Example
```
library(ggplot2)
data.frame(cat = LETTERS[1:5], x = rnorm(5), xend = rnorm(5)) |>
ggplot(aes(x, xend = xend, cat)) +
ggarrow::geom_arrow_segment(
arrow_head = NULL,
stroke_colour = "black", colour = "red",
linewidth = 10
) +
coord_radial(theta = "y")
```
You can use `ggarrow::geom_arrow_segment(arrow_head = NULL)`. It distinguishes `stroke_colour` for the outline and regular `colour` for the interior. This confusing parametrisation was brought to you 'wanna be like `geom_segment`' 😁
A screenshot of two reply messages on bluesky commenting on the same post. The reply on top is very negative about the data.table package, saying 'it was just the worst'. The bottom reply instead claims that data.table's syntax is the best thing produced in #rstats.
Never change #rstats, never change
I've been charmed recently by ggiraph. It feels well-integrated and mostly works with extensions even if doesn't wrap them in interactivity. ggplotly sort of 'translates' ggplot figures and some of the more niche settings or extensions don't have a natural translation.
PDF has a horrible habit messing up font stuff. I'm an SVG enthusiast myself and I use ggrastr when my plot has too many points.
A humble suggestion:
```
`%xtab%` <- function(x, y) {
table(x, y, dnn = list(
deparse(substitute(x)),
deparse(substitute(y))
))
}
```
How can you say this when theme_barbie() exists? (cc @matthewbjane.bsky.social)
'ignored positional aesthetics' are so handy and cool, wanted to share on bsky! (as seen on mastodon 🙃) #rstats #ggplot2
You can use this instead of your `update_geom_defaults()`:
```
theme(geom = element_geom(linewidth = 0.25, borderwidth = 0.25, pointsize = 0.5))
```
And you can use `set_theme()` to globally apply a theme as the default theme.
I wrote some additional tricks here: tidyverse.org/blog/2025/10...
ONE OF US, ONE OF US, ONE OF US ✨https://github.com/ggplot2-extenders/ggplot-extension-club ✨
Look the use of the word 'extension' was very deliberate to absolve ggplot2 from having to absorb more functionality than it already does 😅
I like this use case for a facet extension!
I think this may come from Afrikaans/Dutch and means 'red forest', so bois (woods) wasn't too far of 🤷
dplyr 1.2.0 is out now and we are SO excited!
- `filter_out()` for dropping rows
- `recode_values()`, `replace_values()`, and `replace_when()` that join `case_when()` as a complete family of recoding/replacing tools
These are huge quality of life wins for #rstats!
tidyverse.org/blog/2026/02...
# pretty slick
l <- c(mpg = "Miles per gallon", wt = "Weight (1,000 lbs)", `mpg/2` = "yes this works")
ggplot(mtcars, aes(mpg, wt)) + labs(dictionary = l)
ggplot(mtcars, aes(wt, mpg)) + labs(dictionary = l)
ggplot(mtcars, aes(wt, mpg/2)) + labs(dictionary = l)
This is a very good primer on why we encounter breaking changes whenever ggplot2 has a meaningful update 🙃
A second crime is not putting these on the y-axis and displaying the names horizontally.
It is a (relatively) new thing 🎉
You can specify relative position in ggplot with I(x) I(y).
E.g. annotate("text", x = I(.5), y = I(.5), label = "hello!") will place the text in the middle of the plot.
This, combined with alignment arguments is like 87% of the magic for me.
They're alright if you have mutually inclusive categories, mostly as an alternative for large venn-diagram like data. Here, KO/WT are mutually exclusive and 8h/24h/48h are mutually exclusive. I'd opt for other ways of emphasising the experimental conditions.
Where'd be the artistry in deftly replacing `theme_gray()` with something more sensible? You gotta give the people some sense of accomplishment for free.
lukewarm take, but plotly is uggy and ggplotly should generally be avoided for disrespecting your static ggplot2 formatting (use ggiraph instead)
If there is a fear that some undocumented behaviour you rely on may break on accident, one thing you could do is PR a unit test for the behaviour. At least then, it'll only be broken purposefully 🙃
Side-by-side comparison of two multi-panel bubble charts faceted by world region. The left column shows the default facet labels placed above each panel (“Africa”, “Americas”, “Asia”, “Europe”, “Oceania”). The right column shows the same charts, but the facet labels are moved inside each panel at the top-left using a negative margin. In the center, there is a title reading “Want to place your facet labels inside each panel?” with an arrow pointing right, followed by a short ggplot2 theme code snippet demonstrating how to move strip text inside the panel.
I ignored the strip.clip argument in #ggplot2 for way too long 😲
Combined with a small negative margin tweak, you can place facet labels inside each panel. A tiny trick that makes small multiples feel so much cleaner.
🔵 no manual coordinates
🔵 inherits theme styling
🔵 scales nicely when resizing
You can use character glyphs as shapes too. That includes emoji.
```
library(ggplot2)
ggplot(mpg, aes(displ, hwy, shape = fl)) +
geom_point(size = 5) +
scale_shape_manual(values = c("🫛", "🍓", "🥕", "🍋", "🍇"))
```
I made a geom that can do this, but it had a different purpose in mind, so you have to use `mult = 0`. You can use:
```r
stat_summary(
geom = ggh4x::GeomPointpath, fun = mean, color = "red",
mult = 0
)
```
teunbrand.github.io/ggh4x/refere...
``` r
func <- function(x) deparse(substitute(x))
mtcars |> func()
#> [1] "mtcars"
mtcars |> dplyr::select(hp, mpg) |> func()
#> [1] "dplyr::select(mtcars, hp, mpg)"
```
Also, packages on CRAN come with a DOI that can be included: doi.org/10.32614/CRA...
Everyone can now view my posit::conf 2025 talk "purrrfectly parallel, purrrfectly distributed"!
{purrr} now lets you access the magic of {mirai} directly, as the modern way to make things faster through parallel computation in #rstats.
youtu.be/j1yHjMvbnQM?...
