aleszu

## 538murders.R
# Using the fivethirtyeight package

library(fivethirtyeight)
library(tidyr)
library(tibble)
library(dplyr)

# See names of columns

names(murder_2015_final)

## tidyverse.R
# Getting Started with tidyverse in R

library(tidyr)
library(tibble)

key_value <- tribble(
  ~row, ~key1, ~key2, ~key3, # These are the names of the columns (indicated with ~)
  "1", "1_value_1","1_value_2","1_value_3", # Row 1
  "2", "2_value_1", "2_value_2", "2_value_3", # Row 2
  "3", "3_value_1", "3_value_2", "3_value_3" # Row 3

## line-chart.R
# How to create a line chart in R

library(ggplot2)

venezuela.data <- read.csv(file="venezuela.csv")

p1 <- ggplot() +
    geom_line(aes(y = units, x = year), data = venezuela.data) +
    scale_x_continuous(breaks=seq(2002,2016,2)) +
    theme(text=element_text(family="Tahoma"))

## geocode-csv.R
# Geocoding a csv column of "addresses" in R

#load ggmap
library(ggmap)

# Select the file from the file chooser
fileToLoad <- file.choose(new = TRUE)

# Read in the CSV data and store it in a variable
origAddress <- read.csv(fileToLoad, stringsAsFactors = FALSE)

## postGIS_latimes.js

// example coordinates
var coords = [34.052108, -118.245678];

var sql = new cartodb.SQL({ user: 'latimes' });
sql.execute("SELECT ST_Distance(the_geom::geography, ST_SetSRID(ST_Point("+ coords[1] +", "+ coords[0] +"),4326)::geography) as fDistance FROM socal_freeways")
.done(function(data) {
// distance in feet rounded to nearest 10
var distFeet = Math.round((data.rows[0].fdistance * 3.28084)/10)*10;

## latimes_slider.html
<figure class="graphic-wrapper">
    <h2 class="graphic-hed">Thousands of homes approved near L.A. freeways</h2>
    <p class="graphic-chatter" style="text-align:center;">Use the slider to see where the city has issued building permits within a 1,000 feet of a freeway since 2005.</p>
    <h2 id="year-selected">2005</h2>

    <input type="range" step="0" id="retirement-slider" class="graphic-slider" min="0" max="11" value="0">

    <div class="slider-map-container">
      <ul class="map-images">
        <li><img src="/projects/la-me-freeway-pollution/static/img/timeline-slider-2005-01.png"></li>

## cartodb-drugmap-nyt.css
/** choropleth visualization */

#clean_merge{
  polygon-fill: #FFFFB2;
  polygon-opacity: 1;
  line-color: #FFF;
  line-width: 0;
  line-opacity: 0;
}
#clean_merge [ estimated_age_adjusted_death_rate_11_categories_in_ranges <= 20.1] {

## trumpwsjnodes.js
function tick(e) {

  // Push different nodes in different directions for clustering.
  var k = 6 * e.alpha;
  nodes.forEach(function(o, i) {
    o.y += i & 1 ? k : -k;
    o.x += i & 2 ? k : -k;
  });

  node.attr("cx", function(d) { return d.x; })

## pubrec.html
<!DOCTYPE html>
<meta charset="utf-8">
<script src="http://d3js.org/d3.v3.js"></script>
<style>

path.link {
  fill: none;
  stroke: #666;
  stroke-width: 1.5px;
}

## add_color_d3plus.js
 var attributes = [
    {"name": "concussions", "hex": "#0000ff"}
  ]

  // instantiate d3plus
  var visualization = d3plus.viz()
    .container("#viz")  // container DIV to hold the visualization
    .data(sample_data)  // data to use with the visualization
    .type("line")        // visualization type
    .id("name")         // key for which our data is unique on
	# Using the fivethirtyeight package

	library(fivethirtyeight)
	library(tidyr)
	library(tibble)
	library(dplyr)

	# See names of columns

	names(murder_2015_final)
	# Getting Started with tidyverse in R

	library(tidyr)
	library(tibble)

	key_value <- tribble(
	~row, ~key1, ~key2, ~key3, # These are the names of the columns (indicated with ~)
	"1", "1_value_1","1_value_2","1_value_3", # Row 1
	"2", "2_value_1", "2_value_2", "2_value_3", # Row 2
	"3", "3_value_1", "3_value_2", "3_value_3" # Row 3
	# How to create a line chart in R

	library(ggplot2)

	venezuela.data <- read.csv(file="venezuela.csv")

	p1 <- ggplot() +
	geom_line(aes(y = units, x = year), data = venezuela.data) +
	scale_x_continuous(breaks=seq(2002,2016,2)) +
	theme(text=element_text(family="Tahoma"))
	# Geocoding a csv column of "addresses" in R

	#load ggmap
	library(ggmap)

	# Select the file from the file chooser
	fileToLoad <- file.choose(new = TRUE)

	# Read in the CSV data and store it in a variable
	origAddress <- read.csv(fileToLoad, stringsAsFactors = FALSE)

	// example coordinates
	var coords = [34.052108, -118.245678];

	var sql = new cartodb.SQL({ user: 'latimes' });
	sql.execute("SELECT ST_Distance(the_geom::geography, ST_SetSRID(ST_Point("+ coords[1] +", "+ coords[0] +"),4326)::geography) as fDistance FROM socal_freeways")
	.done(function(data) {
	// distance in feet rounded to nearest 10
	var distFeet = Math.round((data.rows[0].fdistance * 3.28084)/10)*10;
	<figure class="graphic-wrapper">
	<h2 class="graphic-hed">Thousands of homes approved near L.A. freeways</h2>
	<p class="graphic-chatter" style="text-align:center;">Use the slider to see where the city has issued building permits within a 1,000 feet of a freeway since 2005.</p>
	<h2 id="year-selected">2005</h2>

	<input type="range" step="0" id="retirement-slider" class="graphic-slider" min="0" max="11" value="0">

	<div class="slider-map-container">
	<ul class="map-images">
	<li><img src="/projects/la-me-freeway-pollution/static/img/timeline-slider-2005-01.png"></li>
	/** choropleth visualization */

	#clean_merge{
	polygon-fill: #FFFFB2;
	polygon-opacity: 1;
	line-color: #FFF;
	line-width: 0;
	line-opacity: 0;
	}
	#clean_merge [ estimated_age_adjusted_death_rate_11_categories_in_ranges <= 20.1] {
	function tick(e) {

	// Push different nodes in different directions for clustering.
	var k = 6 * e.alpha;
	nodes.forEach(function(o, i) {
	o.y += i & 1 ? k : -k;
	o.x += i & 2 ? k : -k;
	});

	node.attr("cx", function(d) { return d.x; })
	<!DOCTYPE html>
	<meta charset="utf-8">
	<script src="http://d3js.org/d3.v3.js"></script>
	<style>

	path.link {
	fill: none;
	stroke: #666;
	stroke-width: 1.5px;
	}
	var attributes = [
	{"name": "concussions", "hex": "#0000ff"}
	]

	// instantiate d3plus
	var visualization = d3plus.viz()
	.container("#viz") // container DIV to hold the visualization
	.data(sample_data) // data to use with the visualization
	.type("line") // visualization type
	.id("name") // key for which our data is unique on