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<div id="toc"><ul><li><a class="toc-href" href="#" title="淺談資料視覺化以及 ggplot2 實踐">淺談資料視覺化以及 ggplot2 實踐</a><ul><li><a class="toc-href" href="#目錄" title="目錄">目錄</a></li><li><a class="toc-href" href="#資料視覺化是資料與圖的直接映射？" title="資料視覺化是資料與圖的直接映射？">資料視覺化是資料與圖的直接映射？</a></li><li><a class="toc-href" href="#資料視覺化應該是-.." title="資料視覺化應該是 ..">資料視覺化應該是 ..</a><ul><li><a class="toc-href" href="#先確認觀眾及目的" title="先確認觀眾及目的">先確認觀眾及目的</a></li><li><a class="toc-href" href="#正式定義" title="正式定義">正式定義</a></li><li><a class="toc-href" href="#一個簡單例子" title="一個簡單例子">一個簡單例子</a></li><li><a class="toc-href" href="#圖像的分層文法" title="圖像的分層文法">圖像的分層文法</a></li></ul></li><li><a class="toc-href" href="#ggplot2-實踐_1" title="ggplot2 實踐">ggplot2 實踐</a><ul><li><a class="toc-href" href="#載入-packages" title="載入 packages">載入 packages</a></li><li><a class="toc-href" href="#載入資料-+-簡單資料處理" title="載入資料 + 簡單資料處理">載入資料 + 簡單資料處理</a></li><li><a class="toc-href" href="#簡單資料視覺化" title="簡單資料視覺化">簡單資料視覺化</a></li><li><a class="toc-href" href="#基本層數缺一不可" title="基本層數缺一不可">基本層數缺一不可</a></li><li><a class="toc-href" href="#另個簡單例子" title="另個簡單例子">另個簡單例子</a></li><li><a class="toc-href" href="#複雜例子" title="複雜例子">複雜例子</a></li></ul></li><li><a class="toc-href" href="#結語_1" title="結語">結語</a></li><li><a class="toc-href" href="#References" title="References">References</a></li></ul></li></ul></div>
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<a href="https://leemeng.tw/tag/zi-liao-shi-jue-hua.html" rel="tag">資料視覺化</a>
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<a href="https://leemeng.tw/data-visualization-from-matplotlib-to-ggplot2.html" title="">
淺談資料視覺化以及 ggplot2 實踐
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<li class="date">2018-04-14 (Sat)</li>
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<p>這篇主要描述自己以往在利用 Python 做資料視覺化 (data visualization) 時常犯的思維瑕疵，而該思維如何在接觸 R 的 <a href="http://ggplot2.org/">ggplot2</a> 以後得到改善。</p>
<p>本文會試著說明資料視覺化的本質為何，以及在設計視覺化時，概念上應該包含什麼要素以及步驟。最後展示如何透過 ggplot2 活用前述的概念，來實際做資料視覺化。</p>
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<h2 id="目錄">目錄<a class="anchor-link" href="#目錄">¶</a></h2>
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<p>文章內容大致上會分為以下幾個小節：</p>
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<li><a href="#資料視覺化是資料與圖的直接映射？">資料視覺化是資料與圖的直接映射？</a></li>
<li><a href="#資料視覺化應該是-..">資料視覺化應該是 ..</a></li>
<li><a href="#ggplot2-實踐">ggplot2 實踐</a></li>
<li><a href="#結語">結語</a></li>
<li><a href="#References">References</a></li>
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<h2 id="資料視覺化是資料與圖的直接映射？">資料視覺化是資料與圖的直接映射？<a class="anchor-link" href="#資料視覺化是資料與圖的直接映射？">¶</a></h2>
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<p>身為一個 Python 起家的資料科學家，在做資料視覺化的時候，我很自然地使用 Python ecosystem 裡像是 <a href="https://matplotlib.org/">matplotlib</a> 以及 <a href="https://seaborn.pydata.org/">seaborn</a> 等繪圖 packages。針對手中的資料，我會想辦法找到一個「對應」的圖然後把資料塞進去。簡單無腦 <em>(:3 」∠)</em></p>
<p>舉例來說，當我們手上有三個變數 x, y, z 且其各自的資料型態為：</p>
<ul>
