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<!DOCTYPE html>
<html>
<head>
<title>EDA with R</title>
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title: 'EDA with R',
subtitle: 'Data Visualization with <strong>ggplot2</strong>',
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<body style="opacity: 0">
<slides class="layout-widescreen">
<slide class="title-slide segue nobackground">
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<hgroup class="auto-fadein">
<h1 data-config-title><!-- populated from slide_config.json --></h1>
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<p data-config-presenter><!-- populated from slide_config.json --></p>
<p style="margin-top: 6px; margin-left: -2px;">2019-05</p>
</hgroup>
</slide>
<slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>課程綱要</h2></hgroup><article >
</article></slide><slide class=""><hgroup><h2>Agenda</h2></hgroup><article id="agenda">
<div style="float:left;width:48%;">
<ul>
<li>Data Visualization</li>
<li><code>ggplot2</code> in R
<ul>
<li>基本架構介紹</li>
<li>起手式(基本語法)</li>
<li>應用(各種圖形的呈現)
<ul>
<li><code>Bar</code></li>
<li><code>Line</code></li>
<li><code>Histogram</code></li>
<li><code>Point</code></li>
<li><code>Heatmap</code></li>
</ul></li>
<li>進階技巧</li>
</ul></li>
</ul></div>
<div style="float:left;width:48%;">
<ul>
<li>互動式視覺化呈現</li>
<li>Bonus</li>
</ul>
<p><img src='img/a2.jpg' style='height: 350px'></img></p></div>
</article></slide><slide class=""><hgroup><h2>Data Visualization</h2></hgroup><article id="data-visualization">
<div style="float:left;width:48%;">
<ul>
<li>清晰有效地傳達與溝通訊息</li>
<li>教學、研究、宣傳</li>
<li>美學、功能兼顧</li>
<li>統計圖形、訊息可視化</li>
<li>一張好圖，勝過千言萬語</li>
</ul></div>
<div style="float:left;width:48%;">
<p><img src='img/w2.png' style='height: 450px'></img></p></div>
</article></slide><slide class=""><hgroup><h2>ggplot2 簡介</h2></hgroup><article id="ggplot2-">
<div style="float:left;width:50%;">
<ul>
<li>套件下載次數第一名(<a href='https://www.rdocumentation.org/trends?page1=1&sort1=direct&page2=1&sort2=total&page3=1&page4=1' title=''>Source</a>)</li>
<li>R環境下的繪圖套件</li>
<li>取自 “The Grammar of Graphics” (Leland Wilkinson, 2005)</li>
<li><a href='https://github.com/cosname/ggplot2-translation/blob/master/preface.md' title=''>設計理念</a>
<ul>
<li>採用圖層系統</li>
<li>用抽象的概念來控制圖形，</br> 避免細節繁瑣</li>
<li>圖形美觀</li>
</ul></li>
</ul></div>
<div style="float:right;width:48%;">
<p><img src='img/most_download_in_R.png' width=420 align='center'></img></p></div>
</article></slide><slide class=""><hgroup><h2>The Anatomy of a Plot</h2></hgroup><article id="the-anatomy-of-a-plot">
<center>
<img src='img/anatomy.png' width=750 align='center'></img>
</center>
</article></slide><slide class=""><hgroup><h2>ggplot2 基本架構</h2></hgroup><article id="ggplot2-">
<ul>
<li>資料 (data) 和映射 (mapping)</li>
<li>美學對應（<code>aes</code>thetic）</li>
<li>幾何圖案 (<code>geom</code>etric)</li>
<li>座標尺度 (<code>scale</code>)</li>
<li>統計轉換 (<code>stat</code>istics)</li>
<li>座標系統 (<code>coord</code>inante)</li>
<li>圖層 (layer)</li>
<li>繪圖面 (<code>facet</code>)</li>
<li>主題 (<code>theme</code>)</li>
</ul>
</article></slide><slide class=""><hgroup><h2>ggplot2 基本架構(2)</h2></hgroup><article id="ggplot2-2">
<center>
