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<!DOCTYPE html>
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<title>Chapter 4 DataFrame | R for Data Journalism</title>
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<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>
</ul></li>
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<li><a href="https://github.com/rstudio/bookdown" target="blank">Published with bookdown</a></li>
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<h1>
<i class="fa fa-circle-o-notch fa-spin"></i><a href="./">R for Data Journalism</a>
</h1>
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<div class="page-inner">
<section class="normal" id="section-">
<div id="dataframe" class="section level1 hasAnchor" number="4">
<h1><span class="header-section-number">Chapter 4</span> DataFrame<a href="dataframe.html#dataframe" class="anchor-section" aria-label="Anchor link to header"></a></h1>
<p>DataFrame 是 R 語言中最常用的資料結構之一，它是一種表格形式的數據集，類似於 Excel 表格或 SQL 資料表。每一列代表一個變數（Variable），而每一行代表一個觀測值（Observation）。</p>
<p>DataFrame 具備以下特性：</p>
<ul>
<li><p><strong>列（Row）</strong>：每一列都是一個完整的觀測數據。</p></li>
<li><p><strong>欄（Column）</strong>：每一欄是相同型態的變數，例如數字、文字、因子等。</p></li>
<li><p><strong>索引（Index）</strong>：R 自動為 DataFrame 的列加上索引，方便存取數據。</p></li>
</ul>
<div id="基本操作" class="section level2 hasAnchor" number="4.1">
<h2><span class="header-section-number">4.1</span> 基本操作<a href="dataframe.html#基本操作" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<div id="產生新的dataframe" class="section level3 hasAnchor" number="4.1.1">
<h3><span class="header-section-number">4.1.1</span> 產生新的Dataframe<a href="dataframe.html#產生新的dataframe" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>在 R 中，可以透過 <code>data.frame()</code> 函數將數個等長的向量（Vectors）合併為 DataFrame。例如，我們建立台北市各行政區的基本資料，包括區名、面積（平方公里）和人口數（2023 年估計值）。</p>
<ul>
<li>用以下ChatGPT問句來產生測試資料「我現在正在準備R的教學範例， 請協助我產生台北市所有行政區的資料，包含行政區名、面積、人口數 分別指給town, area, population三個變數」。</li>
</ul>
<div class="sourceCode" id="cb174"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb174-1"><a href="dataframe.html#cb174-1" tabindex="-1"></a>town <span class="ot">=</span> <span class="fu">c</span>(<span class="st">"松山區"</span>, <span class="st">"信義區"</span>, <span class="st">"大安區"</span>, <span class="st">"中山區"</span>, <span class="st">"中正區"</span>, <span class="st">"大同區"</span>, <span class="st">"萬華區"</span>, <span class="st">"文山區"</span>, <span class="st">"南港區"</span>, <span class="st">"內湖區"</span>, <span class="st">"士林區"</span>, <span class="st">"北投區"</span>)</span>
<span id="cb174-2"><a href="dataframe.html#cb174-2" tabindex="-1"></a></span>
<span id="cb174-3"><a href="dataframe.html#cb174-3" tabindex="-1"></a>area <span class="ot">=</span> <span class="fu">c</span>(<span class="fl">9.2878</span>, <span class="fl">11.2077</span>, <span class="fl">11.3614</span>, <span class="fl">13.6821</span>, <span class="fl">7.6071</span>, <span class="fl">5.6815</span>, <span class="fl">8.8522</span>, <span class="fl">31.5090</span>, <span class="fl">21.8424</span>, <span class="fl">31.5787</span>, <span class="fl">62.3682</span>, <span class="fl">56.8216</span>) <span class="co"># 單位：平方公里</span></span>
<span id="cb174-4"><a href="dataframe.html#cb174-4" tabindex="-1"></a></span>
<span id="cb174-5"><a href="dataframe.html#cb174-5" tabindex="-1"></a>population <span class="ot">=</span> <span class="fu">c</span>(<span class="dv">206375</span>, <span class="dv">225561</span>, <span class="dv">309835</span>, <span class="dv">203276</span>, <span class="dv">159608</span>, <span class="dv">132397</span>, <span class="dv">194160</span>, <span class="dv">275207</span>, <span class="dv">122103</span>, <span class="dv">287726</span>, <span class="dv">288324</span>, <span class="dv">255688</span>) <span class="co"># 2023年的估計值</span></span></code></pre></div>
