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Chart Types

Source: vignettes/chart-types.Rmd
chart-types.Rmd

Overview

myIO supports 20 chart types via the type argument in addIoLayer(). This vignette shows an example of each.

All examples use built-in R datasets so you can copy and run them directly.

Layer Compatibility

Chart types are organized into compatibility groups. You can freely combine types within compatible groups (e.g. point + line + area), but standalone types like treemap or gauge cannot be mixed with other types.

Group Types
Continuous axes line, point, area, candlestick, bracket, qq
Categorical axes bar, groupedBar, waterfall, boxplot, violin, comparison
Binned axes histogram, ridgeline
Matrix axes heatmap
Calendar calendarHeatmap
Standalone hexbin, treemap, donut, gauge, sankey

Continuous, categorical, and binned groups can be combined with each other. Matrix and standalone groups cannot be mixed with other groups.

Basic Charts

Line Chart 

myIO() |>
  addIoLayer(
    type = "line",
    color = "steelblue",
    label = "temperature",
    data = airquality,
    mapping = list(x_var = "Day", y_var = "Temp")
  )

Point (Scatter) Chart 

myIO() |>
  addIoLayer(
    type = "point",
    color = "coral",
    label = "scatter",
    data = mtcars,
    mapping = list(x_var = "wt", y_var = "mpg")
  )

Bar Chart 

myIO() |>
  addIoLayer(
    type = "bar",
    color = "steelblue",
    label = "bars",
    data = mtcars,
    mapping = list(x_var = "cyl", y_var = "mpg")
  ) |>
  defineCategoricalAxis(xAxis = TRUE)

Area Chart

Area charts require x_var, low_y, and high_y in the mapping:

aq <- airquality[complete.cases(airquality), ]
aq$TempLow <- aq$Temp - 5
aq$TempHigh <- aq$Temp + 5

myIO() |>
  addIoLayer(
    type = "area",
    color = "lightsteelblue",
    label = "area",
    data = aq,
    mapping = list(x_var = "Day", low_y = "TempLow", high_y = "TempHigh")
  )

Grouped Bar Chart 

myIO() |>
  addIoLayer(
    type = "groupedBar",
    color = "steelblue",
    label = "grouped",
    data = mtcars,
    mapping = list(x_var = "cyl", y_var = "mpg")
  ) |>
  defineCategoricalAxis(xAxis = TRUE)

Histogram

Histograms use a value mapping instead of x_var/y_var:

myIO() |>
  addIoLayer(
    type = "histogram",
    color = "steelblue",
    label = "hist",
    data = mtcars,
    mapping = list(value = "mpg")
  )

Donut Chart 

myIO() |>
  addIoLayer(
    type = "donut",
    color = c("#E69F00", "#56B4E9", "#009E73"),
    label = "donut",
    data = aggregate(mpg ~ cyl, data = mtcars, FUN = mean),
    mapping = list(x_var = "cyl", y_var = "mpg")
  )

Gauge Chart

Gauges use a value mapping:

myIO() |>
  addIoLayer(
    type = "gauge",
    color = "steelblue",
    label = "gauge",
    data = data.frame(value = 0.75),
    mapping = list(value = "value")
  )

Hexbin Chart 

myIO() |>
  addIoLayer(
    type = "hexbin",
    color = "steelblue",
    label = "hexbin",
    data = mtcars,
    mapping = list(x_var = "wt", y_var = "mpg", radius = 20)
  )

Treemap

Treemaps use a different mapping with level_1 and level_2 instead of x_var and y_var:

myIO() |>
  addIoLayer(
    type = "treemap",
    label = "cars",
    data = mtcars,
    mapping = list(level_1 = "vs", level_2 = "cyl")
  )

Phase 2 Charts

Heatmap

Heatmaps display a matrix of values with a continuous color scale. Both axes are categorical. Mapping requires x_var, y_var, and value:

df <- expand.grid(
  quarter = c("Q1", "Q2", "Q3", "Q4"),
  segment = c("Low", "Mid", "High"),
  stringsAsFactors = FALSE
)
df$value <- c(2, 4, 6, 5, 7, 9, 4, 6, 8, 3, 5, 7)

myIO() |>
  addIoLayer(
    type = "heatmap",
    color = "#4E79A7",
    label = "Revenue",
    data = df,
    mapping = list(x_var = "quarter", y_var = "segment", value = "value")
  ) |>
  defineCategoricalAxis(xAxis = TRUE, yAxis = TRUE) |>
  setAxisFormat(xLabel = "Quarter", yLabel = "Segment")

