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<!DOCTYPE html>
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<title>Chapter 6 From base R to dplyr | R for Data Journalism</title>
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<meta name="twitter:title" content="Chapter 6 From base R to dplyr | 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="3.3.3" data-path="r-basic.html"><a href="r-basic.html#subsetting-by-logic-comparisons"><i class="fa fa-check"></i><b>3.3.3</b> Subsetting by logic comparisons</a></li>
<li class="chapter" data-level="3.3.4" data-path="r-basic.html"><a href="r-basic.html#sorting-and-ordering"><i class="fa fa-check"></i><b>3.3.4</b> Sorting and ordering</a></li>
<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>
<li class="divider"></li>
<li><a href="https://github.com/rstudio/bookdown" target="blank">Published with bookdown</a></li>
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<div class="body-inner">
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<h1>
<i class="fa fa-circle-o-notch fa-spin"></i><a href="./">R for Data Journalism</a>
</h1>
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<section class="normal" id="section-">
<div id="base2dplyr" class="section level1 hasAnchor" number="6">
<h1><span class="header-section-number">Chapter 6</span> From base R to dplyr<a href="base2dplyr.html#base2dplyr" class="anchor-section" aria-label="Anchor link to header"></a></h1>
<p><strong>From base to tidyverse style</strong></p>
<p>相較於R base的較為傳統的R編程風格，tidyverse style的R programming具有以下幾個特點：</p>
<ol style="list-style-type: decimal">
<li><p>基於tidy data理念：tidyverse style的R programming基於tidy data理念，即資料應該以規律的方式組織，以方便分析和視覺化。tidyverse style的R程式庫提供了一些工具和函數，用於處理和轉換tidy data格式的資料，如dplyr、tidyr等。</p></li>
<li><p>使用管道操作符：tidyverse style的R programming通常使用管道操作符（%>%），將資料通過多個函數連接起來，形成一個清晰和易於理解的資料處理流程。使用管道操作符可以簡化程式碼並提高程式的可讀性。</p></li>
<li><p>強調函數庫的一致性：tidyverse style的R programming強調函數庫之間的一致性，即不同函數庫之間使用相似的函數名稱、參數名稱和返回值等，以方便使用者的學習和使用。</p></li>
<li><p>使用簡潔的命名方式：tidyverse style的R programming通常使用簡潔和易於理解的變數和函數命名方式，例如使用動詞表示操作，使用名詞表示資料，以方便使用者理解程式碼的含義。</p></li>
<li><p>提供高級的視覺化工具：tidyverse style的R programming提供了一些高級的視覺化工具，如ggplot2、gganimate等，可以幫助使用者更加輕鬆地進行資料視覺化和探索。</p></li>
</ol>
<div id="dplyr" class="section level2 hasAnchor" number="6.1">
<h2><span class="header-section-number">6.1</span> dplyr<a href="base2dplyr.html#dplyr" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p>dplyr是一個tidyverse風格的R程式庫，用於對資料進行快速、一致、直觀的操作和轉換。dplyr提供了一些高效能的函數和工具，如<code>filter</code>、<code>select</code>、<code>mutate</code>、<code>group_by</code>和<code>summarize</code>等，用於對資料進行選擇、篩選、轉換、分組和摘要等操作。</p>
<p>以下是dplyr常用的函數：</p>
<ol style="list-style-type: decimal">
<li><p><code>filter</code>：用於選擇符合特定條件的資料列。</p></li>
<li><p><code>select</code>：用於選擇特定的欄位。</p></li>
<li><p><code>mutate</code>：用於新增或修改欄位。</p></li>
<li><p><code>group_by</code>：用於按照特定欄位進行分組。</p></li>
<li><p><code>summarize</code>：用於對分組後的資料進行摘要統計。</p></li>
<li><p><code>arrange</code>：用於按照欄位的特定順序進行排序。</p></li>
</ol>
<p>dplyr具有以下優點：</p>
<ol style="list-style-type: decimal">
<li><p>簡潔而直觀的語法：dplyr的函數名稱和語法都十分簡潔而直觀，易於使用和理解，尤其對於新手來說更加友好。</p></li>
<li><p>高效的運行速度：dplyr的設計考慮了資料處理的效率，使用C++實現了部分函數，因此dplyr在處理大型資料集時運行速度較快。</p></li>
<li><p>與tidyverse相容：dplyr與其他tidyverse程式庫，如ggplot2和tidyr，可以很好地相容，並且能夠與其他常用的R程式庫進行集成，提供更加全面和高效的資料分析和可視化工具。</p></li>
</ol>
</div>
<div id="tptheft_dplyr" class="section level2 hasAnchor" number="6.2">
<h2><span class="header-section-number">6.2</span> Taipie Theft Count (base to dplyr)<a href="base2dplyr.html#tptheft_dplyr" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<div class="sourceCode" id="cb342"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb342-1"><a href="base2dplyr.html#cb342-1" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb342-2"><a href="base2dplyr.html#cb342-2" tabindex="-1"></a><span class="co"># options(stringsAsFactors = F) # default options in R ver.> 4.0</span></span></code></pre></div>
