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<!DOCTYPE html>
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<title>Chapter 22 ggplot | R for Data Journalism</title>
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<li class="chapter" data-level="3.3" data-path="r-basic.html"><a href="r-basic.html#calculating-with-vectors"><i class="fa fa-check"></i><b>3.3</b> Calculating with vectors</a>
<ul>
<li class="chapter" data-level="3.3.1" data-path="r-basic.html"><a href="r-basic.html#arithmetic-operations"><i class="fa fa-check"></i><b>3.3.1</b> Arithmetic operations</a></li>
<li class="chapter" data-level="3.3.2" data-path="r-basic.html"><a href="r-basic.html#logic-comparisons"><i class="fa fa-check"></i><b>3.3.2</b> Logic comparisons</a></li>
<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#預覽dplyr"><i class="fa fa-check"></i><b>4.3.1</b> 預覽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#the-data"><i class="fa fa-check"></i><b>4.4.1</b> The Data</a></li>
<li class="chapter" data-level="4.4.2" data-path="dataframe.html"><a href="dataframe.html#visual-strategies"><i class="fa fa-check"></i><b>4.4.2</b> Visual Strategies</a></li>
<li class="chapter" data-level="4.4.3" data-path="dataframe.html"><a href="dataframe.html#cleaning"><i class="fa fa-check"></i><b>4.4.3</b> Cleaning</a></li>
<li class="chapter" data-level="4.4.4" data-path="dataframe.html"><a href="dataframe.html#plotting"><i class="fa fa-check"></i><b>4.4.4</b> Plotting</a></li>
<li class="chapter" data-level="4.4.5" data-path="dataframe.html"><a href="dataframe.html#practice.-plotting-more"><i class="fa fa-check"></i><b>4.4.5</b> Practice. Plotting more</a></li>
<li class="chapter" data-level="4.4.6" data-path="dataframe.html"><a href="dataframe.html#practice.-selecting-and-filtering-by-dplyr-i"><i class="fa fa-check"></i><b>4.4.6</b> Practice. Selecting and filtering by dplyr I</a></li>
<li class="chapter" data-level="4.4.7" data-path="dataframe.html"><a href="dataframe.html#more-clean-version"><i class="fa fa-check"></i><b>4.4.7</b> (More) Clean version</a></li>
<li class="chapter" data-level="4.4.8" data-path="dataframe.html"><a href="dataframe.html#more-the-fittest-version-to-compute-staysame"><i class="fa fa-check"></i><b>4.4.8</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-table"><i class="fa fa-check"></i><b>6.2.4</b> Long to wide table</a></li>
<li class="chapter" data-level="6.2.5" data-path="base2dplyr.html"><a href="base2dplyr.html#plot-with-long-table"><i class="fa fa-check"></i><b>6.2.5</b> Plot with long table</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#the-data-1"><i class="fa fa-check"></i><b>6.3.1</b> The Data</a></li>
<li class="chapter" data-level="6.3.2" data-path="base2dplyr.html"><a href="base2dplyr.html#advanced-visual-strategies"><i class="fa fa-check"></i><b>6.3.2</b> Advanced Visual Strategies</a></li>
<li class="chapter" data-level="6.3.3" data-path="base2dplyr.html"><a href="base2dplyr.html#code-by-base-r"><i class="fa fa-check"></i><b>6.3.3</b> Code by base R</a></li>
<li class="chapter" data-level="6.3.4" data-path="base2dplyr.html"><a href="base2dplyr.html#code-by-dplyr"><i class="fa fa-check"></i><b>6.3.4</b> Code by dplyr</a></li>
<li class="chapter" data-level="6.3.5" data-path="base2dplyr.html"><a href="base2dplyr.html#generating-each"><i class="fa fa-check"></i><b>6.3.5</b> Generating each</a></li>
<li class="chapter" data-level="6.3.6" data-path="base2dplyr.html"><a href="base2dplyr.html#gathering-subplots-by-cowplot"><i class="fa fa-check"></i><b>6.3.6</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> An 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#案例說明-公投案與人口資料"><i class="fa fa-check"></i><b>7.2</b> 1. 案例說明-公投案與人口資料</a>