<li>x: <a href="https://zh.wikibooks.org/zh-hant/%E7%B5%B1%E8%A8%88%E5%AD%B8/%E7%B5%B1%E8%A8%88%E8%B3%87%E6%96%99">定量變數 (quantitative)</a></li>
<li>y: 定量變數</li>
<li>z: <a href="https://zh.wikibooks.org/zh-hant/%E7%B5%B1%E8%A8%88%E5%AD%B8/%E7%B5%B1%E8%A8%88%E8%B3%87%E6%96%99">定性變數（categorical）</a></li>
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<p>則我們想要進行資料視覺化的時候有幾種選擇：</p>
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<li>想分析 x, y -> 都是定量資料 -> 散佈圖 (scatter plot)</li>
<li>想分析 x, z -> 一定量一定性 -> 長條圖 (bar chart)</li>
</ul>
<p>在這，「資料視覺化」的定義是一種映射關係 (mapping)：也就是如何將資料直接對應到某個「特定」圖表形式（折線圖、散佈圖 etc.）。基本上這種映射關係在做簡單的分析的時候沒有什麼問題，但是當想要同時分析/呈現的變數超過兩個 （例： x & y & z ）的時候就不容易找到適合的圖。一個折衷的方法是我們把變數兩兩畫圖做比較，但這樣會侷限我們能分析的資料維度數目，錯過一些有趣的洞見。</p>
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<h2 id="資料視覺化應該是-..">資料視覺化應該是 ..<a class="anchor-link" href="#資料視覺化應該是-..">¶</a></h2>
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<h3 id="先確認觀眾及目的">先確認觀眾及目的<a class="anchor-link" href="#先確認觀眾及目的">¶</a></h3>
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<p>在完成一些 <a href="#References">ggplot2 的 tutorials</a> 後，可以發現資料視覺化一般依用途可以分為兩種：</p>
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<li>探索、了解資料特性</li>
<li>說故事：將探索過後得到的洞見 (insight) 傳達給其他人</li>
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搞清楚資料視覺化的目的以及觀眾是重要的第一步
（<a href="https://www.datacamp.com/courses/data-visualization-with-ggplot2-1" target="_blank">圖片來源</a>）
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<p>依照目的以及觀眾的不同，資料視覺化的方式會有所不同。一個常見的例子是當我們第一次接觸某個資料集。這時候資料視覺化的觀眾是自己，目的是在最短的時間了解資料特性。則這時我們在做圖的時候的要求就可以很寬鬆，像是不加上標題，或是只要能做出自己能理解的視覺化即可。</p>
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<h3 id="正式定義">正式定義<a class="anchor-link" href="#正式定義">¶</a></h3>
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<p>在確認觀眾及目的以後，我們終於可以開始進行資料視覺化了！資料視覺化的定義因人而異，而這邊我想給出一個非常直觀的定義：</p>
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<p>
資料視覺化是將資料中的變數映射到視覺變數上，進而有效且有意義地呈現資料的樣貌
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<p>一些常見且肉眼容易識別的視覺變數 / 刻度（visual variables / scales）包含：</p>
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<li>位置（x / y axis）</li>
<li>顏色（color）</li>
<li>大小（size）</li>
<li>透明程度（alpha）</li>
<li>填滿（fill）</li>
<li>形狀（shape）</li>
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<p>用更口語的方式來解釋：在做資料視覺化的時候，我們希望能將</p>
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<li>肉眼難以分析的資料</li>
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<p>對應到：</p>
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<li>肉眼容易解讀的視覺元素</li>
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<p>透過這個映射關係，我們可以將原本的變數的數值變化也映射到視覺變數的變化。而因為我們人類容易區別視覺變數的變化（位置差異、大小長度變化 etc），我們能更容易地理解原始資料的樣貌、變化以及模式。</p>
<p>舉例來說，我們可以：</p>
<ul>
<li>把不同捷運路線（文湖線、板南線）對應到不同顏色</li>
<li>把各國的 GDP 對應到點的大小</li>
<li>把某個資料的年份對應到 Ｘ 軸，越右邊代表越接近現代</li>
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<h3 id="一個簡單例子">一個簡單例子<a class="anchor-link" href="#一個簡單例子">¶</a></h3>
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<p>事實上，我們可能平常每天都在做資料視覺化而不自知。比方說我們有一個數列 <code>y</code>：</p>