<div id="reading-data" class="section level3 hasAnchor" number="6.2.1">
<h3><span class="header-section-number">6.2.1</span> Reading data<a href="base2dplyr.html#reading-data" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<div class="sourceCode" id="cb343"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb343-1"><a href="base2dplyr.html#cb343-1" tabindex="-1"></a><span class="co"># Read by read_csv()</span></span>
<span id="cb343-2"><a href="base2dplyr.html#cb343-2" tabindex="-1"></a><span class="co"># Will raise error</span></span>
<span id="cb343-3"><a href="base2dplyr.html#cb343-3" tabindex="-1"></a><span class="co"># Error in make.names(x) : invalid multibyte string at '<bd>s<b8><b9>'</span></span>
<span id="cb343-4"><a href="base2dplyr.html#cb343-4" tabindex="-1"></a><span class="co"># df <- read_csv("data/tp_theft.csv")</span></span>
<span id="cb343-5"><a href="base2dplyr.html#cb343-5" tabindex="-1"></a></span>
<span id="cb343-6"><a href="base2dplyr.html#cb343-6" tabindex="-1"></a><span class="co"># read_csv() with locale = locale(encoding = "Big5")</span></span>
<span id="cb343-7"><a href="base2dplyr.html#cb343-7" tabindex="-1"></a><span class="fu">library</span>(readr)</span>
<span id="cb343-8"><a href="base2dplyr.html#cb343-8" tabindex="-1"></a>df <span class="ot"><-</span> <span class="fu">read_csv</span>(<span class="st">"data/臺北市住宅竊盜點位資訊-UTF8-BOM-1.csv"</span>)</span></code></pre></div>
</div>
<div id="cleaning-data-i" class="section level3 hasAnchor" number="6.2.2">
<h3><span class="header-section-number">6.2.2</span> Cleaning data I<a href="base2dplyr.html#cleaning-data-i" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<ul>
<li>Renaming variables by <code>select()</code></li>
<li>Generating variable year by <code>mutate()</code></li>
<li>Generating variable month by <code>mutate()</code></li>
<li>Retrieving area by <code>mutate()</code></li>
</ul>
<div id="without-pipeline-i" class="section level4 hasAnchor" number="6.2.2.1">
<h4><span class="header-section-number">6.2.2.1</span> (1) Without pipeline I<a href="base2dplyr.html#without-pipeline-i" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<div class="sourceCode" id="cb344"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb344-1"><a href="base2dplyr.html#cb344-1" tabindex="-1"></a>df1 <span class="ot"><-</span> <span class="fu">select</span>(df, <span class="at">id =</span> 編號, <span class="at">cat =</span> 案類, <span class="at">date =</span> <span class="st">`</span><span class="at">發生日期</span><span class="st">`</span>, <span class="at">time =</span> <span class="st">`</span><span class="at">發生時段</span><span class="st">`</span>, <span class="at">location =</span> <span class="st">`</span><span class="at">發生地點</span><span class="st">`</span>)</span>
<span id="cb344-2"><a href="base2dplyr.html#cb344-2" tabindex="-1"></a></span>
<span id="cb344-3"><a href="base2dplyr.html#cb344-3" tabindex="-1"></a>df2 <span class="ot"><-</span> <span class="fu">mutate</span>(df1, <span class="at">year =</span> date <span class="sc">%/%</span> <span class="dv">10000</span>)</span>
<span id="cb344-4"><a href="base2dplyr.html#cb344-4" tabindex="-1"></a>df3 <span class="ot"><-</span> <span class="fu">mutate</span>(df2, <span class="at">month =</span> date <span class="sc">%/%</span> <span class="dv">100</span> <span class="sc">%%</span> <span class="dv">100</span>)</span>