<ul>
<li class="chapter" data-level="7.2.1" data-path="joindata.html"><a href="joindata.html#資料來源"><i class="fa fa-check"></i><b>7.2.1</b> 1.1 資料來源</a></li>
<li class="chapter" data-level="7.2.2" data-path="joindata.html"><a href="joindata.html#處理策略"><i class="fa fa-check"></i><b>7.2.2</b> 1.2 處理策略</a></li>
</ul></li>
<li class="chapter" data-level="7.3" data-path="joindata.html"><a href="joindata.html#moi"><i class="fa fa-check"></i><b>7.3</b> 2. 讀取內政部人口統計資料</a></li>
<li class="chapter" data-level="7.4" data-path="joindata.html"><a href="joindata.html#觀察資料"><i class="fa fa-check"></i><b>7.4</b> 3. 觀察資料</a></li>
<li class="chapter" data-level="7.5" data-path="joindata.html"><a href="joindata.html#彙整列數據為新的變項使用rowwise"><i class="fa fa-check"></i><b>7.5</b> 4. 彙整列數據為新的變項：使用Rowwise()</a>
<ul>
<li class="chapter" data-level="7.5.1" data-path="joindata.html"><a href="joindata.html#補充c_across的應用時機"><i class="fa fa-check"></i><b>7.5.1</b> 補充：<code>c_across()</code>的應用時機</a></li>
</ul></li>
<li class="chapter" data-level="7.6" data-path="joindata.html"><a href="joindata.html#moi_town_groupby"><i class="fa fa-check"></i><b>7.6</b> 5. 將村里指標匯總為鄉鎮市區指標</a></li>
<li class="chapter" data-level="7.7" data-path="joindata.html"><a href="joindata.html#moi_visual_popul"><i class="fa fa-check"></i><b>7.7</b> 6. 視覺化測試（老年人口數 x 曾婚人口數）</a></li>
<li class="chapter" data-level="7.8" data-path="joindata.html"><a href="joindata.html#referendum"><i class="fa fa-check"></i><b>7.8</b> 7. 合併公投資料</a>
<ul>
<li class="chapter" data-level="7.8.1" data-path="joindata.html"><a href="joindata.html#讀取公投資料"><i class="fa fa-check"></i><b>7.8.1</b> 7.1. 讀取公投資料</a></li>
<li class="chapter" data-level="7.8.2" data-path="joindata.html"><a href="joindata.html#moi_join_ref"><i class="fa fa-check"></i><b>7.8.2</b> 7.2. 合併公投資料並視覺化</a></li>
</ul></li>
<li class="chapter" data-level="7.9" data-path="joindata.html"><a href="joindata.html#補充不用rowwise的做法"><i class="fa fa-check"></i><b>7.9</b> 8. 補充：不用<code>rowwise()</code>的做法</a>
<ul>
<li class="chapter" data-level="7.9.1" data-path="joindata.html"><a href="joindata.html#寬表轉長表"><i class="fa fa-check"></i><b>7.9.1</b> <strong>8.1. 寬表轉長表</strong></a></li>
<li class="chapter" data-level="7.9.2" data-path="joindata.html"><a href="joindata.html#切分變項"><i class="fa fa-check"></i><b>7.9.2</b> 8.2. 切分變項</a></li>
<li class="chapter" data-level="7.9.3" data-path="joindata.html"><a href="joindata.html#moi_vil_groupby"><i class="fa fa-check"></i><b>7.9.3</b> 8.3. 使用<code>group_by()</code>建立村里指標</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> Ordered-factor</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> Covert to ordered-factor</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>
<li class="chapter" data-level="8.6" data-path="categorical.html"><a href="categorical.html#plot"><i class="fa fa-check"></i><b>8.6</b> Plot</a>
<ul>
<li class="chapter" data-level="8.6.1" data-path="categorical.html"><a href="categorical.html#plot-by-ggplot"><i class="fa fa-check"></i><b>8.6.1</b> Plot by ggplot()</a></li>
</ul></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#webpage-browsing"><i class="fa fa-check"></i><b>15.1</b> Webpage Browsing</a></li>
<li class="chapter" data-level="15.2" data-path="crawler-overview.html"><a href="crawler-overview.html#scraper"><i class="fa fa-check"></i><b>15.2</b> Scraper</a></li>
<li class="chapter" data-level="15.3" data-path="crawler-overview.html"><a href="crawler-overview.html#type-of-scraper"><i class="fa fa-check"></i><b>15.3</b> Type of Scraper</a>
<ul>
