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<!DOCTYPE html>
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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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<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="crosstab" class="section level1 hasAnchor" number="5">
<h1><span class="header-section-number">Chapter 5</span> Counting and Cross-tabulation<a href="crosstab.html#crosstab" class="anchor-section" aria-label="Anchor link to header"></a></h1>
<p>交叉分析是一種對兩個或多個變數進行聯合分析的方法，通常用於研究不同類別或組別之間的關係和差異。交叉分析可以幫助我們發現變數之間的相互作用，以及不同類別或組別之間的異同點，進而進行更深入的分析和解釋。</p>
<p>在交叉分析中，通常會使用交叉表（cross tabulation）或稱為列聯表（contingency table）來對變數進行分析。交叉表是一種二維資料表格，其中一個變數作為行，另一個變數作為列，每個資料格中則表示兩個變數的交叉次數或百分比。交叉表可以幫助我們從不同角度瞭解變數之間的關係和差異，例如：</p>
<ol style="list-style-type: decimal">
<li>發現變數之間的相關性：可以通過交叉表計算兩個變數之間的相關係數或卡方檢定值，以評估它們之間的相關性程度。</li>
<li>比較不同類別或組別之間的差異：可以通過交叉表比較不同類別或組別之間的差異，例如不同性別、年齡、教育程度、地區等對某一變數的影響。</li>
<li>發現變數之間的交互作用：可以通過交叉表比較不同類別或組別之間的差異，並分析它們之間的交互作用，以進一步瞭解變數之間的關係和影響。</li>
</ol>
<div id="tptheft" class="section level2 hasAnchor" number="5.1">
<h2><span class="header-section-number">5.1</span> Taipei Residential Burglary<a href="crosstab.html#tptheft" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p><strong>觀察值、點位資料</strong>：公部門所發布的開放資料通常會根據某些類別進行統計，例如年齡、性別、教育程度、地區等等，只有少部分的資料會用觀察值（Observation）的方式來記錄，也就是每一個案例紀錄一筆資料。例如疫情一開始人數還少的時候，會逐一記錄每個個案；地理資訊系統上面記錄某些機構或某些特定地點的時候也是點位資料；或在觀察輿情時，每筆發言或留言都是一筆觀察值。「<a href="https://data.taipei/#/dataset/detail?id=68785231-d6c5-47a1-b001-77eec70bec02">臺北市住宅竊盜點位資訊</a>」就是逐案紀錄的點位資料。而以下的例子也是點位資料，主要為主要為噪音、竊盜、交通事故等相關點位資料。</p>
<ul>
<li><a href="https://data.taipei/dataset/detail?id=ea2819ff-c869-480e-b7a9-2e5f7906a696">臺北市街頭隨機強盜案件點位資訊</a>、</li>
<li><a href="https://data.taipei/dataset/detail?id=404ca667-bcc4-4f3b-9217-fdcc1da400b2">臺北市街頭隨機搶奪案件點位資訊</a>、</li>
<li><a href="https://data.taipei/dataset/detail?id=f87ad53e-79c7-48c4-aec4-f0fd8f99bfb2">臺北市汽車竊盜點位資訊</a>、</li>
<li><a href="https://data.taipei/dataset/detail?id=3a0e2289-a605-4eac-af30-f4af613f456d">臺北市機車竊盜點位資訊</a>、</li>
<li><a href="https://data.taipei/dataset/detail?id=5c5e9e13-9803-47c0-bbd2-1a4b3c11c49b">臺北市自行車竊盜點位資訊</a>、</li>
<li><a href="https://data.taipei/dataset/detail?id=0554bac7-cbc2-4ef3-a55e-0aad3dd4ee1d">臺北市道路交通事故斑點圖</a>、</li>
<li><a href="https://data.taipei/dataset/detail?id=b54c0689-61b4-4877-90c0-df0e0586c7dc">臺北市娛樂營業場所噪音告發案件點位資訊</a>、</li>
<li><a href="https://data.taipei/dataset/detail?id=912d38c3-92ca-49aa-9678-e3f85b969abc">臺北市非營業用卡拉OK噪音告發案件點位資訊</a>、</li>
<li><a href="https://data.taipei/dataset/detail?id=1c01c0a5-9bd3-464b-b110-fbb6d0b26e25">臺北市營建工程噪音告發案件點位資訊</a>等，</li>
</ul>
<div id="tptheft_read_file" class="section level3 hasAnchor" number="5.1.1">
