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<!DOCTYPE html>
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<title>Chapter 10 NA Processing | R for Data Journalism</title>
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<meta name="twitter:title" content="Chapter 10 NA Processing | R for Data Journalism" />
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<meta name="author" content="HSIEH, JI-LUNG" />
<meta name="date" content="2024-04-22" />
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<li class="chapter" data-level="3.3.5" data-path="r-basic.html"><a href="r-basic.html#built-in-math-functions"><i class="fa fa-check"></i><b>3.3.5</b> Built-in math functions</a></li>
</ul></li>
<li class="chapter" data-level="3.4" data-path="r-basic.html"><a href="r-basic.html#data-types"><i class="fa fa-check"></i><b>3.4</b> Data types</a>
<ul>
<li class="chapter" data-level="3.4.1" data-path="r-basic.html"><a href="r-basic.html#checking-data-type"><i class="fa fa-check"></i><b>3.4.1</b> Checking data type</a></li>
<li class="chapter" data-level="3.4.2" data-path="r-basic.html"><a href="r-basic.html#converting-data-type"><i class="fa fa-check"></i><b>3.4.2</b> Converting data type</a></li>
</ul></li>
<li class="chapter" data-level="3.5" data-path="r-basic.html"><a href="r-basic.html#character-operations"><i class="fa fa-check"></i><b>3.5</b> Character operations</a></li>
</ul></li>
<li class="chapter" data-level="4" data-path="dataframe.html"><a href="dataframe.html"><i class="fa fa-check"></i><b>4</b> Dataframe</a>
<ul>
<li class="chapter" data-level="4.1" data-path="dataframe.html"><a href="dataframe.html#基本操作"><i class="fa fa-check"></i><b>4.1</b> 基本操作</a>
<ul>
<li class="chapter" data-level="4.1.1" data-path="dataframe.html"><a href="dataframe.html#產生新的dataframe"><i class="fa fa-check"></i><b>4.1.1</b> 產生新的Dataframe</a></li>
<li class="chapter" data-level="4.1.2" data-path="dataframe.html"><a href="dataframe.html#觀察dataframe"><i class="fa fa-check"></i><b>4.1.2</b> 觀察dataframe</a></li>
<li class="chapter" data-level="4.1.3" data-path="dataframe.html"><a href="dataframe.html#操作dataframe"><i class="fa fa-check"></i><b>4.1.3</b> 操作dataframe</a></li>
</ul></li>
<li class="chapter" data-level="4.2" data-path="dataframe.html"><a href="dataframe.html#簡易繪圖"><i class="fa fa-check"></i><b>4.2</b> 簡易繪圖</a></li>
<li class="chapter" data-level="4.3" data-path="dataframe.html"><a href="dataframe.html#延伸學習"><i class="fa fa-check"></i><b>4.3</b> 延伸學習</a>
<ul>
<li class="chapter" data-level="4.3.1" data-path="dataframe.html"><a href="dataframe.html#使用dplyr"><i class="fa fa-check"></i><b>4.3.1</b> 使用dplyr</a></li>
<li class="chapter" data-level="4.3.2" data-path="dataframe.html"><a href="dataframe.html#比較tibble-data_frame-data.frame"><i class="fa fa-check"></i><b>4.3.2</b> 比較tibble, data_frame, data.frame</a></li>
</ul></li>
<li class="chapter" data-level="4.4" data-path="dataframe.html"><a href="dataframe.html#maternity"><i class="fa fa-check"></i><b>4.4</b> Paid Maternity Leave</a>
<ul>
<li class="chapter" data-level="4.4.1" data-path="dataframe.html"><a href="dataframe.html#reading-.xlsx-by-readxl-package"><i class="fa fa-check"></i><b>4.4.1</b> Reading .xlsx by readxl package</a></li>
<li class="chapter" data-level="4.4.2" data-path="dataframe.html"><a href="dataframe.html#previewing-data-by-view-class-dim-str-summary-and-names"><i class="fa fa-check"></i><b>4.4.2</b> Previewing data by <code>View()</code>, <code>class()</code>, <code>dim()</code>, <code>str()</code>, <code>summary()</code> and <code>names()</code></a></li>
<li class="chapter" data-level="4.4.3" data-path="dataframe.html"><a href="dataframe.html#select-variables"><i class="fa fa-check"></i><b>4.4.3</b> Select variables</a></li>
<li class="chapter" data-level="4.4.4" data-path="dataframe.html"><a href="dataframe.html#check-replace-nas"><i class="fa fa-check"></i><b>4.4.4</b> Check & Replace NAs</a></li>
<li class="chapter" data-level="4.4.5" data-path="dataframe.html"><a href="dataframe.html#filtering-data"><i class="fa fa-check"></i><b>4.4.5</b> Filtering data</a></li>
<li class="chapter" data-level="4.4.6" data-path="dataframe.html"><a href="dataframe.html#plotting"><i class="fa fa-check"></i><b>4.4.6</b> Plotting</a></li>
<li class="chapter" data-level="4.4.7" data-path="dataframe.html"><a href="dataframe.html#practice.-plotting-more"><i class="fa fa-check"></i><b>4.4.7</b> Practice. Plotting more</a></li>
<li class="chapter" data-level="4.4.8" data-path="dataframe.html"><a href="dataframe.html#practice.-selecting-and-filtering-by-dplyr-i"><i class="fa fa-check"></i><b>4.4.8</b> Practice. Selecting and filtering by dplyr I</a></li>
<li class="chapter" data-level="4.4.9" data-path="dataframe.html"><a href="dataframe.html#more-clean-version"><i class="fa fa-check"></i><b>4.4.9</b> (More) Clean version</a></li>
<li class="chapter" data-level="4.4.10" data-path="dataframe.html"><a href="dataframe.html#more-the-fittest-version-to-compute-staysame"><i class="fa fa-check"></i><b>4.4.10</b> (More) The fittest version to compute staySame</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="5" data-path="crosstab.html"><a href="crosstab.html"><i class="fa fa-check"></i><b>5</b> Counting and Cross-tabulation</a>
<ul>
<li class="chapter" data-level="5.1" data-path="crosstab.html"><a href="crosstab.html#tptheft"><i class="fa fa-check"></i><b>5.1</b> Taipei Residential Burglary</a>
<ul>
<li class="chapter" data-level="5.1.1" data-path="crosstab.html"><a href="crosstab.html#tptheft_read_file"><i class="fa fa-check"></i><b>5.1.1</b> 讀取檔案</a></li>
<li class="chapter" data-level="5.1.2" data-path="crosstab.html"><a href="crosstab.html#tptheft_mutate_new_var"><i class="fa fa-check"></i><b>5.1.2</b> 萃取所需新變項</a></li>
<li class="chapter" data-level="5.1.3" data-path="crosstab.html"><a href="crosstab.html#tptheft_counting"><i class="fa fa-check"></i><b>5.1.3</b> 使用<code>table()</code>計數</a></li>
<li class="chapter" data-level="5.1.4" data-path="crosstab.html"><a href="crosstab.html#tptheft_filtering"><i class="fa fa-check"></i><b>5.1.4</b> 依變數值篩選資料</a></li>
<li class="chapter" data-level="5.1.5" data-path="crosstab.html"><a href="crosstab.html#tptheft_table"><i class="fa fa-check"></i><b>5.1.5</b> 做雙變數樞紐分析：<code>table()</code></a></li>
<li class="chapter" data-level="5.1.6" data-path="crosstab.html"><a href="crosstab.html#tptheft_plot"><i class="fa fa-check"></i><b>5.1.6</b> 繪圖</a></li>
<li class="chapter" data-level="5.1.7" data-path="crosstab.html"><a href="crosstab.html#practices"><i class="fa fa-check"></i><b>5.1.7</b> Practices</a></li>
</ul></li>
<li class="chapter" data-level="5.2" data-path="crosstab.html"><a href="crosstab.html#tptheft_review_read_file"><i class="fa fa-check"></i><b>5.2</b> Read online files</a></li>
<li class="chapter" data-level="5.3" data-path="crosstab.html"><a href="crosstab.html#tptheft_review_counting"><i class="fa fa-check"></i><b>5.3</b> Counting Review</a>
<ul>
<li class="chapter" data-level="5.3.1" data-path="crosstab.html"><a href="crosstab.html#tapply"><i class="fa fa-check"></i><b>5.3.1</b> <code>tapply()</code></a></li>
<li class="chapter" data-level="5.3.2" data-path="crosstab.html"><a href="crosstab.html#tptheft_review_tapply"><i class="fa fa-check"></i><b>5.3.2</b> <code>tapply()</code> two variables</a></li>
<li class="chapter" data-level="5.3.3" data-path="crosstab.html"><a href="crosstab.html#tptheft_review_count"><i class="fa fa-check"></i><b>5.3.3</b> <code>dplyr::count()</code> two variables</a></li>
</ul></li>
<li class="chapter" data-level="5.4" data-path="crosstab.html"><a href="crosstab.html#tptheft_pivot_table"><i class="fa fa-check"></i><b>5.4</b> Pivoting long-wide tables</a>
<ul>
<li class="chapter" data-level="5.4.1" data-path="crosstab.html"><a href="crosstab.html#tptheft_pivot_wider"><i class="fa fa-check"></i><b>5.4.1</b> long-to-wide</a></li>
<li class="chapter" data-level="5.4.2" data-path="crosstab.html"><a href="crosstab.html#tptheft_pivot_longer"><i class="fa fa-check"></i><b>5.4.2</b> Wide-to-long</a></li>
</ul></li>
<li class="chapter" data-level="5.5" data-path="crosstab.html"><a href="crosstab.html#tptheft_residual"><i class="fa fa-check"></i><b>5.5</b> Residuals analysis</a></li>
</ul></li>
<li class="part"><span><b>II DATA MANIPULATION</b></span></li>
<li class="chapter" data-level="6" data-path="base2dplyr.html"><a href="base2dplyr.html"><i class="fa fa-check"></i><b>6</b> From base R to dplyr</a>
<ul>
<li class="chapter" data-level="6.1" data-path="base2dplyr.html"><a href="base2dplyr.html#dplyr"><i class="fa fa-check"></i><b>6.1</b> dplyr</a></li>
<li class="chapter" data-level="6.2" data-path="base2dplyr.html"><a href="base2dplyr.html#tptheft_dplyr"><i class="fa fa-check"></i><b>6.2</b> Taipie Theft Count (base to dplyr)</a>
<ul>
<li class="chapter" data-level="6.2.1" data-path="base2dplyr.html"><a href="base2dplyr.html#reading-data"><i class="fa fa-check"></i><b>6.2.1</b> Reading data</a></li>
<li class="chapter" data-level="6.2.2" data-path="base2dplyr.html"><a href="base2dplyr.html#cleaning-data-i"><i class="fa fa-check"></i><b>6.2.2</b> Cleaning data I</a></li>
<li class="chapter" data-level="6.2.3" data-path="base2dplyr.html"><a href="base2dplyr.html#cleaning-data-ii"><i class="fa fa-check"></i><b>6.2.3</b> Cleaning data II</a></li>
<li class="chapter" data-level="6.2.4" data-path="base2dplyr.html"><a href="base2dplyr.html#long-to-wide-form"><i class="fa fa-check"></i><b>6.2.4</b> Long to wide form</a></li>
<li class="chapter" data-level="6.2.5" data-path="base2dplyr.html"><a href="base2dplyr.html#setting-time-as-row.name-for-mosaicplot"><i class="fa fa-check"></i><b>6.2.5</b> Setting time as row.name for mosaicplot</a></li>
<li class="chapter" data-level="6.2.6" data-path="base2dplyr.html"><a href="base2dplyr.html#clean-version"><i class="fa fa-check"></i><b>6.2.6</b> Clean version</a></li>
</ul></li>
<li class="chapter" data-level="6.3" data-path="base2dplyr.html"><a href="base2dplyr.html#maternity_dplyr"><i class="fa fa-check"></i><b>6.3</b> Paid Maternity Leave</a>
<ul>
<li class="chapter" data-level="6.3.1" data-path="base2dplyr.html"><a href="base2dplyr.html#visual-strategies"><i class="fa fa-check"></i><b>6.3.1</b> Visual Strategies</a></li>
<li class="chapter" data-level="6.3.2" data-path="base2dplyr.html"><a href="base2dplyr.html#code-by-base-r"><i class="fa fa-check"></i><b>6.3.2</b> Code by base R</a></li>
<li class="chapter" data-level="6.3.3" data-path="base2dplyr.html"><a href="base2dplyr.html#code-by-dplyr"><i class="fa fa-check"></i><b>6.3.3</b> Code by dplyr</a></li>
<li class="chapter" data-level="6.3.4" data-path="base2dplyr.html"><a href="base2dplyr.html#generating-each"><i class="fa fa-check"></i><b>6.3.4</b> Generating each</a></li>