<li class="chapter" data-level="15.3.1" data-path="crawler-overview.html"><a href="crawler-overview.html#type-1.-response-with-json"><i class="fa fa-check"></i><b>15.3.1</b> <strong>Type 1. Response with JSON</strong></a></li>
<li class="chapter" data-level="15.3.2" data-path="crawler-overview.html"><a href="crawler-overview.html#craw_scraping"><i class="fa fa-check"></i><b>15.3.2</b> Type 2. HTML Parsing</a></li>
</ul></li>
<li class="chapter" data-level="15.4" data-path="crawler-overview.html"><a href="crawler-overview.html#supplementary-materials"><i class="fa fa-check"></i><b>15.4</b> Supplementary Materials</a>
<ul>
<li class="chapter" data-level="15.4.1" data-path="crawler-overview.html"><a href="crawler-overview.html#status_code"><i class="fa fa-check"></i><b>15.4.1</b> HTTP Status Code</a></li>
<li class="chapter" data-level="15.4.2" data-path="crawler-overview.html"><a href="crawler-overview.html#using-chrome-devtools"><i class="fa fa-check"></i><b>15.4.2</b> Using Chrome DevTools</a></li>
<li class="chapter" data-level="15.4.3" data-path="crawler-overview.html"><a href="crawler-overview.html#observing-web-request"><i class="fa fa-check"></i><b>15.4.3</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>
</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"><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>
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<li><a href="https://github.com/rstudio/bookdown" target="blank">Published with bookdown</a></li>
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<div id="ggplot" class="section level1 hasAnchor" number="22">
<h1><span class="header-section-number">Chapter 22</span> ggplot<a href="ggplot.html#ggplot" class="anchor-section" aria-label="Anchor link to header"></a></h1>
<p>本節著重在介紹<code>ggplot</code>的基本概念與設定。</p>
<p><strong>小訣竅：</strong>可在一開始便透過<code>knitr::opts_chunk$set(echo = TRUE, fig.width = 2, fig.asp = 0.4)</code>來一次設定所有圖片。<code>fig.width = 8</code>與<code>fig.height = 6</code> 是以英吋（inches）為單位，或用<code>fig.dim = c(8, 6)</code>一次設定長寬<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>。<code>echo = TRUE</code>是設定knit出輸出格式（如html）時，也要包含程式碼。如果<code>echo = FALSE</code>的話，就只會輸出文字和圖形。</p>
<div id="essentials-of-ggplot" class="section level2 hasAnchor" number="22.1">
<h2><span class="header-section-number">22.1</span> Essentials of ggplot<a href="ggplot.html#essentials-of-ggplot" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p>用ggplot來繪製圖形有三個基本函式<code>ggplot()</code> + <code>aes()</code> + <code>geom_圖表類型</code>。</p>
<ol style="list-style-type: decimal">
<li><strong>指定要進行繪圖<code>ggplot()</code></strong>：用<code>%>%</code>將資料（dataframe）pipe給<code>ggplot()</code>後，底下各增添的繪圖選項都用<code>+</code>的符號，類似不斷修正繪圖結果的意思。</li>
<li><strong>指定X／Y軸與群組因子<code>aes()</code></strong>：指定圖表的X/Y軸分別是什麼變數，有些圖表只需要單一個變數（例如Density-chart和Histogram），有些需要X/Y兩個變數（例如Scatter-chart）什麼的變數要做視覺化，Boxplot甚至可以直接指定最大、最小、Q1、Q3和Median等多個變數。</li>
<li><strong>指定要繪製的圖表類型</strong>。例如Line-chart為<code>geom_line()</code>、Scatter-chart為<code>geom_point()</code>、Bar-chart為<code>geom_col()</code>或<code>geom_bar()</code>。查閱<a href="https://www.maths.usyd.edu.au/u/UG/SM/STAT3022/r/current/Misc/data-visualization-2.1.pdf">ggplot cheat sheet</a>可以快速翻閱有哪些圖表類型（如截圖）。</li>
</ol>
<div class="float">
<img src="images/paste-E7629FF5.png" alt="ggplot-cheat-sheet" />
<div class="figcaption">ggplot-cheat-sheet</div>
</div>
<div id="ggplot-秀出預備要繪製的繪圖區" class="section level3 hasAnchor" number="22.1.1">