<span id="cb344-5"><a href="base2dplyr.html#cb344-5" tabindex="-1"></a>df4 <span class="ot"><-</span> <span class="fu">mutate</span>(df3, <span class="at">area =</span> <span class="fu">str_sub</span>(location, <span class="dv">4</span>, <span class="dv">6</span>))</span>
<span id="cb344-6"><a href="base2dplyr.html#cb344-6" tabindex="-1"></a>selected_df <span class="ot"><-</span> <span class="fu">mutate</span>(df4, <span class="at">county =</span> <span class="fu">str_sub</span>(location, <span class="dv">1</span>, <span class="dv">3</span>))</span></code></pre></div>
</div>
<div id="without-pipeline-ii" class="section level4 hasAnchor" number="6.2.2.2">
<h4><span class="header-section-number">6.2.2.2</span> (2) Without pipeline II<a href="base2dplyr.html#without-pipeline-ii" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<div class="sourceCode" id="cb345"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb345-1"><a href="base2dplyr.html#cb345-1" tabindex="-1"></a><span class="fu">library</span>(stringr)</span>
<span id="cb345-2"><a href="base2dplyr.html#cb345-2" tabindex="-1"></a></span>
<span id="cb345-3"><a href="base2dplyr.html#cb345-3" tabindex="-1"></a>selected_df <span class="ot"><-</span> <span class="fu">select</span>(df, <span class="at">id =</span> 編號, <span class="at">cat =</span> 案類, <span class="at">date =</span> <span class="st">`</span><span class="at">發生日期</span><span class="st">`</span>, <span class="at">time =</span> <span class="st">`</span><span class="at">發生時段</span><span class="st">`</span>, <span class="at">location =</span> <span class="st">`</span><span class="at">發生地點</span><span class="st">`</span>)</span>
<span id="cb345-4"><a href="base2dplyr.html#cb345-4" tabindex="-1"></a></span>
<span id="cb345-5"><a href="base2dplyr.html#cb345-5" tabindex="-1"></a>selected_df <span class="ot"><-</span> <span class="fu">mutate</span>(selected_df, <span class="at">year =</span> date <span class="sc">%/%</span> <span class="dv">10000</span>)</span>
<span id="cb345-6"><a href="base2dplyr.html#cb345-6" tabindex="-1"></a>selected_df <span class="ot"><-</span> <span class="fu">mutate</span>(selected_df, <span class="at">month =</span> date <span class="sc">%/%</span> <span class="dv">100</span> <span class="sc">%%</span> <span class="dv">100</span>)</span>
<span id="cb345-7"><a href="base2dplyr.html#cb345-7" tabindex="-1"></a>selected_df <span class="ot"><-</span> <span class="fu">mutate</span>(selected_df, <span class="at">area =</span> <span class="fu">str_sub</span>(location, <span class="dv">4</span>, <span class="dv">6</span>))</span>
<span id="cb345-8"><a href="base2dplyr.html#cb345-8" tabindex="-1"></a>selected_df <span class="ot"><-</span> <span class="fu">mutate</span>(selected_df, <span class="at">county =</span> <span class="fu">str_sub</span>(location, <span class="dv">1</span>, <span class="dv">3</span>))</span></code></pre></div>
</div>
<div id="with-pipeline" class="section level4 hasAnchor" number="6.2.2.3">
<h4><span class="header-section-number">6.2.2.3</span> (3) With pipeline<a href="base2dplyr.html#with-pipeline" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<div class="sourceCode" id="cb346"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb346-1"><a href="base2dplyr.html#cb346-1" tabindex="-1"></a><span class="fu">library</span>(stringr)</span>
<span id="cb346-2"><a href="base2dplyr.html#cb346-2" tabindex="-1"></a>selected_df <span class="ot"><-</span> df <span class="sc">%>%</span></span>
<span id="cb346-3"><a href="base2dplyr.html#cb346-3" tabindex="-1"></a> <span class="fu">select</span>(<span class="at">id =</span> 編號, </span>
<span id="cb346-4"><a href="base2dplyr.html#cb346-4" tabindex="-1"></a> <span class="at">cat =</span> 案類, </span>