<div class="highlight"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">2.055</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.132</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.522</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.229</span><span class="p">,</span> <span class="mf">0.013</span> <span class="o">..</span> <span class="p">]</span>
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<p>光是看這個數字，肉眼無法看出什麼模式，但我們可以簡單畫個圖：</p>
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<p>這邊我們利用視覺變數「Y軸位置」來呈現數值的變化，可以馬上看出數列裡頭的值都落在 -3 到 3 之間，而這是因為我們肉眼很容易辨別「位置」這個視覺變數的變化。</p>
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<h3 id="圖像的分層文法">圖像的分層文法<a class="anchor-link" href="#圖像的分層文法">¶</a></h3>
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<p>在 <a href="http://byrneslab.net/classes/biol607/readings/wickham_layered-grammar.pdf">A Layered Grammar of Graphics</a> 裡頭，<a href="http://hadley.nz/">Hadley Wickham</a> 闡述所謂的圖像（包含由資料視覺化產生的圖像）實際上如同我們平常使用的語言，是有文法的。而其文法可以拆成 7 個部分（層）。前述的</p>
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<li>原始資料 = 資料層（Data）</li>
<li>視覺變數層（Visual variables = Aesthetics）</li>
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<p>則恰好是這個架構裡頭最底下的兩層。視覺變數是我為了方便理解建立的名詞，在原文以及 ggplot2 裡頭被稱作 <strong>Aesthetics</strong>。（中文翻作「美學」，當初看好久也無法理解啊 (╯°Д°)╯ ┻━┻）</p>
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圖像的分層文法
（<a href="https://www.datacamp.com/courses/data-visualization-with-ggplot2-1" target="_blank">圖片來源</a>）
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<p>看到這你一定會「哇靠那我每次畫個圖都要實作七層？」。實際上不需要，上面幾層像是主題（Theme）比較像是裝飾品，給我們更大的自由與彈性來訂製（customize）視覺化結果。在下一節我們會看到，ggplot2 會自動幫我們設定合適的主題或座標。（如果沒特別指定的話）</p>
<p>但一般而言，一個圖像最基本的組成是底下三層。也就是除了前述的兩層（資料、視覺變數）以外還需要加上</p>
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<li>幾何圖形層（Geometries）</li>
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<p>為何還要這層？假如我們有了資料，決定了視覺變數（第二層，例：把資料中的變數 A 對應到 X 軸；變數 B 對應到 Y 軸）後，實際上就可以畫一個充滿點（point）的散佈圖了不是嗎？</p>
<p>這樣的思維如同<a href="#資料視覺化是資料與圖的直接映射？">資料視覺化是資料與圖的直接映射？</a>部分所提到的，有所瑕疵。如果變數 A 是分類型變數（Categorical）的話，單純以<strong>點</strong>為圖形的散佈圖就會變得十分難以理解（下圖左）；這時候以<strong>長條</strong>為圖形（下圖右）的方式會比較清楚：</p>
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<img src="https://leemeng.tw/images/ggplot2/make-geom-layer-independent.png" style="mix-blend-mode: initial;"/>
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獨立幾何圖形層，讓資料視覺化不再侷限於「我要畫什麼圖」，而是「我想要怎麼畫」
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<p>將「幾何圖形」這個選擇獨立出來一層讓我們在資料視覺化的時候有更大的彈性。有了這些基本概念以後，我們可以開始嘗試使用 ggplot2 來實際做一些資料視覺化。</p>
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<h2 id="ggplot2-實踐_1">ggplot2 實踐<a class="anchor-link" href="#ggplot2-實踐">¶</a></h2><p>在這個章節裡頭我們將使用 Kaggle 的 <a href="https://www.kaggle.com/residentmario/ramen-ratings/data">Ramen Ratings</a> 來做資料視覺化。這資料集紀錄了各國泡麵所得到的星星數。首先我們要先載入這次的主角：R 語言裡頭最著名的視覺化 package ggplot2。<a href="http://yaojenkuo.io/r_programming/ch14#(1">dplyr</a> 則是 R 語言用來處理資料的 package。</p>