<h3><span class="header-section-number">22.1.1</span> (1) <code>ggplot()</code> 秀出預備要繪製的繪圖區<a href="ggplot.html#ggplot-秀出預備要繪製的繪圖區" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<div class="sourceCode" id="cb689"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb689-1"><a href="ggplot.html#cb689-1" tabindex="-1"></a><span class="fu">tibble</span>(<span class="at">a=</span><span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, <span class="at">b=</span><span class="dv">5</span><span class="sc">:</span><span class="dv">1</span>) <span class="sc">%>%</span></span>
<span id="cb689-2"><a href="ggplot.html#cb689-2" tabindex="-1"></a> <span class="fu">ggplot</span>()</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-2-1.png" width="576" style="display: block; margin: auto;" /></p>
</div>
<div id="aes-指定xy軸與群組因子" class="section level3 hasAnchor" number="22.1.2">
<h3><span class="header-section-number">22.1.2</span> <strong>(2) <code>aes()</code> 指定X／Y軸與群組因子</strong><a href="ggplot.html#aes-指定xy軸與群組因子" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p><code>aes()</code>會在繪圖區上繪製X與Y軸</p>
<div class="sourceCode" id="cb690"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb690-1"><a href="ggplot.html#cb690-1" tabindex="-1"></a><span class="fu">tibble</span>(<span class="at">a=</span><span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, <span class="at">b=</span><span class="dv">5</span><span class="sc">:</span><span class="dv">1</span>) <span class="sc">%>%</span></span>
<span id="cb690-2"><a href="ggplot.html#cb690-2" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb690-3"><a href="ggplot.html#cb690-3" tabindex="-1"></a> <span class="fu">aes</span>(<span class="at">x=</span>a, <span class="at">y=</span>b)</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-3-1.png" width="576" style="display: block; margin: auto;" /></p>
</div>
<div id="geom_-指定要繪製的圖表類型" class="section level3 hasAnchor" number="22.1.3">
<h3><span class="header-section-number">22.1.3</span> <strong>(3) <code>geom_???()</code> 指定要繪製的圖表類型</strong>。<a href="ggplot.html#geom_-指定要繪製的圖表類型" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>例如折線圖為為<code>geom_line()</code>、X/Y散佈圖為<code>geom_point()</code>、長條圖我多會使用<code>geom_col()</code>。ggplot繪圖種類除了可以參照前面的<a href="https://www.maths.usyd.edu.au/u/UG/SM/STAT3022/r/current/Misc/data-visualization-2.1.pdf">ggplot cheat sheet</a>之外，可以詢問ChatGPT有哪些常見的類別，甚至可以請他舉例給你測試該繪圖方法。</p>
<div class="sourceCode" id="cb691"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb691-1"><a href="ggplot.html#cb691-1" tabindex="-1"></a><span class="fu">tibble</span>(<span class="at">a=</span><span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, <span class="at">b=</span><span class="dv">5</span><span class="sc">:</span><span class="dv">1</span>) <span class="sc">%>%</span></span>
<span id="cb691-2"><a href="ggplot.html#cb691-2" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb691-3"><a href="ggplot.html#cb691-3" tabindex="-1"></a> <span class="fu">aes</span>(<span class="at">x=</span>a, <span class="at">y=</span>b) <span class="sc">+</span> </span>
<span id="cb691-4"><a href="ggplot.html#cb691-4" tabindex="-1"></a> <span class="fu">geom_line</span>()</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-4-1.png" width="576" style="display: block; margin: auto;" /></p>
<p>亦可同時繪製兩種類型的圖表於同一張圖上。例如以下同時繪製了<code>geom_line()</code>與<code>geom_plot()</code>。</p>
<div class="sourceCode" id="cb692"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb692-1"><a href="ggplot.html#cb692-1" tabindex="-1"></a><span class="fu">tibble</span>(<span class="at">a=</span><span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, <span class="at">b=</span><span class="dv">5</span><span class="sc">:</span><span class="dv">1</span>) <span class="sc">%>%</span></span>