<div id="合併等長vector為dataframe" class="section level4 hasAnchor" number="4.1.1.1">
<h4><span class="header-section-number">4.1.1.1</span> 合併等長vector為DataFrame<a href="dataframe.html#合併等長vector為dataframe" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<ul>
<li><p><code>data.frame(town, population, area)</code>：將三個向量合併成一個 DataFrame。</p></li>
<li><p><code>df$density <- df$population / df$area</code>：新增一欄「人口密度」，計算方式為人口數除以區域面積。</p></li>
<li><p><code>str(df)</code>：顯示 DataFrame 的結構資訊。</p></li>
<li><p><code>summary(df)</code>：輸出基本統計摘要，例如平均數、中位數、最小值、最大值等。</p></li>
</ul>
<div class="sourceCode" id="cb175"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb175-1"><a href="dataframe.html#cb175-1" tabindex="-1"></a>df <span class="ot"><-</span> <span class="fu">data.frame</span>(town, population, area)</span>
<span id="cb175-2"><a href="dataframe.html#cb175-2" tabindex="-1"></a>df<span class="sc">$</span>density <span class="ot">=</span> df<span class="sc">$</span>population <span class="sc">/</span> df<span class="sc">$</span>area</span>
<span id="cb175-3"><a href="dataframe.html#cb175-3" tabindex="-1"></a><span class="fu">str</span>(df)</span></code></pre></div>
<pre class="output"><code>## 'data.frame': 6 obs. of 4 variables:
## $ town : chr "中正" "大同" "中山" "松山" ...
## $ population: num 158228 126687 228075 204903 308383 ...
## $ area : num 7.61 5.68 13.68 9.29 11.36 ...
## $ density : num 20800 22298 16670 22062 27143 ...</code></pre>
<div class="sourceCode" id="cb177"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb177-1"><a href="dataframe.html#cb177-1" tabindex="-1"></a><span class="fu">summary</span>(df)</span></code></pre></div>
<pre class="output"><code>## town population area density
## Length:6 Min. :126687 Min. : 5.681 Min. :16670
## Class :character 1st Qu.:165651 1st Qu.: 7.918 1st Qu.:20907
## Mode :character Median :196412 Median : 9.070 Median :21645
## Mean :202366 Mean : 9.412 Mean :21700
## 3rd Qu.:222282 3rd Qu.:10.843 3rd Qu.:22239
## Max. :308383 Max. :13.682 Max. :27143</code></pre>
<div class="sourceCode" id="cb179"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb179-1"><a href="dataframe.html#cb179-1" tabindex="-1"></a><span class="co"># View(df)</span></span></code></pre></div>
</div>
<div id="範例臺灣貿易各國進出口量" class="section level4 hasAnchor" number="4.1.1.2">
<h4><span class="header-section-number">4.1.1.2</span> 範例：臺灣貿易各國進出口量<a href="dataframe.html#範例臺灣貿易各國進出口量" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<ul>
<li>運用<a href="https://cuswebo.trade.gov.tw/">國際貿易署貿易統計系統 (trade.gov.tw)</a>獲取臺灣進出口貿易資料。</li>
</ul>
<div class="sourceCode" id="cb180"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb180-1"><a href="dataframe.html#cb180-1" tabindex="-1"></a>country <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"CN"</span>, <span class="st">"US"</span>, <span class="st">"JP"</span>, <span class="st">"HK"</span>, <span class="st">"KR"</span>, <span class="st">"SG"</span>, <span class="st">"DE"</span>, <span class="st">"MY"</span>, <span class="st">"VN"</span>, <span class="st">"PH"</span>, <span class="st">"TH"</span>, <span class="st">"AU"</span>, <span class="st">"NL"</span>, <span class="st">"SA"</span>, <span class="st">"ID"</span>, <span class="st">"GB"</span>, <span class="st">"IN"</span>, <span class="st">"FR"</span>, <span class="st">"IT"</span>, <span class="st">"AE"</span>)</span>
