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Statistics 1 Zara Shabrina.html
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<!DOCTYPE html>
<html class=""><head>
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<script type="text/javascript">
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<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta name="generator" content="pandoc">
<meta name="author" content="Zahratu Shabrina">
<meta name="date" content="2019-08-21">
<title>Statistic 1: Data Visualisation and Simple Linear Regression</title>
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<h1 class="title toc-ignore">Statistic 1: Data Visualisation and Simple Linear Regression</h1>
<h4 class="author">Zahratu Shabrina</h4>
<h4 class="date">21 August 2019</h4>
</div>
<div id="section-aim" class="section level3">
<h3>Aim</h3>
<p>This session aims to provide a brief overview of statistical computing and data visualisation using <strong>Plotly</strong>, <strong>ggplot2</strong> and <strong>Shiny</strong>. The <strong>Plotly</strong>
graphics library provides a wide range of interactive visualisation
that can be made using R and Python programming. In this tutorial, we’ll
be focusing on using R to produce our visualisation. <strong>ggplot2</strong>
is a visualisation package for creating graphics by telling the package
how to map variables into aesthetics and which of the graphical
primitives to use. <strong>Shiny</strong> makes it possible to create interactive apps straight from R without web development skills.</p>
</div>
<div id="section-download-the-dataset" class="section level3">
<h3>Download the dataset</h3>
<p>Before starting this tutorial, download the dataset available thorugh
the links below. You can also use your own dataset and tailor your
analysis to your data. We are going to use Airbnb and London Profile
& Atlas at Borough level. I have pre-processed the data so that each
borough contains the number of Airbnb supply in the year 2019. The raw
Airbnb data can be downloaded in this website: <a href="http://insideairbnb.com/get-the-data.html" class="uri">http://insideairbnb.com/get-the-data.html</a>
containing the point data of each Airbnb listing in the chosen cities,
including London. The raw borough profile and atlas can be found in
London Datastore <a href="https://data.london.gov.uk/dataset/london-borough-profiles" class="uri">https://data.london.gov.uk/dataset/london-borough-profiles</a>.</p>
<p>The pre-processed data (already aggregated into borough level) and already simplified for the tutorial can be found here.</p>
</div>
<div id="section-data" class="section level3">
<h3>Data</h3>
<ul>
<li>Airbnb data 2019 (borough level) <a href="https://drive.google.com/file/d/1DIZjQZYg2KFW_x7qXseIkrd5mN4a8A7G/view?usp=sharing" class="uri">https://drive.google.com/file/d/1DIZjQZYg2KFW_x7qXseIkrd5mN4a8A7G/view?usp=sharing</a></li>
<li>Borough profile and atlas(Greater London Authority - GLA) <a href="https://drive.google.com/file/d/1eK5KZLzWs5HW2xsFMstGTk1ylFEf8kzE/view?usp=sharing" class="uri">https://drive.google.com/file/d/1eK5KZLzWs5HW2xsFMstGTk1ylFEf8kzE/view?usp=sharing</a></li>
</ul>
<p>By the end of the session, you will be able to identify the methods of summarising and comparing data visually using:</p>
</div>
<div id="section-methods" class="section level3">
<h3>Methods</h3>
<ul>
<li>Box and Whisker Plot</li>
<li>Histogram</li>
<li>Scatter plots</li>
<li>Parallel Coordinates Plot</li>
<li>Donut Chart</li>
<li>Heatmap</li>
<li>Shiny web app</li>
</ul>
</div>
<div id="section-getting-started" class="section level3">
<h3>Getting started</h3>
<p>You should have R and R studio installed, if not: Open a web browser and go to <a href="http://cran.r-project.org/" class="uri">http://cran.r-project.org</a> and download and install it RStudio (download from <a href="http://rstudio.com/" class="uri">http://rstudio.com</a>)</p>
<p>First, install the necessary libraries.</p>
<pre class="r"><code class="hljs"><span class="hljs-comment">#If you don't have these packages, uncomment and install them to your R program</span>
<span class="hljs-comment">#install.packages("plotly")</span>
<span class="hljs-comment">#install.packages("RColorBrewer")</span>
<span class="hljs-comment">#install.packages("shiny")</span>
<span class="hljs-comment">#install.packages("heatmaply")</span>
<span class="hljs-comment">#install.packages("ggplot2")</span>
<span class="hljs-comment"># Load the necessary libraries</span>
<span class="hljs-keyword">library</span>(plotly) <span class="hljs-comment"># Interactive visualisation</span>
<span class="hljs-keyword">library</span>(RColorBrewer) <span class="hljs-comment"># Colour pallette</span>
<span class="hljs-keyword">library</span>(shiny) <span class="hljs-comment"># R-based application</span>
<span class="hljs-keyword">library</span>(heatmaply) <span class="hljs-comment"># Heat map using plotly environment</span>
<span class="hljs-keyword">library</span>(ggplot2) <span class="hljs-comment"># Data visualisation package</span></code></pre>
<p>Now that we have prepared the libraries that we need, we can load the
necessary dataset. Because we have two different dataset, we will add
the Airbnb data into Borough dataset using the match function.</p>
<pre class="r"><code class="hljs"><span class="hljs-comment"># Read in the necessary data set and prepare the data set </span>
<span class="hljs-comment"># IMPORTANT: download the data set to your local server and change the path to your local directory</span>
<span class="hljs-comment">#setwd("/Users/zara/Google Drive/CASA Summer School August 2019/Zara - Stat 1")</span>
airbnb <- read.csv(<span class="hljs-string">"Airbnb Borough 2019.csv"</span>)
data <- read.csv(<span class="hljs-string">"london-borough-profiles.csv"</span>)
