From r-skills
Use when working with R ggplot2 package, especially ggplot2 4.0+ features. Covers S7 migration (@ property access), theme defaults with ink/paper/accent, element_geom(), from_theme(), theme shortcuts (theme_sub_*), palette themes, labels with dictionary/attributes, discrete scale improvements (palette, continuous.limits, minor_breaks, sec.axis), position aesthetics (nudge_x/nudge_y, order), facet_wrap dir/space/layout, boxplot/violin/label styling, stat_manual(), stat_connect(), coord reversal.
How this skill is triggered — by the user, by Claude, or both
Slash command
/r-skills:ggplot2The summary Claude sees in its skill listing — used to decide when to auto-load this skill
ggplot2 is an R package for producing visualizations using a grammar of graphics. You compose plots from data, mappings, layers, scales, facets, coordinates, and themes.
ggplot2 is an R package for producing visualizations using a grammar of graphics. You compose plots from data, mappings, layers, scales, facets, coordinates, and themes.
ggplot(data = mpg, mapping = aes(x = cty, y = hwy))
aes() links data columns to visual properties (x, y, colour, size, etc.)Layers display data using geometry, statistical transformation, and position adjustment:
ggplot(mpg, aes(cty, hwy)) +
geom_point() +
geom_smooth(formula = y ~ x, method = "lm")
Control how data maps to visual properties and create legends/axes:
ggplot(mpg, aes(cty, hwy, colour = class)) +
geom_point() +
scale_colour_viridis_d()
Split data into panels by variables:
ggplot(mpg, aes(cty, hwy)) +
geom_point() +
facet_grid(year ~ drv)
Interpret position aesthetics (Cartesian, polar, map projections):
ggplot(mpg, aes(cty, hwy)) +
geom_point() +
coord_fixed()
Control non-data visual elements:
ggplot(mpg, aes(cty, hwy, colour = class)) +
geom_point() +
theme_minimal() +
theme(legend.position = "top")
ggplot2 4.0.0 (September 2025) introduced S7 classes and major new features.
Access properties with @ instead of $:
# ggplot2 4.0+
ggplot()@data
# Deprecated (still works temporarily)
ggplot()$data
Stricter type validation:
element_text(hjust = "foo")
#> Error: @hjust must be <NULL>, <integer>, or <double>, not <character>
Built-in themes accept ink (foreground), paper (background), accent (highlight):
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_smooth(method = "lm", formula = y ~ x) +
theme_gray(paper = "cornsilk", ink = "navy", accent = "tomato")
Set layer defaults via theme(geom):
ggplot(mpg, aes(class, displ)) +
geom_boxplot(aes(colour = from_theme(accent))) +
theme(geom = element_geom(
accent = "tomato",
paper = "cornsilk",
bordertype = "dashed",
borderwidth = 0.2,
linewidth = 2,
linetype = "solid"
))
Set default palettes in themes:
theme(
palette.colour.continuous = c("chartreuse", "forestgreen"),
palette.shape.discrete = c("triangle", "triangle open")
)
New theme_sub_*() functions reduce verbosity:
| Shortcut | Prefix replaced |
|---|---|
theme_sub_axis() | axis.* |
theme_sub_axis_x() | axis.*.x |
theme_sub_axis_bottom() | axis.*.x.bottom |
theme_sub_legend() | legend.* |
theme_sub_panel() | panel.* |
theme_sub_plot() | plot.* |
theme_sub_strip() | strip.* |
# Concise
theme_sub_axis_x(
ticks = element_line(colour = "red"),
ticks.length = unit(5, "mm")
) +
theme_sub_panel(
widths = unit(5, "cm"),
spacing.x = unit(5, "mm")
)
margin_auto(1) # all sides = 1
margin_auto(1, 2) # t/b=1, l/r=2
margin_auto(1, 2, 3) # t=1, l/r=2, b=3
margin_part(r = 20) # partial (NA inherits)
theme_sub_panel(widths = unit(c(2, 3, 4), "cm")) # per-panel
theme_sub_panel(widths = unit(9, "cm")) # total area
Variables with "label" attribute auto-populate axis labels:
attr(df$bill_dep, "label") <- "Bill depth (mm)"
ggplot(df, aes(bill_dep, bill_len)) + geom_point()
