymin, ymax, alpha, color, linetype, size e + geom_pointrange() x, y, ymin, ymax, alpha, color, fill, linetype, shape, size Visualizing error df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2) e <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se)) g + geom_path(lineend="butt", linejoin="round’, linemitre=1) x, y, alpha, color, linetype, size g + geom_ribbon(aes(ymin=unemploy - 900, ymax=unemploy + 900)) x, ymax, ymin, alpha, color, fill, linetype, size g <- ggplot(economics, aes(date, unemploy)) c <- ggplot(map, aes(long, lat)) data <- data.frame(murder = USArrests$Murder, state = tolower(rownames(USArrests))) map <- map_data("state") e <- ggplot(data, aes(fill = murder)) e + geom_map(aes(map_id = state), map = map) + expand_limits(x = map$long, y = map$lat) map_id, alpha, color, fill, linetype, size Maps F M A = 1 2 3 0 0 1 2 3 4 4 1 2 3 0 0 1 2 3 4 4 + data geom coordinate system plot + F M A = 1 2 3 0 0 1 2 3 4 4 1 2 3 0 0 1 2 3 4 4 data geom coordinate system plot x = F y = A color = F size = A 1 2 3 0 0 1 2 3 4 4 plot + F M A =1 2 3 0 0 1 2 3 4 4 data geom coordinate systemx = F y = A x = F y = A Graphical Primitives Data Visualization with ggplot2 Cheat Sheet RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15 Geoms - Use a geom to represent data points, use the geom’s aesthetic properties to represent variables Basics One Variable a + geom_area(stat = "bin") x, y, alpha, color, fill, linetype, size b + geom_area(aes(y = ..density..), stat = "bin") a + geom_density(kernal = "gaussian") x, y, alpha, color, fill, linetype, size, weight b + geom_density(aes(y = ..county..)) a+ geom_dotplot() x, y, alpha, color, fill a + geom_freqpoly() x, y, alpha, color, linetype, size b + geom_freqpoly(aes(y = ..density..)) a + geom_histogram(binwidth = 5) x, y, alpha, color, fill, linetype, size, weight b + geom_histogram(aes(y = ..density..)) Discrete a <- ggplot(mpg, aes(fl)) b + geom_bar() x, alpha, color, fill, linetype, size, weight Continuous a <- ggplot(mpg, aes(hwy)) Two Variables Discrete X, Discrete Y h <- ggplot(diamonds, aes(cut, color)) h + geom_jitter() x, y, alpha, color, fill, shape, size Discrete X, Continuous Y g <- ggplot(mpg, aes(class, hwy)) g + geom_bar(stat = "identity") x, y, alpha, color, fill, linetype, size, weight g + geom_boxplot() lower, middle, upper, x, ymax, ymin, alpha, color, fill, linetype, shape, size, weight g + geom_dotplot(binaxis = "y", stackdir = "center") x, y, alpha, color, fill g + geom_violin(scale = "area") x, y, alpha, color, fill, linetype, size, weight Continuous X, Continuous Y f <- ggplot(mpg, aes(cty, hwy)) f + geom_blank() f + geom_jitter() x, y, alpha, color, fill, shape, size f + geom_point() x, y, alpha, color, fill, shape, size f + geom_quantile() x, y, alpha, color, linetype, size, weight f + geom_rug(sides = "bl") alpha, color, linetype, size f + geom_smooth(model = lm) x, y, alpha, color, fill, linetype, size, weight f + geom_text(aes(label = cty)) x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust Three Variables i + geom_contour(aes(z = z)) x, y, z, alpha, colour, linetype, size, weight seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2)) i <- ggplot(seals, aes(long, lat)) g <- ggplot(economics, aes(date, unemploy)) Continuous Function g + geom_area() x, y, alpha, color, fill, linetype, size g + geom_line() x, y, alpha, color, linetype, size g + geom_step(direction = "hv") x, y, alpha, color, linetype, size Continuous Bivariate Distribution h <- ggplot(movies, aes(year, rating)) h + geom_bin2d(binwidth = c(5, 0.5)) xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size, weight h + geom_density2d() x, y, alpha, colour, linetype, size h + geom_hex() x, y, alpha, colour, fill size d + geom_segment(aes( xend = long + delta_long, yend = lat + delta_lat)) x, xend, y, yend, alpha, color, linetype, size d + geom_rect(aes(xmin = long, ymin = lat, xmax= long + delta_long, ymax = lat + delta_lat)) xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size c + geom_polygon(aes(group = group)) x, y, alpha, color, fill, linetype, size d<- ggplot(seals, aes(x = long, y = lat)) i + geom_raster(aes(fill = z), hjust=0.5, vjust=0.5, interpolate=FALSE) x, y, alpha, fill i + geom_tile(aes(fill = z)) x, y, alpha, color, fill, linetype, size e + geom_crossbar(fatten = 2) x, y, ymax, ymin, alpha, color, fill, linetype, size e + geom_errorbar() x, ymax, ymin, alpha, color, linetype, size, width (also geom_errorbarh()) e + geom_linerange() x, ymin, ymax, alpha, color, linetype, size e + geom_pointrange() x, y, ymin, ymax, alpha, color, fill, linetype, shape, size Visualizing error df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2) e <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se)) g + geom_path(lineend="butt", linejoin="round’, linemitre=1) x, y, alpha, color, linetype, size g + geom_ribbon(aes(ymin=unemploy - 900, ymax=unemploy + 900)) x, ymax, ymin, alpha, color, fill, linetype, size g <- ggplot(economics, aes(date, unemploy)) c <- ggplot(map, aes(long, lat)) data <- data.frame(murder = USArrests$Murder, state = tolower(rownames(USArrests))) map <- map_data("state") e <- ggplot(data, aes(fill = murder)) e + geom_map(aes(map_id = state), map = map) + expand_limits(x = map$long, y = map$lat) map_id, alpha, color, fill, linetype, size Maps F M A = 1 2 3 0 0 1 2 3 4 4 1 2 3 0 0 1 2 3 4 4 + data geom coordinate system plot + F M A = 1 2 3 0 0 1 2 3 4 4 1 2 3 0 0 1 2 3 4 4 data geom coordinate system plot x = F y = A color = F size = A 1 2 3 0 0 1 2 3 4 4 plot + F M A =1 2 3 0 0 1 2 3 4 4 data geom coordinate systemx = F y = A x = F y = A 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. qplot(x = cty, y = hwy, data = mpg, geom = "point") Creates a complete plot with given data, geom, and mappings. Supplies many useful defaults. aesthetic mappings data geom last_plot() Returns the last plot ggplot(data = <DATA >) + <GEOM_FUNCTION> ( mapping = aes(<MAPPINGS> ), stat = <STAT> , position = <POSITION> ) + <COORDINATE_FUNCTION> + <FACET_FUNCTION> + <SCALE_FUNCTION> + <THEME_FUNCTION><THEME_FUNCTION> <SCALE_FUNCTION> <FACET_FUNCTION> <COORDINATE_FUNCTION> <POSITION> <STAT> <MAPPINGS> <GEOM_FUNCTION> <DATA> Required Not required, sensible defaults supplied 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 a + geom_path(lineend="butt", linejoin="round’, linemitre=1) x, y, alpha, color, group, linetype, size a + geom_polygon(aes(group = group)) x, y, alpha, color, fill, group, linetype, size b + geom_rect(aes(xmin = long, ymin=lat, xmax= long + 1, ymax = lat + 1)) - xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size a + geom_ribbon(aes(ymin=unemploy - 900, ymax=unemploy + 900)) - x, ymax, ymin alpha, color, fill, group, linetype, size Line Segments common aesthetics: x, y, alpha, color, 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 c + geom_area(stat = "bin") x, y, alpha, color, fill, linetype, size 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 c2 + geom_qq(aes(sample = hwy)) x, y, alpha, color, fill, linetype, size, weight Discrete d <- ggplot(mpg, aes(fl)) d + geom_bar() x, alpha, color, fill, linetype, size, weight Continuous c <- ggplot(mpg, aes(hwy)); c2 <- ggplot(mpg)