<h3><span class="header-section-number">5.1.1</span> 讀取檔案<a href="crosstab.html#tptheft_read_file" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>規劃比較完善的開放資料平台會提供API給程式設計者存取，例如<a href="https://data.taipei/#/">臺北資料大平台</a>或內政部開放資料平台。但我們這邊用下載CSV（Common Separated Value）檔的方式來讀取這筆資料，以理解CSV這種檔案型態如何儲存資料。首先要至<a href="https://data.taipei/#/">臺北資料大平台</a>上查詢「住宅竊盜」，可以找到<a href="https://data.taipei/#/dataset/detail?id=68785231-d6c5-47a1-b001-77eec70bec02">臺北市住宅竊盜點位資訊</a>。將該CSV檔下載至個人本機端，置入<code>data</code> 資料夾中，便可以用<code>read.csv()</code>讀取該檔案。或可用tidyverse系列套件中的<code>readr::read_csv()</code>來直接讀取該網址所指到的檔案。</p>
<p>我習慣在<strong>Console</strong>視窗中用<code>??read_csv()</code>查詢到這些函式的用法。</p>
<ul>
<li><code>read.csv()</code> to read csv and convert it to a data.frame</li>
<li><code>readr::read_csv()</code> to read csv or read a csv by an url</li>
</ul>
<p>如果知道這個套件是<code>readr</code>的話，也可以到右下方的工作區塊找到「Packages」工作視窗，裡面有列出現在載入的所有的套件，也有套件中的所有函式。偶而看一看會發現一些自己平常忽略的好用工具。</p>
<div class="sourceCode" id="cb307"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb307-1"><a href="crosstab.html#cb307-1" tabindex="-1"></a><span class="fu">library</span>(knitr)</span>
<span id="cb307-2"><a href="crosstab.html#cb307-2" tabindex="-1"></a><span class="fu">library</span>(kableExtra)</span>
<span id="cb307-3"><a href="crosstab.html#cb307-3" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb307-4"><a href="crosstab.html#cb307-4" tabindex="-1"></a></span>
<span id="cb307-5"><a href="crosstab.html#cb307-5" 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="cb307-6"><a href="crosstab.html#cb307-6" tabindex="-1"></a><span class="fu">head</span>(df) </span></code></pre></div>
<pre class="output"><code>## 編號 案類 發生日期 發生時段 發生地點
## 1 1 住宅竊盜 1030623 08~10 臺北市中正區廈門街91~120號
## 2 2 住宅竊盜 1040101 00~02 臺北市文山區萬美里萬寧街1~30號
## 3 3 住宅竊盜 1040101 00~02 臺北市信義區富台里忠孝東路5段295巷6弄1~30號
## 4 4 住宅竊盜 1040101 06~08 臺北市中山區新生北路1段91~120號
## 5 5 住宅竊盜 1040101 10~12 臺北市文山區明興里興隆路4段1~30號
## 6 6 住宅竊盜 1040102 00~02 臺北市士林區天福里1鄰忠誠路2段130巷1~30號</code></pre>
<p><strong>用read_csv()來讀取。</strong>除了 base套件的<code>read.csv()</code>外，也可使用<code>readr</code>套件的<code>read_csv()</code>函式來讀取，該套件屬於tidyverse套件系的其中一個套件，如果已經有用<code>install.packages("tidyverse")</code>安裝過，只要用<code>library(tidyverse)</code>就可以使用<code>read_csv()</code>函式。在此鼓勵各位使用tidyverse系列套件。普遍來說，<code>read_csv()</code> 的功能和效果都會比<code>read.csv()</code>好，該函式還會自動猜測每個變數的變數型態並直接進行轉換（尤其是有時間欄位的時候，會非常方便）。</p>
<div class="debug">
<p>萬一遇到中文檔案會有讀檔編碼問題時，有可能該檔案是用big5來儲存的，可以在<code>read_csv()</code>中設定<code>locale</code>來指定讀取的編碼方法。如<code>read_csv(url, locale = locale(encoding = "Big5"))</code></p>
</div>
<div class="sourceCode" id="cb309"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb309-1"><a href="crosstab.html#cb309-1" tabindex="-1"></a><span class="fu">library</span>(readr)</span>
<span id="cb309-2"><a href="crosstab.html#cb309-2" 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="cb309-3"><a href="crosstab.html#cb309-3" tabindex="-1"></a><span class="co"># df <- read_csv("data/臺北市住宅竊盜點位資訊-UTF8-BOM-1.csv", locale = locale(encoding = "Big5"))</span></span>
<span id="cb309-4"><a href="crosstab.html#cb309-4" tabindex="-1"></a><span class="fu">head</span>(df)</span></code></pre></div>
<pre class="output"><code>## # A tibble: 6 × 5
