Migrating from plotly to myIO

Why switch from plotly?

Three things changed in 2025 that make plotly a risky dependency for new work:

  1. Documentation retired. The official plotly-r docs site went offline. Learning the API now means reading source code or archived pages.
  2. Broken statistical features. Regression CI bands (#1472) and statistical annotations (#1687) have been broken for years with no fix in sight.
  3. Heavy dependency chain. plotly pulls in htmlwidgets, tidyr, dplyr, rlang, and the full plotly.js bundle (~3.5 MB minified). myIO depends only on htmlwidgets and jsonlite.

If your charts need statistical overlays, the cost of working around plotly’s gaps exceeds the cost of switching.

Side-by-side: 8 common patterns

1. Basic scatter plot

# plotly
plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter", mode = "markers")
# myIO
myIO(data = mtcars) |>
  addIoLayer(type = "point", label = "Cars",
    mapping = list(x_var = "wt", y_var = "mpg"))

myIO uses a layered pipe API instead of a single function with mode flags.

2. Line chart with multiple series

# plotly
plot_ly(economics_long, x = ~date, y = ~value, color = ~variable,
        type = "scatter", mode = "lines")
# myIO
myIO(data = economics_long) |>
  addIoLayer(type = "line", label = "Trends",
    mapping = list(x_var = "date", y_var = "value", group = "variable"))

Groups are declared in the mapping, not as a top-level aesthetic.

3. Bar chart

# plotly
plot_ly(data.frame(x = c("A","B","C"), y = c(10,20,15)),
        x = ~x, y = ~y, type = "bar")
# myIO
myIO(data = data.frame(x = c("A","B","C"), y = c(10,20,15))) |>
  addIoLayer(type = "bar", label = "Values",
    mapping = list(x_var = "x", y_var = "y")) |>
  defineCategoricalAxis(xAxis = TRUE)

myIO requires an explicit defineCategoricalAxis() call for discrete x-axes.

4. Histogram

# plotly
plot_ly(mtcars, x = ~mpg, type = "histogram")
# myIO
myIO(data = mtcars) |>
  addIoLayer(type = "histogram", label = "MPG Distribution",
    mapping = list(x_var = "mpg"),
    options = list(bins = 15))

Bin count is set via options$bins rather than a layout parameter.

5. Box plot

# plotly
plot_ly(iris, y = ~Sepal.Length, color = ~Species, type = "box")
# myIO
myIO(data = iris) |>
  addIoLayer(type = "boxplot", label = "Sepal Length",
    mapping = list(x_var = "Species", y_var = "Sepal.Length"),
    options = list(showOutliers = TRUE)) |>
  defineCategoricalAxis(xAxis = TRUE)

myIO boxplots decompose into sub-layers (IQR box, whiskers, median, outliers), each independently styled and interactive.

6. Regression line with CI band

plotly #1472 – CI ribbons on regression lines do not render correctly.

# plotly (broken — CI band misaligns or disappears)
model <- lm(mpg ~ wt, data = mtcars)
preds <- data.frame(wt = seq(min(mtcars$wt), max(mtcars$wt), length.out = 50))
preds <- cbind(preds, predict(model, preds, interval = "confidence"))
plot_ly() |>
  add_markers(data = mtcars, x = ~wt, y = ~mpg) |>
  add_ribbons(data = preds, x = ~wt, ymin = ~lwr, ymax = ~upr) |>
  add_lines(data = preds, x = ~wt, y = ~fit)
# myIO (one call, CI computed internally)
myIO(data = mtcars) |>
  addIoLayer(type = "regression", label = "MPG vs Weight",
    mapping = list(x_var = "wt", y_var = "mpg"),
    options = list(method = "lm", showCI = TRUE, showStats = TRUE))

myIO computes the CI via stats::predict() and renders it as a first-class area layer – no manual pre-computation.

7. Statistical annotations

plotly #1687 – ggplotly() drops stat_compare_means() annotations.

# plotly (annotations lost in ggplotly conversion)
library(ggpubr)
p <- ggboxplot(iris, x = "Species", y = "Sepal.Length") +
  stat_compare_means(method = "t.test", comparisons = list(
    c("setosa", "versicolor"), c("versicolor", "virginica")))
ggplotly(p)  # brackets and p-values vanish
# myIO (pairwise tests rendered natively)
myIO(data = iris) |>
  addIoLayer(type = "comparison", label = "Sepal Length",
    mapping = list(x_var = "Species", y_var = "Sepal.Length"),
    options = list(method = "t.test"))

The comparison composite expands into boxplots plus significance brackets with p-values, computed in R and rendered in D3.js.

8. Dark mode theming

# plotly
plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter", mode = "markers") |>
  layout(template = "plotly_dark")
# myIO
myIO(data = mtcars) |>
  addIoLayer(type = "point", label = "Cars",
    mapping = list(x_var = "wt", y_var = "mpg")) |>
  setTheme(background = "#1a1a2e", text = "#e0e0e0",
           grid = "#2a2a4a", font = "Inter")

myIO theming uses CSS custom properties, so colors apply consistently across all layers including CI bands, annotations, and export buttons.

What myIO does that plotly can’t

What plotly does that myIO doesn’t

If you need 3D, maps, or broad chart-type coverage, plotly remains the better choice. If you need statistical overlays that actually work, myIO is worth the switch.