- 2. RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Coordinate Systems 60 long lat π + coord_quickmap() π + coord_map(projection = "ortho", orientation=c(41, -74, 0)) projection, orientation, xlim, ylim Map projections from the mapproj package (mercator (default), azequalarea, lagrange, etc.) 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. r <- d + geom_bar() Position Adjustments 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 s <- ggplot(mpg, aes(fl, fill = drv)) Position adjustments determine how to arrange geoms that would otherwise occupy the same space. Each position adjustment can be recast as a function with manual width and height arguments s + geom_bar(position = position_dodge(width = 1)) A B Themes r + theme_classic() r + theme_light() r + theme_linedraw() r + theme_minimal() Minimal themes r + theme_void() Empty theme 0 50 100 150 c d e p r fl count 0 50 100 150 c d e p r fl count 0 50 100 150 c d e p r fl count 0 50 100 150 c d e p r fl count r + theme_bw() White background with grid lines r + theme_gray() Grey background (default theme) r + theme_dark() dark for contrast0 50 100 150 c d e p r fl count Zooming t + coord_cartesian( 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)) Without clipping (preferred) 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. Faceting t <- ggplot(mpg, aes(cty, hwy)) + geom_point() Facets divide a plot into subplots based on the values of one or more discrete variables. 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 t + facet_grid(. ~ fl, labeller = label_both) t + facet_grid(fl ~ ., labeller = label_bquote(alpha ^ .(fl))) t + facet_grid(. ~ fl, labeller = label_parsed) fl: c fl: d fl: e fl: p fl: r c d e p r ↵c ↵d ↵e ↵p ↵r Learn more at docs.ggplot2.org and www.ggplot2-exts.org • ggplot2 2.1.0 • Updated: 11/16 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.. 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.. 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() Stats - An alternative way to build a layer + x ..count.. = 1 2 3 0 0 1 2 3 4 4 1 2 3 0 0 1 2 3 4 4 data geom coordinate system plot x = x y = ..count.. fl cty cyl stat A stat builds new variables to plot (e.g., count, prop). 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. i + stat_density2d(aes(fill = ..level..), geom = "polygon") stat function geom mappings variable created by stat geom to use 1D distributions 2D distributions 3 Variables Comparisons Functions General Purpose Labels t + labs( x = "New x axis label", y = "New y axis label", title ="Add a title above the plot", subtitle = "Add a subtitle below title", caption = "Add a caption below plot", <aes> = "New <aes> legend title") Use scale functions to update legend labels <AES> <AES> t + annotate(geom = "text", x = 8, y = 9, label = "A") geom to place manual values for geom’s aesthetics o + scale_fill_distiller(palette = "Blues") o + scale_fill_gradient(low="red", high="yellow") o + scale_fill_gradient2(low="red", high="blue", mid = "white", midpoint = 25) o + scale_fill_gradientn(colours=topo.colors(6)) Also: rainbow(), heat.colors(), terrain.colors(), cm.colors(), RColorBrewer::brewer.pal() Scales 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))) 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")) scale_ aesthetic to adjust prepackaged scale to use scale specific arguments range of values to include in mapping title to use in legend/axis 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 and Y location scales Color and fill scales (Discrete) Shape and size scales 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") p + scale_shape() + scale_size() p + scale_shape_manual(values = c(3:7)) Color and fill scales (Continuous) o <- c + geom_dotplot(aes(fill = ..x..)) p <- e + geom_point(aes(shape = fl, size = cyl)) p + scale_radius(range = c(1,6)) p + scale_size_area(max_size = 6) Maps to radius of circle, or area c(-1, 26) 0:1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Manual shape values