## 編號 案類 發生日期 發生時段 發生地點
## <dbl> <chr> <dbl> <chr> <chr>
## 1 1 住宅竊盜 1030623 08~10 臺北市中正區廈門街91~120號
## 2 2 住宅竊盜 1040101 00~02 臺北市文山區萬美里萬寧街1~30號
## 3 3 住宅竊盜 1040101 00~02 臺北市信義區富台里忠孝東路5段295巷6弄1~30號
## 4 4 住宅竊盜 1040101 06~08 臺北市中山區新生北路1段91~120號
## 5 5 住宅竊盜 1040101 10~12 臺北市文山區明興里興隆路4段1~30號
## 6 6 住宅竊盜 1040102 00~02 臺北市士林區天福里1鄰忠誠路2段130巷1~30號</code></pre>
<div id="tptheft_var_overview" class="section level4 hasAnchor" number="5.1.1.1">
<h4><span class="header-section-number">5.1.1.1</span> 觀察變數<a href="crosstab.html#tptheft_var_overview" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<ul>
<li><code>names(df)</code> 列出所有變數名稱</li>
<li><code>df$發生地點</code> 顯示該變數內容</li>
<li><code>df$發生時段</code> 顯示該變數內容</li>
<li><code>length(df$發生時段)</code> 顯示該變數的長度（相當於有幾個）</li>
</ul>
</div>
</div>
<div id="tptheft_mutate_new_var" class="section level3 hasAnchor" number="5.1.2">
<h3><span class="header-section-number">5.1.2</span> 萃取所需新變項<a href="crosstab.html#tptheft_mutate_new_var" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>該data.frame包含編號、案類、發生日期、發生時段、發生地點五個變項。其中比較有意義的應該是發生日期、發生時段和發生地點。然而，發生地點幾乎是完整地址，除非要繪製發生的地圖點位地圖，才會需要近乎完整的地址。假設我們的目標是抽取出台北市的「行政區」，發生地點的格式還蠻一致的如「臺北市中正區廈門街91~120號」。因此，我們只要抽出發生地點的第4至6個字即可。</p>
<p>從一個字串中抽取出第n個字到第m個字，要用<code>substr()</code>或<code>stringr</code>套件的<code>str_sub()</code>。可以用<code>?substr</code>或<code>?str_sub</code>查詢help中的相關用法。在此</p>
<ul>
<li>我將中文變數<code>現在時間</code>的資料指給一個新的英文變項<code>time</code>。</li>
<li>從變數<code>發生地點</code>，用<code>substr()</code>取出行政區（<code>region</code>）</li>
<li>或用<code>stringr::str_sub()</code></li>
<li><code>?substr</code>查詢其用法和意義。相當於<code>getting sub string since x to y</code>。</li>
</ul>
<div class="sourceCode" id="cb311"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb311-1"><a href="crosstab.html#cb311-1" tabindex="-1"></a><span class="co"># Get substring of var "發生時段" and assign to a new time var</span></span>
<span id="cb311-2"><a href="crosstab.html#cb311-2" tabindex="-1"></a>df<span class="sc">$</span>time <span class="ot"><-</span> df<span class="sc">$</span>發生時段</span>
<span id="cb311-3"><a href="crosstab.html#cb311-3" tabindex="-1"></a></span>
<span id="cb311-4"><a href="crosstab.html#cb311-4" tabindex="-1"></a><span class="co"># Get substring of var "發生地點" and assign to a new region var</span></span>
<span id="cb311-5"><a href="crosstab.html#cb311-5" tabindex="-1"></a>df<span class="sc">$</span>region <span class="ot"><-</span> <span class="fu">substr</span>(df<span class="sc">$</span>發生地點, <span class="dv">4</span>, <span class="dv">5</span>)</span>
<span id="cb311-6"><a href="crosstab.html#cb311-6" tabindex="-1"></a><span class="fu">head</span>(df)</span></code></pre></div>
<pre class="output"><code>## # A tibble: 6 × 7
## 編號 案類 發生日期 發生時段 發生地點 time region
## <dbl> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 1 住宅竊盜 1030623 08~10 臺北市中正區廈門街91~120號 08~10 中正
## 2 2 住宅竊盜 1040101 00~02 臺北市文山區萬美里萬寧街1~30號 00~02 文山
## 3 3 住宅竊盜 1040101 00~02 臺北市信義區富台里忠孝東路5段29… 00~02 信義
## 4 4 住宅竊盜 1040101 06~08 臺北市中山區新生北路1段91~120號 06~08 中山
## 5 5 住宅竊盜 1040101 10~12 臺北市文山區明興里興隆路4段1~30… 10~12 文山
## 6 6 住宅竊盜 1040102 00~02 臺北市士林區天福里1鄰忠誠路2段1… 00~02 士林</code></pre>
</div>