<img src='img/ggplot.jpeg' width=550 align='center'></img> </br> source: <a href='http://goo.gl/Odt2Rs' title=''>http://goo.gl/Odt2Rs</a>
</center>
</article></slide><slide class=""><hgroup><h2>ggplot2 基本語法</h2></hgroup><article id="ggplot2-">
<pre class = 'prettyprint lang-r'>ggplot(data=..., aes(x=..., y=...)) +
geom_xxx(...) +
stat_xxx(...) +
facet_xxx(...) + ...</pre>
<ul>
<li><code>ggplot</code> 描述 data 從哪來</li>
<li><code>aes</code> 描述圖上的元素跟 data 之類的對應關係</li>
<li><code>geom_xxx</code> 描述要畫圖的類型及相關調整的參數</li>
<li>常用的類型諸如：<code>geom_bar</code>, <code>geom_line</code>, <code>geom_points</code>, …</li>
</ul>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>圖層概念的實作</h2></hgroup><article >
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>前菜 - ggplot2到底有多少種圖</h2></hgroup><article id="---ggplot2">
</article></slide><slide class=""><hgroup><h2>Various functions</h2></hgroup><article id="various-functions">
<pre class = 'prettyprint lang-r'>library(ggplot2)
# list all geom
ls(pattern = '^geom_', env = as.environment('package:ggplot2'))</pre>
<pre > [1] "geom_abline" "geom_area" "geom_bar"
[4] "geom_bin2d" "geom_blank" "geom_boxplot"
[7] "geom_col" "geom_contour" "geom_count"
[10] "geom_crossbar" "geom_curve" "geom_density"
[13] "geom_density_2d" "geom_density2d" "geom_dotplot"
[16] "geom_errorbar" "geom_errorbarh" "geom_freqpoly"
[19] "geom_hex" "geom_histogram" "geom_hline"
[22] "geom_jitter" "geom_label" "geom_line"
[25] "geom_linerange" "geom_map" "geom_path"
[28] "geom_point" "geom_pointrange" "geom_polygon"
[31] "geom_qq" "geom_qq_line" "geom_quantile"
[34] "geom_raster" "geom_rect" "geom_ribbon"
[37] "geom_rug" "geom_segment" "geom_sf"
[40] "geom_sf_label" "geom_sf_text" "geom_smooth"
[43] "geom_spoke" "geom_step" "geom_text"
[46] "geom_tile" "geom_violin" "geom_vline" </pre>
</article></slide><slide class=""><hgroup><h2><strong>注意</strong></h2></hgroup><article >
<ul>
<li>使用 <code>data.frame</code> 儲存資料 (不可以丟 matrix 物件)</li>
<li>使用 <code>wide format</code> to <code>long format</code></li>
</ul>
<p></br></p>
<center>
<img src='img/wide-long.png' title=''/>
</center>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>正片開始</h2></hgroup><article >
</article></slide><slide class=""><hgroup><h2>首先請先安裝以下套件</h2></hgroup><article >
<ul>
<li>安裝套件</li>
</ul>
<pre class = 'prettyprint lang-r'>install.packages(c("tidyr","dplyr","ggplot2"))</pre>
<ul>
<li>載入套件 (<strong>注意</strong>：下載完套件一定要記得 <code>library</code> 才能使用喲！)</li>
</ul>
<pre class = 'prettyprint lang-r'>library(ggplot2)
library(dplyr)
library(tidyr)</pre>
<p><code>or</code></p>
<ul>
<li>安裝套件</li>
</ul>
<pre class = 'prettyprint lang-r'>install.packages("tidyverse")</pre>
<ul>
<li>載入套件 (<strong>注意</strong>：下載完套件一定要記得 <code>library</code> 才能使用喲！)</li>
</ul>
<pre class = 'prettyprint lang-r'>library(tidyverse)</pre>
</article></slide><slide class=""><hgroup><h2>一切從讀檔開始（CSV）</h2></hgroup><article id="csv">
<ul>
<li><a href='https://ling32342.github.io/2017_NCCU_R/data/transaction.csv' title=''>課程資料下載</a></li>
</ul>
<pre class = 'prettyprint lang-r'>############### 相對路徑 ###############
# 瞭解現在我們所處在的路徑
getwd()
# 設定我們檔案存放的路徑
setwd()
# 讀檔起手式
data <- read.csv("transaction.csv")
# 若讀入的是亂碼，試試以下
data <- read.csv("transaction.csv",fileEncoding = 'big5') #如果你是mac
data <- read.csv("transaction.csv",fileEncoding = 'utf-8') #如果你是windows</pre>