<span class="hljs-comment"># Add Airbnb data into the borough dataset (data) based on the same column value (Borough Code)</span>
data$Airbnb <- airbnb$Airbnb[match(data$Code,airbnb$GSS_CODE)]</code></pre>
<p>Now, we can start exploring our dataset to get a rough idea about the structure of the data.</p>
<pre class="r"><code class="hljs"><span class="hljs-comment"># Display the summary statistic of the data for every variable of the dataset</span>
summary(data)</code></pre>
<pre><code class="hljs">## Code New_code Area_name
## E09000001: 1 E09000001: 1 Barking and Dagenham: 1
## E09000002: 1 E09000002: 1 Barnet : 1
## E09000003: 1 E09000003: 1 Bexley : 1
## E09000004: 1 E09000004: 1 Brent : 1
## E09000005: 1 E09000005: 1 Bromley : 1
## E09000006: 1 E09000006: 1 Camden : 1
## (Other) :27 (Other) :27 (Other) :27
## Inner.Outer.London Population_density_2017 Net_internal_migration_2015
## Inner London:14 Min. : 22.00 Min. :-7739
## Outer London:19 1st Qu.: 45.00 1st Qu.:-3361
## Median : 59.00 Median :-2077
## Mean : 74.48 Mean :-2350
## 3rd Qu.:109.00 3rd Qu.:-1176
## Max. :156.00 Max. : 1710
##
## Net_international_migration_2015 Net_natural_change_2015
## Min. : 568 Min. : 30
## 1st Qu.: 2234 1st Qu.:1462
## Median : 3366 Median :2757
## Mean : 4058 Mean :2375
## 3rd Qu.: 5497 3rd Qu.:3164
## Max. :11182 Max. :4616
##
## Med_Income_Estimate_12.13 Med_houseprice_2015 PTAL2014 Airbnb
## Min. :28780 Min. : 243500 Min. :2 Min. : 140
## 1st Qu.:35350 1st Qu.: 345000 1st Qu.:3 1st Qu.: 422
## Median :37040 Median : 410000 Median :3 Median :1040
## Mean :39642 Mean : 465468 Mean :4 Mean :1811
## 3rd Qu.:43060 3rd Qu.: 485000 3rd Qu.:5 3rd Qu.:3094
## Max. :63620 Max. :1200000 Max. :8 Max. :6686
## </code></pre>
<pre class="r"><code class="hljs"><span class="hljs-comment"># Notice that the factors are handled differently to numbers, and are given occurrence counts instead of the summary statistic (min, median, max, quartiles, etc.)</span></code></pre>
</div>
<div id="section-box-and-whisker-plot" class="section level2">
<h2>Box and Whisker Plot</h2>
<p>Our first visualisation is the Box and Whisker plot. This plot can be
used to represent statistical data based on the second and third
quartiles. The vertical line inside the rectangle is used to indicate
the median value and the horizontal lines on either side of the
rectangle used to indicate both the lower and upper quartiles of the
data.</p>
<pre class="r"><code class="hljs"><span class="hljs-comment"># Create the first box plot: Airbnb count</span>
box1 <- plot_ly(data, y=~Airbnb,
name = <span class="hljs-string">"Airbnb count per-borough"</span>,
type=<span class="hljs-string">"box"</span>)
<span class="hljs-comment"># Create the second box plot: Population density</span>
box2 <- plot_ly(data, y=~Population_density_2017,
name=<span class="hljs-string">"Population density 2017"</span>,
type=<span class="hljs-string">"box"</span>)
<span class="hljs-comment"># Create the second box plot: Median house price</span>
box3 <- plot_ly(data, y=~Med_houseprice_2015,
name=<span class="hljs-string">"Median houseprice 2015"</span>,
type=<span class="hljs-string">"box"</span>)
<span class="hljs-comment"># Arrange multiple box plots to be shown simultaneously </span>
box <- subplot(box1, box2, box3)
<span class="hljs-comment"># Show the box plot in the interactive environment</span>
box</code></pre>
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<div id="section-histogram" class="section level2">
<h2>Histogram</h2>
<p>Histogram breaks data into bins (or breaks), showing the frequency distribution of these bins.</p>
<pre class="r"><code class="hljs"><span class="hljs-comment"># Create the first histogram: Airbnb count</span>
hist <- plot_ly(data, x=~Airbnb,
type=<span class="hljs-string">"histogram"</span>,
name=<span class="hljs-string">"Airbnb count per-borough 2019"</span>,
histnorm=<span class="hljs-string">"probability"</span>)
<span class="hljs-comment"># Create the second histogram: Population density</span>
hist2 <- plot_ly(data, x=~Population_density_2017,
type=<span class="hljs-string">"histogram"</span>,
name=<span class="hljs-string">"Population density 2017"</span>,
histnorm=<span class="hljs-string">"probability"</span>)
<span class="hljs-comment"># Arrange multiple histograms to be shown simultaneously </span>
plot_hist <- subplot(hist,hist2)
<span class="hljs-comment"># Show the histogram in the interactive environment</span>
plot_hist</code></pre>
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</div>
<div id="section-scatter-plot" class="section level2">
<h2>Scatter plot</h2>
<p>Scatter plot is a mathematical diagram to display values of the
dataset as a collection of points in the horizontal and vertical axis.</p>
<pre class="r"><code class="hljs"><span class="hljs-comment"># Create scatter plot of Airbnb and Median Houseprice, the colour depends on the Airbnb count and the size represents the median house price</span>
scatter <- plot_ly(
data, x = ~Airbnb, y = ~Med_houseprice_2015,
type=<span class="hljs-string">"scatter"</span>, mode=<span class="hljs-string">"markers"</span>,
color=~Airbnb, size=~Med_houseprice_2015)
<span class="hljs-comment"># Show the scatter plot in the interactive environment</span>
scatter</code></pre>
<pre><code class="hljs">## Warning: `line.width` does not currently support multiple values.</code></pre>
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</div>
<div id="section-parallel-coordinates-plot" class="section level2">
<h2>Parallel Coordinates Plot</h2>
<p>Parallel coordinates plot can be used to examine the relationship
between variables where each variable is given its axes and placed
parallel one another. The downside: For a large dataset, sometimes they
look too cluttered. Parallel coordinates plot is highly interactive, you
