Mara Averick batpigandme

About

Updated the queryAPI and created a new function, stattle that wraps it and makes walking easier. jsonlite is great, but I started to bump into some issues with dplyr::bind_rows. Right now these two functions play nice together, but needs to be further tested.

Load/Install

## source the functions
devtools::source_url('https://gist.githubusercontent.com/tcash21/44667f7b8578cc8e061e/raw/fdac84a0a445e448427cc3557e8c9393ce1fa470/queryAPI.r')

This gist shows how to convert a nested JSON file to an R data.frame. To do this, it uses jsonlite and data.tree.

The gist contains two examples: one is a bit simpler, the second one a bit more advanced.

Example 1

In the first example, we download all the repos from Hadley Wickham's Github account from https://api.github.com/users/hadley/repos . This JSON contains a nested owner object. The code shows how to convert that in a flat data.frame in three statements:

line 5: download
line 8: convert to data.tree

There are packages for this now!

2017-08-03: Since I wrote this in 2014, the universe, specifically Kirill Müller (https://github.com/krlmlr), has provided better solutions to this problem. I now recommend that you use one of these two packages:

rprojroot: This is the main package with functions to help you express paths in a way that will "just work" when developing interactively in an RStudio Project and when you render your file.
here: A lightweight wrapper around rprojroot that anticipates the most likely scenario: you want to write paths relative to the top-level directory, defined as an RStudio project or Git repo. TRY THIS FIRST.

I love these packages so much I wrote an ode to here.

I use these packages now instead of what I describe below. I'll leave this gist up for historical interest. 😆

	library(shiny)
	library(ggplot2)

	ui <- basicPage(
	plotOutput("plot1", brush = "plot_brush"),
	actionButton("select", label="Select Data"),
	actionButton("reset", label="Reset"),
	verbatimTextOutput("info")
	)

	---
	title: "Importing Modern Data into R"
	author: Javier Luraschi
	date: June 29, 2016
	output: revealjs::revealjs_presentation
	---

	## Overview

	* Importing Data

	library(tweenr) # Available on CRAN
	library(ggforce) # Install from thomasp85/ggforce
	library(gganimate) # Install from dgrtwo/gganimate
	set.seed(2)
	x <- sample(9,20, prob=c(1,2,3,4,5,4,3,2,1), replace=T)
	df <- data.frame(x = x, y = 15)
	dfs <- list(df)
	for(i in seq_len(nrow(df))) {
	dftemp <- tail(dfs, 1)
	dftemp[[1]]$y[i] <- sum(dftemp[[1]]$x[seq_len(i)] == dftemp[[1]]$x[i])


	# load_packages -----------------------------------------------------------

	packages <-
	c('nbastatR', #devtools::install_github("abresler/nbastatR")
	'explodingboxplotR', #devtools::install_github("timelyportfolio/explodingboxplotR")
	'ggplot2',
	'dplyr',
	'purrr',
	'magrittr')

	#!/bin/ruby

	require 'awesome_print'
	require 'csv'
	require 'dotenv'
	require 'httparty'
	require 'hashie'
	require 'highline'
	require 'link_header'

	# Twitter Topic Modeling Using R
	# Author: Bryan Goodrich
	# Date Created: February 13, 2015
	# Last Modified: April 3, 2015
	#
	# Use twitteR API to query Twitter, parse the search result, and
	# perform a series of topic models for identifying potentially
	# useful topics from your query content. This has applications for
	# social media, research, or general curiosity
	#

	.tufteize(@gutter, @gap) {
	> p { margin-right: @gutter + @gap; }

	.newthought { font-variant: small-caps; }

	p>strong,
	p>a:not([href^='#'])::before,
	p>a[href^='#'] + * {
	width: @gutter;
	margin-right: -(@gutter + @gap);