</article></slide><slide class=""><hgroup><h2>資料介紹</h2></hgroup><article >
<table class = 'rmdtable'>
<tr class="header">
<th align="right">area_land</th>
<th align="right">price_total</th>
<th align="left">build_type</th>
<th align="right">area_park</th>
<th align="right">area_build</th>
<th align="right">trac_year</th>
</tr>
<tr class="odd">
<td align="right">35.0</td>
<td align="right">6380000</td>
<td align="left">公寓(5樓含以下無電梯)</td>
<td align="right">0.0</td>
<td align="right">61</td>
<td align="right">102</td>
</tr>
<tr class="even">
<td align="right">10.7</td>
<td align="right">12010000</td>
<td align="left">住宅大樓(11層含以上有電梯)</td>
<td align="right">0.0</td>
<td align="right">104</td>
<td align="right">102</td>
</tr>
<tr class="odd">
<td align="right">8.5</td>
<td align="right">10080000</td>
<td align="left">套房(1房1廳1衛)</td>
<td align="right">8.6</td>
<td align="right">52</td>
<td align="right">102</td>
</tr>
<tr class="even">
<td align="right">4.7</td>
<td align="right">4600000</td>
<td align="left">住宅大樓(11層含以上有電梯)</td>
<td align="right">0.0</td>
<td align="right">39</td>
<td align="right">102</td>
</tr>
<tr class="odd">
<td align="right">31.0</td>
<td align="right">23800000</td>
<td align="left">華廈(10層含以下有電梯)</td>
<td align="right">0.0</td>
<td align="right">185</td>
<td align="right">102</td>
</tr>
</table>
</article></slide><slide class=""><hgroup><h2>欄位說明</h2></hgroup><article >
<div style="float:left;width:48%;">
<table class = 'rmdtable'>
<tr class="header">
<th align="left">英文欄位名稱</th>
<th align="left">中文欄位名稱</th>
</tr>
<tr class="odd">
<td align="left">city</td>
<td align="left">縣市</td>
</tr>
<tr class="even">
<td align="left">district</td>
<td align="left">鄉鎮市區</td>
</tr>
<tr class="odd">
<td align="left">trac_year</td>
<td align="left">交易年份</td>
</tr>
<tr class="even">
<td align="left">trac_month</td>
<td align="left">交易月份</td>
</tr>
<tr class="odd">
<td align="left">trac_type</td>
<td align="left">交易標的</td>
</tr>
<tr class="even">
<td align="left">trac_content</td>
<td align="left">交易筆棟數</td>
</tr>
<tr class="odd">
<td align="left">use_type</td>
<td align="left">使用分區或編定</td>
</tr>
</table></div>
<div style="float:right;width:50%;">
<table class = 'rmdtable'>
<tr class="header">
<th align="left">英文欄位名稱</th>
<th align="left">中文欄位名稱</th>
</tr>
<tr class="odd">
<td align="left">build_type</td>
<td align="left">建物型態</td>
</tr>
<tr class="even">
<td align="left">build_ymd</td>
<td align="left">建築完成年月</td>
</tr>
<tr class="odd">
<td align="left">area_land</td>
<td align="left">土地移轉總面積.平方公尺.</td>
</tr>
<tr class="even">
<td align="left">area_build</td>
<td align="left">建物移轉總面積.平方公尺.</td>
</tr>
<tr class="odd">
<td align="left">area_park</td>
<td align="left">車位移轉總面積.平方公尺.</td>
</tr>
<tr class="even">
<td align="left">price_total</td>
<td align="left">總價.元.</td>
</tr>
<tr class="odd">
<td align="left">price_unit</td>
<td align="left">單價.元.平方公尺.</td>
</tr>
</table></div>
</article></slide><slide class=""><hgroup><h2>以為開始了嗎？</h2></hgroup><article >
<ul>
<li>進行分析前，先去了解資料的型態與特性</li>
</ul>
<pre class = 'prettyprint lang-r'>str(data)</pre>
<pre >'data.frame': 153598 obs. of 15 variables:
$ city : Factor w/ 4 levels "高雄市","臺北市",..: 2 2 2 2 2 2 2 2 2 2 ...