can add constraints so the data only shows a certain range of values.</p>
<pre class="r"><code class="hljs">par <- data %>%
plot_ly(width=<span class="hljs-number">750</span>, height = <span class="hljs-number">350</span>) %>%
add_trace(type = <span class="hljs-string">'parcoords'</span>,
line = list(color = ~Airbnb),
dimensions = list(
list(range = c(~min(Airbnb),~max(Airbnb)),
label = <span class="hljs-string">'Airbnb'</span>, values = ~Airbnb),
list(range = c(~min(Med_houseprice_2015),~max(Med_houseprice_2015)),
label = <span class="hljs-string">'Median House Price 2015'</span>, values = ~Med_houseprice_2015),
list(range = c(~min(Med_Income_Estimate_12.13),~max(Med_Income_Estimate_12.13)),
label = <span class="hljs-string">'Median Income Estimate 12/13'</span>, values = ~Med_Income_Estimate_12.13),
list(range = c(~min(Net_international_migration_2015),~max(Net_international_migration_2015)),
label = <span class="hljs-string">'Net International Migration 2015'</span>, values = ~Net_international_migration_2015),
list(range = c(~min(PTAL2014),~max(PTAL2014)),
label = <span class="hljs-string">'PTAL2014'</span>, values = ~PTAL2014)
)
)
par </code></pre>
<div id="htmlwidget-ea94c02166a6cf6299bf" style="width:750px;height:350px;" class="plotly html-widget html-widget-static-bound js-plotly-plot"><div class="plot-container plotly"><div class="svg-container" style="position: relative; width: 750px; height: 350px;"><svg class="main-svg" xmlns="http://www.w3.org/2000/svg" xlink="http://www.w3.org/1999/xlink" width="750" height="350" style="background: rgb(255, 255, 255) none repeat scroll 0% 0%;"><defs id="defs-60edb4"><g class="clips"></g><g class="gradients"></g></defs><g class="bglayer"></g><g class="draglayer"></g><g class="layer-below"><g class="imagelayer"></g><g class="shapelayer"></g></g><g class="cartesianlayer"></g><g class="polarlayer"></g><g class="ternarylayer"></g><g class="geolayer"></g><g class="pielayer"></g><g class="sunburstlayer"></g><g class="glimages"></g></svg><div class="gl-container"><canvas class="gl-canvas gl-canvas-context" style="position: absolute; top: 0px; left: 0px; overflow: visible; pointer-events: none; opacity: 1;" width="750" height="350"></canvas><canvas class="gl-canvas gl-canvas-focus" style="position: absolute; 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font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 2k</text></g><g class="tick" style="opacity: 1;" transform="translate(0,86.52734494347692)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 4k</text></g><g class="tick" style="opacity: 1;" transform="translate(0,23.588145432325074)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 6k</text></g><path class="domain" d="M-2,2H0V208H-2" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></path></g><g class="axis-heading"><text class="axis-title" text-anchor="middle" style="cursor: ew-resize; pointer-events: auto; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;" transform="translate(0,-28)">Airbnb</text></g><g class="axis-extent"><g class="axis-extent-top" transform="translate(0,-10)"><text class="axis-extent-top-text axis-extent-text" text-anchor="middle" style="cursor: default; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;">6.686k</text></g><g class="axis-extent-bottom" transform="translate(0,220)"><text class="axis-extent-bottom-text axis-extent-text" dy="0.75em" text-anchor="middle" style="cursor: default; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;">140</text></g></g><g class="axis-brush"><rect class="background" x="-5" width="10" visibility="visible" style="visibility: visible; pointer-events: auto;" fill="yellow" opacity="0" transform="translate(0 2)" height="208"></rect><line class="highlight-shadow" x="-2" stroke-width="5" stroke="white" opacity="1" stroke-linecap="butt" y1="210" stroke-dasharray="0,210"></line><line class="highlight" x="-2" stroke-width="3" stroke="magenta" opacity="1" stroke-linecap="butt" y1="210" stroke-dasharray="0,210"></line></g></g></g><g class="y-axis" transform="translate(170, 0)"><g class="axis-overlays"><g class="axis"><g class="tick" style="opacity: 1;" transform="translate(0,174.29482488238372)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0">0.4M</text></g><g class="tick" style="opacity: 1;" transform="translate(0,131.22111866178778)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0">0.6M</text></g><g class="tick" style="opacity: 1;" transform="translate(0,88.14741244119186)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0">0.8M</text></g><g class="tick" style="opacity: 1;" transform="translate(0,45.07370622059593)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0">1.0M</text></g><g class="tick" style="opacity: 1;" transform="translate(0,2)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0">1.2M</text></g><path class="domain" d="M-2,2H0V208H-2" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></path></g><g class="axis-heading"><text class="axis-title" text-anchor="middle" style="cursor: ew-resize; pointer-events: auto; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;" transform="translate(0,-28)">Median House Price 2015</text></g><g class="axis-extent"><g class="axis-extent-top" transform="translate(0,-10)"><text class="axis-extent-top-text axis-extent-text" text-anchor="middle" style="cursor: default; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;">1.2M</text></g><g class="axis-extent-bottom" transform="translate(0,220)"><text class="axis-extent-bottom-text axis-extent-text" dy="0.75em" text-anchor="middle" style="cursor: default; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;">243.5k</text></g></g><g class="axis-brush"><rect class="background" x="-5" width="10" visibility="visible" style="visibility: visible; pointer-events: auto;" fill="yellow" opacity="0" transform="translate(0 2)" height="208"></rect><line class="highlight-shadow" x="-2" stroke-width="5" stroke="white" opacity="1" stroke-linecap="butt" y1="210" stroke-dasharray="0,210"></line><line class="highlight" x="-2" stroke-width="3" stroke="magenta" opacity="1" stroke-linecap="butt" y1="210" stroke-dasharray="0,210"></line></g></g></g><g class="y-axis" transform="translate(340, 0)"><g class="axis-overlays"><g class="axis"><g class="tick" style="opacity: 1;" transform="translate(0,200.7864523536165)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 30k</text></g><g class="tick" style="opacity: 1;" transform="translate(0,141.65901262916188)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 40k</text></g><g class="tick" style="opacity: 1;" transform="translate(0,82.53157290470723)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 50k</text></g><g class="tick" style="opacity: 1;" transform="translate(0,23.40413318025259)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 60k</text></g><path class="domain" d="M-2,2H0V208H-2" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></path></g><g class="axis-heading"><text class="axis-title" text-anchor="middle" style="cursor: ew-resize; pointer-events: auto; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;" transform="translate(0,-28)">Median Income Estimate 12/13</text></g><g class="axis-extent"><g class="axis-extent-top" transform="translate(0,-10)"><text class="axis-extent-top-text axis-extent-text" text-anchor="middle" style="cursor: default; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;">63.62k</text></g><g class="axis-extent-bottom" transform="translate(0,220)"><text class="axis-extent-bottom-text axis-extent-text" dy="0.75em" text-anchor="middle" style="cursor: default; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;">28.78k</text></g></g><g class="axis-brush"><rect class="background" x="-5" width="10" visibility="visible" style="visibility: visible; pointer-events: auto;" fill="yellow" opacity="0" transform="translate(0 2)" height="208"></rect><line class="highlight-shadow" x="-2" stroke-width="5" stroke="white" opacity="1" stroke-linecap="butt" y1="210" stroke-dasharray="0,210"></line><line class="highlight" x="-2" stroke-width="3" stroke="magenta" opacity="1" stroke-linecap="butt" y1="210" stroke-dasharray="0,210"></line></g></g></g><g class="y-axis" transform="translate(510, 0)"><g class="axis-overlays"><g class="axis"><g class="tick" style="opacity: 1;" transform="translate(0,180.20727341247408)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 2k</text></g><g class="tick" style="opacity: 1;" transform="translate(0,141.39061616732616)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 4k</text></g><g class="tick" style="opacity: 1;" transform="translate(0,102.57395892217825)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 6k</text></g><g class="tick" style="opacity: 1;" transform="translate(0,63.75730167703033)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 8k</text></g><g class="tick" style="opacity: 1;" transform="translate(0,24.940644431882408)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 10k</text></g><path class="domain" d="M-2,2H0V208H-2" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></path></g><g class="axis-heading"><text class="axis-title" text-anchor="middle" style="cursor: ew-resize; pointer-events: auto; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;" transform="translate(0,-28)">Net International Migration 2015</text></g><g class="axis-extent"><g class="axis-extent-top" transform="translate(0,-10)"><text class="axis-extent-top-text axis-extent-text" text-anchor="middle" style="cursor: default; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;">11.182k</text></g><g class="axis-extent-bottom" transform="translate(0,220)"><text class="axis-extent-bottom-text axis-extent-text" dy="0.75em" text-anchor="middle" style="cursor: default; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1;">568</text></g></g><g class="axis-brush"><rect class="background" x="-5" width="10" visibility="visible" style="visibility: visible; pointer-events: auto;" fill="yellow" opacity="0" transform="translate(0 2)" height="208"></rect><line class="highlight-shadow" x="-2" stroke-width="5" stroke="white" opacity="1" stroke-linecap="butt" y1="210" stroke-dasharray="0,210"></line><line class="highlight" x="-2" stroke-width="3" stroke="magenta" opacity="1" stroke-linecap="butt" y1="210" stroke-dasharray="0,210"></line></g></g></g><g class="y-axis" transform="translate(680, 0)"><g class="axis-overlays"><g class="axis"><g class="tick" style="opacity: 1;" transform="translate(0,208)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 2</text></g><g class="tick" style="opacity: 1;" transform="translate(0,139.33333333333334)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; font-size: 10px; fill: rgb(68, 68, 68); fill-opacity: 1; text-shadow: rgb(255, 255, 255) 1px 1px 1px, rgb(255, 255, 255) -1px -1px 1px, rgb(255, 255, 255) 1px -1px 1px, rgb(255, 255, 255) -1px 1px 1px; cursor: default;" x="-7" y="0"> 4</text></g><g class="tick" style="opacity: 1;" transform="translate(0,70.66666666666667)"><line x2="-4" y2="0" fill="none" stroke="black" stroke-opacity="0.25" stroke-width="1px"></line><text dy=".32em" style="text-anchor: end; font-family: "Open Sans", verdana, arial, sans-serif; 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</div>
<div id="section-donut-chart" class="section level2">
<h2>Donut Chart</h2>
<p>Donut chart visually shows the proportion of data in a way that is simple but informative.</p>
<pre class="r"><code class="hljs"><span class="hljs-comment"># Subset the boroughs only to include those with high Airbnb counts to be displayed in the donut chart</span>