dict <- c(species = "Species", bill_dep = "Bill depth (mm)")
ggplot(penguins, aes(bill_dep, bill_len, colour = species)) +
geom_point() +
labs(dictionary = dict)
scale_colour_discrete(name = toupper)
guides(x = guide_axis(title = tools::toTitleCase))
labs(y = \(x) paste0(x, " variable"))
Label hierarchy (lowest to highest): aes() < labs(dictionary) < column attribute < labs() < scale_*(name) < guide_*(title)
scale_colour_discrete(breaks = c(
"Pygoscelis adeliae" = "Adelie",
"Pygoscelis papua" = "Gentoo"
))
# Palette for spacing
scale_x_discrete(palette = scales::pal_manual(c(1:3, 5:7)))
# Consistent limits across facets
scale_x_discrete(continuous.limits = c(1, 5))
# Minor breaks
scale_x_discrete(
minor_breaks = scales::breaks_width(1, offset = 0.5),
guide = guide_axis(minor.ticks = TRUE)
)
# Secondary axis
scale_x_discrete(sec.axis = dup_axis(
name = "Counts",
breaks = seq_len(7),
labels = paste0("n = ", table(mpg$class))
))
geom_text(aes(nudge_x = sign(value) * 3, label = value))
ggplot(data, aes(x, y, fill = group)) +
geom_boxplot(position = position_dodge(preserve = "single")) +
aes(order = group)
8 direction options for facet_wrap(dir):
| dir | Start | Fill |
|---|---|---|
"lt" | top-left | left-to-right |
"tl" | top-left | top-to-bottom |
"lb" | bottom-left | left-to-right |
"bl" | bottom-left | bottom-to-top |
"rt" | top-right | right-to-left |
"tr" | top-right | top-to-bottom |
"rb" | bottom-right | right-to-left |
"br" | bottom-right | bottom-to-top |
facet_wrap(~ island, scales = "free_x", space = "free_x")
geom_point(colour = "grey", layout = "fixed_rows") # repeat in rows
geom_point(layout = NULL) # use facet vars
annotate("text", label = "X", layout = 6) # specific panel
Options: NULL, "fixed", <integer>, "fixed_cols", "fixed_rows"
geom_boxplot(
whisker.linetype = "dashed",
box.colour = "black",
median.linewidth = 2,
staplewidth = 0.5,
staple.colour = "grey50"
)
geom_violin(
quantiles = c(0.1, 0.9),
quantile.linetype = 1,
quantile.colour = "red"
)
geom_label(
aes(linetype = factor(vs), linewidth = factor(am)),
text.colour = "black",
border.colour = "blue"
)
geom_area(aes(fill = continuous_var)) # gradient (R 4.1+)
make_centroids <- function(df) {
transform(df, xend = mean(x), yend = mean(y))
}
stat_manual(geom = "segment", fun = make_centroids)
geom_line(stat = "connect") # stairstep
geom_ribbon(stat = "connect", alpha = 0.4)
# Custom connection shape
smooth <- cbind(x = seq(0, 1, length.out = 20)[-1],
y = scales::rescale(plogis(x, 0.5, 0.1)))
stat_connect(connection = smooth)
coord_cartesian(reverse = "x") # "y", "xy", "none"
coord_sf(reverse = "y")
coord_radial(reverse = "theta") # "r", "thetar", "none"
| Old | New |
|---|---|
fatten | median.linewidth / middle.linewidth |
draw_quantiles | quantiles |
geom_errorbarh() | geom_errorbar(orientation = "y") |
coord_trans() | coord_transform() |
borders() | annotation_borders() |
facet_wrap(as.table) | facet_wrap(dir) |
theme_get/set/update/replace() | get/set/update/replace_theme() |
last_plot() | get_last_plot() |
layer_data/grob/scales() | get_layer_data/grob(), get_panel_scales() |
npx claudepluginhub jsperger/llm-r-skills --plugin r-skillsHelps author, edit, and debug Gribouille grammar-of-graphics plots in Typst documents, including plot calls, aesthetic mappings, geom/stat/scale layers, coords, facets, guides, and themes.
Creates submission-grade scientific figures for Nature/high-impact journals using Python (matplotlib/seaborn) or R (ggplot2/patchwork/ComplexHeatmap). Guides figure planning, export, and QA.
Builds statistical plots with seaborn and pandas, including box plots, violin plots, pair plots, and heatmaps. Use for quick exploration of distributions, relationships, and categorical comparisons.