<span id="cb692-2"><a href="ggplot.html#cb692-2" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb692-3"><a href="ggplot.html#cb692-3" tabindex="-1"></a> <span class="fu">aes</span>(<span class="at">x=</span>a, <span class="at">y=</span>b) <span class="sc">+</span> </span>
<span id="cb692-4"><a href="ggplot.html#cb692-4" tabindex="-1"></a> <span class="fu">geom_line</span>() <span class="sc">+</span> </span>
<span id="cb692-5"><a href="ggplot.html#cb692-5" tabindex="-1"></a> <span class="fu">geom_point</span>()</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-5-1.png" width="576" style="display: block; margin: auto;" /></p>
<p>注意：ggplot是以變數為基礎的視覺化套件，也就是說，當準備好dataframe後，就可以在ggplot中指定要用哪些變數來繪圖。也因此，務必把dataframe整理為tidy型態，也就是長表格（long-form）的型態。整理完資料後，我會習慣地用<code>names(plot)</code>或<code>glimpse(plot)</code>來看一下該資料所有的變項，好可以在下一階段的繪圖做參考。</p>
</div>
</div>
<div id="nyt-inequality" class="section level2 hasAnchor" number="22.2">
<h2><span class="header-section-number">22.2</span> NYT: Inequality<a href="ggplot.html#nyt-inequality" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p>以下將以紐時的這個<a href="https://www.nytimes.com/2021/05/11/learning/lesson-plans/teach-about-inequality-with-these-28-new-york-times-graphs.html">Teach About Inequality With These 28 New York Times Graphs</a> 案例來做繪圖教學。該教學引用了<a href="https://www.nytimes.com/interactive/2020/04/10/opinion/coronavirus-us-economy-inequality.html">Opinion | America Will Struggle After Coronavirus. These Charts Show Why.</a>這篇新聞中的圖表，我們拿來做範例的這張圖，主要是在說財富趨勢對年輕人而言尤其艱難。35歲以下美國人的凈資產中位數 - 他們平均比年長的美國人差得多 - 比2004年35歲以下美國人的凈資產低40%。相比之下，65歲以上美國人的凈資產在同一時期增長了9%。簡而言之，嬰兒潮一代比他們的前輩更富有，而千禧一代和X世代比他們的前輩更窮；或者說，年輕人拿自己和10年前的年輕人相比，現在的年輕人更窮；而現在的老年人拿自己和10年前的老年人比，現在的老年人更富有。</p>
<div id="loading-data-1" class="section level3 hasAnchor" number="22.2.1">
<h3><span class="header-section-number">22.2.1</span> (1) Loading data<a href="ggplot.html#loading-data-1" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>仔細觀察一下資料，你會怎樣描述這個資料？</p>
<p>這個Dataframe包含三個變數（Category, year, Net_Worth），共66個觀測值。變數「Category」描述的是年齡範圍，包含六個類別（Level）。變數「year」代表年份，從1989年到2019年，以三年為一個週期觀察，共有11個Levels。變數「Net_Worth」則表示在該年齡範圍內的淨資產。從資料可以觀察到，在不同的時間點，不同年齡範圍的人群的財富狀況看似有明顯差異。例如，比較1989年和2019年，45-54歲的年齡組在這段期間內的淨值似乎較35-44歲組要高，這可能反映了隨著年齡增長，個人或家庭的財富累積增加的趨勢。</p>
<div class="sourceCode" id="cb693"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb693-1"><a href="ggplot.html#cb693-1" tabindex="-1"></a>NW <span class="ot"><-</span> <span class="fu">read_csv</span>(<span class="st">"data/interactive_bulletin_charts_agecl_median.csv"</span>) <span class="sc">%>%</span></span>
<span id="cb693-2"><a href="ggplot.html#cb693-2" tabindex="-1"></a> <span class="fu">select</span>(Category, year, Net_Worth) <span class="sc">%>%</span></span>
<span id="cb693-3"><a href="ggplot.html#cb693-3" tabindex="-1"></a> <span class="fu">group_by</span>(Category) <span class="sc">%>%</span></span>
<span id="cb693-4"><a href="ggplot.html#cb693-4" tabindex="-1"></a> <span class="fu">arrange</span>(year) <span class="sc">%>%</span></span>
<span id="cb693-5"><a href="ggplot.html#cb693-5" tabindex="-1"></a> <span class="fu">ungroup</span>()</span></code></pre></div>
<pre><code>## Rows: 66 Columns: 37
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Category
## dbl (36): year, Before_Tax_Income, Net_Worth, Assets, Financial_Assets, Tran...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.</code></pre>
<div class="sourceCode" id="cb695"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb695-1"><a href="ggplot.html#cb695-1" tabindex="-1"></a>NW <span class="sc">%>%</span> <span class="fu">head</span>(<span class="dv">12</span>)</span></code></pre></div>