<li class="chapter" data-level="6.3.5" data-path="base2dplyr.html"><a href="base2dplyr.html#gathering-subplots-by-cowplot"><i class="fa fa-check"></i><b>6.3.5</b> Gathering subplots by cowplot</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="7" data-path="joindata.html"><a href="joindata.html"><i class="fa fa-check"></i><b>7</b> Data manipultaiton: Join data</a>
<ul>
<li class="chapter" data-level="7.1" data-path="joindata.html"><a href="joindata.html#simple"><i class="fa fa-check"></i><b>7.1</b> A Simple Example: Joining Two Data Frames</a>
<ul>
<li class="chapter" data-level="7.1.1" data-path="joindata.html"><a href="joindata.html#left_join-right_join"><i class="fa fa-check"></i><b>7.1.1</b> <code>left_join()</code> & <code>right_join()</code></a></li>
<li class="chapter" data-level="7.1.2" data-path="joindata.html"><a href="joindata.html#inner_join-and-full_join"><i class="fa fa-check"></i><b>7.1.2</b> <code>inner_join()</code> and <code>full_join()</code></a></li>
<li class="chapter" data-level="7.1.3" data-path="joindata.html"><a href="joindata.html#join-by-different-keys"><i class="fa fa-check"></i><b>7.1.3</b> <code>join()</code> by different keys</a></li>
</ul></li>
<li class="chapter" data-level="7.2" data-path="joindata.html"><a href="joindata.html#moi"><i class="fa fa-check"></i><b>7.2</b> 讀取內政部人口統計資料</a>
<ul>
<li class="chapter" data-level="7.2.1" data-path="joindata.html"><a href="joindata.html#moi_plan"><i class="fa fa-check"></i><b>7.2.1</b> 分析規劃</a></li>
<li class="chapter" data-level="7.2.2" data-path="joindata.html"><a href="joindata.html#moi_clean"><i class="fa fa-check"></i><b>7.2.2</b> 清理資料</a></li>
<li class="chapter" data-level="7.2.3" data-path="joindata.html"><a href="joindata.html#moi_rowwise"><i class="fa fa-check"></i><b>7.2.3</b> 進階：運用<code>rowwise()</code></a></li>
<li class="chapter" data-level="7.2.4" data-path="joindata.html"><a href="joindata.html#moi_vil"><i class="fa fa-check"></i><b>7.2.4</b> 建立鄉鎮市區與村里指標</a></li>
<li class="chapter" data-level="7.2.5" data-path="joindata.html"><a href="joindata.html#moi_visual_popul"><i class="fa fa-check"></i><b>7.2.5</b> 視覺化測試（老年人口數 x 曾婚人口數）</a></li>
</ul></li>
<li class="chapter" data-level="7.3" data-path="joindata.html"><a href="joindata.html#referendum"><i class="fa fa-check"></i><b>7.3</b> 讀取公投資料</a>
<ul>
<li class="chapter" data-level="7.3.1" data-path="joindata.html"><a href="joindata.html#moi_join_ref"><i class="fa fa-check"></i><b>7.3.1</b> 合併公投資料並視覺化</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="8" data-path="categorical.html"><a href="categorical.html"><i class="fa fa-check"></i><b>8</b> Categorical Data Analysis</a>
<ul>
<li class="chapter" data-level="8.1" data-path="categorical.html"><a href="categorical.html#survey-analysis"><i class="fa fa-check"></i><b>8.1</b> Survey Analysis</a></li>
<li class="chapter" data-level="8.2" data-path="categorical.html"><a href="categorical.html#the-case-misinformation-perception"><i class="fa fa-check"></i><b>8.2</b> The Case: Misinformation Perception</a></li>
<li class="chapter" data-level="8.3" data-path="categorical.html"><a href="categorical.html#factorize"><i class="fa fa-check"></i><b>8.3</b> Factorizing data</a>
<ul>
<li class="chapter" data-level="8.3.1" data-path="categorical.html"><a href="categorical.html#factor2order"><i class="fa fa-check"></i><b>8.3.1</b> factor-to-order</a></li>
<li class="chapter" data-level="8.3.2" data-path="categorical.html"><a href="categorical.html#excluding"><i class="fa fa-check"></i><b>8.3.2</b> Excluding</a></li>
<li class="chapter" data-level="8.3.3" data-path="categorical.html"><a href="categorical.html#groupup"><i class="fa fa-check"></i><b>8.3.3</b> Grouping-up</a></li>
</ul></li>
<li class="chapter" data-level="8.4" data-path="categorical.html"><a href="categorical.html#order2factor"><i class="fa fa-check"></i><b>8.4</b> Order-to-factor</a></li>
<li class="chapter" data-level="8.5" data-path="categorical.html"><a href="categorical.html#crosstabing"><i class="fa fa-check"></i><b>8.5</b> Cross-tabulating</a></li>
</ul></li>
<li class="chapter" data-level="9" data-path="timeline.html"><a href="timeline.html"><i class="fa fa-check"></i><b>9</b> Processing Timeline</a>
<ul>
<li class="chapter" data-level="9.1" data-path="timeline.html"><a href="timeline.html#time-object"><i class="fa fa-check"></i><b>9.1</b> Time object</a></li>
<li class="chapter" data-level="9.2" data-path="timeline.html"><a href="timeline.html#example-processing-time-object-in-social-opinions"><i class="fa fa-check"></i><b>9.2</b> Example: Processing time object in social opinions</a>
<ul>
<li class="chapter" data-level="9.2.1" data-path="timeline.html"><a href="timeline.html#char-to-time"><i class="fa fa-check"></i><b>9.2.1</b> Char-to-Time</a></li>
<li class="chapter" data-level="9.2.2" data-path="timeline.html"><a href="timeline.html#density-plot-along-time"><i class="fa fa-check"></i><b>9.2.2</b> Density plot along time</a></li>
<li class="chapter" data-level="9.2.3" data-path="timeline.html"><a href="timeline.html#freq-by-month"><i class="fa fa-check"></i><b>9.2.3</b> Freq by month</a></li>
<li class="chapter" data-level="9.2.4" data-path="timeline.html"><a href="timeline.html#freq-by-date-good"><i class="fa fa-check"></i><b>9.2.4</b> Freq-by-date (good)</a></li>
<li class="chapter" data-level="9.2.5" data-path="timeline.html"><a href="timeline.html#freq-by-hour"><i class="fa fa-check"></i><b>9.2.5</b> Freq-by-hour</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="10" data-path="na.html"><a href="na.html"><i class="fa fa-check"></i><b>10</b> NA Processing</a>
<ul>
<li class="chapter" data-level="10.1" data-path="na.html"><a href="na.html#cleaning-gov-annual-budget"><i class="fa fa-check"></i><b>10.1</b> Cleaning Gov Annual Budget</a>
<ul>
<li class="chapter" data-level="10.1.1" data-path="na.html"><a href="na.html#basic-cleaning"><i class="fa fa-check"></i><b>10.1.1</b> Basic Cleaning</a></li>
<li class="chapter" data-level="10.1.2" data-path="na.html"><a href="na.html#processing-na"><i class="fa fa-check"></i><b>10.1.2</b> Processing NA</a></li>
<li class="chapter" data-level="10.1.3" data-path="na.html"><a href="na.html#complete-code"><i class="fa fa-check"></i><b>10.1.3</b> Complete Code</a></li>
</ul></li>
<li class="chapter" data-level="10.2" data-path="na.html"><a href="na.html#cleaning-covid-vaccinating-data"><i class="fa fa-check"></i><b>10.2</b> Cleaning Covid Vaccinating data</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="na.html"><a href="na.html#觀察並評估資料概況"><i class="fa fa-check"></i><b>10.2.1</b> 觀察並評估資料概況</a></li>
<li class="chapter" data-level="10.2.2" data-path="na.html"><a href="na.html#按月對齊資料"><i class="fa fa-check"></i><b>10.2.2</b> 按月對齊資料</a></li>
<li class="chapter" data-level="10.2.3" data-path="na.html"><a href="na.html#處理遺漏資料的月份"><i class="fa fa-check"></i><b>10.2.3</b> 處理遺漏資料的月份</a></li>
<li class="chapter" data-level="10.2.4" data-path="na.html"><a href="na.html#完整程式碼"><i class="fa fa-check"></i><b>10.2.4</b> 完整程式碼</a></li>
</ul></li>
</ul></li>
<li class="part"><span><b>III TEXT PROCESSING</b></span></li>
<li class="chapter" data-level="11" data-path="tm.html"><a href="tm.html"><i class="fa fa-check"></i><b>11</b> Text Processing</a></li>
<li class="chapter" data-level="12" data-path="trump.html"><a href="trump.html"><i class="fa fa-check"></i><b>12</b> Trump’s tweets</a>
<ul>
<li class="chapter" data-level="12.1" data-path="trump.html"><a href="trump.html#loading-data"><i class="fa fa-check"></i><b>12.1</b> Loading data</a></li>
<li class="chapter" data-level="12.2" data-path="trump.html"><a href="trump.html#cleaning-data"><i class="fa fa-check"></i><b>12.2</b> Cleaning data</a></li>
<li class="chapter" data-level="12.3" data-path="trump.html"><a href="trump.html#visual-exploring"><i class="fa fa-check"></i><b>12.3</b> Visual Exploring</a>
<ul>
<li class="chapter" data-level="12.3.1" data-path="trump.html"><a href="trump.html#productivity-by-time"><i class="fa fa-check"></i><b>12.3.1</b> Productivity by time</a></li>
<li class="chapter" data-level="12.3.2" data-path="trump.html"><a href="trump.html#tweeting-with-figures"><i class="fa fa-check"></i><b>12.3.2</b> Tweeting with figures</a></li>
</ul></li>
<li class="chapter" data-level="12.4" data-path="trump.html"><a href="trump.html#keyness"><i class="fa fa-check"></i><b>12.4</b> Keyness</a>
<ul>
<li class="chapter" data-level="12.4.1" data-path="trump.html"><a href="trump.html#log-likelihood-ratio"><i class="fa fa-check"></i><b>12.4.1</b> Log-likelihood ratio</a></li>
<li class="chapter" data-level="12.4.2" data-path="trump.html"><a href="trump.html#plotting-keyness"><i class="fa fa-check"></i><b>12.4.2</b> Plotting keyness</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="13" data-path="re.html"><a href="re.html"><i class="fa fa-check"></i><b>13</b> Regular expression</a>
<ul>
<li class="chapter" data-level="13.1" data-path="re.html"><a href="re.html#re-applications-on-string-operations"><i class="fa fa-check"></i><b>13.1</b> <strong>RE applications on string operations</strong></a>
<ul>
<li class="chapter" data-level="13.1.1" data-path="re.html"><a href="re.html#extracting"><i class="fa fa-check"></i><b>13.1.1</b> Extracting</a></li>
<li class="chapter" data-level="13.1.2" data-path="re.html"><a href="re.html#detecting-with-non-greedy"><i class="fa fa-check"></i><b>13.1.2</b> Detecting with non-greedy</a></li>
<li class="chapter" data-level="13.1.3" data-path="re.html"><a href="re.html#detecting-multiple-patterns"><i class="fa fa-check"></i><b>13.1.3</b> Detecting multiple patterns</a></li>
<li class="chapter" data-level="13.1.4" data-path="re.html"><a href="re.html#extracting-nearby-words"><i class="fa fa-check"></i><b>13.1.4</b> Extracting nearby words</a></li>
</ul></li>
<li class="chapter" data-level="13.2" data-path="re.html"><a href="re.html#re-case-studies"><i class="fa fa-check"></i><b>13.2</b> RE Case studies</a>
<ul>
<li class="chapter" data-level="13.2.1" data-path="re.html"><a href="re.html#getting-the-last-page-of-ptt-hatepolitics"><i class="fa fa-check"></i><b>13.2.1</b> Getting the last page of PTT HatePolitics</a></li>
<li class="chapter" data-level="13.2.2" data-path="re.html"><a href="re.html#practice.-ask-chatgpt"><i class="fa fa-check"></i><b>13.2.2</b> Practice. Ask CHATGPT</a></li>
</ul></li>
<li class="chapter" data-level="13.3" data-path="re.html"><a href="re.html#useful-cases"><i class="fa fa-check"></i><b>13.3</b> Useful cases</a>
<ul>
<li class="chapter" data-level="13.3.1" data-path="re.html"><a href="re.html#matching-url"><i class="fa fa-check"></i><b>13.3.1</b> Matching URL</a></li>