<span id="cb346-5"><a href="base2dplyr.html#cb346-5" tabindex="-1"></a> <span class="at">date =</span> <span class="st">`</span><span class="at">發生日期</span><span class="st">`</span>, </span>
<span id="cb346-6"><a href="base2dplyr.html#cb346-6" tabindex="-1"></a> <span class="at">time =</span> <span class="st">`</span><span class="at">發生時段</span><span class="st">`</span>, </span>
<span id="cb346-7"><a href="base2dplyr.html#cb346-7" tabindex="-1"></a> <span class="at">location =</span> <span class="st">`</span><span class="at">發生地點</span><span class="st">`</span>) <span class="sc">%>%</span></span>
<span id="cb346-8"><a href="base2dplyr.html#cb346-8" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">year =</span> date <span class="sc">%/%</span> <span class="dv">10000</span>) <span class="sc">%>%</span></span>
<span id="cb346-9"><a href="base2dplyr.html#cb346-9" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">month =</span> date <span class="sc">%/%</span> <span class="dv">100</span> <span class="sc">%%</span> <span class="dv">100</span>) <span class="sc">%>%</span></span>
<span id="cb346-10"><a href="base2dplyr.html#cb346-10" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">area =</span> <span class="fu">str_sub</span>(location, <span class="dv">4</span>, <span class="dv">6</span>)) <span class="sc">%>%</span></span>
<span id="cb346-11"><a href="base2dplyr.html#cb346-11" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">county =</span> <span class="fu">str_sub</span>(location, <span class="dv">1</span>, <span class="dv">3</span>))</span></code></pre></div>
</div>
</div>
<div id="cleaning-data-ii" class="section level3 hasAnchor" number="6.2.3">
<h3><span class="header-section-number">6.2.3</span> Cleaning data II<a href="base2dplyr.html#cleaning-data-ii" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<ul>
<li>Filtering out irrelevant data records</li>
</ul>
<div class="sourceCode" id="cb347"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb347-1"><a href="base2dplyr.html#cb347-1" tabindex="-1"></a><span class="co"># readr::guess_encoding("data/tp_theft.csv")</span></span>
<span id="cb347-2"><a href="base2dplyr.html#cb347-2" tabindex="-1"></a>filtered_df <span class="ot"><-</span> selected_df <span class="sc">%>%</span></span>
<span id="cb347-3"><a href="base2dplyr.html#cb347-3" tabindex="-1"></a> <span class="co"># count(year) %>% View</span></span>
<span id="cb347-4"><a href="base2dplyr.html#cb347-4" tabindex="-1"></a> <span class="fu">filter</span>(county <span class="sc">==</span> <span class="st">"臺北市"</span>) <span class="sc">%>%</span></span>
<span id="cb347-5"><a href="base2dplyr.html#cb347-5" tabindex="-1"></a> <span class="fu">filter</span>(year <span class="sc">>=</span> <span class="dv">104</span>) <span class="sc">%>%</span></span>
<span id="cb347-6"><a href="base2dplyr.html#cb347-6" tabindex="-1"></a> <span class="co"># count(time) %>% View</span></span>
<span id="cb347-7"><a href="base2dplyr.html#cb347-7" tabindex="-1"></a> <span class="co"># count(location) %>%</span></span>
<span id="cb347-8"><a href="base2dplyr.html#cb347-8" tabindex="-1"></a> <span class="fu">filter</span>(<span class="sc">!</span>area <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"中和市"</span>, <span class="st">"板橋市"</span>))</span></code></pre></div>
</div>
<div id="long-to-wide-form" class="section level3 hasAnchor" number="6.2.4">
<h3><span class="header-section-number">6.2.4</span> Long to wide form<a href="base2dplyr.html#long-to-wide-form" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<ul>
<li><code>count()</code> two variables</li>
<li><code>pivot_wider()</code> spread one variable as columns to wide form</li>
</ul>
