Misalignment when layering on top of plots with adjusted axis breaks

[grantmcdermott]

I don't mean w.r.t. the new dodge argument, but rather the internal axis tick adjustment that we do for some types. For example, grouped boxplots or violin plots:

library("tinyplot")

plt(
  body_mass ~ sex | species, data = penguins,
  flip = TRUE,
  type = "box",
  theme = "clean"
)
plt_add(type = "j", cex = 0.5)

Another example would be ridge plots (and, in this case, we note that there's a colour mismatch too).

plt(
  sex ~ body_mass, data = penguins,
  type = "ridge",
  theme = "ridge"
)
plt_add(type = "j", cex = 0.5)

^{Created on 2025-09-22 with reprex v2.1.1}

[grantmcdermott]

Actually, it's not just "dodged" types. We also face a similar problem when adding on top of plot types that respect a certain order.

library(tinyplot)
library(broom)

coefs = tidy(lm(mpg ~ wt + drat, mtcars), conf.int = TRUE)

plt(
  estimate ~ term,
  ymin = conf.low, ymax = conf.high,
  data = coefs,
  type = "errorbar",
  theme = "basic"
)
plt_add(type = 'ribbon')

^{Created on 2025-11-19 with reprex v2.1.1}

[grantmcdermott]

I don't mean w.r.t. the new dodge argument

I should clarify; this should have been "I don't just mean w.r..t. the new dodge argument".

In particularly, adding on top of dodged errobars etc. also produces misalignment. For example:

library(tinyplot)

models = list(
    "Model A" = lm(mpg ~ cyl, data = mtcars),
    "Model B" = lm(mpg ~ hp + cyl, data = mtcars)
)

results = lapply(names(models), function(m) {
    data.frame(
        model = m,
        term = names(coef(models[[m]])),
        estimate = coef(models[[m]]),
        setNames(data.frame(confint(models[[m]])), c("conf.low", "conf.high"))
    )
})
results = do.call(rbind, results)

tinyplot(estimate ~ term | model,
         ymin = conf.low, ymax = conf.high,
         data = results,
         type = "pointrange",
         dodge = 0.1,
         theme = "basic")
tinyplot_add(type = "l")

^{Created on 2025-11-21 with reprex v2.1.1}