<li class="chapter" data-level="13.3.2" data-path="re.html"><a href="re.html#removing-all-html-tags-but-keeping-comment-content"><i class="fa fa-check"></i><b>13.3.2</b> Removing all html tags but keeping comment content</a></li>
<li class="chapter" data-level="13.3.3" data-path="re.html"><a href="re.html#removing-space"><i class="fa fa-check"></i><b>13.3.3</b> Removing space</a></li>
<li class="chapter" data-level="13.3.4" data-path="re.html"><a href="re.html#testing"><i class="fa fa-check"></i><b>13.3.4</b> Testing</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="14" data-path="tmchi.html"><a href="tmchi.html"><i class="fa fa-check"></i><b>14</b> Text processing in Chinese</a>
<ul>
<li class="chapter" data-level="14.1" data-path="tmchi.html"><a href="tmchi.html#preprocessing"><i class="fa fa-check"></i><b>14.1</b> Preprocessing</a>
<ul>
<li class="chapter" data-level="14.1.1" data-path="tmchi.html"><a href="tmchi.html#assigning-unique-id-to-each-doc"><i class="fa fa-check"></i><b>14.1.1</b> Assigning unique id to each doc</a></li>
</ul></li>
<li class="chapter" data-level="14.2" data-path="tmchi.html"><a href="tmchi.html#tokenization"><i class="fa fa-check"></i><b>14.2</b> Tokenization</a>
<ul>
<li class="chapter" data-level="14.2.1" data-path="tmchi.html"><a href="tmchi.html#initializer-tokenizer"><i class="fa fa-check"></i><b>14.2.1</b> Initializer tokenizer</a></li>
<li class="chapter" data-level="14.2.2" data-path="tmchi.html"><a href="tmchi.html#tokenization-1"><i class="fa fa-check"></i><b>14.2.2</b> Tokenization</a></li>
</ul></li>
<li class="chapter" data-level="14.3" data-path="tmchi.html"><a href="tmchi.html#exploring-wording-features"><i class="fa fa-check"></i><b>14.3</b> Exploring wording features</a>
<ul>
<li class="chapter" data-level="14.3.1" data-path="tmchi.html"><a href="tmchi.html#word-frequency-distribution"><i class="fa fa-check"></i><b>14.3.1</b> Word frequency distribution</a></li>
<li class="chapter" data-level="14.3.2" data-path="tmchi.html"><a href="tmchi.html#keyness-by-logratio"><i class="fa fa-check"></i><b>14.3.2</b> Keyness by logratio</a></li>
<li class="chapter" data-level="14.3.3" data-path="tmchi.html"><a href="tmchi.html#keyness-by-scatter"><i class="fa fa-check"></i><b>14.3.3</b> Keyness by scatter</a></li>
</ul></li>
<li class="chapter" data-level="14.4" data-path="tmchi.html"><a href="tmchi.html#tf-idf"><i class="fa fa-check"></i><b>14.4</b> TF-IDF</a>
<ul>
<li class="chapter" data-level="14.4.1" data-path="tmchi.html"><a href="tmchi.html#term-frequency"><i class="fa fa-check"></i><b>14.4.1</b> Term-frequency</a></li>
<li class="chapter" data-level="14.4.2" data-path="tmchi.html"><a href="tmchi.html#tf-idf-to-filter-significant-words"><i class="fa fa-check"></i><b>14.4.2</b> TF-IDF to filter significant words</a></li>
<li class="chapter" data-level="14.4.3" data-path="tmchi.html"><a href="tmchi.html#practice.-understanding-tf-idf"><i class="fa fa-check"></i><b>14.4.3</b> Practice. Understanding TF-IDF</a></li>
</ul></li>
</ul></li>
<li class="part"><span><b>IV CRAWLER</b></span></li>
<li class="chapter" data-level="15" data-path="crawler-overview.html"><a href="crawler-overview.html"><i class="fa fa-check"></i><b>15</b> Introduction to Web Scraping</a>
<ul>
<li class="chapter" data-level="15.1" data-path="crawler-overview.html"><a href="crawler-overview.html#webapi"><i class="fa fa-check"></i><b>15.1</b> Using Web API</a></li>
<li class="chapter" data-level="15.2" data-path="crawler-overview.html"><a href="crawler-overview.html#craw_scraping"><i class="fa fa-check"></i><b>15.2</b> Webpage Scraping</a>
<ul>
<li class="chapter" data-level="15.2.1" data-path="crawler-overview.html"><a href="crawler-overview.html#status_code"><i class="fa fa-check"></i><b>15.2.1</b> HTTP Status Code</a></li>
</ul></li>
<li class="chapter" data-level="15.3" data-path="crawler-overview.html"><a href="crawler-overview.html#webpage-browsing"><i class="fa fa-check"></i><b>15.3</b> Webpage Browsing</a></li>
<li class="chapter" data-level="15.4" data-path="crawler-overview.html"><a href="crawler-overview.html#using-chrome-devtools"><i class="fa fa-check"></i><b>15.4</b> Using Chrome DevTools</a>
<ul>
<li class="chapter" data-level="15.4.1" data-path="crawler-overview.html"><a href="crawler-overview.html#observing-web-request"><i class="fa fa-check"></i><b>15.4.1</b> Observing web request</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="16" data-path="scraping-104.html"><a href="scraping-104.html"><i class="fa fa-check"></i><b>16</b> Scraping 104.com</a>
<ul>
<li class="chapter" data-level="16.1" data-path="scraping-104.html"><a href="scraping-104.html#complete-code-1"><i class="fa fa-check"></i><b>16.1</b> Complete Code</a></li>
<li class="chapter" data-level="16.2" data-path="scraping-104.html"><a href="scraping-104.html#step-by-step"><i class="fa fa-check"></i><b>16.2</b> Step-by-Step</a>
<ul>
<li class="chapter" data-level="16.2.1" data-path="scraping-104.html"><a href="scraping-104.html#get-the-first-pages"><i class="fa fa-check"></i><b>16.2.1</b> Get the first pages</a></li>
<li class="chapter" data-level="16.2.2" data-path="scraping-104.html"><a href="scraping-104.html#get-the-first-page-by-modifying-url"><i class="fa fa-check"></i><b>16.2.2</b> Get the first page by modifying url</a></li>
<li class="chapter" data-level="16.2.3" data-path="scraping-104.html"><a href="scraping-104.html#combine-two-data-with-the-same-variables"><i class="fa fa-check"></i><b>16.2.3</b> Combine two data with the same variables</a></li>
<li class="chapter" data-level="16.2.4" data-path="scraping-104.html"><a href="scraping-104.html#drop-out-hierarchical-variables"><i class="fa fa-check"></i><b>16.2.4</b> Drop out hierarchical variables</a></li>
<li class="chapter" data-level="16.2.5" data-path="scraping-104.html"><a href="scraping-104.html#dropping-hierarchical-variables-by-dplyr-way"><i class="fa fa-check"></i><b>16.2.5</b> Dropping hierarchical variables by dplyr way</a></li>
<li class="chapter" data-level="16.2.6" data-path="scraping-104.html"><a href="scraping-104.html#finding-out-the-last-page-number"><i class="fa fa-check"></i><b>16.2.6</b> Finding out the last page number</a></li>
<li class="chapter" data-level="16.2.7" data-path="scraping-104.html"><a href="scraping-104.html#using-for-loop-to-get-all-pages"><i class="fa fa-check"></i><b>16.2.7</b> Using for-loop to get all pages</a></li>
<li class="chapter" data-level="16.2.8" data-path="scraping-104.html"><a href="scraping-104.html#combine-all-data.frame"><i class="fa fa-check"></i><b>16.2.8</b> combine all data.frame</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="17" data-path="read_json.html"><a href="read_json.html"><i class="fa fa-check"></i><b>17</b> Read JSON</a>
<ul>
<li class="chapter" data-level="17.1" data-path="read_json.html"><a href="read_json.html#reading-json"><i class="fa fa-check"></i><b>17.1</b> Reading JSON</a>
<ul>
<li class="chapter" data-level="17.1.1" data-path="read_json.html"><a href="read_json.html#json-as-a-string"><i class="fa fa-check"></i><b>17.1.1</b> JSON as a string</a></li>
<li class="chapter" data-level="17.1.2" data-path="read_json.html"><a href="read_json.html#json-as-a-local-file"><i class="fa fa-check"></i><b>17.1.2</b> JSON as a local file</a></li>
<li class="chapter" data-level="17.1.3" data-path="read_json.html"><a href="read_json.html#json-as-a-web-file"><i class="fa fa-check"></i><b>17.1.3</b> JSON as a web file</a></li>
<li class="chapter" data-level="17.1.4" data-path="read_json.html"><a href="read_json.html#practice.-convert-ubike-json-to-data.frame"><i class="fa fa-check"></i><b>17.1.4</b> Practice. Convert ubike json to data.frame</a></li>
</ul></li>
<li class="chapter" data-level="17.2" data-path="read_json.html"><a href="read_json.html#case-1-air-quality-well-formatted"><i class="fa fa-check"></i><b>17.2</b> Case 1: Air-Quality (well-formatted )</a>
<ul>
<li class="chapter" data-level="17.2.1" data-path="read_json.html"><a href="read_json.html#using-knitrkable-for-better-printing"><i class="fa fa-check"></i><b>17.2.1</b> Using knitr::kable() for better printing</a></li>
<li class="chapter" data-level="17.2.2" data-path="read_json.html"><a href="read_json.html#step-by-step-parse-json-format-string-to-r-objects"><i class="fa fa-check"></i><b>17.2.2</b> Step-by-step: Parse JSON format string to R objects</a></li>
<li class="chapter" data-level="17.2.3" data-path="read_json.html"><a href="read_json.html#combining-all"><i class="fa fa-check"></i><b>17.2.3</b> Combining all</a></li>
</ul></li>
<li class="chapter" data-level="17.3" data-path="read_json.html"><a href="read_json.html#practices-traversing-json-data"><i class="fa fa-check"></i><b>17.3</b> <strong>Practices: traversing json data</strong></a></li>
<li class="chapter" data-level="17.4" data-path="read_json.html"><a href="read_json.html#case-2-cnyes-news-well-formatted"><i class="fa fa-check"></i><b>17.4</b> Case 2: cnyes news (well-formatted)</a>
<ul>
<li class="chapter" data-level="17.4.1" data-path="read_json.html"><a href="read_json.html#option-取回資料並寫在硬碟"><i class="fa fa-check"></i><b>17.4.1</b> (option) 取回資料並寫在硬碟</a></li>
</ul></li>
<li class="chapter" data-level="17.5" data-path="read_json.html"><a href="read_json.html#case-3-footrumor-ill-formatted"><i class="fa fa-check"></i><b>17.5</b> Case 3: footRumor (ill-formatted)</a>
<ul>
<li class="chapter" data-level="17.5.1" data-path="read_json.html"><a href="read_json.html#處理非典型的json檔"><i class="fa fa-check"></i><b>17.5.1</b> 處理非典型的JSON檔</a></li>
</ul></li>
<li class="chapter" data-level="17.6" data-path="read_json.html"><a href="read_json.html#reviewing-json"><i class="fa fa-check"></i><b>17.6</b> Reviewing JSON</a>
<ul>
<li class="chapter" data-level="17.6.1" data-path="read_json.html"><a href="read_json.html#type-i-well-formatted-json-uvi-aqi-hospital_revisits"><i class="fa fa-check"></i><b>17.6.1</b> Type I: Well-formatted JSON: UVI, AQI, Hospital_revisits</a></li>
<li class="chapter" data-level="17.6.2" data-path="read_json.html"><a href="read_json.html#type-ii-hierarchical-json-rent591-facebook-graph-api-google-map"><i class="fa fa-check"></i><b>17.6.2</b> Type II: hierarchical JSON: rent591, facebook graph api, google map</a></li>
<li class="chapter" data-level="17.6.3" data-path="read_json.html"><a href="read_json.html#type-iii-ill-formatted-json-food_rumors-ubike"><i class="fa fa-check"></i><b>17.6.3</b> Type III: Ill-formatted JSON: food_rumors, ubike</a></li>
</ul></li>
<li class="chapter" data-level="17.7" data-path="read_json.html"><a href="read_json.html#section"><i class="fa fa-check"></i><b>17.7</b> </a></li>
</ul></li>
<li class="chapter" data-level="18" data-path="html-parser.html"><a href="html-parser.html"><i class="fa fa-check"></i><b>18</b> HTML Parser</a>
