l_hist is a generic function for creating an interactive histogram display that can be linked with loon's other displays

l_hist(x, yshows = c("frequency", "density"), showStackedColors = TRUE,
  origin = NULL, binwidth = NULL, showBinHandle = FALSE,
  xlabel = NULL, parent = NULL, ...)



vector with numerical data to perform the binning onx,


one of "frequency" (default) or "density"


if TRUE (default) then bars will be coloured according to colours of the points; if FALSE, then the bars will be a uniform colour except for highlighted points.


numeric scalar to define the binning origin


a numeric scalar to specify the binwidth If NULL binwidth is set using David Scott's rule when x is numeric and and using the minumum numerical difference between factor levels when x is a factor or a character vector.


If TRUE, then an interactive "bin handle" appears on the plot whose movement resets the origin and the binwidth. Default is FALSE


label to be used on the horizontal axis. If NULL, an attempt at a meaningful label inferred from x will be made.


parent widget path


named arguments to modify the histogram plot states


widget handle


Note that when changing the yshows state form 'frequency' to 'density' you might have to use l_scaleto_world to show the complete histogram in the plotting region.

For more information run: l_help("learn_R_display_hist")

See also


h <- l_hist(iris$Sepal.Length) names(h)
#> [1] "linkingGroup" "linkingKey" "zoomX" #> [4] "zoomY" "panX" "panY" #> [7] "deltaX" "deltaY" "xlabel" #> [10] "ylabel" "title" "showLabels" #> [13] "showScales" "swapAxes" "showGuides" #> [16] "background" "foreground" "guidesBackground" #> [19] "guidelines" "minimumMargins" "labelMargins" #> [22] "scalesMargins" "default_ylabels" "x" #> [25] "binwidth" "origin" "showBinHandle" #> [28] "yshows" "colorStackingOrder" "showOutlines" #> [31] "showStackedColors" "colorFill" "colorOutline" #> [34] "color" "selected" "active" #> [37] "selectBy" "selectionLogic" "useLoonInspector"
h["xlabel"] <- "Sepal length" h["showOutlines"] <- FALSE h["yshows"]
#> [1] "frequency"
h["yshows"] <- "density" l_scaleto_plot(h) h["showStackedColors"] <- TRUE h['color'] <- iris$Species h["showStackedColors"] <- FALSE h["showOutlines"] <- TRUE h["showGuides"] <- TRUE # link another plot with the previous plot h['linkingGroup'] <- "iris_data" h2 <- with(iris, l_hist(Petal.Width, linkingGroup="iris_data", showStackedColors = TRUE)) # Get an R (grid) graphics plot of the current loon plot plot(h)
# or with more control about grid parameters grid.loon(h)
# or to save the grid data structure (grob) for later use hg <- loonGrob(h)