<pre><code>## # A tibble: 12 × 3
## Category year Net_Worth
## <chr> <dbl> <dbl>
## 1 Less than 35 1989 16.2
## 2 35-44 1989 112.
## 3 45-54 1989 195.
## 4 55-64 1989 195.
## 5 65-74 1989 154.
## 6 75 or older 1989 144.
## 7 Less than 35 1992 16.6
## 8 35-44 1992 79.9
## 9 45-54 1992 140.
## 10 55-64 1992 203.
## 11 65-74 1992 176.
## 12 75 or older 1992 155.</code></pre>
<div id="group_by的概念" class="section level4 hasAnchor" number="22.2.1.1">
<h4><span class="header-section-number">22.2.1.1</span> (1.1) <code>group_by()</code>的概念<a href="ggplot.html#group_by的概念" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<p>在提供的程式碼中，<code>group_by(Category)</code>是一個關鍵步驟，它影響了數據處理的方式，尤其是在隨後的操作中。以下是有和沒有<code>group_by(Category)</code>時的主要差異：</p>
<ul>
<li><p>有<code>group_by(Category)</code>：當在程式碼中使用<code>group_by(Category)</code>時，這意味著接下來的操作將在每個<code>Category</code>類別的子集上單獨進行。這對於需要按類別分析或操作數據時非常有用。在此程式碼中，<code>arrange(year)</code>將會在每個<code>Category</code>內部對<code>year</code>進行排序。這意味著每個類別內的年份會從最小到最大排序，但這種排序是獨立於其他類別的。</p></li>
<li><p>沒有<code>group_by(Category)</code>：如果省略<code>group_by(Category)</code>，則後續的操作將考慮所有的數據作為一個整體來進行。</p>
<p>省略<code>group_by(Category)</code>後，<code>arrange(year)</code>會對整個數據集按<code>year</code>進行全局排序，而不會考慮<code>Category</code>的界限。由於<code>year</code>是一個類別變項，出現在多個<code>Category</code>組中，因此，會有多個相同<code>year</code>的列排在一起。</p></li>
</ul>
</div>
</div>
<div id="visualizing" class="section level3 hasAnchor" number="22.2.2">
<h3><span class="header-section-number">22.2.2</span> (2) Visualizing<a href="ggplot.html#visualizing" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>這是預期視覺化的結果。</p>
<p><img src="images/paste-B46B3CD7.png" /></p>
<div id="plot-without-group" class="section level4 hasAnchor" number="22.2.2.1">
<h4><span class="header-section-number">22.2.2.1</span> (2.1) Plot without group<a href="ggplot.html#plot-without-group" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<p>先將<code>year</code>和<code>Net_worth</code>分別繪製在X與Y軸上，並用<code>geom_line()</code>繪製為折線圖。結果圖表中呈現鋸齒狀的折線，看似有問題，但其實是合理的。因為<code>year</code>是一個離散變數，而我們希望每個年齡層一條線的話，那就要照年齡層來分組。也因此，每一年都有有每個年齡層的資料，當我們把「年」作為X軸時，自然同一年就會有數筆不同年齡層的資料，因此才會是鋸齒狀的。</p>
<div class="sourceCode" id="cb697"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb697-1"><a href="ggplot.html#cb697-1" tabindex="-1"></a>NW <span class="sc">%>%</span></span>
<span id="cb697-2"><a href="ggplot.html#cb697-2" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb697-3"><a href="ggplot.html#cb697-3" tabindex="-1"></a> <span class="fu">aes</span>(<span class="at">x=</span>year, <span class="at">y=</span>Net_Worth) <span class="sc">+</span> </span>
<span id="cb697-4"><a href="ggplot.html#cb697-4" tabindex="-1"></a> <span class="fu">geom_line</span>()</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-7-1.png" width="576" style="display: block; margin: auto;" /></p>
<p><strong>不同的圖表類型是可以疊加在同一張圖上的</strong>。我們也可以把<code>geom_point()</code> 另一種圖表型態加入，也是可以的，兩者的X與Y不相衝突。<code>geom_line()</code>、<code>geom_point()</code>、<code>geom_text()</code>三者會經常伴隨出現。</p>
<div class="sourceCode" id="cb698"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb698-1"><a href="ggplot.html#cb698-1" tabindex="-1"></a>NW <span class="sc">%>%</span></span>
<span id="cb698-2"><a href="ggplot.html#cb698-2" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb698-3"><a href="ggplot.html#cb698-3" tabindex="-1"></a> <span class="fu">aes</span>(<span class="at">x=</span>year, <span class="at">y=</span>Net_Worth) <span class="sc">+</span> </span>
<span id="cb698-4"><a href="ggplot.html#cb698-4" tabindex="-1"></a> <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb698-5"><a href="ggplot.html#cb698-5" tabindex="-1"></a> <span class="fu">geom_point</span>()</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-8-1.png" width="576" style="display: block; margin: auto;" /></p>
</div>
<div id="grouping" class="section level4 hasAnchor" number="22.2.2.2">
<h4><span class="header-section-number">22.2.2.2</span> (2.2) Grouping<a href="ggplot.html#grouping" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<p>上圖是我們把多個年齡層的逐年資料畫在同一條折線上，所以會呈現鋸齒狀折現的狀況。但這些年齡層並非在同一條線上呀？因此，我們要根據<code>Category</code>這個變數來做分組。</p>
<div class="sourceCode" id="cb699"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb699-1"><a href="ggplot.html#cb699-1" tabindex="-1"></a>NW <span class="sc">%>%</span></span>
<span id="cb699-2"><a href="ggplot.html#cb699-2" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
