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We are excited to announce that a new package leaflet has been released on CRAN. The R package leaflet is an interface to the JavaScript library Leaflet to create interactive web maps. It was developed on top of the htmlwidgets framework, which means the maps can be rendered in R Markdown (v2) documents, Shiny apps, and RStudio IDE / the R console. Please see http://rstudio.github.io/leaflet for the full documentation. To install the package, run

install.packages('leaflet')

We quietly introduced this package in December when we announced htmlwidgets, but in the months since then we’ve added a lot of new features and launched a new set of documentation. If you haven’t looked at leaflet lately, now is a great time to get reacquainted!

The Map Widget

The basic usage of this package is that you create a map widget using the leaflet() function, and add layers to the map using the layer functions such as addTiles(), addMarkers(), and so on. Adding layers can be done through the pipe operator %>% from magrittr (you are not required to use %>%, though):

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

leaflet-basic

There are a variety of layers that you can add to a map widget, including:

  • Map tiles
  • Markers / Circle Markers
  • Polygons / Rectangles
  • Lines
  • Popups
  • GeoJSON / TopoJSON
  • Raster Images
  • Color Legends
  • Layer Groups and Layer Control

There are a sets of methods to manipulate the attributes of a map, such as setView() and fitBounds(), etc. You can find the details from the help page ?setView.

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We are happy to announce a new package DT is available on CRAN now. DT is an interface to the JavaScript library DataTables based on the htmlwidgets framework, to present rectangular R data objects (such as data frames and matrices) as HTML tables. You can filter, search, and sort the data in the table. See http://rstudio.github.io/DT/ for the full documentation and examples of this package. To install the package, run

install.packages('DT')
# run DT::datatable(iris) to see a "hello world" example

DataTables

The main function in this package is datatable(), which returns a table widget that can be rendered in R Markdown documents, Shiny apps, and the R console. It is easy to customize the style (cell borders, row striping, and row highlighting, etc), theme (default or Bootstrap), row/column names, table caption, and so on.

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We’re pleased to announce d3heatmap, our new package for generating interactive heat maps using d3.js and htmlwidgets. Tal Galili, author of dendextend, collaborated with us on this package.

d3heatmap is designed to have a familiar feature set and API for anyone who has used heatmap or heatmap.2 to create static heatmaps. You can specify dendrogram, clustering, and scaling options in the same way.

d3heatmap includes the following features:

  • Shows the row/column/value under the mouse cursor
  • Click row/column labels to highlight
  • Drag a rectangle over the image to zoom in
  • Works from the R console, in RStudio, with R Markdown, and with Shiny

Installation

install.packages("d3heatmap")

Examples

Here’s a very simple example (source: flowingdata):

library(d3heatmap)
url <- "http://datasets.flowingdata.com/ppg2008.csv"
nba_players <- read.csv(url, row.names = 1)
d3heatmap(nba_players, scale = "column")

d3heatmap

You can easily customize the colors using the colors parameter. This can take an RColorBrewer palette name, a vector of colors, or a function that takes (potentially scaled) data points as input and returns colors.

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