R ggplot2 cheatsheet - Data Visualization with ggplot2 - CHEAT SHEET

Q: How do you start building a ggplot2 chart from a dataset?

Use ggplot(data = , aes(...)) to begin a plot, then add one geom_* layer at a time. Each geom function returns a layer that you can further configure.

Q: What are the common components you can add to ggplot2 plots?

You can add required layers with geom functions, then optionally include coordinate functions, facet functions, scale functions, and theme functions to complete the visualization.

Q: How can you visualize statistical uncertainty or error bars in ggplot2?

Create a data frame containing fit and standard error bounds, then use geoms such as geom_crossbar(), geom_errorbar(), geom_linerange(), and geom_pointrange() to draw uncertainty intervals.

Oct 13, 2026

Upload by Rizky

ggplot2 cheat sheet summarizing how to build plots from the grammar of graphics: a dataset, a coordinate system, and geoms as visual marks. Covers core setup with ggplot(data, aes()) and adding layers via geom_* functions, plus optional stat, position, coordinates, facets, scales, and themes. Lists graphical primitives, common aesthetics, continuous and discrete variables, and many geom types including points, lines, areas, histograms, labels, jitter, smooth, violin, maps, error visualization, and multi-variable plots like contours and tiles.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages2

Reading Time1 min

AccessFree to download

Basics

Plot construction with ggplot2 layers

Graphical primitives and aesthetics

Geoms by data type

One variable (continuous)

Discrete variables

Line segments and segments

Advanced examples

Visualizing error

Maps

Three variables (contour, raster, tile)

Basics 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. data geom x = F · y = A + data geom x = F · y = A color = F size = A Complete the template below to build a graph. ggplot (data = ) + required (mapping = aes( ), stat = , position = ) + + + + Not required, sensible defaults supplied ggplot(data = mpg, aes(x = cty, y = hwy)) Begins a plot that you finish by adding layers to. Add one geom function per layer. 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. Data Visualization with ggplot2 : : CHEAT SHEET + = coordinate system To display values, map variables in the data to visual properties of the geom (aesthetics) like size, color, and x and y locations. coordinate system Geo ms Use a geom function to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer. 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 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 dds-creteggplot(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, 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 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, fi

FAQs

How do you start building a ggplot2 chart from a dataset?

Use ggplot(data = , aes(...)) to begin a plot, then add one geom_* layer at a time. Each geom function returns a layer that you can further configure.

What are the common components you can add to ggplot2 plots?

You can add required layers with geom functions, then optionally include coordinate functions, facet functions, scale functions, and theme functions to complete the visualization.

How can you visualize statistical uncertainty or error bars in ggplot2?

Create a data frame containing fit and standard error bounds, then use geoms such as geom_crossbar(), geom_errorbar(), geom_linerange(), and geom_pointrange() to draw uncertainty intervals.

Oct 13, 2026

Upload by Rizky