<span id="cb699-3"><a href="ggplot.html#cb699-3" tabindex="-1"></a> <span class="fu">aes</span>(<span class="at">x=</span>year, <span class="at">y=</span>Net_Worth, <span class="at">group=</span>Category) <span class="sc">+</span> </span>
<span id="cb699-4"><a href="ggplot.html#cb699-4" tabindex="-1"></a> <span class="fu">geom_line</span>() <span class="sc">+</span> </span>
<span id="cb699-5"><a href="ggplot.html#cb699-5" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="at">stat=</span><span class="st">"identity"</span>)</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-9-1.png" width="576" style="display: block; margin: auto;" /></p>
<p>如希望不同線條上不一樣的色彩，應指定<code>color=Category</code>。</p>
<div class="sourceCode" id="cb700"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb700-1"><a href="ggplot.html#cb700-1" tabindex="-1"></a>NW <span class="sc">%>%</span> </span>
<span id="cb700-2"><a href="ggplot.html#cb700-2" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span> <span class="fu">aes</span>(year, Net_Worth, <span class="at">color=</span>Category) <span class="sc">+</span> </span>
<span id="cb700-3"><a href="ggplot.html#cb700-3" tabindex="-1"></a> <span class="fu">geom_line</span>()</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-10-1.png" width="576" style="display: block; margin: auto;" /></p>
<dl>
<dt>用color、fill或group來做分組？</dt>
<dd>
<p>在使用<code>geom_line()</code>函數時，顏色的設定是針對線條本身，而非填充面積。當我們希望指定點（透過<code>geom_point()</code>）或線條（使用<code>geom_line()</code>）的顏色時，我們會使用<code>color</code>參數來定義顏色。</p>
</dd>
</dl>
<pre><code>相對地，當使用`geom_area()`函數進行視覺化時，由於它涉及的是面積的填充，因此我們應該使用`fill`參數來指定填充色。在某些情況下，我們可能會同時使用`color=Category`和`fill=Category`來對`geom_area()`進行設定，這樣做能夠同時定義邊線顏色和填充顏色。然而，當利用`geom_area()`來展示折線圖時，建議限制使用的顏色種類不超過兩種，以避免顏色層疊導致的視覺混淆，即便是設定了`alpha=0.2`以降低透明度。
`geom_area()`函數默認展示的是累積分佈圖，即不同群組的數值會在Y軸方向上疊加。若目的是比較兩個群組之間的差異，而非觀察整體趨勢，則可以通過添加`position="dodge"`參數來調整分佈方式，並將`alpha`設定為小於1的值以增加圖形的透明度，從而更清晰地分辨不同群組之間的差異。</code></pre>
<div class="sourceCode" id="cb702"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb702-1"><a href="ggplot.html#cb702-1" tabindex="-1"></a>NW <span class="sc">%>%</span> </span>
<span id="cb702-2"><a href="ggplot.html#cb702-2" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span> <span class="fu">aes</span>(year, Net_Worth, <span class="at">color=</span>Category, <span class="at">fill=</span>Category) <span class="sc">+</span> </span>
<span id="cb702-3"><a href="ggplot.html#cb702-3" tabindex="-1"></a> <span class="fu">geom_area</span>(<span class="at">position=</span><span class="st">"dodge"</span>, <span class="at">alpha=</span><span class="fl">0.2</span>)</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-11-1.png" width="576" style="display: block; margin: auto;" /></p>
</div>
</div>
</div>
<div id="adjusting-chart" class="section level2 hasAnchor" number="22.3">
<h2><span class="header-section-number">22.3</span> Adjusting Chart<a href="ggplot.html#adjusting-chart" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<div id="type-of-points-and-lines" class="section level3 hasAnchor" number="22.3.1">
<h3><span class="header-section-number">22.3.1</span> Type of Points and Lines<a href="ggplot.html#type-of-points-and-lines" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>下面的例子同時用了<code>geom_line()</code>和<code>geom_point()</code>，且分別設定了線寬（<code>size=1</code>）、點的大小(<code>size=2</code>)，折線型態（<code>linetype="dashed"</code>）、半透明程度（<code>alpha</code>）。</p>
<p><a href="http://www.sthda.com/english/wiki/ggplot2-line-types-how-to-change-line-types-of-a-graph-in-r-software">ggplot2 line types : How to change line types of a graph in R software? - Easy Guides - Wiki - STHDA</a></p>
<div class="sourceCode" id="cb703"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb703-1"><a href="ggplot.html#cb703-1" tabindex="-1"></a>NW <span class="sc">%>%</span> </span>
<span id="cb703-2"><a href="ggplot.html#cb703-2" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span> <span class="fu">aes</span>(year, Net_Worth, <span class="at">color=</span>Category) <span class="sc">+</span> </span>
<span id="cb703-3"><a href="ggplot.html#cb703-3" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="at">size=</span><span class="dv">1</span>, <span class="at">linetype =</span> <span class="st">"dashed"</span>, <span class="at">alpha=</span><span class="fl">0.5</span>) <span class="sc">+</span> </span>
<span id="cb703-4"><a href="ggplot.html#cb703-4" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="at">size=</span><span class="dv">2</span>, <span class="at">color=</span><span class="st">"dimgrey"</span>, <span class="at">alpha=</span><span class="fl">0.5</span>)</span></code></pre></div>