data_subset <- subset(data, Airbnb > <span class="hljs-number">1000</span>)
<span class="hljs-comment"># Create the donut chart based on the subsetted data, you can change the size of the pie hole / change the values to get more familiar with how the donut chart works</span>
donut <- data_subset %>%
group_by(Area_name) %>%
plot_ly(labels = ~Area_name, values = ~Airbnb) %>%
add_pie(hole = <span class="hljs-number">0.6</span>) %>%
layout(title = <span class="hljs-string">"Airbnb per-borough"</span>, showlegend = <span class="hljs-literal">F</span>,
xaxis = list(showgrid = <span class="hljs-literal">FALSE</span>, zeroline = <span class="hljs-literal">FALSE</span>, showticklabels = <span class="hljs-literal">FALSE</span>),
yaxis = list(showgrid = <span class="hljs-literal">FALSE</span>, zeroline = <span class="hljs-literal">FALSE</span>, showticklabels = <span class="hljs-literal">FALSE</span>))
<span class="hljs-comment"># Show the interactive donut chart in the widget</span>
donut</code></pre>
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font-size: 17px; fill: rgb(68, 68, 68); opacity: 1; font-weight: normal; white-space: pre;" x="336" y="12.5" text-anchor="middle" dy="0em" data-unformatted="Airbnb per-borough" data-math="N">Airbnb per-borough</text></g></g><g class="menulayer"></g><g class="zoomlayer"></g></svg><div class="modebar-container" style="position: absolute; top: 0px; right: 0px; width: 100%;"><div id="modebar-c91d6a" class="modebar modebar--hover ease-bg" style="z-index: 1;"><div class="modebar-group"><a rel="tooltip" class="modebar-btn" data-title="Download plot as a png" data-toggle="false" data-gravity="n"><svg viewBox="0 0 1000 1000" class="icon" height="1em" width="1em"><path d="m500 450c-83 0-150-67-150-150 0-83 67-150 150-150 83 0 150 67 150 150 0 83-67 150-150 150z m400 150h-120c-16 0-34 13-39 29l-31 93c-6 15-23 28-40 28h-340c-16 0-34-13-39-28l-31-94c-6-15-23-28-40-28h-120c-55 0-100-45-100-100v-450c0-55 45-100 100-100h800c55 0 100 45 100 100v450c0 55-45 100-100 100z m-400-550c-138 0-250 112-250 250 0 138 112 250 250 250 138 0 250-112 250-250 0-138-112-250-250-250z m365 380c-19 0-35 16-35 35 0 19 16 35 35 35 19 0 35-16 35-35 0-19-16-35-35-35z" transform="matrix(1 0 0 -1 0 850)"></path></svg></a></div><div class="modebar-group"><a rel="tooltip" class="modebar-btn active" data-title="Toggle show closest data on hover" data-attr="hovermode" data-val="closest" data-toggle="false" data-gravity="ne"><svg viewBox="0 0 1500 1000" class="icon" height="1em" width="1em"><path d="m375 725l0 0-375-375 375-374 0-1 1125 0 0 750-1125 0z" transform="matrix(1 0 0 -1 0 850)"></path></svg></a></div><div class="modebar-group"><a href="https://plot.ly/" target="_blank" data-title="Produced with Plotly" class="modebar-btn plotlyjsicon modebar-btn--logo"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 132 132" height="1em" width="1em"><defs><style>.cls-1 {fill: #119dff;} .cls-2 {fill: #25fefd;} .cls-3 {fill: #fff;}</style></defs><title>plotly-logomark</title><g id="symbol"><rect class="cls-1" width="132" height="132" rx="6" ry="6"></rect><circle class="cls-2" cx="78" cy="54" r="6"></circle><circle class="cls-2" cx="102" cy="30" r="6"></circle><circle class="cls-2" cx="78" cy="30" r="6"></circle><circle class="cls-2" cx="54" cy="30" r="6"></circle><circle class="cls-2" cx="30" cy="30" r="6"></circle><circle class="cls-2" cx="30" cy="54" r="6"></circle><path class="cls-3" d="M30,72a6,6,0,0,0-6,6v24a6,6,0,0,0,12,0V78A6,6,0,0,0,30,72Z"></path><path class="cls-3" d="M78,72a6,6,0,0,0-6,6v24a6,6,0,0,0,12,0V78A6,6,0,0,0,78,72Z"></path><path class="cls-3" d="M54,48a6,6,0,0,0-6,6v48a6,6,0,0,0,12,0V54A6,6,0,0,0,54,48Z"></path><path class="cls-3" d="M102,48a6,6,0,0,0-6,6v48a6,6,0,0,0,12,0V54A6,6,0,0,0,102,48Z"></path></g></svg></a></div></div></div><svg class="main-svg" xmlns="http://www.w3.org/2000/svg" xlink="http://www.w3.org/1999/xlink" width="672" height="480"><g class="hoverlayer"></g></svg></div></div></div>
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</div>
<div id="section-heatmap" class="section level2">
<h2>Heatmap</h2>
<p>An interactive heatmap is a grid of coloured cells highlighting the
patterns of our data accompanied by dendrograms. As other interactive
visualisation, you can hover over the areas with your mouse to show the
correlation values. Heatmaply is based on ggplot2 and plotly.js engine.</p>
<p>We are going to visualise the correlations between 5 variables:
Airbnb, Population Density, Net Natural Change (migration), Median House
price, and Median Income Estimate.</p>
<pre class="r"><code class="hljs"><span class="hljs-comment"># Remove # if you have not install heatmaply in your R environment</span>
<span class="hljs-comment">#install.packages("heatmaply")</span>
<span class="hljs-keyword">library</span>(heatmaply)
<span class="hljs-comment"># Subset the data to only include numeric data</span>
data_heatmap <- data[c(<span class="hljs-number">5</span>:<span class="hljs-number">12</span>)]
<span class="hljs-comment"># Create the heatmap showing the correlations between the five variables</span>
heatmap <- heatmaply(cor(data_heatmap), margins = c(<span class="hljs-number">50</span>, <span class="hljs-number">50</span>),
k_col = <span class="hljs-number">2</span>, k_row = <span class="hljs-number">2</span>,
limits = c(-<span class="hljs-number">1</span>,<span class="hljs-number">1</span>))
<span class="hljs-comment"># Show the heatmap in an interactive environment</span>
heatmap</code></pre>
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</div>
<div id="section-simple-linear-regression" class="section level2">
<h2>Simple Linear Regression</h2>
<p>A simple linear regression model describes the relationship between
two variables x and y that can be expressed by the following equation.