<div id="tptheft_counting" class="section level3 hasAnchor" number="5.1.3">
<h3><span class="header-section-number">5.1.3</span> 使用<code>table()</code>計數<a href="crosstab.html#tptheft_counting" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>清理完資料後，我們要回答的第一個數據問題通常是「那XXX的案例有幾個？」例如：大安區有多少竊盜案？10~12這個時段有多少案例。</p>
<p><code>table()</code>函式可以對Vector中的值進行計數（Counting）。<code>table(df$time)</code> 相當於去計數不同的時間區間出現多少起案例；<code>table(df$region)</code> 相當於去計數不同地區各出現多少起案例。</p>
<p>提示：可以用<code>class(tb_1)</code> 觀察用<code>table()</code> 計數後所產生的資料型態（<code>table</code>）。</p>
<div class="sourceCode" id="cb313"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb313-1"><a href="crosstab.html#cb313-1" tabindex="-1"></a><span class="do">## table</span></span>
<span id="cb313-2"><a href="crosstab.html#cb313-2" tabindex="-1"></a><span class="co"># counting the frequency of region variable</span></span>
<span id="cb313-3"><a href="crosstab.html#cb313-3" tabindex="-1"></a></span>
<span id="cb313-4"><a href="crosstab.html#cb313-4" tabindex="-1"></a>(<span class="fu">table</span>(df<span class="sc">$</span>region))</span></code></pre></div>
<pre class="output"><code>##
## 中山 中正 信義 內湖 北投 南港 士林 大同 大安 文山 松山 萬華
## 438 263 214 303 318 181 373 172 311 204 220 350</code></pre>
<div class="sourceCode" id="cb315"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb315-1"><a href="crosstab.html#cb315-1" tabindex="-1"></a><span class="co"># counting the frequency of time variable</span></span>
<span id="cb315-2"><a href="crosstab.html#cb315-2" tabindex="-1"></a>(tb_1 <span class="ot"><-</span> <span class="fu">table</span>(df<span class="sc">$</span>time)) <span class="co"># %>% View</span></span></code></pre></div>
<pre class="output"><code>##
## 00~02 02~04 03~05 04~06 05~07 06~08 08~10 09~11 10~12 11~03 11~13 12~14 12~15
## 272 214 8 156 23 191 305 6 338 1 26 338 2
## 14~16 15~17 15~18 16~18 17~19 18~20 18~21 19~21 20~22 21~23 21~24 22~24 23~01
## 342 3 1 246 21 314 1 4 303 5 1 206 20</code></pre>
<div class="sourceCode" id="cb317"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb317-1"><a href="crosstab.html#cb317-1" tabindex="-1"></a><span class="fu">class</span>(tb_1) </span></code></pre></div>
<pre class="output"><code>## [1] "table"</code></pre>
</div>
<div id="tptheft_filtering" class="section level3 hasAnchor" number="5.1.4">
<h3><span class="header-section-number">5.1.4</span> 依變數值篩選資料<a href="crosstab.html#tptheft_filtering" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>該項竊盜案資料整理時經常不慎用不同的時間區間來標記，有時候也會不小心把新北市的資料給那進來，所以需要做資料篩選。從各個時間區間的竊盜案出現次數來觀察，有少數的案件出現在奇數的時間區間如<code>09~11</code>或<code>12~15</code>等等需要篩除；從各個行政區的竊盜案出現次數來觀察，確實都是台北市的竊盜案。</p>
<p>接下來要用base套件的R，根據某個變數值（例如上述的時間）來篩出符合條件的資料，或者篩去不符合條件的資料。其語法是要在<code>df[ , ]</code>逗號前加上篩選的條件，也就是對資料列進行篩選，篩出或篩除都是以整列為單位。在此的條件是<code>df$time</code><span style="color:hotpink">在</span><code>00~02</code>、<code>02~04</code>、…之間；或者是<code>df$time</code><span style="color:hotpink">不在</span><code>03~05</code>、<code>05~07</code>、…之間。表示法分別如下：</p>