<ul>
<li class="chapter" data-level="18.1" data-path="html-parser.html"><a href="html-parser.html#html"><i class="fa fa-check"></i><b>18.1</b> HTML</a></li>
<li class="chapter" data-level="18.2" data-path="html-parser.html"><a href="html-parser.html#detecting-element-path"><i class="fa fa-check"></i><b>18.2</b> Detecting Element Path</a>
<ul>
<li class="chapter" data-level="18.2.1" data-path="html-parser.html"><a href="html-parser.html#xpath"><i class="fa fa-check"></i><b>18.2.1</b> XPath</a></li>
<li class="chapter" data-level="18.2.2" data-path="html-parser.html"><a href="html-parser.html#css-selector"><i class="fa fa-check"></i><b>18.2.2</b> CSS Selector</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="19" data-path="ptt-scrape.html"><a href="ptt-scrape.html"><i class="fa fa-check"></i><b>19</b> Scraping PTT</a>
<ul>
<li class="chapter" data-level="19.1" data-path="ptt-scrape.html"><a href="ptt-scrape.html#ptt_load_pkgs"><i class="fa fa-check"></i><b>19.1</b> Step 1. 載入所需套件</a></li>
<li class="chapter" data-level="19.2" data-path="ptt-scrape.html"><a href="ptt-scrape.html#ptt_parsehtml"><i class="fa fa-check"></i><b>19.2</b> Step 2. 取回並剖析HTML檔案</a>
<ul>
<li class="chapter" data-level="19.2.1" data-path="ptt-scrape.html"><a href="ptt-scrape.html#ptt_read_html"><i class="fa fa-check"></i><b>19.2.1</b> <strong>Step 2-1. <code>read_html()</code> 將網頁取回並轉為xml_document</strong></a></li>
<li class="chapter" data-level="19.2.2" data-path="ptt-scrape.html"><a href="ptt-scrape.html#ptt_html_nodes"><i class="fa fa-check"></i><b>19.2.2</b> <strong>Step 2-2 以<code>html_nodes()</code> 以選擇所需的資料節點</strong></a></li>
<li class="chapter" data-level="19.2.3" data-path="ptt-scrape.html"><a href="ptt-scrape.html#ptt_xpath_css"><i class="fa fa-check"></i><b>19.2.3</b> <strong>Step 2-2 補充說明與XPath、CSS Selector的最佳化</strong></a></li>
<li class="chapter" data-level="19.2.4" data-path="ptt-scrape.html"><a href="ptt-scrape.html#ptt_html_text"><i class="fa fa-check"></i><b>19.2.4</b> <strong>Step 2-3 <code>html_text()</code>或<code>html_attr()</code>轉出所要的資料</strong></a></li>
</ul></li>
<li class="chapter" data-level="19.3" data-path="ptt-scrape.html"><a href="ptt-scrape.html#ptt_for"><i class="fa fa-check"></i><b>19.3</b> Step 3. 用for迴圈打撈多頁的連結</a></li>
<li class="chapter" data-level="19.4" data-path="ptt-scrape.html"><a href="ptt-scrape.html#ptt_scrape_post"><i class="fa fa-check"></i><b>19.4</b> Step 4. 根據連結取回所有貼文</a></li>
<li class="chapter" data-level="19.5" data-path="ptt-scrape.html"><a href="ptt-scrape.html#ptt_method2"><i class="fa fa-check"></i><b>19.5</b> 補充(1) 較好的寫法</a></li>
<li class="chapter" data-level="19.6" data-path="ptt-scrape.html"><a href="ptt-scrape.html#ptt_best"><i class="fa fa-check"></i><b>19.6</b> 補充(2) 最佳的寫法</a></li>
</ul></li>
<li class="chapter" data-level="20" data-path="lebron.html"><a href="lebron.html"><i class="fa fa-check"></i><b>20</b> NYT: LeBron James Achievement</a>
<ul>
<li class="chapter" data-level="20.1" data-path="lebron.html"><a href="lebron.html#get-top250-players"><i class="fa fa-check"></i><b>20.1</b> Get top250 players</a></li>
<li class="chapter" data-level="20.2" data-path="lebron.html"><a href="lebron.html#scraping-live-scores"><i class="fa fa-check"></i><b>20.2</b> Scraping live scores</a>
<ul>
<li class="chapter" data-level="20.2.1" data-path="lebron.html"><a href="lebron.html#testing-scrape-one"><i class="fa fa-check"></i><b>20.2.1</b> Testing: Scrape one</a></li>
<li class="chapter" data-level="20.2.2" data-path="lebron.html"><a href="lebron.html#scrape-life-time-scores-of-all-top-250-players"><i class="fa fa-check"></i><b>20.2.2</b> Scrape life time scores of all top-250 players</a></li>
</ul></li>
<li class="chapter" data-level="20.3" data-path="lebron.html"><a href="lebron.html#cleaning-data-1"><i class="fa fa-check"></i><b>20.3</b> Cleaning data</a></li>
<li class="chapter" data-level="20.4" data-path="lebron.html"><a href="lebron.html#visualization"><i class="fa fa-check"></i><b>20.4</b> Visualization</a>
<ul>
<li class="chapter" data-level="20.4.1" data-path="lebron.html"><a href="lebron.html#line-age-x-cumpts"><i class="fa fa-check"></i><b>20.4.1</b> Line: Age x cumPTS</a></li>
<li class="chapter" data-level="20.4.2" data-path="lebron.html"><a href="lebron.html#line-year-x-cumpts"><i class="fa fa-check"></i><b>20.4.2</b> Line: year x cumPTS</a></li>
<li class="chapter" data-level="20.4.3" data-path="lebron.html"><a href="lebron.html#line-age-x-per_by_year"><i class="fa fa-check"></i><b>20.4.3</b> Line: Age x PER_by_year</a></li>
<li class="chapter" data-level="20.4.4" data-path="lebron.html"><a href="lebron.html#comparing-lebron-james-and-jabbar"><i class="fa fa-check"></i><b>20.4.4</b> Comparing LeBron James and Jabbar</a></li>
</ul></li>
<li class="chapter" data-level="20.5" data-path="lebron.html"><a href="lebron.html#scraping-and-cleaning"><i class="fa fa-check"></i><b>20.5</b> Scraping and cleaning</a>
<ul>
<li class="chapter" data-level="20.5.1" data-path="lebron.html"><a href="lebron.html#vis-ljames-and-jabbar"><i class="fa fa-check"></i><b>20.5.1</b> VIS LJames and jabbar</a></li>
</ul></li>
<li class="chapter" data-level="20.6" data-path="lebron.html"><a href="lebron.html#more-scraping-all-players"><i class="fa fa-check"></i><b>20.6</b> (More) Scraping all players</a>
<ul>
<li class="chapter" data-level="20.6.1" data-path="lebron.html"><a href="lebron.html#testing-1"><i class="fa fa-check"></i><b>20.6.1</b> Testing</a></li>
<li class="chapter" data-level="20.6.2" data-path="lebron.html"><a href="lebron.html#scrape-from-a-z-except-xno-x"><i class="fa fa-check"></i><b>20.6.2</b> Scrape from a-z except x(no x)</a></li>
</ul></li>
</ul></li>
<li class="part"><span><b>V VISUALIZATION</b></span></li>
<li class="chapter" data-level="21" data-path="visualization-1.html"><a href="visualization-1.html"><i class="fa fa-check"></i><b>21</b> Visualization</a>
<ul>
<li class="chapter" data-level="21.1" data-path="visualization-1.html"><a href="visualization-1.html#ggplot2"><i class="fa fa-check"></i><b>21.1</b> ggplot2</a></li>
<li class="chapter" data-level="21.2" data-path="visualization-1.html"><a href="visualization-1.html#vis-packages"><i class="fa fa-check"></i><b>21.2</b> VIS packages</a></li>
<li class="chapter" data-level="21.3" data-path="visualization-1.html"><a href="visualization-1.html#case-gallery"><i class="fa fa-check"></i><b>21.3</b> Case Gallery</a>
<ul>
<li class="chapter" data-level="21.3.1" data-path="visualization-1.html"><a href="visualization-1.html#wp-paid-maternity-leave-產假支薪-barplot"><i class="fa fa-check"></i><b>21.3.1</b> WP: Paid Maternity Leave (產假支薪): barplot</a></li>
<li class="chapter" data-level="21.3.2" data-path="visualization-1.html"><a href="visualization-1.html#nyt-population-changes-over-more-than-20000-years-coordinate-lineplot"><i class="fa fa-check"></i><b>21.3.2</b> NYT: Population Changes Over More Than 20,000 Years: Coordinate, lineplot</a></li>
<li class="chapter" data-level="21.3.3" data-path="visualization-1.html"><a href="visualization-1.html#nyt-lebron-james-achievement-coordinate-lineplot"><i class="fa fa-check"></i><b>21.3.3</b> NYT: LeBron James’ Achievement: Coordinate, lineplot</a></li>
<li class="chapter" data-level="21.3.4" data-path="visualization-1.html"><a href="visualization-1.html#taiwan-village-population-distribution-coordinate-lineplot"><i class="fa fa-check"></i><b>21.3.4</b> Taiwan Village Population Distribution: Coordinate, lineplot</a></li>
<li class="chapter" data-level="21.3.5" data-path="visualization-1.html"><a href="visualization-1.html#nyt-net-worth-by-age-group-coordinate-barplot"><i class="fa fa-check"></i><b>21.3.5</b> NYT: Net Worth by Age Group: Coordinate, barplot</a></li>
<li class="chapter" data-level="21.3.6" data-path="visualization-1.html"><a href="visualization-1.html#nyt-optimistic-of-different-generation-association-scatter"><i class="fa fa-check"></i><b>21.3.6</b> NYT: Optimistic of different generation: Association, scatter</a></li>
<li class="chapter" data-level="21.3.7" data-path="visualization-1.html"><a href="visualization-1.html#vaccinating-proportion-by-countries-amount-heatmap"><i class="fa fa-check"></i><b>21.3.7</b> Vaccinating Proportion by countries: Amount, heatmap</a></li>
<li class="chapter" data-level="21.3.8" data-path="visualization-1.html"><a href="visualization-1.html#taiwan-salary-distribution-distribution-boxmap"><i class="fa fa-check"></i><b>21.3.8</b> Taiwan salary distribution: Distribution, boxmap</a></li>
<li class="chapter" data-level="21.3.9" data-path="visualization-1.html"><a href="visualization-1.html#taiwan-income-distribution-by-each-town-distribution-boxmap"><i class="fa fa-check"></i><b>21.3.9</b> Taiwan income distribution by each town: Distribution, boxmap</a></li>
<li class="chapter" data-level="21.3.10" data-path="visualization-1.html"><a href="visualization-1.html#nyt-carbon-by-countries-proportion-treemap"><i class="fa fa-check"></i><b>21.3.10</b> NYT: Carbon by countries: Proportion, Treemap</a></li>
<li class="chapter" data-level="21.3.11" data-path="visualization-1.html"><a href="visualization-1.html#taiwan-annual-expenditure-proportion-treemap"><i class="fa fa-check"></i><b>21.3.11</b> Taiwan Annual Expenditure: Proportion, Treemap</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="22" data-path="ggplot.html"><a href="ggplot.html"><i class="fa fa-check"></i><b>22</b> ggplot</a>
<ul>
<li class="chapter" data-level="22.1" data-path="ggplot.html"><a href="ggplot.html#essentials-of-ggplot"><i class="fa fa-check"></i><b>22.1</b> Essentials of ggplot</a>
<ul>
<li class="chapter" data-level="22.1.1" data-path="ggplot.html"><a href="ggplot.html#ggplot-秀出預備要繪製的繪圖區"><i class="fa fa-check"></i><b>22.1.1</b> (1) <code>ggplot()</code> 秀出預備要繪製的繪圖區</a></li>
<li class="chapter" data-level="22.1.2" data-path="ggplot.html"><a href="ggplot.html#aes-指定xy軸與群組因子"><i class="fa fa-check"></i><b>22.1.2</b> <strong>(2) <code>aes()</code> 指定X／Y軸與群組因子</strong></a></li>
<li class="chapter" data-level="22.1.3" data-path="ggplot.html"><a href="ggplot.html#geom_-指定要繪製的圖表類型"><i class="fa fa-check"></i><b>22.1.3</b> <strong>(3) <code>geom_???()</code> 指定要繪製的圖表類型</strong>。</a></li>
</ul></li>
<li class="chapter" data-level="22.2" data-path="ggplot.html"><a href="ggplot.html#nyt-inequality"><i class="fa fa-check"></i><b>22.2</b> NYT: Inequality</a>
<ul>
<li class="chapter" data-level="22.2.1" data-path="ggplot.html"><a href="ggplot.html#loading-data-1"><i class="fa fa-check"></i><b>22.2.1</b> (1) Loading data</a></li>
<li class="chapter" data-level="22.2.2" data-path="ggplot.html"><a href="ggplot.html#visualizing"><i class="fa fa-check"></i><b>22.2.2</b> (2) Visualizing</a></li>