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<h3 id="載入-packages">載入 packages<a class="anchor-link" href="#載入-packages">¶</a></h3>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">library</span><span class="p">(</span><span class="n">ggplot2</span><span class="p">)</span>
<span class="n">library</span><span class="p">(</span><span class="n">dplyr</span><span class="p">)</span>
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<p>值得一提的是它們都是同屬於 <a href="https://medium.com/datainpoint/tidyverse-r-%E8%AA%9E%E8%A8%80%E5%AD%B8%E7%BF%92%E4%B9%8B%E6%97%85%E7%9A%84%E6%96%B0%E8%B5%B7%E9%BB%9E-3b01ca6a348c">TidyVerse</a> 的一員。TidyVerse 是 R 裡頭常被用來做資料科學的 packages 的集合，以 Python 來說大概就像是 Pandas + Matplotlib + Numpy 的感覺吧。</p>
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<h3 id="載入資料-+-簡單資料處理">載入資料 + 簡單資料處理<a class="anchor-link" href="#載入資料-+-簡單資料處理">¶</a></h3><p>如下註解所示，這邊將資料集讀入，做一些簡單的資料型態轉變後選擇一部分的資料集（subset）來做之後的視覺化：</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># 將 CSV 檔案載入成資料框架（dataframe）</span>
<span class="n">ramen_all</span> <span class="o"><-</span> <span class="n">read</span><span class="o">.</span><span class="n">csv</span><span class="p">(</span><span class="s2">"datasets//ramen-ratings.csv"</span><span class="p">)</span>
<span class="c1"># 將「星星數」轉成定量資料</span>
<span class="n">ramen_all</span><span class="err">$</span><span class="n">Stars</span> <span class="o"><-</span> <span class="k">as</span><span class="o">.</span><span class="n">numeric</span><span class="p">(</span><span class="n">ramen_all</span><span class="err">$</span><span class="n">Stars</span><span class="p">)</span>
<span class="c1"># Subset 資料，選擇拉麵數量前幾多的國家方便 demo</span>
<span class="n">ramen</span> <span class="o"><-</span> <span class="n">ramen_all</span> <span class="o">%>%</span>
<span class="nb">filter</span><span class="p">(</span><span class="n">Country</span> <span class="o">%</span><span class="k">in</span>% count(ramen_all, Country, sort = TRUE)[1:6, 1, drop=TRUE]) %>%
<span class="nb">filter</span><span class="p">(</span><span class="n">Style</span> <span class="o">%</span><span class="k">in</span>% count(ramen_all, Style, sort = TRUE)[1:4, 1 , drop=TRUE])
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<p>除了我們使用 dplyr 的 <code>filter</code> 依照條件 subset 資料集以外，值得一提的是 pipe 運算子 <code>%>%</code>。它是前面提到的 TidyVerse 裡頭的 packages 共享的介面（interface），將前一個函示的輸出當作下一個函式的輸入，讓我們可以把運算全部串（chain）在一起。在 Linux 裡頭就是如同 <code>|</code> 的存在。</p>
<p>而實際我們的資料長這樣：</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">head</span><span class="p">(</span><span class="n">ramen</span><span class="p">)</span>
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<thead><tr><th scope="col">Review..</th><th scope="col">Brand</th><th scope="col">Variety</th><th scope="col">Style</th><th scope="col">Country</th><th scope="col">Stars</th><th scope="col">Top.Ten</th></tr></thead>
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<tr><td>2580 </td><td>New Touch </td><td>T's Restaurant Tantanmen </td><td>Cup </td><td>Japan </td><td>37 </td><td> </td></tr>