$ district : Factor w/ 99 levels "阿蓮區","八里區",..: 63 96 96 5 96 5 5 38 38 42 ...
$ trac_year : int 102 102 102 102 102 102 102 102 102 102 ...
$ trac_month : Factor w/ 12 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
$ trac_type : Factor w/ 2 levels "房地(土地+建物)",..: 1 1 2 1 1 2 2 2 2 2 ...
$ trac_content: Factor w/ 327 levels "土地0建物0車位0",..: 57 57 58 57 57 62 62 58 64 167 ...
$ use_type : Factor w/ 5 levels "工","農","其他",..: 5 4 4 4 5 5 5 5 1 5 ...
$ build_type : Factor w/ 12 levels "辦公商業大樓",..: 6 12 10 12 7 7 7 12 1 12 ...
$ build_ymd : int 701109 701228 970114 851218 970624 1010724 1010724 1000414 1010531 870910 ...
$ area_land : num 34.96 10.71 8.51 4.7 30.97 ...
$ area_build : num 60.6 104.5 51.9 39.4 185.2 ...
$ area_park : num 0 0 8.55 0 0 ...
$ price_total : num 6380000 12010000 10080000 4600000 23800000 ...
$ price_unit : int 105263 114928 194070 116900 128510 218147 204716 174613 133648 27658 ...
$ age : int 32 32 5 17 5 1 1 2 1 15 ...</pre>
</article></slide><slide class=""><hgroup><h2>身為資料分析師，一定要有的好習慣！</h2></hgroup><article >
<ul>
<li>暸解基本的各變數統計量值</li>
</ul>
<pre class = 'prettyprint lang-r'>summary(data) </pre>
<pre > city district trac_year trac_month
高雄市:34460 淡水區 : 7172 Min. :102 12 :15206
臺北市:24238 西屯區 : 5974 1st Qu.:102 5 :15079
臺中市:37482 新莊區 : 5955 Median :102 4 :14682
新北市:57418 北屯區 : 5881 Mean :102 3 :14523
新店區 : 5873 3rd Qu.:102 7 :13805
中和區 : 5719 Max. :102 6 :13392
(Other):117024 (Other):66911
trac_type trac_content use_type
房地(土地+建物) :91613 土地1建物1車位0:66792 工 : 3233
房地(土地+建物)+車位:61985 土地1建物1車位1:41031 農 : 577
土地2建物1車位0:14537 其他: 8206
土地1建物1車位2: 7195 商 : 26205
土地2建物1車位1: 4787 住 :115377
土地3建物1車位0: 4691
(Other) :14565
build_type build_ymd area_land
住宅大樓(11層含以上有電梯):70725 Min. : 100602 Min. : 0
公寓(5樓含以下無電梯) :23211 1st Qu.: 780326 1st Qu.: 13
透天厝 :21954 Median : 870506 Median : 22
華廈(10層含以下有電梯) :20365 Mean : 868754 Mean : 42
套房(1房1廳1衛) : 9709 3rd Qu.: 991201 3rd Qu.: 36
店面(店鋪) : 2888 Max. :1030313 Max. :127088
(Other) : 4746
area_build area_park price_total price_unit
Min. : 0 Min. : 0 Min. : 0 Min. : 0
1st Qu.: 85 1st Qu.: 0 1st Qu.: 4900000 1st Qu.: 42685
Median : 124 Median : 0 Median : 8400000 Median : 67880
Mean : 153 Mean : 25 Mean : 12879580 Mean : 86176
3rd Qu.: 179 3rd Qu.: 9 3rd Qu.: 14580000 3rd Qu.: 111173
Max. :79669 Max. :2400000 Max. :8800000000 Max. :4284119
NA's :461
age
Min. :-1
1st Qu.: 3
Median :15
Mean :15
3rd Qu.:24
Max. :92
</pre>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>質化 v.s. 量化：Bar Chart</h2></hgroup><article id="-v.s.-bar-chart">
</article></slide><slide class=""><hgroup><h2><code>geom_bar</code></h2></hgroup><article id="geom_bar">
<ul>
<li>先來<code>比較</code>看看2013年在各縣市的案件交易量</li>
<li>SimHei字體<a href='http://www.fontpalace.com/font-details/SimHei/' title=''>下載連結</a></li>
</ul>
<pre class = 'prettyprint lang-r'>thm <- function() theme(text = element_text(size = 15, family = "SimHei")) # 控制字體與大小
# SimHei是只有Mac才有的字體, 用來解決Mac系統中文顯示錯誤的問題
# Windows系統使用者請忽略 `+ thm()` 指令
data %>%
ggplot(aes(x = city)) + geom_bar(stat = "count") + thm() # stat = "count" 算個數</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-16-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>Change labels !</h2></hgroup><article id="change-labels">
<pre class = 'prettyprint lang-r'>data %>%
ggplot(aes(x = city)) + geom_bar(stat = "count") + thm() +