</ul></li>
<li class="chapter" data-level="22.3" data-path="ggplot.html"><a href="ggplot.html#adjusting-chart"><i class="fa fa-check"></i><b>22.3</b> Adjusting Chart</a>
<ul>
<li class="chapter" data-level="22.3.1" data-path="ggplot.html"><a href="ggplot.html#type-of-points-and-lines"><i class="fa fa-check"></i><b>22.3.1</b> Type of Points and Lines</a></li>
<li class="chapter" data-level="22.3.2" data-path="ggplot.html"><a href="ggplot.html#line-types"><i class="fa fa-check"></i><b>22.3.2</b> Line Types</a></li>
<li class="chapter" data-level="22.3.3" data-path="ggplot.html"><a href="ggplot.html#title-labels-and-legends"><i class="fa fa-check"></i><b>22.3.3</b> Title, Labels and Legends</a></li>
<li class="chapter" data-level="22.3.4" data-path="ggplot.html"><a href="ggplot.html#font"><i class="fa fa-check"></i><b>22.3.4</b> Font</a></li>
<li class="chapter" data-level="22.3.5" data-path="ggplot.html"><a href="ggplot.html#color-themes"><i class="fa fa-check"></i><b>22.3.5</b> Color Themes</a></li>
<li class="chapter" data-level="22.3.6" data-path="ggplot.html"><a href="ggplot.html#set-up-default-theme"><i class="fa fa-check"></i><b>22.3.6</b> Set-up Default Theme</a></li>
<li class="chapter" data-level="22.3.7" data-path="ggplot.html"><a href="ggplot.html#show-chinese-text"><i class="fa fa-check"></i><b>22.3.7</b> Show Chinese Text</a></li>
<li class="chapter" data-level="22.3.8" data-path="ggplot.html"><a href="ggplot.html#xy-axis"><i class="fa fa-check"></i><b>22.3.8</b> X/Y axis</a></li>
</ul></li>
<li class="chapter" data-level="22.4" data-path="ggplot.html"><a href="ggplot.html#highlighting-storytelling"><i class="fa fa-check"></i><b>22.4</b> Highlighting & Storytelling</a>
<ul>
<li class="chapter" data-level="22.4.1" data-path="ggplot.html"><a href="ggplot.html#依群組指定顏色"><i class="fa fa-check"></i><b>22.4.1</b> 依群組指定顏色</a></li>
<li class="chapter" data-level="22.4.2" data-path="ggplot.html"><a href="ggplot.html#使用gghighlight套件"><i class="fa fa-check"></i><b>22.4.2</b> 使用gghighlight套件</a></li>
<li class="chapter" data-level="22.4.3" data-path="ggplot.html"><a href="ggplot.html#為視覺化建立群組"><i class="fa fa-check"></i><b>22.4.3</b> 為視覺化建立群組</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="23" data-path="coordinate.html"><a href="coordinate.html"><i class="fa fa-check"></i><b>23</b> Coordinate</a>
<ul>
<li class="chapter" data-level="23.1" data-path="coordinate.html"><a href="coordinate.html#population_growth"><i class="fa fa-check"></i><b>23.1</b> NYT: Population Growth</a>
<ul>
<li class="chapter" data-level="23.1.1" data-path="coordinate.html"><a href="coordinate.html#parsing-table-from-pdf"><i class="fa fa-check"></i><b>23.1.1</b> Parsing table from pdf</a></li>
<li class="chapter" data-level="23.1.2" data-path="coordinate.html"><a href="coordinate.html#x-and-y-with-log-scale"><i class="fa fa-check"></i><b>23.1.2</b> X and Y with log-scale</a></li>
</ul></li>
<li class="chapter" data-level="23.2" data-path="coordinate.html"><a href="coordinate.html#vilpopulation"><i class="fa fa-check"></i><b>23.2</b> Order as axis</a></li>
<li class="chapter" data-level="23.3" data-path="coordinate.html"><a href="coordinate.html#log-scale"><i class="fa fa-check"></i><b>23.3</b> Log-scale</a></li>
<li class="chapter" data-level="23.4" data-path="coordinate.html"><a href="coordinate.html#section-1"><i class="fa fa-check"></i><b>23.4</b> </a></li>
<li class="chapter" data-level="23.5" data-path="coordinate.html"><a href="coordinate.html#square-root-scale"><i class="fa fa-check"></i><b>23.5</b> Square-root scale</a></li>
<li class="chapter" data-level="23.6" data-path="coordinate.html"><a href="coordinate.html#increasing-percentage-as-y"><i class="fa fa-check"></i><b>23.6</b> Increasing percentage as Y</a>
<ul>
<li class="chapter" data-level="23.6.1" data-path="coordinate.html"><a href="coordinate.html#networth"><i class="fa fa-check"></i><b>23.6.1</b> NYT: Net Worth by Age Group</a></li>
<li class="chapter" data-level="23.6.2" data-path="coordinate.html"><a href="coordinate.html#read-and-sort-data"><i class="fa fa-check"></i><b>23.6.2</b> Read and sort data</a></li>
</ul></li>
<li class="chapter" data-level="23.7" data-path="coordinate.html"><a href="coordinate.html#xy-aspect-ratio"><i class="fa fa-check"></i><b>23.7</b> X/Y aspect ratio</a>
<ul>
<li class="chapter" data-level="23.7.1" data-path="coordinate.html"><a href="coordinate.html#optimistic"><i class="fa fa-check"></i><b>23.7.1</b> UNICEF-Optimistic (WGOITH)</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="24" data-path="amount.html"><a href="amount.html"><i class="fa fa-check"></i><b>24</b> AMOUNT</a>
<ul>
<li class="chapter" data-level="24.1" data-path="amount.html"><a href="amount.html#bar-chart"><i class="fa fa-check"></i><b>24.1</b> Bar chart</a></li>
<li class="chapter" data-level="24.2" data-path="amount.html"><a href="amount.html#vaccinating"><i class="fa fa-check"></i><b>24.2</b> Heatmap: Vaccination</a>
<ul>
<li class="chapter" data-level="24.2.1" data-path="amount.html"><a href="amount.html#the-case-vaccinating-coverage-by-month"><i class="fa fa-check"></i><b>24.2.1</b> The case: Vaccinating coverage by month</a></li>
<li class="chapter" data-level="24.2.2" data-path="amount.html"><a href="amount.html#data-cleaning"><i class="fa fa-check"></i><b>24.2.2</b> Data cleaning</a></li>
<li class="chapter" data-level="24.2.3" data-path="amount.html"><a href="amount.html#visualization-2"><i class="fa fa-check"></i><b>24.2.3</b> Visualization</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="25" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html"><i class="fa fa-check"></i><b>25</b> DISTRIBUTION: Histogram & Density</a>
<ul>
<li class="chapter" data-level="25.1" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#density-plot"><i class="fa fa-check"></i><b>25.1</b> Density plot</a>
<ul>
<li class="chapter" data-level="25.1.1" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#density-with-different-bandwidth"><i class="fa fa-check"></i><b>25.1.1</b> Density with different bandwidth</a></li>
</ul></li>
<li class="chapter" data-level="25.2" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#histogram"><i class="fa fa-check"></i><b>25.2</b> Histogram</a>
<ul>
<li class="chapter" data-level="25.2.1" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#histogram-with-different-number-of-bins"><i class="fa fa-check"></i><b>25.2.1</b> Histogram with different number of bins</a></li>
<li class="chapter" data-level="25.2.2" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#density-vs-histogram"><i class="fa fa-check"></i><b>25.2.2</b> Density vs histogram</a></li>
<li class="chapter" data-level="25.2.3" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#positions-of-bar-chart"><i class="fa fa-check"></i><b>25.2.3</b> Positions of bar chart</a></li>
<li class="chapter" data-level="25.2.4" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#display-two-groups-histogram-by-facet_wrap"><i class="fa fa-check"></i><b>25.2.4</b> Display two groups histogram by facet_wrap()</a></li>
</ul></li>
<li class="chapter" data-level="25.3" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#pyramid"><i class="fa fa-check"></i><b>25.3</b> Pyramid Plot</a>
<ul>
<li class="chapter" data-level="25.3.1" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#modify-geom_col-to-pyramid-plot"><i class="fa fa-check"></i><b>25.3.1</b> Modify geom_col() to pyramid plot</a></li>
</ul></li>
<li class="chapter" data-level="25.4" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#box-plot-muitiple-distrubution"><i class="fa fa-check"></i><b>25.4</b> Box plot: Muitiple Distrubution</a>
<ul>
<li class="chapter" data-level="25.4.1" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#twsalary"><i class="fa fa-check"></i><b>25.4.1</b> TW-Salary (boxplot)</a></li>
<li class="chapter" data-level="25.4.2" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#twincome"><i class="fa fa-check"></i><b>25.4.2</b> TW-Income (boxplot)</a></li>
</ul></li>
<li class="chapter" data-level="25.5" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#likert-plot"><i class="fa fa-check"></i><b>25.5</b> Likert plot</a>
<ul>
<li class="chapter" data-level="25.5.1" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#stacked-or-dodged-bar"><i class="fa fa-check"></i><b>25.5.1</b> Stacked or dodged bar</a></li>
<li class="chapter" data-level="25.5.2" data-path="distribution-histogram-density.html"><a href="distribution-histogram-density.html#likert-graph"><i class="fa fa-check"></i><b>25.5.2</b> Likert Graph</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="26" data-path="proportion.html"><a href="proportion.html"><i class="fa fa-check"></i><b>26</b> PROPORTION</a>
<ul>
<li class="chapter" data-level="26.1" data-path="proportion.html"><a href="proportion.html#pie-chart"><i class="fa fa-check"></i><b>26.1</b> Pie Chart</a></li>
<li class="chapter" data-level="26.2" data-path="proportion.html"><a href="proportion.html#dodged-bar-chart"><i class="fa fa-check"></i><b>26.2</b> Dodged Bar Chart</a></li>
<li class="chapter" data-level="26.3" data-path="proportion.html"><a href="proportion.html#treemap-nested-proportion"><i class="fa fa-check"></i><b>26.3</b> Treemap: Nested Proportion</a>
<ul>
<li class="chapter" data-level="26.3.1" data-path="proportion.html"><a href="proportion.html#carbon"><i class="fa fa-check"></i><b>26.3.1</b> NYT: Carbon by countries</a></li>
<li class="chapter" data-level="26.3.2" data-path="proportion.html"><a href="proportion.html#twbudget"><i class="fa fa-check"></i><b>26.3.2</b> TW: Taiwan Annual Expenditure</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="27" data-path="association.html"><a href="association.html"><i class="fa fa-check"></i><b>27</b> ASSOCIATION</a>
<ul>
<li class="chapter" data-level="27.1" data-path="association.html"><a href="association.html#等比例座標軸"><i class="fa fa-check"></i><b>27.1</b> 等比例座標軸</a>
<ul>
<li class="chapter" data-level="27.1.1" data-path="association.html"><a href="association.html#unicef-optimistic-wgoith"><i class="fa fa-check"></i><b>27.1.1</b> UNICEF-Optimistic (WGOITH)</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="28" data-path="time-trends.html"><a href="time-trends.html"><i class="fa fa-check"></i><b>28</b> TIME & TRENDS</a>
<ul>
<li class="chapter" data-level="28.1" data-path="time-trends.html"><a href="time-trends.html#highlighting-unemployed-population"><i class="fa fa-check"></i><b>28.1</b> Highlighting: Unemployed Population</a>
<ul>
<li class="chapter" data-level="28.1.1" data-path="time-trends.html"><a href="time-trends.html#the-econimics-data"><i class="fa fa-check"></i><b>28.1.1</b> The econimics data</a></li>