<span id="cb180-2"><a href="dataframe.html#cb180-2" tabindex="-1"></a></span>
<span id="cb180-3"><a href="dataframe.html#cb180-3" tabindex="-1"></a>import <span class="ot"><-</span> <span class="fu">c</span>(<span class="fl">26.142</span>, <span class="fl">12.008</span>, <span class="fl">7.032</span>, <span class="fl">13.646</span>, <span class="fl">4.589</span>, <span class="fl">5.768</span>, <span class="fl">2.131</span>, <span class="fl">2.802</span>, <span class="fl">3.428</span>, <span class="fl">3.019</span>, <span class="fl">1.976</span>, <span class="fl">1.118</span>, <span class="fl">1.624</span>, <span class="fl">0.449</span>, <span class="fl">0.983</span>, <span class="fl">1.302</span>, <span class="fl">1.027</span>, <span class="fl">0.553</span>, <span class="fl">0.670</span>, <span class="fl">0.455</span>)</span>
<span id="cb180-4"><a href="dataframe.html#cb180-4" tabindex="-1"></a></span>
<span id="cb180-5"><a href="dataframe.html#cb180-5" tabindex="-1"></a>export <span class="ot"><-</span> <span class="fu">c</span>(<span class="fl">22.987</span>, <span class="fl">12.204</span>, <span class="fl">11.837</span>, <span class="fl">7.739</span>, <span class="fl">5.381</span>, <span class="fl">4.610</span>, <span class="fl">2.866</span>, <span class="fl">2.784</span>, <span class="fl">2.414</span>, <span class="fl">2.092</span>, <span class="fl">1.839</span>, <span class="fl">1.788</span>, <span class="fl">1.665</span>, <span class="fl">1.409</span>, <span class="fl">1.391</span>, <span class="fl">1.075</span>, <span class="fl">0.974</span>, <span class="fl">0.899</span>, <span class="fl">0.800</span>, <span class="fl">0.728</span>)</span></code></pre></div>
</div>
<div id="合併vector為data.frame" class="section level4 hasAnchor" number="4.1.1.3">
<h4><span class="header-section-number">4.1.1.3</span> 合併vector為data.frame<a href="dataframe.html#合併vector為data.frame" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<p>在 R 早期版本中，當我們讀取或創建資料框架（DataFrame）時，R 預設會將字串類型的變數轉換為因子（Factors）。這樣的設計對於統計分析是有益的，因為統計方法通常將文字數據視為類別變數來處理。然而，隨著資料科學領域的快速發展，需要處理大量文字數據的情況日益增多，因此將字串自動轉換為因子可能不再適用於所有情境。</p>
<div class="sourceCode" id="cb181"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb181-1"><a href="dataframe.html#cb181-1" tabindex="-1"></a>df <span class="ot"><-</span> <span class="fu">data.frame</span>(country, import, export)</span>
<span id="cb181-2"><a href="dataframe.html#cb181-2" tabindex="-1"></a><span class="fu">str</span>(df)</span></code></pre></div>
<pre class="output"><code>## 'data.frame': 20 obs. of 3 variables:
## $ country: chr "CN" "US" "JP" "HK" ...
## $ import : num 26.14 12.01 7.03 13.65 4.59 ...
## $ export : num 22.99 12.2 11.84 7.74 5.38 ...</code></pre>
<p>自 <strong>R 4.0</strong> 起，R 的預設行為已改變，<strong>現在建立 DataFrame 或讀取數據時，字串變數預設會保持為字串（Character），而不會自動轉換為因子</strong>。這意味著，當我們使用 <code>data.frame()</code> 或 <code>read.csv()</code> 等函數讀取數據時，除非明確指定，R 不會自動將字串轉換為因子。</p>
<p>如果你在統計分析中仍然希望將文字型態的變數作為類別變數（即因子）處理，你需要手動進行轉換。例如：在合併資料時，若希望所有<code>Character</code>變數自動轉換為<code>Factor</code>，可以這樣設定：</p>
<div class="sourceCode" id="cb183"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb183-1"><a href="dataframe.html#cb183-1" tabindex="-1"></a>df <span class="ot"><-</span> <span class="fu">data.frame</span>(country, import, export, <span class="at">stringsAsFactors =</span> <span class="cn">TRUE</span>)</span>
<span id="cb183-2"><a href="dataframe.html#cb183-2" tabindex="-1"></a><span class="fu">str</span>(df)</span></code></pre></div>
<pre class="output"><code>## 'data.frame': 20 obs. of 3 variables:
## $ country: Factor w/ 20 levels "AE","AU","CN",..: 3 19 11 7 12 17 4 13 20 15 ...
## $ import : num 26.14 12.01 7.03 13.65 4.59 ...
## $ export : num 22.99 12.2 11.84 7.74 5.38 ...</code></pre>
<p>其他功能：建立一個新且空的<code>data.frame</code>。</p>
<div class="sourceCode" id="cb185"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb185-1"><a href="dataframe.html#cb185-1" tabindex="-1"></a>df.test <span class="ot"><-</span> <span class="fu">data.frame</span>()</span></code></pre></div>
</div>
</div>
<div id="觀察dataframe" class="section level3 hasAnchor" number="4.1.2">
<h3><span class="header-section-number">4.1.2</span> 觀察dataframe<a href="dataframe.html#觀察dataframe" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>當我們處理數據框架（dataframe）時，有幾種常用的方法可以幫助我們更好地了解和觀察數據的結構和內容。</p>
<ol style="list-style-type: decimal">
<li><p><code>View(df)</code>: 使用RStudio提供的圖形使用者介面直接觀看dataframe。這個功能允許你直觀地瀏覽整個數據集，方便地查看不同行（變數）和列（觀測值）。這對於初步瞭解數據的分佈和檢查數據的格式特別有用。</p></li>
<li><p><code>head(df)</code>: 這個函數用於取出數據框架的前六筆資料（也就是前六列）。這可以讓我們快速概覽數據集的開頭部分，了解數據的基本結構和內容。如果需要查看更多或更少的列，可以向<code>head</code>函數傳遞一個額外的參數，如<code>head(df, n = 10)</code>來查看前十列。</p></li>
<li><p><code>class(df)</code>: 此函數返回該變數的類型。對於dataframe，它將返回”DataFrame”，表明該對象是一個dataframe。了解對象的類型是重要的基礎步驟，尤其是在R中，不同類型的變項能夠做的操作和應用的函數也不同。</p></li>
<li><p><code>str(df)</code>: <code>str</code>是結構（structure）的縮寫，這個函數提供了dataframe的詳細結構信息，包括變項的數量、變項名稱、變項數據類型以及每個變項前幾個值。這是一個非常強大的函數，用於深入了解數據集的內部結構，特別是當處理大型數據集時。</p></li>
<li><p><code>summary(df)</code>: 此函數提供了數據框架的摘要統計信息，包括數值變數的最小值、最大值、中位數、平均值、第一四分位數和第三四分位數，以及因子變數的水平計數。這對於快速獲取數據集的統計概述非常有用。</p></li>
</ol>
<div class="sourceCode" id="cb186"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb186-1"><a href="dataframe.html#cb186-1" tabindex="-1"></a><span class="co"># View(df)</span></span>
<span id="cb186-2"><a href="dataframe.html#cb186-2" tabindex="-1"></a><span class="fu">head</span>(df) <span class="co"># get first part of the data.frame</span></span></code></pre></div>
<pre class="output"><code>## country import export
## 1 CN 26.142 22.987
## 2 US 12.008 12.204
## 3 JP 7.032 11.837
## 4 HK 13.646 7.739
## 5 KR 4.589 5.381
## 6 SG 5.768 4.610</code></pre>
<div class="sourceCode" id="cb188"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb188-1"><a href="dataframe.html#cb188-1" tabindex="-1"></a><span class="fu">class</span>(df)</span></code></pre></div>
<pre class="output"><code>## [1] "data.frame"</code></pre>
<div class="sourceCode" id="cb190"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb190-1"><a href="dataframe.html#cb190-1" tabindex="-1"></a><span class="fu">str</span>(df)</span></code></pre></div>
<pre class="output"><code>## 'data.frame': 20 obs. of 3 variables:
## $ country: Factor w/ 20 levels "AE","AU","CN",..: 3 19 11 7 12 17 4 13 20 15 ...
## $ import : num 26.14 12.01 7.03 13.65 4.59 ...
## $ export : num 22.99 12.2 11.84 7.74 5.38 ...</code></pre>
<div class="sourceCode" id="cb192"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb192-1"><a href="dataframe.html#cb192-1" tabindex="-1"></a><span class="fu">summary</span>(df)</span></code></pre></div>
<pre class="output"><code>## country import export
## AE : 1 Min. : 0.449 Min. : 0.728
## AU : 1 1st Qu.: 1.016 1st Qu.: 1.312
## CN : 1 Median : 2.054 Median : 1.966
## DE : 1 Mean : 4.536 Mean : 4.374
## FR : 1 3rd Qu.: 4.884 3rd Qu.: 4.803
## GB : 1 Max. :26.142 Max. :22.987
## (Other):14</code></pre>
<div class="sourceCode" id="cb194"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb194-1"><a href="dataframe.html#cb194-1" tabindex="-1"></a><span class="co"># look up help</span></span>
<span id="cb194-2"><a href="dataframe.html#cb194-2" tabindex="-1"></a><span class="fu">help</span>(summary)</span>
<span id="cb194-3"><a href="dataframe.html#cb194-3" tabindex="-1"></a>?summary</span></code></pre></div>
<div id="觀察資料維度" class="section level4 hasAnchor" number="4.1.2.1">
<h4><span class="header-section-number">4.1.2.1</span> 觀察資料維度<a href="dataframe.html#觀察資料維度" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<div class="sourceCode" id="cb195"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb195-1"><a href="dataframe.html#cb195-1" tabindex="-1"></a><span class="fu">dim</span>(df)</span></code></pre></div>
<pre class="output"><code>## [1] 20 3</code></pre>
<div class="sourceCode" id="cb197"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb197-1"><a href="dataframe.html#cb197-1" tabindex="-1"></a><span class="fu">ncol</span>(df)</span></code></pre></div>
<pre class="output"><code>## [1] 3</code></pre>
<div class="sourceCode" id="cb199"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb199-1"><a href="dataframe.html#cb199-1" tabindex="-1"></a><span class="fu">nrow</span>(df)</span></code></pre></div>
<pre class="output"><code>## [1] 20</code></pre>
<div class="sourceCode" id="cb201"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb201-1"><a href="dataframe.html#cb201-1" tabindex="-1"></a><span class="fu">length</span>(df)</span></code></pre></div>
<pre class="output"><code>## [1] 3</code></pre>
</div>
</div>
<div id="操作dataframe" class="section level3 hasAnchor" number="4.1.3">
<h3><span class="header-section-number">4.1.3</span> 操作dataframe<a href="dataframe.html#操作dataframe" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>以下將介紹 DataFrame 的基本操作，包括變數提取、變數創建、篩選數據、排序等常見應用。</p>
<div id="選擇某變項select" class="section level4 hasAnchor" number="4.1.3.1">
<h4><span class="header-section-number">4.1.3.1</span> 選擇某變項（Select）<a href="dataframe.html#選擇某變項select" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<ul>
<li><code>names(df)</code> 列出變數名稱。</li>
<li><code>df$發生.現.地點</code> 顯示該變數內容</li>
<li><code>df$發生時段</code> 顯示該變數內容</li>
<li><code>length(df$發生時段)</code> 顯示該變數的長度（相當於有幾個）</li>
<li><code>summary()</code> 函數可以用來查看數據的基本統計資訊，例如最小值、最大值、平均數、四分位數等：</li>
</ul>
<div class="sourceCode" id="cb203"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb203-1"><a href="dataframe.html#cb203-1" tabindex="-1"></a><span class="fu">names</span>(df)</span></code></pre></div>
<pre class="output"><code>## [1] "country" "import" "export"</code></pre>