<div class="sourceCode" id="cb348"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb348-1"><a href="base2dplyr.html#cb348-1" tabindex="-1"></a><span class="co"># count() then pivot_wider()</span></span>
<span id="cb348-2"><a href="base2dplyr.html#cb348-2" tabindex="-1"></a>df.wide <span class="ot"><-</span> filtered_df <span class="sc">%>%</span> </span>
<span id="cb348-3"><a href="base2dplyr.html#cb348-3" tabindex="-1"></a> <span class="fu">count</span>(time, area) <span class="sc">%>%</span></span>
<span id="cb348-4"><a href="base2dplyr.html#cb348-4" tabindex="-1"></a> <span class="fu">pivot_wider</span>(<span class="at">names_from =</span> area, <span class="at">values_from =</span> n, <span class="at">values_fill =</span> <span class="dv">0</span>)</span>
<span id="cb348-5"><a href="base2dplyr.html#cb348-5" tabindex="-1"></a>??pivot_wider</span></code></pre></div>
</div>
<div id="setting-time-as-row.name-for-mosaicplot" class="section level3 hasAnchor" number="6.2.5">
<h3><span class="header-section-number">6.2.5</span> Setting time as row.name for mosaicplot<a href="base2dplyr.html#setting-time-as-row.name-for-mosaicplot" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<div class="sourceCode" id="cb349"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb349-1"><a href="base2dplyr.html#cb349-1" tabindex="-1"></a><span class="fu">row.names</span>(df.wide) <span class="ot"><-</span> df.wide<span class="sc">$</span>time</span>
<span id="cb349-2"><a href="base2dplyr.html#cb349-2" tabindex="-1"></a>df.wide<span class="sc">$</span>time <span class="ot"><-</span> <span class="cn">NULL</span></span></code></pre></div>
<div class="sourceCode" id="cb350"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb350-1"><a href="base2dplyr.html#cb350-1" tabindex="-1"></a><span class="co"># Specify fonts for Chinese</span></span>
<span id="cb350-2"><a href="base2dplyr.html#cb350-2" tabindex="-1"></a><span class="co"># par(family=('STKaiti')) </span></span>
<span id="cb350-3"><a href="base2dplyr.html#cb350-3" tabindex="-1"></a><span class="fu">par</span>(<span class="at">family=</span>(<span class="st">'Heiti TC Light'</span>)) <span class="co"># for mac</span></span>
<span id="cb350-4"><a href="base2dplyr.html#cb350-4" tabindex="-1"></a></span>
<span id="cb350-5"><a href="base2dplyr.html#cb350-5" tabindex="-1"></a><span class="co"># Specify colors</span></span>
<span id="cb350-6"><a href="base2dplyr.html#cb350-6" tabindex="-1"></a>colors <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">'#D0104C'</span>, <span class="st">'#DB4D6D'</span>, <span class="st">'#E83015'</span>, <span class="st">'#F75C2F'</span>,</span>
<span id="cb350-7"><a href="base2dplyr.html#cb350-7" tabindex="-1"></a> <span class="st">'#E79460'</span>, <span class="st">'#E98B2A'</span>, <span class="st">'#9B6E23'</span>, <span class="st">'#F7C242'</span>,</span>
<span id="cb350-8"><a href="base2dplyr.html#cb350-8" tabindex="-1"></a> <span class="st">'#BEC23F'</span>, <span class="st">'#90B44B'</span>, <span class="st">'#66BAB7'</span>, <span class="st">'#1E88A8'</span>)</span>
<span id="cb350-9"><a href="base2dplyr.html#cb350-9" tabindex="-1"></a></span>
<span id="cb350-10"><a href="base2dplyr.html#cb350-10" tabindex="-1"></a><span class="co"># mosaicplot()</span></span>
<span id="cb350-11"><a href="base2dplyr.html#cb350-11" tabindex="-1"></a><span class="fu">mosaicplot</span>(df.wide, <span class="at">color=</span>colors, <span class="at">border=</span><span class="dv">0</span>, <span class="at">off =</span> <span class="dv">3</span>,</span>
<span id="cb350-12"><a href="base2dplyr.html#cb350-12" tabindex="-1"></a> <span class="at">main=</span><span class="st">"Theft rate of Taipei city (region by hour)"</span>)</span></code></pre></div>