Calendar Heatmap

Calendar heatmaps render daily values as a GitHub-contributions-style grid: one cell per day, seven rows per week, with month labels across the top. The chart is scoped to a single calendar year in v1.2. Mapping requires date (any Date or ISO "YYYY-MM-DD" string) and value (numeric):

set.seed(42)
df <- data.frame(
  day     = as.Date("2026-01-01") + 0:364,
  commits = rpois(365, lambda = 3)
)

myIO() |>
  addIoLayer(
    type = "calendarHeatmap",
    color = "#4E79A7",
    label = "Daily commits",
    data = df,
    mapping = list(date = "day", value = "commits")
  )

Use options$weekStart = "monday" for ISO week ordering. Two calendars linked via linkCharts(a, b, on = "day", cursor = TRUE) synchronize the hover crosshair by date across both charts.

Candlestick (OHLC)

Candlestick charts show open-high-low-close data. Green bars indicate close >= open, red bars indicate close < open. Mapping requires x_var, open, high, low, and close:

df <- data.frame(
  day = 1:10,
  open  = c(10, 12, 11, 14, 13, 15, 14, 16, 15, 17),
  high  = c(14, 15, 14, 17, 16, 18, 17, 19, 18, 20),
  low   = c(9,  11, 10, 13, 12, 14, 13, 15, 14, 16),
  close = c(12, 11, 14, 13, 15, 14, 16, 15, 17, 19)
)

myIO() |>
  addIoLayer(
    type = "candlestick",
    label = "Price",
    data = df,
    mapping = list(
      x_var = "day", open = "open",
      high = "high", low = "low", close = "close"
    )
  ) |>
  setAxisFormat(xAxis = ".0f", yAxis = "$,.0f",
                xLabel = "Day", yLabel = "Price")

Waterfall

Waterfall charts show how an initial value is affected by sequential positive and negative changes. The cumulative transform is auto-applied. Mark summary rows with a total mapping:

df <- data.frame(
  step  = c("Revenue", "COGS", "Gross Profit", "OpEx", "Net Income"),
  value = c(500, -200, NA, -150, NA),
  is_total = c(FALSE, FALSE, TRUE, FALSE, TRUE)
)

myIO() |>
  addIoLayer(
    type = "waterfall",
    label = "P&L",
    data = df,
    mapping = list(x_var = "step", y_var = "value", total = "is_total")
  ) |>
  defineCategoricalAxis(xAxis = TRUE) |>
  setAxisFormat(yAxis = "$,.0f", xLabel = "Step", yLabel = "Amount")

Sankey

Sankey diagrams show flows between nodes. Mapping requires source, target, and value. This is a standalone type and cannot be mixed with other charts:

df <- data.frame(
  source = c("Budget", "Budget", "Sales", "Sales", "Marketing"),
  target = c("Sales", "Marketing", "Revenue", "Leads", "Leads"),
  value  = c(40, 20, 30, 10, 15)
)

myIO() |>
  addIoLayer(
    type = "sankey",
    color = c("#4E79A7", "#F28E2B", "#E15759", "#76B7B2", "#59A14F"),
    label = "Flow",
    data = df,
    mapping = list(source = "source", target = "target", value = "value")
  )

Boxplot (Composite)

Boxplots are composite charts that automatically expand into area (IQR box), point (whiskers, median), and optional outlier layers. Mapping requires x_var (categorical) and y_var (numeric):

myIO() |>
  addIoLayer(
    type = "boxplot",
    color = "#4E79A7",
    label = "Sepal Length",
    data = iris,
    mapping = list(x_var = "Species", y_var = "Sepal.Length"),
    options = list(showOutliers = TRUE)
  ) |>
  setAxisFormat(xLabel = "Species", yLabel = "Sepal Length (cm)")

Options: showOutliers (default TRUE), whiskerType (“tukey” or “minmax”).

