data-visualization.pdf
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This document provides a cheat sheet for creating data visualizations with ggplot2 in R. It summarizes the key components of ggplot2 including data, geoms, stats, scales, facets, and themes. Geoms like geom_point and geom_bar are used to represent data points. Stats like stat_density can be used to calculate new variables for plotting. Scales map data values to visual properties. The cheat sheet provides examples of customizing plots by adding geoms, stats, scales, coordinate systems, and facets.
data-visualization.pdf
- 1. Data visualization with ggplot2 : : CHEAT SHEET 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. Basics GRAPHICAL PRIMITIVES a + geom_blank() and a + expand_limits() Ensure limits include values across all plots. b + geom_curve(aes(yend = lat + 1, xend = long + 1), curvature = 1) - 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(alpha = 50)) - x, y, alpha, color, fill, group, subgroup, 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 + = To display values, map variables in the data to visual properties of the geom (aesthetics) like size, color, and x and y locations. + = data geom x = F · y = A coordinate system plot data geom x = F · y = A color = F size = A coordinate system plot Complete the template below to build a graph. required ggplot(data = mpg, aes(x = cty, y = hwy)) Begins a plot that you finish by adding layers to. Add one geom function per layer. 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. F M A F M A 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)) a <- ggplot(economics, aes(date, unemploy)) b <- ggplot(seals, aes(x = long, y = lat)) ONE VARIABLE continuous c <- ggplot(mpg, aes(hwy)); c2 <- ggplot(mpg) 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 e + geom_label(aes(label = cty), nudge_x = 1, nudge_y = 1) - x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust 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) - x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust one discrete, one continuous 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 both discrete g <- ggplot(diamonds, aes(cut, color)) g + geom_count() x, y, alpha, color, fill, shape, size, stroke e + geom_jitter(height = 2, width = 2) x, y, alpha, color, fill, shape, size THREE VARIABLES seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2)); l <- ggplot(seals, aes(long, lat)) l + geom_raster(aes(fill = z), hjust = 0.5, vjust = 0.5, interpolate = FALSE) x, y, alpha, fill l + geom_tile(aes(fill = z)) x, y, alpha, color, fill, linetype, size, width h + geom_bin2d(binwidth = c(0.25, 500)) x, y, alpha, color, fill, linetype, size, weight h + geom_density_2d() x, y, alpha, color, group, linetype, size h + geom_hex() x, y, alpha, color, fill, size continuous function i <- ggplot(economics, aes(date, unemploy)) visualizing error df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2) j <- ggplot(df, aes(grp, fit, ymin = fit - se, ymax = fit + se)) maps data <- data.frame(murder = USArrests$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 Not required, sensible defaults supplied Geoms Use a geom function to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer. TWO VARIABLES both continuous e <- ggplot(mpg, aes(cty, hwy)) continuous bivariate distribution h <- ggplot(diamonds, aes(carat, price)) RStudio® is a trademark of RStudio, PBC • CC BY SA RStudio • info@rstudio.com • 844-448-1212 • rstudio.com • Learn more at ggplot2.tidyverse.org • ggplot2 3.3.5 • Updated: 2021-08 ggplot (data = <DATA> ) + <GEOM_FUNCTION> (mapping = aes( <MAPPINGS> ), stat = <STAT> , position = <POSITION> ) + <COORDINATE_FUNCTION> + <FACET_FUNCTION> + <SCALE_FUNCTION> + <THEME_FUNCTION> l + geom_contour(aes(z = z)) x, y, z, alpha, color, group, linetype, size, weight l + geom_contour_filled(aes(fill = z)) x, y, alpha, color, fill, group, linetype, size, subgroup 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 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 Aes color and fill - string ("red", "#RRGGBB") linetype - integer or string (0 = "blank", 1 = "solid", 2 = "dashed", 3 = "dotted", 4 = "dotdash", 5 = "longdash", 6 = "twodash") lineend - string ("round", "butt", or "square") linejoin - string ("round", "mitre", or "bevel") size - integer (line width in mm) shape - integer/shape name or a single character ("a") Common aesthetic values.