<p><img src="R21-tptheft_dplyr_files/figure-html/unnamed-chunk-10-1.png" width="672" /></p>
</div>
<div id="clean-version" class="section level3 hasAnchor" number="6.2.6">
<h3><span class="header-section-number">6.2.6</span> Clean version<a href="base2dplyr.html#clean-version" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<div class="sourceCode" id="cb351"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb351-1"><a href="base2dplyr.html#cb351-1" tabindex="-1"></a><span class="fu">library</span>(readr)</span>
<span id="cb351-2"><a href="base2dplyr.html#cb351-2" tabindex="-1"></a><span class="co"># options(stringsAsFactors = F)</span></span>
<span id="cb351-3"><a href="base2dplyr.html#cb351-3" tabindex="-1"></a>df <span class="ot"><-</span> <span class="fu">read_csv</span>(<span class="st">"data/臺北市住宅竊盜點位資訊-UTF8-BOM-1.csv"</span>)</span>
<span id="cb351-4"><a href="base2dplyr.html#cb351-4" tabindex="-1"></a></span>
<span id="cb351-5"><a href="base2dplyr.html#cb351-5" tabindex="-1"></a>selected_df <span class="ot"><-</span> df <span class="sc">%>%</span></span>
<span id="cb351-6"><a href="base2dplyr.html#cb351-6" tabindex="-1"></a> <span class="fu">select</span>(<span class="at">id =</span> 編號, </span>
<span id="cb351-7"><a href="base2dplyr.html#cb351-7" tabindex="-1"></a> <span class="at">cat =</span> 案類,</span>
<span id="cb351-8"><a href="base2dplyr.html#cb351-8" tabindex="-1"></a> <span class="at">date =</span> <span class="st">`</span><span class="at">發生日期</span><span class="st">`</span>, </span>
<span id="cb351-9"><a href="base2dplyr.html#cb351-9" tabindex="-1"></a> <span class="at">time =</span> <span class="st">`</span><span class="at">發生時段</span><span class="st">`</span>, </span>
<span id="cb351-10"><a href="base2dplyr.html#cb351-10" tabindex="-1"></a> <span class="at">location =</span> <span class="st">`</span><span class="at">發生地點</span><span class="st">`</span>) <span class="sc">%>%</span></span>
<span id="cb351-11"><a href="base2dplyr.html#cb351-11" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">year =</span> date <span class="sc">%/%</span> <span class="dv">10000</span>) <span class="sc">%>%</span></span>
<span id="cb351-12"><a href="base2dplyr.html#cb351-12" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">month =</span> date <span class="sc">%/%</span> <span class="dv">100</span> <span class="sc">%%</span> <span class="dv">100</span>) <span class="sc">%>%</span></span>
<span id="cb351-13"><a href="base2dplyr.html#cb351-13" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">area =</span> stringr<span class="sc">::</span><span class="fu">str_sub</span>(location, <span class="dv">4</span>, <span class="dv">6</span>)) <span class="sc">%>%</span></span>
<span id="cb351-14"><a href="base2dplyr.html#cb351-14" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">county =</span> stringr<span class="sc">::</span><span class="fu">str_sub</span>(location, <span class="dv">1</span>, <span class="dv">3</span>))</span>
<span id="cb351-15"><a href="base2dplyr.html#cb351-15" tabindex="-1"></a></span>
<span id="cb351-16"><a href="base2dplyr.html#cb351-16" tabindex="-1"></a>selected_df <span class="sc">%>%</span> <span class="fu">count</span>(year)</span></code></pre></div>
<pre class="output"><code>## # A tibble: 9 × 2
## year n
## <dbl> <int>
## 1 103 1
## 2 104 687
## 3 105 663
## 4 106 560
## 5 107 501
## 6 108 411
## 7 109 304
## 8 110 189
## 9 111 31</code></pre>
<div class="sourceCode" id="cb353"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb353-1"><a href="base2dplyr.html#cb353-1" tabindex="-1"></a>selected_df <span class="sc">%>%</span> <span class="fu">count</span>(time) <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 × 2