<tr><td>2579 </td><td>Just Way </td><td>Noodles Spicy Hot Sesame Spicy Hot Sesame Guan-miao Noodles</td><td>Pack </td><td>Taiwan </td><td> 7 </td><td> </td></tr>
<tr><td>2578 </td><td>Nissin </td><td>Cup Noodles Chicken Vegetable </td><td>Cup </td><td>USA </td><td>16 </td><td> </td></tr>
<tr><td>2577 </td><td>Wei Lih </td><td>GGE Ramen Snack Tomato Flavor </td><td>Pack </td><td>Taiwan </td><td>19 </td><td> </td></tr>
<tr><td>2575 </td><td>Samyang Foods </td><td>Kimchi song Song Ramen </td><td>Pack </td><td>South Korea </td><td>47 </td><td> </td></tr>
<tr><td>2574 </td><td>Acecook </td><td>Spice Deli Tantan Men With Cilantro </td><td>Cup </td><td>Japan </td><td>39 </td><td> </td></tr>
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<h3 id="簡單資料視覺化">簡單資料視覺化<a class="anchor-link" href="#簡單資料視覺化">¶</a></h3>
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<p>有了資料，讓我們再確定一下資料視覺化的目的及觀眾：</p>
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<li>目的：探索資料</li>
<li>觀眾：我們自己</li>
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<p>這樣的條件讓我們知道視覺化的條件是快速做出結果，不需調整如標題、主題的設定。</p>
<p>現在讓我們問一些簡單的問題。像是</p>
<ol>
<li>泡麵的包裝（碗裝、袋裝等）各佔多少比例？</li>
<li>不同國家各有多少泡麵在資料集裡頭？</li>
<li>不同包裝的泡麵所得到的星星總數，在不同國家有什麼差異嗎？</li>
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<p>其中一種能解決第一個問題的資料視覺化是：</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">ggplot</span><span class="p">(</span><span class="n">ramen</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span> <span class="o">=</span> <span class="n">Style</span><span class="p">))</span> <span class="o">+</span> <span class="n">geom_bar</span><span class="p">()</span>
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<p>在</p>
<div class="highlight"><pre><span></span><span class="nf">ggplot</span><span class="p">(</span><span class="n">ramen</span><span class="p">,</span><span class="w"> </span><span class="nf">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Style</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="nf">geom_bar</span><span class="p">()</span>
</pre></div>
<p>裡頭，我們實際上已經建構了圖表最基礎的三層元素：</p>
<ul>
<li>資料層： <code>ramen</code> 告訴 ggplot2 使用此資料框架</li>
<li>視覺變數層： <code>aes(x = Style)</code> 告訴 ggplot2 我們將使用「 X 軸位置」這個視覺變數來反映泡麵包裝 <code>Style</code> 這個變數的變化<ul>
<li>因為包裝的值有四種可能，你可以想像 ggplot2 已經準備好要幫你在 X 軸上的四個位置畫圖</li>
<li><code>aes</code> 是我們前面提到 <strong>aesthetics</strong> 的縮寫</li>
</ul>
</li>
<li>幾何圖形層： <code>geom_bar()</code> 告訴 ggplot 去計算對應到 <code>x</code> 視覺變數的變數裡頭，所有值的出現次數後將結果以<strong>長條</strong>來呈現</li>
</ul>
<p>我們通常透過 <code>+</code> 來疊加不同層的結果。</p>
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<h3 id="基本層數缺一不可">基本層數缺一不可<a class="anchor-link" href="#基本層數缺一不可">¶</a></h3>
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<p>上面的例子很簡單，但假如我們沒有指定幾何圖形層的話，圖會長什麼樣子呢？</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">ggplot</span><span class="p">(</span><span class="n">ramen</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span> <span class="o">=</span> <span class="n">Style</span><span class="p">))</span>
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