- 2. Scales Coordinate Systems A stat builds new variables to plot (e.g., count, prop). Stats An alternative way to build a layer. + = data geom x = x · y = ..count.. coordinate system plot fl cty cyl x ..count.. stat 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_density_2d(aes(fill = ..level..), geom = "polygon") stat function geommappings variable created by stat geom to use c + stat_bin(binwidth = 1, boundary = 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() + xlim(-5, 5) + stat_function(fun = dnorm, n = 20, geom = “point”) x | ..x.., ..y.. ggplot() + stat_qq(aes(sample = 1:100)) x, y, sample | ..sample.., ..theoretical.. e + stat_sum() x, y, size | ..n.., ..prop.. e + stat_summary(fun.data = "mean_cl_boot") h + stat_summary_bin(fun = "mean", geom = "bar") e + stat_identity() e + stat_unique() 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 title to use in legend/axis labels to use in legend/axis breaks to use in legend/axis range of values to include in mapping GENERAL PURPOSE SCALES Use with most aesthetics scale_*_continuous() - Map cont’ values to visual ones. scale_*_discrete() - Map discrete values to visual ones. scale_*_binned() - Map continuous values to discrete bins. 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 values as date times. Same as scale_*_date(). See ?strptime for label formats. X & Y LOCATION SCALES Use with x or y aesthetics (x shown here) scale_x_log10() - Plot x on log10 scale. scale_x_reverse() - Reverse the direction of the x axis. scale_x_sqrt() - Plot x on square root scale. COLOR AND FILL SCALES (DISCRETE) 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") COLOR AND FILL SCALES (CONTINUOUS) o <- c + geom_dotplot(aes(fill = ..x..)) 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(colors = 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() p + scale_shape_manual(values = c(3:7)) p + scale_radius(range = c(1,6)) p + scale_size_area(max_size = 6) 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. ggplot(mpg, aes(y = fl)) + geom_bar() Flip cartesian coordinates by switching x and y aesthetic mappings. r + coord_polar(theta = "x", direction=1) theta, start, direction - Polar coordinates. r + coord_trans(y = “sqrt") - x, y, xlim, ylim Transformed cartesian coordinates. Set xtrans and ytrans to the name of a window function. π + coord_quickmap() π + coord_map(projection = "ortho", orientation = c(41, -74, 0)) - projection, 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. 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. 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_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 theme. r + theme_void() Empty theme. Faceting Facets divide a plot into subplots based on the values of one or more discrete variables. t <- ggplot(mpg, aes(cty, hwy)) + geom_point() t + facet_grid(cols = vars(fl)) Facet into columns based on fl. t + facet_grid(rows = vars(year)) Facet into rows based on year. t + facet_grid(rows = vars(year), cols = vars(fl)) Facet into both rows and columns. t + facet_wrap(vars(fl)) Wrap facets into a rectangular layout. Set scales to let axis limits vary across facets. t + facet_grid(rows = vars(drv), cols = vars(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 label: t + facet_grid(cols = vars(fl), labeller = label_both) t + facet_grid(rows = vars(fl), labeller = label_bquote(alpha ^ .(fl))) fl: c fl: d fl: e fl: p fl: r ↵c ↵d ↵e ↵p ↵r Labels and Legends Use labs() to label the elements of your plot. 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", alt = "Add alt text to the plot", <aes> = "New <aes> legend title") t + annotate(geom = "text", x = 8, y = 9, label = “A") Places a geom with manually selected aesthetics. p + guides(x = guide_axis(n.dodge = 2)) Avoid crowded or overlapping labels with guide_axis(n.dodge or angle). n + guides(fill = “none") Set legend type for each aesthetic: colorbar, legend, or none (no legend). n + theme(legend.position = "bottom") Place legend at "bottom", "top", "le ft ", or “right”. n + scale_fill_discrete(name = "Title", labels = c("A", "B", "C", "D", "E")) Set legend title and labels with a scale function. <AES> <AES> Zooming Without clipping (preferred): 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)) RStudio® is a trademark of RStudio, PBC • CC BY SA RStudio • info@rstudio.com • 844-448-1212 • rstudio.com • Learn more at ggplot2.tidyverse.org • ggplot2 3.3.5 • Updated: 2021-08 60 long lat r + theme() Customize aspects of the theme such as axis, legend, panel, and facet properties. r + ggtitle(“Title”) + theme(plot.title.postion = “plot”) r + theme(panel.background = element_rect(fill = “blue”)) Override defaults with scales package.