labs(title = "各縣市交易量", x = "縣市", y = "交易量") # lab用來幫圖形的標題、x軸與y軸做命名</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-17-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>Change colors !</h2></hgroup><article id="change-colors">
<ul>
<li>顏色調整：<code>color</code> vs <code>fill</code>?</li>
</ul>
<pre class = 'prettyprint lang-r'>data %>%
ggplot(aes(x = city)) + geom_bar(stat = "count") + thm() +
labs(title = "各縣市交易量", x = "縣市", y = "交易量") +
geom_bar(fill = 'snow', color = 'black') # see colors() if you're picky</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-18-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>Bar chart</h2></hgroup><article id="bar-chart">
<ul>
<li><code>比較</code>臺北市各行政區的案件交易量</li>
</ul>
<pre class = 'prettyprint lang-r'># 資料整理
table = data %>%
filter(city == "臺北市") %>%
group_by(district) %>%
summarise(Count = n()) # dplyr::n 用來計數
table</pre>
<pre ># A tibble: 12 x 2
district Count
<fct> <int>
1 北投區 2416
2 大安區 1755
3 大同區 1092
4 南港區 989
5 內湖區 3598
6 士林區 1859
7 松山區 1556
8 萬華區 1584
9 文山區 2197
10 信義區 1585
11 中山區 4410
12 中正區 1197</pre>
</article></slide><slide class=""><hgroup><h2>Bar chart</h2></hgroup><article id="bar-chart-1">
<pre class = 'prettyprint lang-r'>table %>%
ggplot(aes(x = district, y = Count)) +
geom_bar(stat = "identity") + thm() # stat='identity'以表格的值做為bar的高度</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-20-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>Reoder x</h2></hgroup><article id="reoder-x">
<pre class = 'prettyprint lang-r'>table %>%
ggplot(aes(x = reorder(district, -Count), y = Count)) +
geom_bar(stat = 'identity') + thm() +
labs(titles = "臺北市各行政區交易量", x = "行政區", y = "Count")</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-21-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>小挑戰</h2></hgroup><article >
<ul>
<li>計算並<code>比較</code>臺北市&高雄市的各使用分區或編定(<code>use_type</code>)所佔比例</li>
</ul>
<pre ># A tibble: 10 x 4
# Groups: city [2]
city use_type Count rate
<fct> <fct> <int> <dbl>
1 高雄市 工 196 0.01
2 高雄市 農 123 0
3 高雄市 其他 532 0.02
4 高雄市 商 7582 0.22
5 高雄市 住 26027 0.76
6 臺北市 工 442 0.02
7 臺北市 農 3 0
8 臺北市 其他 1709 0.07
9 臺北市 商 7323 0.3
10 臺北市 住 14761 0.61</pre>
</article></slide><slide class=""><hgroup><h2>參考解答</h2></hgroup><article >
<pre class = 'prettyprint lang-r'>table = data %>%
filter(city == "臺北市" | city == "高雄市" ) %>%
group_by(city, use_type) %>%
summarise(Count = n()) %>%
mutate(rate = round(Count/sum(Count), 2)) </pre>
<center>
<img src='img/white2.png' width=600 align='center'></img>
</center>
</article></slide><slide class=""><hgroup><h2>Grouping：<code>stack</code></h2></hgroup><article id="groupingstack">
<pre class = 'prettyprint lang-r'>table %>%
ggplot(aes(x = city, y = Count, fill = use_type)) +
geom_bar(stat = 'identity', position = 'stack') + thm() # stack類別堆疊</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-24-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>Grouping：<code>dodge</code></h2></hgroup><article id="groupingdodge">
<pre class = 'prettyprint lang-r'>table %>%
ggplot(aes(x = city, y = rate, fill = use_type)) +
geom_bar(stat = 'identity', position = 'dodge') + # dodge類別並排
thm() + scale_fill_discrete(name ="使用分區或編定") # 設定圖例的顯示</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-25-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>量化 v.s. 量化：Line Chart</h2></hgroup><article id="-v.s.-line-chart">
</article></slide><slide class=""><hgroup><h2><code>geom_line</code></h2></hgroup><article id="geom_line">
<ul>
<li>各月份交易量</li>