The numbers α and β are called parameters, and ϵ is the error term.</p>
<p><span class="math display"><span class="MathJax_Preview" style="color: inherit; display: none;"></span><div class="MathJax_Display" style="text-align: center;"><span class="MathJax" id="MathJax-Element-1-Frame" tabindex="0" style="text-align: center; position: relative;" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mi>y</mi><mo>=</mo><mrow class="MJX-TeXAtom-ORD"><mo>&#x3B1;</mo></mrow><mo>+</mo><mrow class="MJX-TeXAtom-ORD"><mo>&#x3B2;</mo></mrow><mi>x</mi><mo>+</mo><mrow class="MJX-TeXAtom-ORD"><mo>&#x3F5;</mo></mrow></math>" role="presentation"><nobr aria-hidden="true"><span class="math" id="MathJax-Span-1" style="width: 7.324em; display: inline-block;"><span style="display: inline-block; position: relative; width: 6.071em; height: 0px; font-size: 120%;"><span style="position: absolute; clip: rect(1.698em, 1006.04em, 2.955em, -1000em); top: -2.56em; left: 0em;"><span class="mrow" id="MathJax-Span-2"><span class="mi" id="MathJax-Span-3" style="font-family: STIXGeneral; font-style: italic;">y</span><span class="mo" id="MathJax-Span-4" style="font-family: STIXGeneral; padding-left: 0.313em;">=</span><span class="texatom" id="MathJax-Span-5" style="padding-left: 0.313em;"><span class="mrow" id="MathJax-Span-6"><span class="mo" id="MathJax-Span-7" style="font-family: STIXGeneral;">α</span></span></span><span class="mo" id="MathJax-Span-8" style="font-family: STIXGeneral; padding-left: 0.25em;">+</span><span class="texatom" id="MathJax-Span-9" style="padding-left: 0.25em;"><span class="mrow" id="MathJax-Span-10"><span class="mo" id="MathJax-Span-11" style="font-family: STIXGeneral;">β</span></span></span><span class="mi" id="MathJax-Span-12" style="font-family: STIXGeneral; font-style: italic;">x<span style="display: inline-block; overflow: hidden; height: 1px; width: 0.003em;"></span></span><span class="mo" id="MathJax-Span-13" style="font-family: STIXGeneral; padding-left: 0.25em;">+</span><span class="texatom" id="MathJax-Span-14" style="padding-left: 0.25em;"><span class="mrow" id="MathJax-Span-15"><span class="mo" id="MathJax-Span-16" style="font-family: STIXGeneral;">ϵ</span></span></span></span><span style="display: inline-block; width: 0px; height: 2.56em;"></span></span></span><span style="display: inline-block; overflow: hidden; vertical-align: -0.332em; border-left: 0px solid; width: 0px; height: 1.223em;"></span></span></nobr><span class="MJX_Assistive_MathML MJX_Assistive_MathML_Block" role="presentation"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mi>y</mi><mo>=</mo><mrow class="MJX-TeXAtom-ORD"><mo>α</mo></mrow><mo>+</mo><mrow class="MJX-TeXAtom-ORD"><mo>β</mo></mrow><mi>x</mi><mo>+</mo><mrow class="MJX-TeXAtom-ORD"><mo>ϵ</mo></mrow></math></span></span></div><script type="math/tex; mode=display" id="MathJax-Element-1"> y = α + βx+ ϵ </script></span>
The lm function is applied to a formula that describes the variable
House price by the variable Income estimate, and saves the linear
regression model in a new variable Houseprice.lm.</p>
<pre class="r"><code class="hljs">Houseprice.lm <- lm(Med_houseprice_2015 ~ Med_Income_Estimate_12.13, data = data)
<span class="hljs-comment"># Show the summary of the regression</span>
summary(Houseprice.lm)</code></pre>
<pre><code class="hljs">##
## Call:
## lm(formula = Med_houseprice_2015 ~ Med_Income_Estimate_12.13,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -182716 -99456 -19333 63259 396844
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.724e+05 1.289e+05 -2.889 0.00699 **
## Med_Income_Estimate_12.13 2.114e+01 3.198e+00 6.609 2.2e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 133800 on 31 degrees of freedom
## Multiple R-squared: 0.5849, Adjusted R-squared: 0.5715
## F-statistic: 43.68 on 1 and 31 DF, p-value: 2.198e-07</code></pre>
<p>Plot the linear regression result with the model fit.</p>
<pre class="r"><code class="hljs"><span class="hljs-comment"># Create regression line</span>
fit <- Houseprice.lm
<span class="hljs-comment"># Using plotly for regression</span>
data %>%
plot_ly(x = ~Med_Income_Estimate_12.13) %>%
add_markers(y = ~Med_houseprice_2015) %>%
add_lines(x = ~Med_Income_Estimate_12.13, y = fitted(fit))%>%
<span class="hljs-comment"># Change the X and Y axis label</span>
layout(yaxis = list(title =<span class="hljs-string">"Median House Price"</span>), xaxis = list(title =<span class="hljs-string">"Median Income Estimate"</span>))</code></pre>