<pre><code>df$time %in% c("00~02", "02~04", "04~6",...)
!df$time %in% c("03~05", "05~07", ...)</code></pre>
<ul>
<li><p><code>%in%</code> 表示的是左方<code>df$time</code>的值是否是右方Vector中的其中一個</p></li>
<li><p>如果要表示不包含，就在<code>df%time</code>加一個NOT，也就是<code>!</code>。</p></li>
</ul>
<p>依照各組時間的案例個數統計後，篩除資料未足100的時間區間如下，最後再用<code>table(df$time)</code> 計算一次，發現每個時段都兩三、百個案例，且涵蓋整日的時間。清理後沒有重疊的時間區間，做類別資料分析會比較準確。</p>
<div class="sourceCode" id="cb320"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb320-1"><a href="crosstab.html#cb320-1" tabindex="-1"></a><span class="co"># filter out irrelevant timestamp</span></span>
<span id="cb320-2"><a href="crosstab.html#cb320-2" tabindex="-1"></a>df <span class="ot"><-</span> df[<span class="sc">!</span>df<span class="sc">$</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">"11~03"</span>, <span class="st">"12~15"</span>, <span class="st">"15~17"</span>, <span class="st">"15~18"</span>, <span class="st">"17~19"</span>, <span class="st">"18~21"</span>, <span class="st">"19~21"</span>, <span class="st">"21~23"</span>, <span class="st">"21~24"</span>, <span class="st">"23~01"</span>), ]</span>
<span id="cb320-3"><a href="crosstab.html#cb320-3" tabindex="-1"></a></span>
<span id="cb320-4"><a href="crosstab.html#cb320-4" tabindex="-1"></a><span class="fu">table</span>(df<span class="sc">$</span>time)</span></code></pre></div>
<pre class="output"><code>##
## 00~02 02~04 04~06 06~08 08~10 10~12 12~14 14~16 16~18 18~20 20~22 22~24
## 272 214 156 191 305 338 338 342 246 314 303 206</code></pre>
<div class="sourceCode" id="cb322"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb322-1"><a href="crosstab.html#cb322-1" tabindex="-1"></a><span class="co"># filter out irrelevant region(area)</span></span>
<span id="cb322-2"><a href="crosstab.html#cb322-2" tabindex="-1"></a><span class="co"># df <- df[!df$region %in% c("三重", "中和", "淡水", "板橋"), ]</span></span></code></pre></div>
</div>
<div id="tptheft_table" class="section level3 hasAnchor" number="5.1.5">
<h3><span class="header-section-number">5.1.5</span> 做雙變數樞紐分析：<code>table()</code><a href="crosstab.html#tptheft_table" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>類別變項分析通常是要考驗兩個變項間的關係，從上述的計數中，我可以看見不同行政區或者不同時間的竊盜案數量，但我進一步想知道，那不同行政區的竊盜案常發生時間是否不同？這時後就要做時間和行政區的交叉分析。我們同樣可以用<code>table()</code>和<code>tapply()</code>來做兩個變項的交叉分析，寫法如下。</p>
<p>用<code>table()</code>來交叉分析的結果如下，所得到的結果之變數型態仍是<code>table</code>型態。</p>
<div class="sourceCode" id="cb323"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb323-1"><a href="crosstab.html#cb323-1" tabindex="-1"></a><span class="co"># Tabulating time and region variables</span></span>
<span id="cb323-2"><a href="crosstab.html#cb323-2" tabindex="-1"></a>(res_table <span class="ot"><-</span> <span class="fu">table</span>(df<span class="sc">$</span>time, df<span class="sc">$</span>region))</span></code></pre></div>
<pre class="output"><code>##
