Code
library(tidyverse) library(patchwork) library(gt) # sysfonts::font_add_google(name = "fira code") # showtext::showtext_auto() knitr::opts_chunk$set(collapse = TRUE, comment = "#>", dev = "ragg_png")
In honor of the release of {ggplot2} 4.0.0 🥳, I wanted to explore the relationships between the geometric objects (“geoms”) and statistical transformations (“statistics”) offered by the core {ggplot2} Functions.
Inside The layered graphic grammar framework, suddenly in it {ggplot2} are built by adding layer()S. each layer() consists of a geom (the actual thing that is drawn), with different geoms that have different aes()Thetics that can (and must) be assigned to variables in the data.
All of them all layer()S Also Have a stat – this can be considered as a function that is applied to the data that it transforms in a certain way before it is passed on to the geom and its aesthetics.
For example, geom_histogram() uses the "bin" Stand standard:
geom_histogram()$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 unprocessed data, the number of cases of each value of the x aesthetics within every X-bin.
p1 <- ggplot(mpg, aes(hwy)) + geom_histogram(bins = 10, color = "black") p1
Figure 1
We can see the product of this transformation through the layer_data() function, which extremes the data after it has been transformed by the stat:
# access the data from the first (and only) layer layer_data(p1, i = 1) |> # 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 reflect the underlying data shown in the final plot in Figure 1: 10 columns, their X location and their heights (count).1
Let’s look deep into the geoms and statistics {ggplot2} And how they relate to each other.
Data collection
# list all functions in ggplot2 that start with geom_ or stat_:
get_statgeom <- function(fname) {
layer <- match.fun(fname)()
data.frame(
stat = class(layer$stat)[1],
geom = class(layer$geom)[1]
)
}
has_statgeom.args <- function(fname) {
frmls <- formals(match.fun(fname))
data.frame(
has_stat_arg = "stat" %in% names(frmls),
has_geom_arg = "geom" %in% names(frmls)
)
}
ggdata <- data.frame(
ggfunction = ls("package:ggplot2", pattern = "^(geom|stat)_")
) |>
filter(
!str_detect(ggfunction, "(bin2d|density2d|binhex|summary2d)"),
!ggfunction %in% c("geom_errobarh")
) |>
mutate(
type = case_when(
str_starts(ggfunction, "geom_") ~ "geom",
str_starts(ggfunction, "stat_") ~ "stat",
TRUE ~ NA_character_
),
help_page = map(ggfunction, help) |>
map(as.character) |>
map_chr(str_extract, "(?<=help/).*"),
map(ggfunction, possibly(get_statgeom, data.frame(NA))) |>
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 <- ggdata |>
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 the statutory. What is that about?
The identity Stat is a stat that does nothing.2 It takes the data as it is and passes it on to the aesthetics of the geom.
Geoms who use the identity state can be considered the most basic building blocks of {ggplot2} – Many types of lines, bars/tiles, points, areas, etc. These are the geoms that you would probably use annotate().3 We can see that such geoms are often also used with other statistics:
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 statistics.
{ggplot2} Offers many statistics that in principle can only be used with a specific geom (and vice versa). These geom stating couples are almost exclusive to each other – think of a boxplot, for example, which is a unique geom that has little meaning without the Boxplot Stat.
p2 <- ggplot(mpg, aes(class, hwy)) + geom_boxplot() p2
layer_data(p2, i = 1) |> # 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 gives all geom-state couples in {ggplot2}:
Code
ggdata_pairs <- ggdata |>
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"
)| 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_Mooth (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 standard geom= And stat= arguments, these pairs are interchangeable – which means that you stat_ function for the geom_ function from the table above. For example:
Code
# g1 <- ggplot(mpg, aes(class))
# g1 + geom_bar() + g1 + stat_count()
# g2 <- ggplot(diamonds, aes(x, y)) + xlim(4, 10) + ylim(4, 10)
# g2 + geom_bin_2d() + g2 + stat_bin_2d()
# g3 <- ggplot(mpg, aes(class, hwy))
# g3 + geom_boxplot() + g3 + stat_boxplot()
# g4 <- ggplot(faithfuld, aes(waiting, eruptions, z = density))
# g4 + geom_contour() + g4 + stat_contour()
# g4 + geom_contour_filled() + g4 + stat_contour_filled()
# g5 <- ggplot(mpg, aes(cty, hwy))
# g5 + geom_count() + g5 + stat_sum()
# g7 <- ggplot(faithful, aes(x = eruptions, y = waiting))
# g7 + geom_density_2d() + g7 + stat_density_2d()
# g7 + geom_density_2d_filled() + g7 + stat_density_2d_filled()
# g8 <- ggplot(diamonds, aes(carat, price))
# g8 + geom_hex() + g8 + stat_bin_hex()
# g9 <- ggplot(diamonds, aes(carat))
# g9 + geom_histogram() + g9 + stat_bin()
# df <- data.frame(y = rt(200, df = 5))
# g10 <- ggplot(df, aes(sample = y))
# g10 + stat_qq() + stat_qq_line() + g10 + geom_qq() + geom_qq_line()
# g11 <- ggplot(mpg, aes(displ, 1 / hwy))
# g11 + geom_quantile() + g11 + stat_quantile()
# huron <- data.frame(year = 1875:1972, level = as.vector(LakeHuron))
# g12 <- ggplot(huron, aes(year, level))
# g12 + geom_area() + g12 + stat_align()
# g13 <- ggplot(mpg, aes(displ, hwy))
# g13 + geom_smooth() + g13 + stat_smooth()
g14 <- ggplot(mtcars, aes(factor(cyl), mpg))
(g14 + geom_violin() + ggtitle("geom_violin()")) +
(g14 + stat_ydensity() + ggtitle("stat_ydensity()")) &
theme(plot.title = element_text(family = "fira code"))
The only exception is geom/stat_density() For some reason?
Code
g6 <- ggplot(diamonds, aes(carat))
(g6 + geom_density() +
labs(title = "geom_density()",
subtitle="stat = "density", geom = "area"")) +
(g6 + stat_density() +
labs(title = "stat_density()",
subtitle="stat = "density", geom = "ribbon"")) &
theme(plot.title = element_text(family = "fira code"),
plot.subtitle = element_text(family = "fira code"))
Understanding the relationship between geoms and statistics is an important part of mastering {ggplot2}. Build a mental model of how {ggplot2} processes your data – understand the underlying underlying grammar – This allows you to leave simply the use of standard values to adjust your suddenly confidence. If you understand a function such as geom_bar() is just a shortcut for layer(stat = "count", geom = "bar")You can start thinking about how you can link different geoms to existing statistics, or how you can use the different calculated variables that are provided by a stat.
I personally look forward to experimenting with the new stat_manual()!
So the next time you make a plot, I encourage you to use layer_data() and don’t ask what the data can do for you, but what You Can do your data!
Related
#Research #geoms #statistics #ggplot2 #Rbloggers