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<script type="application/json" data-for="htmlwidget-40217dd7e457fa66a445">{"x":{"visdat":{"12b4a38ace":["function () ","plotlyVisDat"]},"cur_data":"12b4a38ace","attrs":{"12b4a38ace":{"x":{},"alpha_stroke":1,"sizes":[10,100],"spans":[1,20],"y":{},"type":"scatter","mode":"markers","inherit":true},"12b4a38ace.1":{"x":{},"alpha_stroke":1,"sizes":[10,100],"spans":[1,20],"y":[972233.561575491,249426.369347681,484233.033255604,409416.148446059,306912.789314337,537703.857709884,552286.809833778,409627.495578289,389972.212280866,327413.461140682,374755.218760281,370316.928983444,553766.23975939,376234.648685893,449360.756437596,402653.040214688,410472.884107211,374332.52449582,468593.345470558,803155.855791208,556302.405346155,441118.218280612,386379.31103295,514455.673164545,235900.152884939,406668.635727064,757716.222361682,411740.966900593,471763.552454013,365878.639206606,326779.419743991,631119.2901557,631753.331552391],"type":"scatter","mode":"lines","inherit":true}},"layout":{"margin":{"b":40,"l":60,"t":25,"r":10},"yaxis":{"domain":[0,1],"automargin":true,"title":"Median House Price"},"xaxis":{"domain":[0,1],"automargin":true,"title":"Median Income Estimate"},"hovermode":"closest","showlegend":true},"source":"A","config":{"showSendToCloud":false},"data":[{"x":[63620,29420,40530,36990,32140,43060,43750,37000,36070,33110,35350,35140,43820,35420,38880,36670,37040,35330,39790,55620,43940,38490,35900,41960,28780,36860,53470,37100,39940,34930,33080,47480,47510],"y":[799999,243500,445000,275000,407250,374975,700000,300000,430000,320000,340000,485000,730000,432500,396150,287500,350000,355000,583000,1200000,410000,450000,352000,415000,305000,345000,575000,475000,320000,415000,366569,557000,920000],"type":"scatter","mode":"markers","marker":{"color":"rgba(31,119,180,1)","line":{"color":"rgba(31,119,180,1)"}},"error_y":{"color":"rgba(31,119,180,1)"},"error_x":{"color":"rgba(31,119,180,1)"},"line":{"color":"rgba(31,119,180,1)"},"xaxis":"x","yaxis":"y","frame":null},{"x":[28780,29420,32140,33080,33110,34930,35140,35330,35350,35420,35900,36070,36670,36860,36990,37000,37040,37100,38490,38880,39790,39940,40530,41960,43060,43750,43820,43940,47480,47510,53470,55620,63620],"y":[235900.152884939,249426.369347681,306912.789314337,326779.419743991,327413.461140682,365878.639206606,370316.928983444,374332.52449582,374755.218760281,376234.648685893,386379.31103295,389972.212280866,402653.040214688,406668.635727064,409416.148446059,409627.495578289,410472.884107211,411740.966900593,441118.218280612,449360.756437596,468593.345470558,471763.552454013,484233.033255604,514455.673164545,537703.857709884,552286.809833778,553766.23975939,556302.405346155,631119.2901557,631753.331552391,757716.222361682,803155.855791208,972233.561575491],"type":"scatter","mode":"lines","marker":{"color":"rgba(255,127,14,1)","line":{"color":"rgba(255,127,14,1)"}},"error_y":{"color":"rgba(255,127,14,1)"},"error_x":{"color":"rgba(255,127,14,1)"},"line":{"color":"rgba(255,127,14,1)"},"xaxis":"x","yaxis":"y","frame":null}],"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>
<p>We have used plotly to visualise our data. Another convenient package for statistical programming in R is ggplot.</p>
<pre class="r"><code class="hljs">p <- ggplot(data = data,aes(x=Med_Income_Estimate_12.13, y=Med_houseprice_2015)) + geom_point() +
geom_smooth(method=<span class="hljs-string">'lm'</span>,se = <span class="hljs-literal">FALSE</span>, color=<span class="hljs-string">"black"</span>) +
xlab(<span class="hljs-string">"Median Income Estimate 12-13"</span>) +
ylab(<span class="hljs-string">"Median Houseprice 2015"</span>)
p</code></pre>
<p><img src="Statistic%201%20%20Data%20Visualisation%20and%20Simple%20Linear%20Regression_files/unnamed-chunk-12-1.png" width="672"></p>
</div>
<div id="section-shiny---an-r-based-interactive-web-app" class="section level2">
<h2>Shiny - an R based Interactive Web App</h2>
<p>Now let’s create a simple regression app using Shiny. We’ll use
Airbnb and Borough data. In this app, the user will be able to select
variables for regression and display the outcome in a form of scatter
plot and display the regression summary.</p>
<p>The architecture of shiny always contains a user interface (ui), a
server (backend) and app that can call the ui & server as an
application.</p>
<pre class="r"><code class="hljs"><span class="hljs-comment"># In the UI, you can arrange how you want the website to appear and how to choose variables (slider, selectable, etc)</span>
ui <- fluidPage(
<span class="hljs-comment"># Application title</span>
titlePanel(<span class="hljs-string">"Simple Linear Regression using Airbnb and Borough Profile dataset"</span>),
<span class="hljs-comment"># You can construct the application to have a side bar and main panel. The side bar will contain the dependent and independent variables while the main panel can be divided into several clickable tabs</span>
<span class="hljs-comment"># Setting the side bar with a selection input for chosen variables</span>
<span class="hljs-comment"># Here is where we design the layout for our sidebar</span>
sidebarLayout(
sidebarPanel(
selectInput(<span class="hljs-string">"Dependent"</span>, label=h3(<span class="hljs-string">"Dependent Variable"</span>), choices=list(