## 中山 中正 信義 內湖 北投 南港 士林 大同 大安 文山 松山 萬華
## 00~02 62 15 27 20 24 19 28 15 24 17 4 17
## 02~04 26 22 12 15 17 12 29 10 15 14 13 29
## 04~06 22 7 11 15 17 6 14 15 14 8 5 22
## 06~08 20 19 13 16 24 13 17 9 19 9 11 21
## 08~10 45 27 20 27 22 16 24 17 31 18 24 34
## 10~12 38 20 18 33 35 19 35 12 34 18 35 41
## 12~14 30 25 20 26 34 15 46 12 49 25 23 33
## 14~16 43 19 18 39 32 20 40 26 32 19 22 32
## 16~18 21 19 8 24 33 11 30 13 25 16 20 26
## 18~20 39 42 23 22 40 18 31 13 23 23 17 23
## 20~22 40 13 22 34 17 20 41 13 26 15 25 37
## 22~24 33 20 16 18 15 9 23 9 12 17 14 20</code></pre>
<div class="sourceCode" id="cb325"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb325-1"><a href="crosstab.html#cb325-1" tabindex="-1"></a><span class="co"># Checking it class and its content</span></span>
<span id="cb325-2"><a href="crosstab.html#cb325-2" tabindex="-1"></a><span class="fu">class</span>(res_table)</span></code></pre></div>
<pre class="output"><code>## [1] "table"</code></pre>
<div class="sourceCode" id="cb327"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb327-1"><a href="crosstab.html#cb327-1" tabindex="-1"></a><span class="do">## [1] "table"</span></span></code></pre></div>
</div>
<div id="tptheft_plot" class="section level3 hasAnchor" number="5.1.6">
<h3><span class="header-section-number">5.1.6</span> 繪圖<a href="crosstab.html#tptheft_plot" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>通常這種類別資料交叉分析最常用的圖表型態之一便是Mosaic Plot（但事實上Mosaic Plot不見能夠被一眼就了解）。我們可以把交叉分析後的變項<code>res_table</code>直接用MosaicPlot來繪圖。</p>
<div class="sourceCode" id="cb328"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb328-1"><a href="crosstab.html#cb328-1" tabindex="-1"></a><span class="co"># mosaicplot() to plot 2-dim categorical vars.</span></span>
<span id="cb328-2"><a href="crosstab.html#cb328-2" tabindex="-1"></a><span class="fu">mosaicplot</span>(res_table)</span></code></pre></div>
<p><img src="R12_tptheft_base_files/figure-html/unnamed-chunk-9-1.png" width="672" /></p>
<div class="sourceCode" id="cb329"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb329-1"><a href="crosstab.html#cb329-1" tabindex="-1"></a><span class="co"># Add argument main (figure title)</span></span>
<span id="cb329-2"><a href="crosstab.html#cb329-2" tabindex="-1"></a><span class="fu">mosaicplot</span>(res_table, <span class="at">main=</span><span class="st">"mosaic plot"</span>)</span></code></pre></div>
<p><img src="R12_tptheft_base_files/figure-html/unnamed-chunk-9-2.png" width="672" /></p>
<div id="tptheft_show_chi" class="section level4 hasAnchor" number="5.1.6.1">
<h4><span class="header-section-number">5.1.6.1</span> 解決圖表無法顯示中文<a href="crosstab.html#tptheft_show_chi" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<p>大部分的視覺化套件都無法順利顯示中文，除非特別指定所要用的中文字型。這方面網路上可以找到很多的說明，但非常討厭的是，幾乎每換一套視覺化工具，換一套語言，就有不同的中文字體指定方式。例如用base的<code>plot()</code>來繪圖或用<code>ggplot()</code>的中文字型指定方法便不同，且軸上面有中文、圖標有中文、或者圖內有中文都要分開指定，非常討人厭。</p>