Violin (Composite)

Violin plots show the distribution shape via a mirrored kernel density estimate. They expand into area (density), optional box (IQR), and optional median point layers:

myIO() |>
  addIoLayer(
    type = "violin",
    color = "#59A14F",
    label = "Distribution",
    data = iris,
    mapping = list(x_var = "Species", y_var = "Sepal.Length"),
    options = list(showBox = TRUE, showMedian = TRUE)
  ) |>
  setAxisFormat(xLabel = "Species", yLabel = "Sepal Length (cm)")

Options: showBox (TRUE), showMedian (TRUE), showPoints (FALSE), bandwidth (KDE bandwidth, default “nrd0”).

Ridgeline (Composite)

Ridgeline plots stack multiple density curves vertically for comparing distributions across groups. They expand into one area sub-layer per group value. Mapping requires x_var (numeric values), y_var (numeric), and group (categorical):

df <- mtcars
df$cyl <- as.character(df$cyl)

myIO() |>
  addIoLayer(
    type = "ridgeline",
    color = c("#4E79A7", "#F28E2B", "#E15759"),
    label = "MPG by Cylinders",
    data = df,
    mapping = list(x_var = "hp", y_var = "mpg", group = "cyl"),
    options = list(overlap = 0.5)
  ) |>
  setAxisFormat(xLabel = "Horsepower", yLabel = "Density")

Options: overlap (0-1, default 0.6), bandwidth (KDE bandwidth).

Q-Q Plot (Composite)

Q-Q (quantile-quantile) plots compare sample quantiles against a theoretical distribution to assess normality. The composite expands into a point layer (Q-Q scatter), a reference line (through Q1/Q3), and an optional confidence envelope. Only y_var is required in the mapping:

myIO() |>
  addIoLayer(
    type = "qq",
    color = "#4E79A7",
    label = "MPG Normality",
    data = mtcars,
    mapping = list(y_var = "mpg")
  ) |>
  setAxisFormat(xLabel = "Theoretical Quantiles", yLabel = "Sample Quantiles")

Options: envelope (TRUE), conf_level (0.95), distribution (“norm”), qfunc (custom quantile function). Supports grouped Q-Q via group mapping.

Comparison (Composite)

Comparison plots combine boxplots with pairwise significance brackets showing p-values from hypothesis tests. The composite expands into boxplot sub-layers plus a bracket layer with test results:

myIO() |>
  addIoLayer(
    type = "comparison",
    color = "#4E79A7",
    label = "Species Comparison",
    data = iris,
    mapping = list(x_var = "Species", y_var = "Sepal.Width"),
    options = list(method = "t.test", p_adjust = "bonferroni")
  ) |>
  setAxisFormat(xLabel = "Species", yLabel = "Sepal Width (cm)")

Options: method (“t.test” or “wilcox.test”), p_adjust (“none”, “bonferroni”, “holm”, “BH”), comparisons (list of specific pairs), showOutliers (TRUE).

Bracket (Standalone Layer)

Brackets can also be added as a standalone layer on top of any grouped chart. Use transform = "pairwise_test" to compute significance tests:

myIO() |>
  addIoLayer(
    type = "boxplot", color = "#4E79A7",
    label = "Sepal Width", data = iris,
    mapping = list(x_var = "Species", y_var = "Sepal.Width")
  ) |>
  addIoLayer(
    type = "bracket", label = "Tests",
    transform = "pairwise_test", data = iris,
    mapping = list(x_var = "Species", y_var = "Sepal.Width"),
    options = list(method = "wilcox.test", p_adjust = "holm")
  )

Combining Chart Types

You can overlay compatible chart types on a single widget. Here we layer points, a connecting line, and a linear model trend line:

myIO() |>
  addIoLayer(
    type = "point",
    color = "steelblue",
    label = "points",
    data = mtcars,
    mapping = list(x_var = "wt", y_var = "mpg")
  ) |>
  addIoLayer(
    type = "line",
    color = "orange",
    label = "line",
    data = mtcars,
    mapping = list(x_var = "wt", y_var = "mpg")
  ) |>
  addIoLayer(
    type = "line",
    transform = "lm",
    color = "red",
    label = "trend",
    data = mtcars,
    mapping = list(x_var = "wt", y_var = "mpg")
  )