<li class="chapter" data-level="28.1.2" data-path="time-trends.html"><a href="time-trends.html#setting-marking-area"><i class="fa fa-check"></i><b>28.1.2</b> Setting marking area</a></li>
</ul></li>
<li class="chapter" data-level="28.2" data-path="time-trends.html"><a href="time-trends.html#smoothing-unemployed"><i class="fa fa-check"></i><b>28.2</b> Smoothing: Unemployed</a>
<ul>
<li class="chapter" data-level="28.2.1" data-path="time-trends.html"><a href="time-trends.html#polls_2008"><i class="fa fa-check"></i><b>28.2.1</b> Polls_2008</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="29" data-path="geospatial.html"><a href="geospatial.html"><i class="fa fa-check"></i><b>29</b> GEOSPATIAL</a>
<ul>
<li class="chapter" data-level="29.1" data-path="geospatial.html"><a href="geospatial.html#world-map"><i class="fa fa-check"></i><b>29.1</b> World Map</a>
<ul>
<li class="chapter" data-level="29.1.1" data-path="geospatial.html"><a href="geospatial.html#bind-data-to-map-data"><i class="fa fa-check"></i><b>29.1.1</b> Bind data to map data</a></li>
<li class="chapter" data-level="29.1.2" data-path="geospatial.html"><a href="geospatial.html#drawing-map"><i class="fa fa-check"></i><b>29.1.2</b> Drawing Map</a></li>
<li class="chapter" data-level="29.1.3" data-path="geospatial.html"><a href="geospatial.html#drawing-map-by-specific-colors"><i class="fa fa-check"></i><b>29.1.3</b> Drawing map by specific colors</a></li>
<li class="chapter" data-level="29.1.4" data-path="geospatial.html"><a href="geospatial.html#practice.-drawing-map-for-every-years"><i class="fa fa-check"></i><b>29.1.4</b> Practice. Drawing map for every years</a></li>
</ul></li>
<li class="chapter" data-level="29.2" data-path="geospatial.html"><a href="geospatial.html#read-spatial-data-from-segis"><i class="fa fa-check"></i><b>29.2</b> Read Spatial Data from SEGIS</a>
<ul>
<li class="chapter" data-level="29.2.1" data-path="geospatial.html"><a href="geospatial.html#the-case-population-and-density-of-taipei"><i class="fa fa-check"></i><b>29.2.1</b> The case: Population and Density of Taipei</a></li>
<li class="chapter" data-level="29.2.2" data-path="geospatial.html"><a href="geospatial.html#projection-投影的概念"><i class="fa fa-check"></i><b>29.2.2</b> Projection 投影的概念</a></li>
</ul></li>
<li class="chapter" data-level="29.3" data-path="geospatial.html"><a href="geospatial.html#town-level-taipei-income"><i class="fa fa-check"></i><b>29.3</b> Town-level: Taipei income</a>
<ul>
<li class="chapter" data-level="29.3.1" data-path="geospatial.html"><a href="geospatial.html#reading-income-data"><i class="fa fa-check"></i><b>29.3.1</b> Reading income data</a></li>
<li class="chapter" data-level="29.3.2" data-path="geospatial.html"><a href="geospatial.html#read-taipei-zip-code"><i class="fa fa-check"></i><b>29.3.2</b> Read Taipei zip code</a></li>
</ul></li>
<li class="chapter" data-level="29.4" data-path="geospatial.html"><a href="geospatial.html#twmap"><i class="fa fa-check"></i><b>29.4</b> Voting map - County level</a>
<ul>
<li class="chapter" data-level="29.4.1" data-path="geospatial.html"><a href="geospatial.html#loading-county-level-president-voting-rate"><i class="fa fa-check"></i><b>29.4.1</b> Loading county-level president voting rate</a></li>
<li class="chapter" data-level="29.4.2" data-path="geospatial.html"><a href="geospatial.html#sf-to-load-county-level-shp"><i class="fa fa-check"></i><b>29.4.2</b> sf to load county level shp</a></li>
<li class="chapter" data-level="29.4.3" data-path="geospatial.html"><a href="geospatial.html#simplfying-map-polygon"><i class="fa fa-check"></i><b>29.4.3</b> Simplfying map polygon</a></li>
<li class="chapter" data-level="29.4.4" data-path="geospatial.html"><a href="geospatial.html#practice.-drawing-taiwan-county-scale-map-from-segis-data"><i class="fa fa-check"></i><b>29.4.4</b> Practice. Drawing Taiwan county-scale map from SEGIS data</a></li>
</ul></li>
<li class="chapter" data-level="29.5" data-path="geospatial.html"><a href="geospatial.html#mapping-data-with-grid"><i class="fa fa-check"></i><b>29.5</b> Mapping data with grid</a>
<ul>
<li class="chapter" data-level="29.5.1" data-path="geospatial.html"><a href="geospatial.html#loading-taiwan-map"><i class="fa fa-check"></i><b>29.5.1</b> Loading Taiwan map</a></li>
<li class="chapter" data-level="29.5.2" data-path="geospatial.html"><a href="geospatial.html#building-grid"><i class="fa fa-check"></i><b>29.5.2</b> Building grid</a></li>
<li class="chapter" data-level="29.5.3" data-path="geospatial.html"><a href="geospatial.html#loading-data-2"><i class="fa fa-check"></i><b>29.5.3</b> loading data</a></li>
<li class="chapter" data-level="29.5.4" data-path="geospatial.html"><a href="geospatial.html#merging-data"><i class="fa fa-check"></i><b>29.5.4</b> Merging data</a></li>
</ul></li>
<li class="chapter" data-level="29.6" data-path="geospatial.html"><a href="geospatial.html#mapping-youbike-location"><i class="fa fa-check"></i><b>29.6</b> Mapping Youbike Location</a>
<ul>
<li class="chapter" data-level="29.6.1" data-path="geospatial.html"><a href="geospatial.html#creating-a-new-variable"><i class="fa fa-check"></i><b>29.6.1</b> Creating a new variable</a></li>
<li class="chapter" data-level="29.6.2" data-path="geospatial.html"><a href="geospatial.html#mapping-with-sf"><i class="fa fa-check"></i><b>29.6.2</b> Mapping with sf</a></li>
<li class="chapter" data-level="29.6.3" data-path="geospatial.html"><a href="geospatial.html#using-ggmap-deprecated"><i class="fa fa-check"></i><b>29.6.3</b> Using ggmap (Deprecated)</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="30" data-path="network-vis.html"><a href="network-vis.html"><i class="fa fa-check"></i><b>30</b> NETWORK VIS</a>
<ul>
<li class="chapter" data-level="30.1" data-path="network-vis.html"><a href="network-vis.html#generating-networks"><i class="fa fa-check"></i><b>30.1</b> Generating networks</a>
<ul>
<li class="chapter" data-level="30.1.1" data-path="network-vis.html"><a href="network-vis.html#random-network"><i class="fa fa-check"></i><b>30.1.1</b> Random network</a></li>
<li class="chapter" data-level="30.1.2" data-path="network-vis.html"><a href="network-vis.html#random-network-1"><i class="fa fa-check"></i><b>30.1.2</b> Random network</a></li>
</ul></li>
<li class="chapter" data-level="30.2" data-path="network-vis.html"><a href="network-vis.html#retrieve-top3-components"><i class="fa fa-check"></i><b>30.2</b> Retrieve Top3 Components</a>
<ul>
<li class="chapter" data-level="30.2.1" data-path="network-vis.html"><a href="network-vis.html#visualize-again"><i class="fa fa-check"></i><b>30.2.1</b> Visualize again</a></li>
</ul></li>
<li class="chapter" data-level="30.3" data-path="network-vis.html"><a href="network-vis.html#motif-visualization-and-analysis"><i class="fa fa-check"></i><b>30.3</b> Motif visualization and analysis</a>
<ul>
<li class="chapter" data-level="30.3.1" data-path="network-vis.html"><a href="network-vis.html#motif-type"><i class="fa fa-check"></i><b>30.3.1</b> Motif type</a></li>
<li class="chapter" data-level="30.3.2" data-path="network-vis.html"><a href="network-vis.html#motif-analysis"><i class="fa fa-check"></i><b>30.3.2</b> Motif analysis</a></li>
<li class="chapter" data-level="30.3.3" data-path="network-vis.html"><a href="network-vis.html#generate-motives"><i class="fa fa-check"></i><b>30.3.3</b> Generate motives</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="31" data-path="interactivity.html"><a href="interactivity.html"><i class="fa fa-check"></i><b>31</b> Interactivity</a>
<ul>
<li class="chapter" data-level="31.1" data-path="interactivity.html"><a href="interactivity.html#ggplotly"><i class="fa fa-check"></i><b>31.1</b> ggplotly</a>
<ul>
<li class="chapter" data-level="31.1.1" data-path="interactivity.html"><a href="interactivity.html#line-chart"><i class="fa fa-check"></i><b>31.1.1</b> LINE CHART</a></li>
<li class="chapter" data-level="31.1.2" data-path="interactivity.html"><a href="interactivity.html#scatter"><i class="fa fa-check"></i><b>31.1.2</b> SCATTER</a></li>
<li class="chapter" data-level="31.1.3" data-path="interactivity.html"><a href="interactivity.html#barplot"><i class="fa fa-check"></i><b>31.1.3</b> Barplot</a></li>
<li class="chapter" data-level="31.1.4" data-path="interactivity.html"><a href="interactivity.html#boxplot"><i class="fa fa-check"></i><b>31.1.4</b> Boxplot</a></li>
<li class="chapter" data-level="31.1.5" data-path="interactivity.html"><a href="interactivity.html#treemap-global-carbon"><i class="fa fa-check"></i><b>31.1.5</b> Treemap (Global Carbon)</a></li>
</ul></li>
<li class="chapter" data-level="31.2" data-path="interactivity.html"><a href="interactivity.html#產製圖表動畫"><i class="fa fa-check"></i><b>31.2</b> 產製圖表動畫</a>
<ul>
<li class="chapter" data-level="31.2.1" data-path="interactivity.html"><a href="interactivity.html#地圖下載與轉換投影方法"><i class="fa fa-check"></i><b>31.2.1</b> 地圖下載與轉換投影方法</a></li>
<li class="chapter" data-level="31.2.2" data-path="interactivity.html"><a href="interactivity.html#靜態繪圖測試"><i class="fa fa-check"></i><b>31.2.2</b> 靜態繪圖測試</a></li>
</ul></li>
</ul></li>
<li class="part"><span><b>VI CASE STUDIES</b></span></li>
<li class="chapter" data-level="32" data-path="wgoitg.html"><a href="wgoitg.html"><i class="fa fa-check"></i><b>32</b> WGOITG of NyTimes</a></li>
<li class="chapter" data-level="33" data-path="inequality-net-worth-by-age-group.html"><a href="inequality-net-worth-by-age-group.html"><i class="fa fa-check"></i><b>33</b> Inequality: Net Worth by Age Group</a></li>
<li class="chapter" data-level="34" data-path="optimism-survey-by-countries.html"><a href="optimism-survey-by-countries.html"><i class="fa fa-check"></i><b>34</b> Optimism Survey by Countries</a></li>
<li class="chapter" data-level="35" data-path="taiwan.html"><a href="taiwan.html"><i class="fa fa-check"></i><b>35</b> Case Studies (Taiwan)</a>
<ul>
<li class="chapter" data-level="35.1" data-path="taiwan.html"><a href="taiwan.html#tw-aqi-visual-studies"><i class="fa fa-check"></i><b>35.1</b> TW AQI Visual Studies</a>
<ul>
<li class="chapter" data-level="35.1.1" data-path="taiwan.html"><a href="taiwan.html#eda-load-data-from-github"><i class="fa fa-check"></i><b>35.1.1</b> eda-load-data-from-github</a></li>
<li class="chapter" data-level="35.1.2" data-path="taiwan.html"><a href="taiwan.html#trending-central-tendency"><i class="fa fa-check"></i><b>35.1.2</b> Trending: Central tendency</a></li>
<li class="chapter" data-level="35.1.3" data-path="taiwan.html"><a href="taiwan.html#trending-extreme-value"><i class="fa fa-check"></i><b>35.1.3</b> Trending: Extreme value</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="36" data-path="appendix.html"><a href="appendix.html"><i class="fa fa-check"></i><b>36</b> Appendix</a>