<p>Mosaic Plot屬於base R的<code>plot()</code>，其中文指定方法要指定在繪圖前的<code>par()</code>函式中（<code>par</code>為parameter的意思），指定方法為<code>par(family=('Heiti TC Light'))</code>，Heiti TC Light為字體名稱，為OSX上在用的黑體細字，STKaiti則為標楷體。然後，<code>par()</code>和<code>mosaicplot()</code>兩個函式要「同時執行」，也就是請你直接用shift-cmd(ctrl)-Enter執行整個code-cell，或者將該兩個函式選起來一次執行。</p>
<div class="sourceCode" id="cb330"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb330-1"><a href="crosstab.html#cb330-1" tabindex="-1"></a><span class="fu">par</span>(<span class="at">family=</span>(<span class="st">'STKaiti'</span>))</span>
<span id="cb330-2"><a href="crosstab.html#cb330-2" tabindex="-1"></a><span class="co"># par(family=('Heiti TC Light'))</span></span>
<span id="cb330-3"><a href="crosstab.html#cb330-3" tabindex="-1"></a><span class="fu">mosaicplot</span>(res_table, <span class="at">main=</span><span class="st">"mosaic plot"</span>, <span class="at">color=</span>T)</span></code></pre></div>
<p><img src="R12_tptheft_base_files/figure-html/unnamed-chunk-10-1.png" width="672" /></p>
</div>
<div id="tptheft_define_color" class="section level4 hasAnchor" number="5.1.6.2">
<h4><span class="header-section-number">5.1.6.2</span> 自訂顏色<a href="crosstab.html#tptheft_define_color" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<p>目前顏色實在過醜，你可以自訂顏色指給<code>mosaicplot()</code>。例如我底下便產製了12種顏色後，將其作為<code>mosaicplot()</code>的參數</p>
<div class="sourceCode" id="cb331"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb331-1"><a href="crosstab.html#cb331-1" tabindex="-1"></a><span class="co"># Set up color by yourself.</span></span>
<span id="cb331-2"><a href="crosstab.html#cb331-2" 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="cb331-3"><a href="crosstab.html#cb331-3" 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="cb331-4"><a href="crosstab.html#cb331-4" 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="cb331-5"><a href="crosstab.html#cb331-5" tabindex="-1"></a><span class="co"># par(family=('STKaiti'))</span></span>
<span id="cb331-6"><a href="crosstab.html#cb331-6" tabindex="-1"></a><span class="fu">par</span>(<span class="at">family=</span>(<span class="st">'Heiti TC Light'</span>))</span>
<span id="cb331-7"><a href="crosstab.html#cb331-7" tabindex="-1"></a><span class="fu">mosaicplot</span>(res_table, <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="cb331-8"><a href="crosstab.html#cb331-8" 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="R12_tptheft_base_files/figure-html/unnamed-chunk-11-1.png" width="672" /></p>
</div>
</div>
<div id="practices" class="section level3 hasAnchor" number="5.1.7">
<h3><span class="header-section-number">5.1.7</span> Practices<a href="crosstab.html#practices" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<div id="tptheft_p1" class="section level4 hasAnchor" number="5.1.7.1">
<h4><span class="header-section-number">5.1.7.1</span> 萃取月份作為新變項month<a href="crosstab.html#tptheft_p1" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<div class="practice" style="color:OliveDrab;">
<p>除了時間和地區可能會有差別外，那月份會不會竊盜案的數量也有差異呢？會不會冬天小偷也都在家休息了，夏天多呢？請嘗試從發生日期萃取出竊盜案發生的月份，並儲存為一個新的變項<code>month</code>。</p>