</ul>
<pre class = 'prettyprint lang-r'>table = data %>%
group_by(trac_month) %>%
summarise(Count=n())
table</pre>
<pre ># A tibble: 12 x 2
trac_month Count
<fct> <int>
1 1 10367
2 2 7871
3 3 14523
4 4 14682
5 5 15079
6 6 13392
7 7 13805
8 8 11714
9 9 10814
10 10 13166
11 11 12979
12 12 15206</pre>
</article></slide><slide class=""><hgroup><h2>Line chart</h2></hgroup><article id="line-chart">
<pre class = 'prettyprint lang-r'>table %>%
ggplot(aes(x = trac_month, y = Count, group = 1)) +
geom_line() + thm() + labs(x = "交易月份" , y = "交易數量")</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-27-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2><code>Multiple</code> Line</h2></hgroup><article id="multiple-line">
<ul>
<li>各縣市各月份交易量比較</li>
</ul>
<pre class = 'prettyprint lang-r'>table = data %>%
group_by(city, trac_month) %>%
summarise(Count = n())
table</pre>
<pre ># A tibble: 48 x 3
# Groups: city [?]
city trac_month Count
<fct> <fct> <int>
1 高雄市 1 2511
2 高雄市 2 1897
3 高雄市 3 3355
4 高雄市 4 3524
5 高雄市 5 3378
6 高雄市 6 3097
7 高雄市 7 3063
8 高雄市 8 2573
9 高雄市 9 2354
10 高雄市 10 2590
# ... with 38 more rows</pre>
</article></slide><slide class=""><hgroup><h2><code>Multiple</code> Line</h2></hgroup><article id="multiple-line-1">
<pre class = 'prettyprint lang-r'>table %>%
ggplot(aes(x = trac_month, y = Count, group = city, color = city)) +
geom_line() + geom_point() + thm() +
labs(x = "交易月份" , y = "交易數量")</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-29-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>小挑戰</h2></hgroup><article id="-1">
<ul>
<li>計算臺北市&高雄市不同屋齡的交易量，並畫出 <code>Multiple</code> Line plot</li>
<li>如下圖：</li>
</ul>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-30-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>參考答案 (有發現什麼問題嗎？！)</h2></hgroup><article id="-">
<pre class = 'prettyprint lang-r'>data %>%
filter(city == "臺北市" | city == "高雄市" ) %>%
group_by(city, age) %>%
summarise(Count = n()) %>%
ggplot(aes(x = age, y = Count, group = city, color = city)) +
geom_line() + geom_point() + thm()</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-31-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>調整 x 軸的 scale 再看一次</h2></hgroup><article id="-x--scale-">
<ul>
<li>EDA的價值之一就是找出資料中的<code>不合理</code></li>
</ul>
<pre class = 'prettyprint lang-r'>data %>%
filter(city == "臺北市" | city == "高雄市" ) %>%
group_by(city, age) %>%
summarise(Count = n()) %>%
ggplot(aes(x = age, y = Count, group = city, color = city)) +
geom_line() + geom_point() + thm() + coord_cartesian(xlim = c(-3, 10))</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-32-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>單一數值：Histogram</h2></hgroup><article id="histogram">
</article></slide><slide class=""><hgroup><h2><code>geom_histogram</code></h2></hgroup><article id="geom_histogram">
<pre class = 'prettyprint lang-r'>data %>%
ggplot(aes(x = age, y =..count..)) +
geom_histogram()</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-33-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>Histogram</h2></hgroup><article id="histogram-1">
<ul>
<li><code>aes(y=..count..)</code> vs. <code>aes(y=..density..)</code></li>
</ul>
<pre class = 'prettyprint lang-r'>data %>%
ggplot(aes(x = age, y =..density..)) +
geom_histogram()</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-34-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>Histogram</h2></hgroup><article id="histogram-2">
