etpinard

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timelyportfolio

Introduction

This d3.js parallel coordinates plot is another experiment in how we might use interactive plots in Javascript to represent a partykit / rpart object from R. The example builds on this d3.js collapsible tree plot. Eventually, it would be nice to combine the tree and parallel coordinates into a layout with synced interactivity.

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### Almost No [`rCharts`](http://rcharts.io) Also, this is fairly different from most of my interactive plots from R in that it almost completely avoids [`rCharts`](http://rcharts.io) (almost because I did use its `publish` to make this gist). I chose to exclude `rCharts` for two reasons:

1. demo how we can use htmltools to build html/js in R
2. help users understand some of the things rCharts does f

hadley

iclass <- function(x) {
  c(
    if (is.matrix(x)) "matrix",
    if (is.array(x) && !is.matrix(x)) "array",
    if (is.double(x)) "double",
    if (is.integer(x)) "integer",
    mode(x)
  )
}

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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aammd

Bootstrap confidence intervals for linear and nonlinear models

Another version of this gist with figures included is on Rpubs

Recently I was trying to put confidence intervals on a regression line, and I got some excellent advice from @davidjayharris on Twitter, who suggested the below method in an excellent

woobe

Introduction

I created a function called "extract_colours()" in my "rPlotter" package. Using this function, any image in PNG, JPG, JPEG or TIFF format can be converted into a simple colour palette.

YET, this is my very first attempt, the colours are only sorted by alphabetical order at the moment. More work is needed to arrange colours based on colour theory and other factors.

Example 1a - R Logo

ramnathv

I very frequently get asked this question as to what is the best way to get started with building an rCharts library. While rCharts ships with its own bindings, I tend to see it more as a framework that allows one to seamlessly plug into other javascript visualization libraries, or even custom visualizations for that matter.

I will be writing a more detailed developer guide on how to build rCharts bindings for a library or chart of interest. For now, the knowledge is scattered across blog posts and my objective here is to consolidate them to provide some structure for anyone trying to build their own binding.

I will start with two posts of my own, where I deconstruct the process of going from a javascript visualization library to an rCharts binding. I take the example of uvCharts, which is a wrapper around d3.js, and explain how to go about integrating it within the rCharts framework.

• [How rCharts Works -

wch

Type: Shiny
Title: conditionalPanel demo
License: MIT
Author: Winston Chang <winston@rstudio.com>
AuthorUrl: http://www.rstudio.com/
Tags: conditionalpanel
DisplayMode: Showcase

jcheng5

library(shinyIncubator)

shinyServer(function(input, output, session) {
  output$plot <- renderPlot({
    if (input$go == 0)
      return()

    # Wrap the entire expensive operation with withProgress
    withProgress(session, {
      setProgress(message = "Calculating, please wait",

hadley

Limitations of lapply()

lapply() is a really useful function but it has some limitations. For example, with lapply() there is no way to:

• add new elements
• remove existing elements
• change the names of list elements

To overcome these problems we could create a variant of lapply() than I'm going to call fapply() (where f is for flexible). Rather than calling the supplied function with with an element of the list extract with [[, it will call it with a sub-list extracted with [.