Axis Minor Ticks¶

The axisMinorTicks and axisMinorTicksLength parameters in theme() control the appearance of axis minor ticks, i.e., the tick marks drawn between major ticks.

  • axisMinorTicks - tick line attributes set via elementLine()
  • axisMinorTicksLength - tick length in pixels
  • axisMinorTicksX / axisMinorTicksY, axisMinorTicksLengthX / axisMinorTicksLengthY - directional overrides allow controlling only horizontal or vertical axes.

Minor ticks are placed at positions computed by the scale (e.g., midpoints between major breaks for continuous scales). They do not carry labels as for now.

In [1]:
%useLatestDescriptors
%use lets-plot
In [2]:
LetsPlot.getInfo()
Out[2]:
Lets-Plot Kotlin API v.4.12.2-SNAPSHOT. Frontend: Notebook with dynamically loaded JS. Lets-Plot JS v.4.9.0.
Outputs: Web (HTML+JS), Kotlin Notebook (Swing), Static SVG (hidden)

General Usage¶

Minor ticks can improve readability of continuous axes, especially when major breaks are sparse.

In [3]:
val rand = java.util.Random(42)
val x = List(2000) { rand.nextGaussian() }
val dfHist = mapOf("x" to x)

val plotNorm = letsPlot(dfHist) +
    geomHistogram(bins = 40, color = "white", fill = "light_sky_blue", alpha = 0.7) { x = "x"; y = "..density.." } +
    geomDensity(color = "tomato", size = 1.2, alpha = 0.0) { x = "x" } +
    scaleXContinuous(breaks = listOf(-3, -2, -1, 0, 1, 2, 3)) +
    labs(title = "Normal Distribution", x = "Value", y = "Count / Density")
plotNorm
Out[3]:
-3 -2 -1 0 1 2 3 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Normal Distribution Count / Density Value
In [4]:
plotNorm +
    ggtitle("Normal Distribution with Minor Ticks") +
    theme(
        axisTicksLength = 8,

        axisMinorTicks = elementLine(color = "gray85"),
        axisMinorTicksLength = 5,

        axisMinorTicksX = elementLine(color = "gray66", size = 2),
    )
Out[4]:
-3 -2 -1 0 1 2 3 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Normal Distribution with Minor Ticks Count / Density Value

Minor Ticks as Separators¶

Minor ticks can be styled as vertical separators, helping distinguish categories more clearly.

In [5]:
val csvUrl = "https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/midwest.csv"
val lines = java.net.URL(csvUrl).readText().lines()
val header = lines.first().split(",")
val stateIdx = header.indexOfFirst { it.trim('"') == "state" }
val poptotalIdx = header.indexOfFirst { it.trim('"') == "poptotal" }
val stateNames = mapOf("IL" to "Illinois", "IN" to "Indiana", "MI" to "Michigan", "OH" to "Ohio", "WI" to "Wisconsin")

val stateCol = mutableListOf<String>()
val poptotalCol = mutableListOf<Int>()
for (line in lines.drop(1)) {
    if (line.isBlank()) continue
    val parts = line.split(",")
    val state = parts.getOrNull(stateIdx)?.trim('"') ?: continue
    val fullName = stateNames[state] ?: continue
    val pop = parts.getOrNull(poptotalIdx)?.trim('"')?.toIntOrNull() ?: continue
    stateCol.add(fullName)
    poptotalCol.add(pop)
}
val df = mapOf("state" to stateCol, "poptotal" to poptotalCol)
println("Rows: ${stateCol.size}")

Rows: 437
In [6]:
val basePlot = letsPlot(df) +
    geomJitter(alpha = 0.3, seed = 42) { x = "state"; y = "poptotal" } +
    ggtitle("Population Distribution by State") +
    themeGrey()
basePlot
Out[6]:
Illinois Indiana Michigan Ohio Wisconsin 0 10 ​6​ 2·10 ​6​ 3·10 ​6​ 4·10 ​6​ 5·10 ​6​ Population Distribution by State poptotal state
In [7]:
basePlot +
    ggtitle("Minor ticks as separators") +
    theme(
        // disable major ticks
        axisTicksX = elementBlank(),

        // and add enlarged minor ticks
        axisMinorTicksLengthX = 15.0,

        axisMinorTicksX = elementLine(color = "grey")
    )
Out[7]:
Illinois Indiana Michigan Ohio Wisconsin 0 10 ​6​ 2·10 ​6​ 3·10 ​6​ 4·10 ​6​ 5·10 ​6​ Minor ticks as separators poptotal state