<span class="hljs-string">"Population Density"</span> = <span class="hljs-string">"Population_density_2017"</span>,
<span class="hljs-string">"Airbnb"</span> = <span class="hljs-string">"Airbnb"</span>,
<span class="hljs-string">"Median Houseprice"</span> = <span class="hljs-string">"Med_houseprice_2015"</span>,
<span class="hljs-string">"Median Income Estimate"</span> = <span class="hljs-string">"Med_Income_Estimate_12.13"</span>
)),
selectInput(<span class="hljs-string">"Independent"</span>, label=h3(<span class="hljs-string">"Independent Variable"</span>), choices=list(
<span class="hljs-string">"Population Density"</span> = <span class="hljs-string">"Population_density_2017"</span>,
<span class="hljs-string">"Airbnb"</span> = <span class="hljs-string">"Airbnb"</span>,
<span class="hljs-string">"Median Houseprice"</span> = <span class="hljs-string">"Med_houseprice_2015"</span>,
<span class="hljs-string">"Median Income Estimate"</span> = <span class="hljs-string">"Med_Income_Estimate_12.13"</span>
)),
h6(<span class="hljs-string">"We can see the top Airbnb proportion in London"</span>),
plotlyOutput(<span class="hljs-string">"Donut"</span>)
),
<span class="hljs-comment"># Now designing for the main panel to showcase the regression plot and summary</span>
mainPanel(
tabsetPanel(
tabPanel(<span class="hljs-string">"Plot"</span>, plotOutput(<span class="hljs-string">"Plot"</span>)),
tabPanel(<span class="hljs-string">"Summary"</span>, verbatimTextOutput(<span class="hljs-string">"Summary"</span>))
)
)
))
<span class="hljs-comment"># Server is all the analysis for the output. Everytime we select variable in the sidebar, R will work in the background and show the result </span>
server <- <span class="hljs-keyword">function</span>(input,output){
<span class="hljs-comment"># Creating the donut chart for the sidebar panel</span>
output$Donut <- renderPlotly({data_subset %>%
group_by(Area_name) %>%
plot_ly(labels = ~Area_name, values = ~Airbnb) %>%
add_pie(hole = <span class="hljs-number">0.6</span>) %>%
layout(title = <span class="hljs-string">"Airbnb per-borough"</span>, showlegend = <span class="hljs-literal">F</span>,
xaxis = list(showgrid = <span class="hljs-literal">FALSE</span>, zeroline = <span class="hljs-literal">FALSE</span>, showticklabels = <span class="hljs-literal">FALSE</span>),
yaxis = list(showgrid = <span class="hljs-literal">FALSE</span>, zeroline = <span class="hljs-literal">FALSE</span>, showticklabels = <span class="hljs-literal">FALSE</span>))
})
<span class="hljs-comment"># Creating the regression plot</span>
output$Plot <- renderPlot(
<span class="hljs-comment">#plot(data[,input$Independent], data[,input$Dependent], main="Scatterplot")</span>
ggplot(data = data, aes(x = data[,input$Independent], y = data[,input$Dependent])) +
geom_point(color=<span class="hljs-string">'black'</span>) +
geom_smooth(method = <span class="hljs-string">"lm"</span>, se = <span class="hljs-literal">FALSE</span>, color=<span class="hljs-string">"black"</span>) + xlab(input$Independent) + ylab(input$Dependent)
)
<span class="hljs-comment"># Displaying the summary statistic for the regression </span>
output$Summary <- renderPrint({
fit <- lm(data[,input$Dependent] ~ data[,input$Independent])
names(fit$coefficients) <- c(<span class="hljs-string">"Intercept"</span>, input$var2)
summary(fit)
})
}
<span class="hljs-comment"># Run the application by calling ui and server as an application </span>
shinyApp(ui = ui, server = server)</code></pre>
<iframe class="shiny-frame" src="Statistic%201%20%20Data%20Visualisation%20and%20Simple%20Linear%20Regression_files/a.html" width="100%" height="400"></iframe>
<div id="section-uploading-you-shiny-app" class="section level3">
<h3>Uploading you shiny app</h3>
<ul>
<li>Shinyapps.io</li>
<li>Shiny Server</li>
<li>RStudio Connect <a href="https://shiny.rstudio.com/articles/deployment-web.html" class="uri">https://shiny.rstudio.com/articles/deployment-web.html</a></li>
</ul>
<p>Check the app that we just made by clicking on the link here <a href="https://zahratushabrina.shinyapps.io/Airbnb_Regression/" class="uri">https://zahratushabrina.shinyapps.io/Airbnb_Regression/</a></p>
</div>
<div id="section-more-to-learn" class="section level3">
<h3>More to learn</h3>
<ul>
<li>Check more stuff you can do with plotly <a href="https://plot.ly/r/" class="uri">https://plot.ly/r/</a></li>
<li>The ggplot2 cheatsheet <a href="https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf" class="uri">https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf</a></li>
<li>Some ggplot2 inspiration here <a href="http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html" class="uri">http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html</a></li>
<li>Shiny gallery <a href="https://shiny.rstudio.com/gallery/" class="uri">https://shiny.rstudio.com/gallery/</a></li>
</ul>
</div>
</div>
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