## time n
## <chr> <int>
## 1 00~02 272
## 2 02~04 214
## 3 03~05 8
## 4 04~06 156
## 5 05~07 23
## 6 06~08 191
## 7 08~10 305
## 8 09~11 6
## 9 10~12 338
## 10 11~03 1</code></pre>
<div class="sourceCode" id="cb355"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb355-1"><a href="base2dplyr.html#cb355-1" tabindex="-1"></a>selected_df <span class="sc">%>%</span> <span class="fu">arrange</span>(time) <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
## id cat date time location year month area county
## <dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr>
## 1 2 住宅竊盜 1040101 00~02 臺北市文山區萬美里萬寧… 104 1 文山… 臺北市
## 2 3 住宅竊盜 1040101 00~02 臺北市信義區富台里忠孝… 104 1 信義… 臺北市
## 3 6 住宅竊盜 1040102 00~02 臺北市士林區天福里1鄰… 104 1 士林… 臺北市
## 4 12 住宅竊盜 1040105 00~02 臺北市中山區南京東路3… 104 1 中山… 臺北市
## 5 33 住宅竊盜 1040115 00~02 臺北市松山區饒河街181~… 104 1 松山… 臺北市
## 6 74 住宅竊盜 1040131 00~02 臺北市南港區重陽路57巷… 104 1 南港… 臺北市
## 7 75 住宅竊盜 1040201 00~02 臺北市北投區中心里中和… 104 2 北投… 臺北市
## 8 92 住宅竊盜 1040210 00~02 臺北市北投區大同路200… 104 2 北投… 臺北市
## 9 95 住宅竊盜 1040212 00~02 臺北市萬華區萬大路493… 104 2 萬華… 臺北市
## 10 106 住宅竊盜 1040216 00~02 臺北市信義區吳興街269… 104 2 信義… 臺北市</code></pre>
<div class="sourceCode" id="cb357"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb357-1"><a href="base2dplyr.html#cb357-1" tabindex="-1"></a>filtered_df <span class="ot"><-</span> selected_df <span class="sc">%>%</span></span>
<span id="cb357-2"><a href="base2dplyr.html#cb357-2" tabindex="-1"></a> <span class="co"># count(year) %>% View</span></span>
<span id="cb357-3"><a href="base2dplyr.html#cb357-3" tabindex="-1"></a> <span class="fu">filter</span>(year <span class="sc">>=</span> <span class="dv">104</span>) <span class="sc">%>%</span></span>
<span id="cb357-4"><a href="base2dplyr.html#cb357-4" tabindex="-1"></a> <span class="fu">filter</span>(<span class="sc">!</span>time <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"03~05"</span>, <span class="st">"05~07"</span>, <span class="st">"09~11"</span>, <span class="st">"11~13"</span>, <span class="st">"15~17"</span>, <span class="st">"17~19"</span>, <span class="st">"18~21"</span>, <span class="st">"21~23"</span>, <span class="st">"23~01"</span>))</span>
<span id="cb357-5"><a href="base2dplyr.html#cb357-5" tabindex="-1"></a> <span class="co"># count(time) %>% View</span></span>
<span id="cb357-6"><a href="base2dplyr.html#cb357-6" tabindex="-1"></a> <span class="co"># count(location) %>%</span></span>
<span id="cb357-7"><a href="base2dplyr.html#cb357-7" tabindex="-1"></a> <span class="co"># filter(!area %in% c("中和市", "板橋市"))</span></span>
<span id="cb357-8"><a href="base2dplyr.html#cb357-8" tabindex="-1"></a></span>
<span id="cb357-9"><a href="base2dplyr.html#cb357-9" tabindex="-1"></a>df.wide <span class="ot"><-</span> filtered_df <span class="sc">%>%</span> </span>
<span id="cb357-10"><a href="base2dplyr.html#cb357-10" tabindex="-1"></a> <span class="fu">count</span>(time, area) <span class="sc">%>%</span></span>
<span id="cb357-11"><a href="base2dplyr.html#cb357-11" tabindex="-1"></a> <span class="fu">pivot_wider</span>(<span class="at">names_from =</span> area, <span class="at">values_from =</span> n, <span class="at">values_fill =</span> <span class="dv">0</span>) <span class="sc">%>%</span></span>
<span id="cb357-12"><a href="base2dplyr.html#cb357-12" tabindex="-1"></a> <span class="fu">as.data.frame</span>()</span>
<span id="cb357-13"><a href="base2dplyr.html#cb357-13" tabindex="-1"></a></span>
<span id="cb357-14"><a href="base2dplyr.html#cb357-14" tabindex="-1"></a><span class="fu">row.names</span>(df.wide) <span class="ot"><-</span> df.wide<span class="sc">$</span>time</span>