Quarto example

HTMLWidgets

Leaflet

library(leaflet)

m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=174.768, lat=-36.852, popup="The birthplace of R")
m  # Print the map

DataTable

library(DT)
library(dplyr)
penguins = read.csv("https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/inst/extdata/penguins.csv") %>%
  mutate(species = as.factor(species),
         island = as.factor(island),
         sex = as.factor(sex))
datatable(penguins, filter = "top")

$('#DataTables_Table_0').on("search.dt", function() { 
  console.log($(this).DataTable().rows( { filter : 'applied'} ).nodes().length);
});

data = FileAttachment("https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/inst/extdata/penguins.csv").csv({typed: true})

ObservableJS

This piece of code implements the data filtering based on the input selectors below. bill_length_min (slider) and islands (checkboxes) are inputs initialized in the code chunk after this one.

filtered = data.filter(function(penguin) {
  return bill_length_min < penguin.bill_length_mm &&
         islands.includes(penguin.island);
})

Interestingly, previous code chunks can use variables created in future code chunks with ojs chunks since code is executed non-linearly.

Code

viewof bill_length_min = Inputs.range(
  [32, 50], 
  {value: 35, step: 1, label: "Bill length (min):"}
)

// Create checkboxes
viewof islands = Inputs.checkbox(
  ["Torgersen", "Biscoe", "Dream"], 
  { value: ["Torgersen", "Biscoe"], 
    label: "Islands:"
  }
)

Observable Plot
Data

Code

Plot.rectY(filtered, 
  Plot.binX(
    {y: "count"}, 
    {x: "body_mass_g", fill: "species", thresholds: 20}
  ))
  .plot({
    facet: {
      data: filtered,
      x: "sex",
      y: "species",
      marginRight: 80
    },
    marks: [
      Plot.frame(),
    ]
  }
)

Code

Inputs.table(filtered)