<ul>
<li class="chapter" data-level="36.1" data-path="appendix.html"><a href="appendix.html#dataset"><i class="fa fa-check"></i><b>36.1</b> Dataset</a></li>
</ul></li>
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<li><a href="https://github.com/rstudio/bookdown" target="blank">Published with bookdown</a></li>
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<h1>
<i class="fa fa-circle-o-notch fa-spin"></i><a href="./">R for Data Journalism</a>
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<section class="normal" id="section-">
<div id="na" class="section level1 hasAnchor" number="10">
<h1><span class="header-section-number">Chapter 10</span> NA Processing<a href="na.html#na" class="anchor-section" aria-label="Anchor link to header"></a></h1>
<p>許多統計資料都會有不同程度的<code>NA</code>（缺失值、遺漏值）。缺失值產生的原因不一，可能有以下原因：</p>
<ol style="list-style-type: decimal">
<li>資料運算的時候產生的填缺失值。例如<code>spread()</code>和<code>pivot_wider()</code>經常會產生<code>NA</code>，也經常會指定值（例如0）來取代可能產生的<code>NA</code>。</li>
<li>資料紀錄的時候遺漏某些時間點的資料。</li>
<li>開放資料在開放時已經被整理成階層化、易於展示、一般人易懂的表格型態。此時，若將其讀入也會產生非常大量的<code>NA</code>。例如本章節所要提到的政府各部會預算比例。</li>
<li>紀錄資料筆數非常龐大、來源眾多、紀錄時間不一時，雖然有很多紀錄，但這些紀錄必須要被對齊、刪減，才能夠獲得有意義的可計算資料。例如本章節會提到的世界各國疫苗注射資料。</li>
</ol>
<div id="cleaning-gov-annual-budget" class="section level2 hasAnchor" number="10.1">
<h2><span class="header-section-number">10.1</span> Cleaning Gov Annual Budget<a href="na.html#cleaning-gov-annual-budget" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p>這個範例將清理中央政府111年度的歲出預算表。由於政府預算有款、科、目、節與機構，會呈現一個大部會到小布會的樹狀階層，因此非常適合用Treemap來做視覺化，預期視覺化的結果如下（視覺化的部分可參考章節<a href="proportion.html#treemap-nested-proportion">26.3</a>）：</p>
<p><img src="images/paste-38DB44D3.png" /></p>
<p>在讀入資料後，由於資料具有階層性、從最大的科款、項、目，由於是給一般讀者所閱讀的資料，在原本的EXCEL表格中，比較大的階層可能會合併數個資料格來表示，這會使得較大的階層會有相當多的缺失值。此時，我們會需要依據其他列的值，來回填這些NA值，所用的函式為<code>zoo::na.locf()</code>。</p>
<div class="sourceCode" id="cb455"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb455-1"><a href="na.html#cb455-1" tabindex="-1"></a>raw <span class="ot"><-</span> readxl<span class="sc">::</span><span class="fu">read_excel</span>(<span class="st">"data/111B歲出政事別預算表.xls"</span>, <span class="at">skip=</span><span class="dv">3</span>, <span class="at">col_names =</span> F)</span>
<span id="cb455-2"><a href="na.html#cb455-2" tabindex="-1"></a>raw <span class="sc">%>%</span> <span class="fu">head</span>(<span class="dv">10</span>)</span></code></pre></div>
<pre class="output"><code>## # A tibble: 10 × 9
## ...1 ...2 ...3 ...4 ...5 ...6 ...7 ...8 ...9
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 科 … <NA> <NA> <NA> <NA> 本年… 上年… 前年… "本…
## 2 款 項 目 節 "名 … <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> "\n… 2262… 2135… 2039… "126…
## 4 <NA> <NA> <NA> <NA> "\n(… 2101… 2026… 1907… "750…
## 5 1 <NA> <NA> <NA> "310… 1210… 1186… 1176… "233…
## 6 <NA> 1 <NA> <NA> "310… 1004… 9789… 9973… "258…
## 7 <NA> <NA> 1 <NA> "310… 9205… 8963… 8821… "241…
## 8 <NA> <NA> 2 <NA> "310… 30000 30000 2999… "-"
## 9 <NA> <NA> 3 <NA> "310… 15760 15760 4557… "-"
## 10 <NA> <NA> 4 <NA> "310… 5332 5332 6720… "-"</code></pre>
<div id="basic-cleaning" class="section level3 hasAnchor" number="10.1.1">
<h3><span class="header-section-number">10.1.1</span> Basic Cleaning<a href="na.html#basic-cleaning" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<ol style="list-style-type: decimal">
<li>重新命名欄位名稱</li>
<li>刪去被當成表格標題的多於列（通常是前兩三列）<code>slice(-(1:2))</code>。</li>
<li>觀察資料，「款」可以說是支出大類的代號，例如總統府、行政支出、立法支出、軍事支出、教育支出等。「科」為該單位底下的部門或者項目，例如「行政支出」下有行政院、主計總處支出等。更底下的細類「目」並非本例的分析對象，所以可以刪除。所以，如果款、科均為缺失值的話，代表其為更細的「目」。因此篩去款科為缺失值的所有項目。<code>filter(!is.na(款) | !is.na(科))</code></li>
<li>將機構id和機構名稱切分開來，視覺化的時候只會用到機構名稱。<code>separate(機構, c("oid", "org"), sep="\n")</code></li>
</ol>
<div class="sourceCode" id="cb457"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb457-1"><a href="na.html#cb457-1" tabindex="-1"></a><span class="fu">names</span>(raw) <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"款"</span>, <span class="st">"科"</span>, <span class="st">"目"</span>, <span class="st">"節"</span>, <span class="st">"機構"</span>, <span class="st">"本年度預算"</span>, <span class="st">"上年度預算"</span>, <span class="st">"上年度決算"</span>, <span class="st">"預算差"</span>)</span>
<span id="cb457-2"><a href="na.html#cb457-2" tabindex="-1"></a></span>
<span id="cb457-3"><a href="na.html#cb457-3" tabindex="-1"></a>cleaned <span class="ot"><-</span> raw <span class="sc">%>%</span></span>
<span id="cb457-4"><a href="na.html#cb457-4" tabindex="-1"></a> <span class="fu">slice</span>(<span class="sc">-</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>)) <span class="sc">%>%</span></span>
<span id="cb457-5"><a href="na.html#cb457-5" tabindex="-1"></a> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(款) <span class="sc">|</span> <span class="sc">!</span><span class="fu">is.na</span>(科)) <span class="sc">%>%</span></span>
<span id="cb457-6"><a href="na.html#cb457-6" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>目, <span class="sc">-</span>節) <span class="sc">%>%</span></span>
<span id="cb457-7"><a href="na.html#cb457-7" tabindex="-1"></a> <span class="fu">separate</span>(機構, <span class="fu">c</span>(<span class="st">"oid"</span>, <span class="st">"org"</span>), <span class="at">sep=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span>
<span id="cb457-8"><a href="na.html#cb457-8" tabindex="-1"></a>cleaned <span class="sc">%>%</span> <span class="fu">head</span>(<span class="dv">10</span>)</span></code></pre></div>
<pre class="output"><code>## # A tibble: 10 × 8
## 款 科 oid org 本年度預算 上年度預算 上年度決算 預算差
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 <NA> 3100000000 國務支出 1210301 1186955 1176955.12… 23346
## 2 <NA> 1 3102010000 總統府 1004797 978916 997305.545… 25881
## 3 <NA> 2 3102100000 國家安全會議 205504 208039 179649.579… -2535
## 4 2 <NA> 3200000000 行政支出 6134276 5836481 5477154.58… 297795
## 5 <NA> 1 3203010000 行政院 1256043 1286646 1268295.23 -30603
## 6 <NA> 2 3203100000 主計總處 1604967 1478173 1578781.89… 126794
## 7 <NA> 3 3203300000 人事行政總處 555363 573447 489516.177… -18084
## 8 <NA> 4 3203340000 公務人力發展… 244346 239453 229852.261… 4893
## 9 <NA> 5 3203420000 檔案管理局 787429 646081 443133.207… 141348
## 10 <NA> 6 3203900000 大陸委員會 900896 900866 792491.221… 30</code></pre>
</div>
<div id="processing-na" class="section level3 hasAnchor" number="10.1.2">
<h3><span class="header-section-number">10.1.2</span> Processing NA<a href="na.html#processing-na" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>觀察一下現在的資料，發現，行政院、主計總處等均屬於行政支出，但行政支出卻自有一列。依照長表格的格式來說，應嘗試把「款」作為機構的變項。所以將款的數字取代為「行政支出」等支出類別的名稱。</p>
<div class="sourceCode" id="cb459"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb459-1"><a href="na.html#cb459-1" tabindex="-1"></a>cleaned <span class="sc">%>%</span> <span class="fu">mutate</span>(款 <span class="ot">=</span> <span class="fu">ifelse</span>(<span class="sc">!</span><span class="fu">is.na</span>(款), org, 款)) <span class="sc">%>%</span></span>
<span id="cb459-2"><a href="na.html#cb459-2" tabindex="-1"></a> <span class="fu">head</span>(<span class="dv">10</span>)</span></code></pre></div>
<pre class="output"><code>## # A tibble: 10 × 8
## 款 科 oid org 本年度預算 上年度預算 上年度決算 預算差
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 國務支出 <NA> 3100000000 國務支出 1210301 1186955 1176955.1… 23346
## 2 <NA> 1 3102010000 總統府 1004797 978916 997305.54… 25881
## 3 <NA> 2 3102100000 國家安全會… 205504 208039 179649.57… -2535
## 4 行政支出 <NA> 3200000000 行政支出 6134276 5836481 5477154.5… 297795
## 5 <NA> 1 3203010000 行政院 1256043 1286646 1268295.23 -30603
## 6 <NA> 2 3203100000 主計總處 1604967 1478173 1578781.8… 126794
## 7 <NA> 3 3203300000 人事行政總… 555363 573447 489516.17… -18084
## 8 <NA> 4 3203340000 公務人力發… 244346 239453 229852.26… 4893
## 9 <NA> 5 3203420000 檔案管理局 787429 646081 443133.20… 141348
## 10 <NA> 6 3203900000 大陸委員會 900896 900866 792491.22… 30</code></pre>
<p>接下來，希望能夠在「款==<code>NA</code>」的地方填入該欄的「前一個值」例如行政支出。查詢一下（關鍵字如「Fill in NA column values with the last value that was not NA」）還真的有這樣的函式可以操作：</p>
<div class="notes">
<p><strong><code>zoo::na.locf()</code></strong>：<strong><code>zoo::na.locf()</code></strong> 是 R 語言中 <strong><code>zoo</code></strong> 套件提供的函式，其作用是將缺失值（NA）用最後一個非缺失值（non-missing value）填充。具體而言，<strong><code>na.locf()</code></strong> 函式將會從第一個非缺失值開始向下填充，直到下一個非缺失值出現為止。這種方法稱為 “last observation carried forward”（LOCF），意思是最後觀測值向前填充。</p>
</div>
<div class="sourceCode" id="cb461"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb461-1"><a href="na.html#cb461-1" tabindex="-1"></a><span class="fu">library</span>(zoo)</span>
<span id="cb461-2"><a href="na.html#cb461-2" tabindex="-1"></a>cleaned <span class="sc">%>%</span> </span>
<span id="cb461-3"><a href="na.html#cb461-3" tabindex="-1"></a> <span class="fu">mutate</span>(款 <span class="ot">=</span> <span class="fu">ifelse</span>(<span class="sc">!</span><span class="fu">is.na</span>(款), org, 款)) <span class="sc">%>%</span></span>
<span id="cb461-4"><a href="na.html#cb461-4" tabindex="-1"></a> <span class="fu">mutate</span>(款 <span class="ot">=</span> zoo<span class="sc">::</span><span class="fu">na.locf</span>(款)) <span class="sc">%>%</span></span>
<span id="cb461-5"><a href="na.html#cb461-5" tabindex="-1"></a> <span class="fu">head</span>(<span class="dv">10</span>)</span></code></pre></div>
<pre class="output"><code>## # A tibble: 10 × 8
## 款 科 oid org 本年度預算 上年度預算 上年度決算 預算差
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 國務支出 <NA> 3100000000 國務支出 1210301 1186955 1176955.1… 23346
## 2 國務支出 1 3102010000 總統府 1004797 978916 997305.54… 25881
## 3 國務支出 2 3102100000 國家安全會… 205504 208039 179649.57… -2535
## 4 行政支出 <NA> 3200000000 行政支出 6134276 5836481 5477154.5… 297795
## 5 行政支出 1 3203010000 行政院 1256043 1286646 1268295.23 -30603
## 6 行政支出 2 3203100000 主計總處 1604967 1478173 1578781.8… 126794
## 7 行政支出 3 3203300000 人事行政總… 555363 573447 489516.17… -18084
## 8 行政支出 4 3203340000 公務人力發… 244346 239453 229852.26… 4893
## 9 行政支出 5 3203420000 檔案管理局 787429 646081 443133.20… 141348
## 10 行政支出 6 3203900000 大陸委員會 900896 900866 792491.22… 30</code></pre>
<p>太神奇了！看見沒！接下來只要把「科 is <code>NA</code>」的那些該大類支出總數的紀錄給刪除，資料就乾淨了。最後就只會剩下一些資料清理的功伕。完整程式碼可以看下一節。</p>
</div>
<div id="complete-code" class="section level3 hasAnchor" number="10.1.3">
<h3><span class="header-section-number">10.1.3</span> Complete Code<a href="na.html#complete-code" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<div class="sourceCode" id="cb463"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb463-1"><a href="na.html#cb463-1" tabindex="-1"></a><span class="fu">library</span>(zoo)</span>
<span id="cb463-2"><a href="na.html#cb463-2" tabindex="-1"></a><span class="co"># raw <- readxl::read_excel("data/111B歲出政事別預算總表.xls")</span></span>