<pre class = 'prettyprint lang-r'>data %>%
ggplot(aes(x = age, y =..density.., fill =..count..)) + # fill 依指定欄位填色
geom_histogram(binwidth = .5) </pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-35-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>Histogram + density</h2></hgroup><article id="histogram-density">
<ul>
<li><code>geom_histogram()</code> + <code>geom_density()</code></li>
</ul>
<pre class = 'prettyprint lang-r'>data %>%
ggplot(aes(x = age, y = ..density..)) +
geom_histogram(color = "black", fill = "white") +
geom_density(alpha = .2, fill = "#FF6666") # alpha設定透明度</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-36-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>量化 v.s. 量化：Scatter Plot</h2></hgroup><article id="-v.s.-scatter-plot">
</article></slide><slide class=""><hgroup><h2>Scatter plot</h2></hgroup><article id="scatter-plot">
<ul>
<li><code>geom_point</code></li>
</ul>
<pre class = 'prettyprint lang-r'>iris %>%
ggplot(aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
geom_point(shape = 1, size = 2) # shape控制圖示；size控制點的大小</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-37-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>point shape types in <code>ggplot2</code></h2></hgroup><article id="point-shape-types-in-ggplot2">
<center>
<img src='img/point_shape_types.png' style='height: 500px' align="middle"></img>
</center>
</article></slide><slide class=""><hgroup><h2>Scatter plot</h2></hgroup><article id="scatter-plot-1">
<ul>
<li>參數放在 aes() 函數裡面，表示資料依據指定欄位內容做不同的shape/size變化</li>
</ul>
<pre class = 'prettyprint lang-r'>iris %>%
ggplot(aes(x = Sepal.Length, y = Petal.Length, color = Species, shape = Species, size = Species)) +
geom_point() + scale_shape_manual(values = c(1,5,7)) + # 控制 shape 顯示圖示
scale_size_manual(values = c(1,2,3)) # 控制圖示 size 顯示大小</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-38-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>插播</h2></hgroup><article >
<pre class = 'prettyprint lang-r'>install.packages("devtools")</pre>
<center>
<img src='img/devtools.png' width=750 align='center'></img>
</center>
<ul>
<li><a href='https://github.com/cttobin/ggthemr' title=''>配色懶人包ggthemr</a></li>
</ul>
</article></slide><slide class=""><hgroup><h2>小挑戰</h2></hgroup><article id="-2">
<ul>
<li>士林區,文山區,信義區三個區域的<strong>套房(1房1廳1衛)</strong>在屋齡與每平方公尺單價存在著怎樣的特性？</li>
</ul>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-40-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>參考答案</h2></hgroup><article >
<pre class = 'prettyprint lang-r'>library(ggthemr)
ggthemr('flat')
ans = data %>% filter(district == "文山區" | district == "士林區" |district == "信義區") %>% filter(build_type == "套房(1房1廳1衛)") %>% ggplot(aes(x = age, y = price_unit, color = district)) + geom_point(position = "jitter") + thm()
ans</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-41-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>HeatMap</h2></hgroup><article id="heatmap">
</article></slide><slide class=""><hgroup><h2>相關性熱圖</h2></hgroup><article >
<ul>
<li>通常我們會好奇變數與變數之間是不是存在著相關性</li>
<li>在統計學上有一種簡單的方式去計算變數間的關係指標，我們稱之為相關係數係數！</li>
<li>以下將以 iris 資料做為示範，用熱圖來呈現</li>
</ul>
<pre class = 'prettyprint lang-r'># 挑出前四欄位
dat <- iris %>%
select(1:4)
dat %>%
head()</pre>
<pre > Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
4 4.6 3.1 1.5 0.2
5 5.0 3.6 1.4 0.2
6 5.4 3.9 1.7 0.4</pre>