<pre><code>## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.</code></pre>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-12-1.png" width="576" style="display: block; margin: auto;" /></p>
</div>
<div id="line-types" class="section level3 hasAnchor" number="22.3.2">
<h3><span class="header-section-number">22.3.2</span> Line Types<a href="ggplot.html#line-types" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>See more from ggthemes <a href="https://github.com/BTJ01/ggthemes/tree/master/inst/examples" class="uri">https://github.com/BTJ01/ggthemes/tree/master/inst/examples</a></p>
<div class="sourceCode" id="cb705"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb705-1"><a href="ggplot.html#cb705-1" tabindex="-1"></a><span class="fu">library</span>(ggthemes)</span>
<span id="cb705-2"><a href="ggplot.html#cb705-2" tabindex="-1"></a>rescale01 <span class="ot"><-</span> <span class="cf">function</span>(x) {</span>
<span id="cb705-3"><a href="ggplot.html#cb705-3" tabindex="-1"></a> (x <span class="sc">-</span> <span class="fu">min</span>(x)) <span class="sc">/</span> <span class="fu">diff</span>(<span class="fu">range</span>(x))</span>
<span id="cb705-4"><a href="ggplot.html#cb705-4" tabindex="-1"></a> }</span>
<span id="cb705-5"><a href="ggplot.html#cb705-5" tabindex="-1"></a></span>
<span id="cb705-6"><a href="ggplot.html#cb705-6" tabindex="-1"></a><span class="fu">gather</span>(economics, variable, value, <span class="sc">-</span>date) <span class="sc">%>%</span></span>
<span id="cb705-7"><a href="ggplot.html#cb705-7" tabindex="-1"></a> <span class="fu">group_by</span>(variable) <span class="sc">%>%</span></span>
<span id="cb705-8"><a href="ggplot.html#cb705-8" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">value =</span> <span class="fu">rescale01</span>(value)) <span class="sc">%>%</span></span>
<span id="cb705-9"><a href="ggplot.html#cb705-9" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> date, <span class="at">y =</span> value, <span class="at">linetype =</span> variable)) <span class="sc">+</span></span>
<span id="cb705-10"><a href="ggplot.html#cb705-10" tabindex="-1"></a> <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb705-11"><a href="ggplot.html#cb705-11" tabindex="-1"></a> <span class="fu">scale_linetype_stata</span>() <span class="sc">+</span> <span class="fu">theme_minimal</span>()</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-13-1.png" width="576" style="display: block; margin: auto;" /></p>
</div>
<div id="title-labels-and-legends" class="section level3 hasAnchor" number="22.3.3">
<h3><span class="header-section-number">22.3.3</span> Title, Labels and Legends<a href="ggplot.html#title-labels-and-legends" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>Titles, labels, and legend <strong>設定標題與X／Y軸標題（法一）</strong>：以下設定了圖表的圖表標題、和X軸與Y軸的軸標題（<code>xlab</code>與<code>ylab</code>）。</p>
<div class="sourceCode" id="cb706"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb706-1"><a href="ggplot.html#cb706-1" tabindex="-1"></a>NW <span class="sc">%>%</span> </span>
<span id="cb706-2"><a href="ggplot.html#cb706-2" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span> <span class="fu">aes</span>(year, Net_Worth, <span class="at">color=</span>Category) <span class="sc">+</span> </span>
<span id="cb706-3"><a href="ggplot.html#cb706-3" tabindex="-1"></a> <span class="fu">geom_line</span>() <span class="sc">+</span> </span>
<span id="cb706-4"><a href="ggplot.html#cb706-4" tabindex="-1"></a> <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb706-5"><a href="ggplot.html#cb706-5" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">"Year"</span>) <span class="sc">+</span> </span>
<span id="cb706-6"><a href="ggplot.html#cb706-6" tabindex="-1"></a> <span class="fu">ylab</span>(<span class="st">"Net Worth"</span>) <span class="sc">+</span> </span>
<span id="cb706-7"><a href="ggplot.html#cb706-7" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"Net Worth by year grouped by age groups"</span>)</span></code></pre></div>
<p><img src="V01_Learning_ggplot_files/figure-html/unnamed-chunk-14-1.png" width="576" style="display: block; margin: auto;" /></p>
<p><strong>設定標題與X／Y軸標題（法二）</strong>：這是一次設定圖表標題（<code>title</code>）、次標題（<code>suttitle</code>）、X軸與Y軸標題的方法。</p>
<div class="sourceCode" id="cb707"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb707-1"><a href="ggplot.html#cb707-1" tabindex="-1"></a>NW <span class="sc">%>%</span> </span>