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R-bloggers | R news and tutorials contributed by (750) R bloggers

16.09.2025

Exploring {ggplot2}’s Geoms and Stats | R-bloggers

Code library(tidyverse) library(patchwork) library(gt) # sysfonts::font_add_google(name = "fira code") # showtext::showtext_auto() knitr::opts_chunk$set(collapse = TRUE, comment = "#>", dev = "ragg_png") In celebration of the release of {ggplot2} 4.0.0 🥳, I wanted to explore the relationships between the geometric objects (“geoms”) and statistical transformations (“stats”) that are offered by the core {ggplot2} functions. Geoms and What? Within The Layered Grammar of Graphics framework, plots in {ggplot2} are built by adding layer()s. Each layer() consists of a geom (the actual thing being drawn), with different geoms having different aes()thetics that can (and should) be mapped to variables in the data. However, all layer()s also have a stat - this can be thought of as a function applied to the data, transforming it in some way before it is passed to the geom and its aesthetics. For example, geom_histogram() uses the "bin" stat by default: geom_histogram()$stat #> #> aesthetics: function #> compute_group: function #> compute_layer: function #> compute_panel: function #> default_aes: ggplot2::mapping, uneval, gg, S7_object #> dropped_aes: weight #> extra_params: na.rm orientation #> finish_layer: function #> non_missing_aes: #> optional_aes: #> parameters: function #> required_aes: x|y #> retransform: TRUE #> setup_data: function #> setup_params: function #> super: This stat takes the raw data, counts the number of occurrences of each value of the x aesthetic within each x-bin. p1 # see computed variables: select(x, count, density, ncount, ndensity, width) #> x count density ncount ndensity width #> 1 12.00000 5 0.006009615 0.07352941 0.07352941 0.9 #> 2 15.55556 50 0.060096154 0.73529412 0.73529412 0.9 #> 3 19.11111 34 0.040865385 0.50000000 0.50000000 0.9 #> 4 22.66667 29 0.034855769 0.42647059 0.42647059 0.9 #> 5 26.22222 68 0.081730769 1.00000000 1.00000000 0.9 #> 6 29.77778 33 0.039663462 0.48529412 0.48529412 0.9 #> 7 33.33333 9 0.010817308 0.13235294 0.13235294 0.9 #> 8 36.88889 3 0.003605769 0.04411765 0.04411765 0.9 #> 9 40.44444 1 0.001201923 0.01470588 0.01470588 0.9 #> 10 44.00000 2 0.002403846 0.02941176 0.02941176 0.9 We can see that this data has been transformed, and reflects the underlying data represented in the final plot in Figure 1: 10 columns, their x location, and their heights (count).1 Let’s take a deeper look at the geoms and stats in {ggplot2} and how they relate to each other. Data collection # list all functions in ggplot2 that start with geom_ or stat_: get_statgeom bind_rows(), map(ggfunction, has_statgeom.args) |> bind_rows() ) |> select(-`NA.`) |> filter(!(is.na(stat) & is.na(geom)), !help_page %in% c("ggsf")) Plot geom-stat combinations ggdata_tidy mutate( stat = str_remove(stat, "Stat") |> str_to_lower(), geom = str_remove(geom, "Geom") |> str_to_lower(), stat = fct_reorder(factor(stat), stat, .fun = length), geom = fct_reorder(factor(geom), stat, .fun = length) ) ggplot(ggdata_tidy, aes(geom, stat)) + geom_point() + scale_x_discrete(limits = rev, guide = guide_axis(angle = -40)) + theme(text = element_text(family = "fira code")) We can see that the most common stat by far is the identity stat. What’s that about? The Identity Stat and the Basic Geom Builing Blocks The identity stat is a stat that does nothing.2 It takes the data as-is and passes it to the geom’s aesthetics. Geoms that use the identity stat can be thought of a the most basic building blocks of {ggplot2} - many types of lines, bars/tiles, points, areas, etc. These are the geoms you’d probably use with annotate().3 We can see such geoms are often used with other stats as well: Code ggdata |> filter("StatIdentity" %in% stat, .by = geom) |> mutate( stat = str_remove(stat, "Stat") |> str_to_lower(), geom = str_remove(geom, "Geom") |> str_to_lower(), is_identity = if_else(stat == "identity", "identity", "other"), geom = fct_reorder(factor(geom), geom, .fun = length) ) |> ggplot(aes(stat, geom, fill = geom)) + facet_grid(cols = vars(is_identity), scales = "free_x", space = "free_x") + geom_point( shape = 21, color = "black", size = 3 ) + ggrepel::geom_label_repel( aes(label = ggfunction), family = "fira code", layout = 2, force = 10, max.overlaps = Inf ) + theme( title = element_text(family = "fira code"), axis.text = element_text(family = "fira code") ) + scale_fill_viridis_d(option = "A", end = 1, begin = 0.2) + scale_x_discrete(guide = guide_axis(angle = -40)) + guides(fill = "none") For example, the point geom is used by {ggplot2} together with 7 other non-identity stats. Geom-Stat Pairs {ggplot2} provides many stats that basically can only be used with a specific geom (and vice versa). These geom-stat pairs are almost exclusive to one another - think of a boxplot for example, which is a unique geom that has little meaning without the boxplot stat. p2 # see computed variables: select(x, ymin:ymax, width, outliers) #> x ymin lower middle upper ymax width outliers #> 1 1 23 24.0 25.0 26.0 26 0.9 #> 2 2 23 26.0 27.0 29.0 33 0.9 35, 37, 35, 44 #> 3 3 23 26.0 27.0 29.0 32 0.9 #> 4 4 21 22.0 23.0 24.0 24 0.9 17 #> 5 5 15 16.0 17.0 18.0 20 0.9 12, 12, 12, 22 #> 6 6 20 24.5 26.0 30.5 36 0.9 44, 41 #> 7 7 14 17.0 17.5 19.0 22 0.9 12, 12, 25, 24, 27, 25, 26, 23 The table below lists all the geom-stat pairs in {ggplot2}: Code ggdata_pairs filter( stat != "StatIdentity", !help_page %in% c( "geom_abline", "geom_linerange", "ggsf", "stat_summary", "stat_summary_2d" ), !ggfunction %in% c("geom_col", "geom_freqpoly"), !str_detect(ggfunction, "(bin2d|density2d|binhex)") ) |> filter(n() > 1, .by = c(help_page)) |> arrange(help_page) |> mutate( stat = str_remove(stat, "Stat") |> str_to_lower(), geom = str_remove(geom, "Geom") |> str_to_lower() ) ggdata_pairs |> pivot_wider( names_from = type, values_from = ggfunction, id_cols = c(help_page, geom, stat), names_prefix = "type_" ) |> group_by(help_page) |> fill(starts_with("type_"), .direction = "downup") |> ungroup() |> distinct(help_page, type_geom, type_stat, .keep_all = TRUE) |> mutate( help_page = case_when(n() > 1 ~ help_page, .default = "Other topics"), .by = help_page ) |> gt(groupname_col = "help_page") |> cols_merge( columns = c(type_geom, stat), pattern = '{1}(stat = "{2}")' ) |> cols_merge( columns = c(type_stat, geom), pattern = '{1}(geom = "{2}")' ) |> opt_table_font(font = google_font("Fira Code")) |> tab_style( style = list( cell_text(weight = "bold") ), locations = cells_column_labels() ) |> tab_style( style = list( cell_text(weight = "bold") ), locations = cells_row_groups() ) |> cols_label( "type_geom" = "geom_", "type_stat" = "stat_", "help_page" = "Topic" ) geom_ stat_ Other topics geom_bar(stat = "count") stat_count(geom = "bar") geom_bin_2d(stat = "bin2d") stat_bin_2d(geom = "bin2d") geom_boxplot(stat = "boxplot") stat_boxplot(geom = "boxplot") geom_count(stat = "sum") stat_sum(geom = "point") geom_density(stat = "density") stat_density(geom = "density") geom_hex(stat = "binhex") stat_bin_hex(geom = "hex") geom_histogram(stat = "bin") stat_bin(geom = "bar") geom_quantile(stat = "quantile") stat_quantile(geom = "quantile") geom_area(stat = "align") stat_align(geom = "area") geom_smooth(stat = "smooth") stat_smooth(geom = "smooth") geom_violin(stat = "ydensity") stat_ydensity(geom = "violin") geom_contour geom_contour(stat = "contour") stat_contour(geom = "contour") geom_contour_filled(stat = "contourfilled") stat_contour_filled(geom = "contourfilled") geom_density_2d geom_density_2d(stat = "density2d") stat_density_2d(geom = "density2d") geom_density_2d_filled(stat = "density2dfilled") stat_density_2d_filled(geom = "density2dfilled") geom_qq geom_qq(stat = "qq") stat_qq(geom = "point") geom_qq_line(stat = "qqline") stat_qq_line(geom = "abline") When using the default geom= and stat= arguments, these pairs are interchangeable - meaning you can swap the stat_ function for the geom_ function from the table above. For example: Code # g1

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