</article></slide><slide class=""><hgroup><h2><code>寬轉長</code> 計算相關性</h2></hgroup><article id="-">
<pre class = 'prettyprint lang-r'>p = dat %>%
cor() %>%
as.data.frame() %>%
mutate(names = row.names(.)) %>%
gather(class, cor, 1:4)
p %>% head(10)</pre>
<pre > names class cor
1 Sepal.Length Sepal.Length 1.00
2 Sepal.Width Sepal.Length -0.12
3 Petal.Length Sepal.Length 0.87
4 Petal.Width Sepal.Length 0.82
5 Sepal.Length Sepal.Width -0.12
6 Sepal.Width Sepal.Width 1.00
7 Petal.Length Sepal.Width -0.43
8 Petal.Width Sepal.Width -0.37
9 Sepal.Length Petal.Length 0.87
10 Sepal.Width Petal.Length -0.43</pre>
</article></slide><slide class=""><hgroup><h2>Heatmap的好處</h2></hgroup><article id="heatmap">
<ul>
<li>藉由 Heatmap ，快速地看出變數之間的關聯程度</li>
</ul>
<pre class = 'prettyprint lang-r'>p %>%
ggplot(aes(x = names, y = class, fill= cor)) +
geom_tile() + labs(x = "", y = "") +
scale_fill_gradient2(limits = c(-1, 1))</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-44-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>進階技巧</h2></hgroup><article >
</article></slide><slide class=""><hgroup><h2><code>stat</code>istics</h2></hgroup><article id="statistics">
<ul>
<li>Fit regression line</li>
</ul>
<pre class = 'prettyprint lang-r'>library(ggthemr)
ggthemr('flat')
iris %>%
ggplot(aes(x = Sepal.Length, y = Petal.Length)) +
geom_point() +
stat_smooth(method = lm, level = .95) # add se=FALSE to disable CI </pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-45-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2><code>facet</code></h2></hgroup><article id="facet">
<ul>
<li>比較各縣市在各月份下的交易量</li>
</ul>
<pre class = 'prettyprint lang-r'>table <- data %>%
group_by(city, trac_month) %>% # 選擇縣市、交易月份作為分群
summarise(total = n()) # 計算分群下的總數
table %>%
ggplot(aes(x = trac_month, y = total ,fill = city))+
geom_bar(stat = 'identity') + thm()</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-46-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2><code>facet</code></h2></hgroup><article id="facet-1">
<pre class = 'prettyprint lang-r'>table %>%
ggplot(aes(x = trac_month, y = total ,fill = city))+
geom_bar(stat = 'identity') + thm() +
facet_wrap( ~city , nrow = 2) +
labs(x = "交易月份", y="交易量")</pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-47-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2><code>coord</code>_flip()</h2></hgroup><article id="coord_flip">
<pre class = 'prettyprint lang-r'>table %>%
ggplot(aes(x = trac_month, y = total, fill = city)) +
geom_bar(stat = 'identity') + thm() + facet_wrap( ~ city, nrow = 2)+
labs(x = "交易月份", y = "交易量") + coord_flip() </pre>
<p><img src="R_EDA_Visualization_files/figure-html/unnamed-chunk-48-1.png" width="720" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>圖形輸出</h2></hgroup><article >
<ul>
<li>利用 RStudio UI 介面存擋</li>
<li>畫完圖之後，再存檔</li>
</ul>
<pre class = 'prettyprint lang-r'>ggsave('檔案名稱')
ggsave("plot.pdf", width = 4, height = 4)
ggsave("plot.png", width = 4, height = 4, dpi = 300)</pre>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>互動式視覺化呈現</h2></hgroup><article >
</article></slide><slide class=""><hgroup><h2>package：<code>Plotly</code></h2></hgroup><article id="packageplotly">
<ul>
<li>Plotly是一個資料視覺化的R套件，以簡單的方式，讓資料能夠產生互動的效果。</li>
<li>提供一個合作平台，使用者能夠將自己在R中繪製的圖存上屬於自己的Plotly平台上。</li>
<li><a href='https://plot.ly/' title=''>Plotly官方網站</a></li>
<li>結合了各式各樣的API，包裝<code>Python</code>、<code>R</code>、<code>Malab</code>、…等等</li>
</ul>
<ul class = 'build'>