<span id="cb463-3"><a href="na.html#cb463-3" tabindex="-1"></a>raw <span class="ot"><-</span> readxl<span class="sc">::</span><span class="fu">read_excel</span>(<span class="st">"data/111B歲出政事別預算表.xls"</span>, <span class="at">skip=</span><span class="dv">3</span>, <span class="at">col_names =</span> F) </span>
<span id="cb463-4"><a href="na.html#cb463-4" tabindex="-1"></a></span>
<span id="cb463-5"><a href="na.html#cb463-5" tabindex="-1"></a><span class="fu">names</span>(raw) <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"款"</span>, <span class="st">"科"</span>, <span class="st">"目"</span>, <span class="st">"節"</span>, <span class="st">"機構"</span>, <span class="st">"本年度預算"</span>, <span class="st">"上年度預算"</span>, <span class="st">"上年度決算"</span>, <span class="st">"預算差"</span>)</span>
<span id="cb463-6"><a href="na.html#cb463-6" tabindex="-1"></a><span class="co"># raw$款 <- na.locf(raw$款)</span></span>
<span id="cb463-7"><a href="na.html#cb463-7" tabindex="-1"></a></span>
<span id="cb463-8"><a href="na.html#cb463-8" tabindex="-1"></a>cleaned <span class="ot"><-</span> raw <span class="sc">%>%</span></span>
<span id="cb463-9"><a href="na.html#cb463-9" tabindex="-1"></a> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(款) <span class="sc">|</span> <span class="sc">!</span><span class="fu">is.na</span>(科)) <span class="sc">%>%</span></span>
<span id="cb463-10"><a href="na.html#cb463-10" tabindex="-1"></a> <span class="fu">slice</span>(<span class="sc">-</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>)) <span class="sc">%>%</span></span>
<span id="cb463-11"><a href="na.html#cb463-11" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>目, <span class="sc">-</span>節) <span class="sc">%>%</span></span>
<span id="cb463-12"><a href="na.html#cb463-12" tabindex="-1"></a> <span class="fu">separate</span>(機構, <span class="fu">c</span>(<span class="st">"oid"</span>, <span class="st">"org"</span>), <span class="at">sep=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>) <span class="sc">%>%</span></span>
<span id="cb463-13"><a href="na.html#cb463-13" tabindex="-1"></a> <span class="fu">mutate</span>(款 <span class="ot">=</span> <span class="fu">ifelse</span>(<span class="sc">!</span><span class="fu">is.na</span>(款), org, 款)) <span class="sc">%>%</span></span>
<span id="cb463-14"><a href="na.html#cb463-14" tabindex="-1"></a> <span class="fu">mutate</span>(款 <span class="ot">=</span> zoo<span class="sc">::</span><span class="fu">na.locf</span>(款)) <span class="sc">%>%</span></span>
<span id="cb463-15"><a href="na.html#cb463-15" tabindex="-1"></a> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(科)) <span class="sc">%>%</span></span>
<span id="cb463-16"><a href="na.html#cb463-16" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>科) <span class="sc">%>%</span> <span class="fu">type_convert</span>() <span class="sc">%>%</span></span>
<span id="cb463-17"><a href="na.html#cb463-17" tabindex="-1"></a> <span class="fu">mutate</span>(上年度預算 <span class="ot">=</span> <span class="fu">as.numeric</span>(上年度預算), </span>
<span id="cb463-18"><a href="na.html#cb463-18" tabindex="-1"></a> 上年度決算 <span class="ot">=</span> <span class="fu">as.integer</span>(上年度決算),</span>
<span id="cb463-19"><a href="na.html#cb463-19" tabindex="-1"></a> 預算差 <span class="ot">=</span> <span class="fu">as.numeric</span>(預算差)) <span class="sc">%>%</span></span>
<span id="cb463-20"><a href="na.html#cb463-20" tabindex="-1"></a> <span class="fu">replace_na</span>(<span class="fu">list</span>(上年度預算 <span class="ot">=</span> <span class="dv">0</span>, 上年度決算 <span class="ot">=</span> <span class="dv">0</span>)) <span class="sc">%>%</span></span>
<span id="cb463-21"><a href="na.html#cb463-21" tabindex="-1"></a> <span class="fu">mutate</span>(預算差 <span class="ot">=</span> 本年度預算 <span class="sc">-</span> 上年度預算)</span>
<span id="cb463-22"><a href="na.html#cb463-22" tabindex="-1"></a></span>
<span id="cb463-23"><a href="na.html#cb463-23" tabindex="-1"></a>cleaned <span class="sc">%>%</span> <span class="fu">head</span>()</span></code></pre></div>
<pre class="output"><code>## # A tibble: 6 × 7
## 款 oid org 本年度預算 上年度預算 上年度決算 預算差
## <chr> <dbl> <chr> <dbl> <dbl> <int> <dbl>
## 1 國務支出 3102010000 總統府 1004797 978916 997305 25881
## 2 國務支出 3102100000 國家安全會議 205504 208039 179649 -2535
## 3 行政支出 3203010000 行政院 1256043 1286646 1268295 -30603
## 4 行政支出 3203100000 主計總處 1604967 1478173 1578781 126794
## 5 行政支出 3203300000 人事行政總處 555363 573447 489516 -18084
## 6 行政支出 3203340000 公務人力發展學院 244346 239453 229852 4893</code></pre>
</div>
</div>
<div id="cleaning-covid-vaccinating-data" class="section level2 hasAnchor" number="10.2">
<h2><span class="header-section-number">10.2</span> Cleaning Covid Vaccinating data<a href="na.html#cleaning-covid-vaccinating-data" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p>這個案例是希望視覺化不同國家（Y）在不同時間點（X）的疫苗施打涵蓋率（將使用熱區圖，所以將用顏色來表示涵蓋率）。涵蓋率的表示法在該資料中為每百萬人施打疫苗數，但也可以轉為百分比，有多少比例的人已經施打過第一劑、第二劑或第三劑等。</p>
<p>資料來源為：</p>
<ul>
<li><p><a href="https://ourworldindata.org/covid-vaccinations" class="uri">https://ourworldindata.org/covid-vaccinations</a></p></li>
<li><p><a href="https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations" class="uri">https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations</a></p></li>
</ul>
<p>預期希望看見的結果如下，如何將這份疫苗施打比例的資料做視覺化，請見視覺化的章節<a href="#heatmap-vaccination"><strong>??</strong></a>：</p>
<p><img src="images/paste-3609B9F5.png" /></p>
<div id="觀察並評估資料概況" class="section level3 hasAnchor" number="10.2.1">
<h3><span class="header-section-number">10.2.1</span> 觀察並評估資料概況<a href="na.html#觀察並評估資料概況" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p><strong>這是一份資料缺失相當多的資料</strong>。評估資料概況後可發現這個資料集每一列就是某一個國家某一天所上傳的紀錄。所以，一個國家會有很多列。乍聽之下不難處理，但事實上每個國家不會每天上傳、也不會固定某一天上傳、哪一週、哪一個月開始上傳也不一定，也有可能會漏掉一些月份或週次。所以，制定出一個時間單位（例如週、月）、然後延著時間軸將資料「對齊」，讓每個國家在每個時間單位都有資料。但每個國家疫情發展程度不一，所以也不可能有一個完美的對齊，所以通常會建議就所要觀察的國家進行對齊即可。至於想刪除的那些資料列，幾乎都可以當成是所謂的缺失值。</p>
<div class="sourceCode" id="cb465"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb465-1"><a href="na.html#cb465-1" tabindex="-1"></a>raw <span class="ot"><-</span> <span class="fu">read_csv</span>(<span class="st">"data/vaccinations.csv"</span>)</span>
<span id="cb465-2"><a href="na.html#cb465-2" tabindex="-1"></a><span class="fu">dim</span>(raw)</span></code></pre></div>
<pre class="output"><code>## [1] 99442 16</code></pre>
<div class="sourceCode" id="cb467"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb467-1"><a href="na.html#cb467-1" tabindex="-1"></a>raw <span class="sc">%>%</span> <span class="fu">head</span>(<span class="dv">20</span>)</span></code></pre></div>
<pre class="output"><code>## # A tibble: 20 × 16
## location iso_code date total_vaccinations people_vaccinated
## <chr> <chr> <date> <dbl> <dbl>
## 1 Afghanistan AFG 2021-02-22 0 0
## 2 Afghanistan AFG 2021-02-23 NA NA
## 3 Afghanistan AFG 2021-02-24 NA NA
## 4 Afghanistan AFG 2021-02-25 NA NA
## 5 Afghanistan AFG 2021-02-26 NA NA
## 6 Afghanistan AFG 2021-02-27 NA NA
## 7 Afghanistan AFG 2021-02-28 8200 8200
## 8 Afghanistan AFG 2021-03-01 NA NA
## 9 Afghanistan AFG 2021-03-02 NA NA
## 10 Afghanistan AFG 2021-03-03 NA NA
## 11 Afghanistan AFG 2021-03-04 NA NA
## 12 Afghanistan AFG 2021-03-05 NA NA
## 13 Afghanistan AFG 2021-03-06 NA NA
## 14 Afghanistan AFG 2021-03-07 NA NA
## 15 Afghanistan AFG 2021-03-08 NA NA
## 16 Afghanistan AFG 2021-03-09 NA NA
## 17 Afghanistan AFG 2021-03-10 NA NA
## 18 Afghanistan AFG 2021-03-11 NA NA
## 19 Afghanistan AFG 2021-03-12 NA NA
## 20 Afghanistan AFG 2021-03-13 NA NA
## # ℹ 11 more variables: people_fully_vaccinated <dbl>, total_boosters <dbl>,
## # daily_vaccinations_raw <dbl>, daily_vaccinations <dbl>,
## # total_vaccinations_per_hundred <dbl>, people_vaccinated_per_hundred <dbl>,
## # people_fully_vaccinated_per_hundred <dbl>,
## # total_boosters_per_hundred <dbl>, daily_vaccinations_per_million <dbl>,
## # daily_people_vaccinated <dbl>, daily_people_vaccinated_per_hundred <dbl></code></pre>
</div>
<div id="按月對齊資料" class="section level3 hasAnchor" number="10.2.2">
<h3><span class="header-section-number">10.2.2</span> 按月對齊資料<a href="na.html#按月對齊資料" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>首先要挑選要拿來做視覺化的資料欄位。這邊所選擇的是<code>people_fully_vaccinated_per_hundred</code>，也就是每百人接種二劑疫苗的人數，相當於接種二劑疫苗的百分比。</p>
<p>接下來便是缺失值處理，如果這個欄位沒有數值的就直接用<code>drop_na()</code>篩除即可。</p>
<p>這個範例希望把該資料視覺化為Y軸為年、X軸為時間的熱區圖。但整個疫情資料橫亙二年多，如果以週為彙整單位的話，那勢必X軸會有近百個資料點。所以打算以「月」為單位來彙整這些資料，因為且資料中也有不少國家缺數週的資料，所以以月為彙整單位是一個權衡後的選擇（仍可以嘗試用週作為彙整單位試試看）。所以，運用了<code>lubridate::floor_date()</code>來將日期資料轉換為月，例如2022-03-12和2022-03-14都會被轉換為2022-03-01。</p>
<p>依照國家與時間群組彙整資料。接下來就依照各國的月份來做彙整（注意，此時會有不少資料同屬於某個月的資料）。彙整的方法是，經過對「日期」（不是對月）做排序後，僅留下第一筆資料，也就是僅留下最接近月份開頭的資料。經由這樣的操作，會使得各國在每個月剛好留下一筆資料，如下面程式的範例輸出。</p>
<div class="sourceCode" id="cb469"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb469-1"><a href="na.html#cb469-1" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
<span id="cb469-2"><a href="na.html#cb469-2" tabindex="-1"></a>fullvaccinated <span class="ot"><-</span> raw <span class="sc">%>%</span> <span class="fu">select</span>(<span class="at">country =</span> location, date, </span>
<span id="cb469-3"><a href="na.html#cb469-3" tabindex="-1"></a> people_fully_vaccinated_per_hundred) <span class="sc">%>%</span></span>
<span id="cb469-4"><a href="na.html#cb469-4" tabindex="-1"></a> <span class="fu">drop_na</span>(people_fully_vaccinated_per_hundred) <span class="sc">%>%</span></span>
<span id="cb469-5"><a href="na.html#cb469-5" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">m =</span> <span class="fu">floor_date</span>(date, <span class="at">unit =</span> <span class="st">"month"</span>)) <span class="sc">%>%</span></span>
<span id="cb469-6"><a href="na.html#cb469-6" tabindex="-1"></a> <span class="fu">group_by</span>(country, m) <span class="sc">%>%</span></span>
<span id="cb469-7"><a href="na.html#cb469-7" tabindex="-1"></a> <span class="fu">arrange</span>(date) <span class="sc">%>%</span></span>
<span id="cb469-8"><a href="na.html#cb469-8" tabindex="-1"></a> <span class="fu">slice</span>(<span class="dv">1</span>) <span class="sc">%>%</span></span>
<span id="cb469-9"><a href="na.html#cb469-9" tabindex="-1"></a> <span class="fu">ungroup</span>() <span class="sc">%>%</span></span>
<span id="cb469-10"><a href="na.html#cb469-10" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>date)</span>
<span id="cb469-11"><a href="na.html#cb469-11" tabindex="-1"></a></span>
<span id="cb469-12"><a href="na.html#cb469-12" tabindex="-1"></a>fullvaccinated <span class="sc">%>%</span> <span class="fu">head</span>(<span class="dv">10</span>)</span></code></pre></div>
<pre class="output"><code>## # A tibble: 10 × 3
## country people_fully_vaccinated_per_hundred m