<div class="sourceCode" id="cb205"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb205-1"><a href="dataframe.html#cb205-1" tabindex="-1"></a><span class="fu">head</span>(df<span class="sc">$</span>export)</span></code></pre></div>
<pre class="output"><code>## [1] 22.987 12.204 11.837 7.739 5.381 4.610</code></pre>
<div class="sourceCode" id="cb207"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb207-1"><a href="dataframe.html#cb207-1" tabindex="-1"></a><span class="fu">length</span>(df<span class="sc">$</span>import)</span></code></pre></div>
<pre class="output"><code>## [1] 20</code></pre>
<div class="sourceCode" id="cb209"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb209-1"><a href="dataframe.html#cb209-1" tabindex="-1"></a><span class="fu">summary</span>(df)</span></code></pre></div>
<pre class="output"><code>## country import export
## AE : 1 Min. : 0.449 Min. : 0.728
## AU : 1 1st Qu.: 1.016 1st Qu.: 1.312
## CN : 1 Median : 2.054 Median : 1.966
## DE : 1 Mean : 4.536 Mean : 4.374
## FR : 1 3rd Qu.: 4.884 3rd Qu.: 4.803
## GB : 1 Max. :26.142 Max. :22.987
## (Other):14</code></pre>
</div>
<div id="創建新變數mutate" class="section level4 hasAnchor" number="4.1.3.2">
<h4><span class="header-section-number">4.1.3.2</span> 創建新變數（Mutate）<a href="dataframe.html#創建新變數mutate" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<ul>
<li>在 DataFrame 中，我們可以使用 <code>$</code> 符號來新增一個變數，並且透過四則運算來計算新變數的值。在 R 中，尤其是未來運用到 dplyr 套件時，會把DataFrame新增一個變項的動作稱為Mutate，亦即透過運算新增變項。</li>
<li>這裡容易犯錯的是，要記得跟程式講說你要加總或四則運算的是哪個df的variable。</li>
<li>從下面的這個操作中，該data.frame會產生一個新的變數<code>sub</code>，這就相當於Excel中的某一行減去某一行，然後把資料放在新的一行。</li>
</ul>
<div class="sourceCode" id="cb211"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb211-1"><a href="dataframe.html#cb211-1" tabindex="-1"></a>df<span class="sc">$</span>sub <span class="ot"><-</span> df<span class="sc">$</span>import <span class="sc">-</span> df<span class="sc">$</span>export</span></code></pre></div>
</div>
<div id="篩選資料filter" class="section level4 hasAnchor" number="4.1.3.3">
<h4><span class="header-section-number">4.1.3.3</span> 篩選資料（Filter）<a href="dataframe.html#篩選資料filter" class="anchor-section" aria-label="Anchor link to header"></a></h4>
<ul>
<li><p>注意，要告訴程式<code>import</code>和<code>export</code>是哪個<code>data.frame</code>的。</p></li>
<li><p><code>df[,]</code>為存取<code>df</code>中某個區段的數值或某個數值的方法。因此<code>df[1, 1]</code>會取出第一行第一列，也就是第一筆資料的第一個vector。<code>df[2, 3]</code>則會取出第二筆資料的第三個variable。</p></li>
<li><p>下面的例子<code>nrow(df)</code>為1894，有1894筆資料，所以自然df<span class="math inline">\(import與df\)</span>export的長度都是1894。因此，比較這兩個變數的大小會得到一個長度為1894的boolean (logical) variable。因此把這個長度為1894、充滿TRUE和FALSE的logical vector丟進df的row之處，因為取自df，大小判斷式結果的長度自然和原本的df的列數相同。因此當這個TRUE/FALSE被丟在df的列之處，便會篩選出<code>import</code>大於<code>p.xport</code>的數值。</p></li>
<li><p>原本的df有五個variable，而上述的操作是篩選資料，所以被篩選的是列，因此行的數量、名稱都不會變。因此，我篩選完後，直接存取這個被篩選過的data.frame的country variable，自然是可以的。</p></li>
</ul>
<div class="sourceCode" id="cb212"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb212-1"><a href="dataframe.html#cb212-1" tabindex="-1"></a>df</span></code></pre></div>
<pre class="output"><code>## country import export sub
## 1 CN 26.142 22.987 3.155
## 2 US 12.008 12.204 -0.196
## 3 JP 7.032 11.837 -4.805
## 4 HK 13.646 7.739 5.907
## 5 KR 4.589 5.381 -0.792
## 6 SG 5.768 4.610 1.158
## 7 DE 2.131 2.866 -0.735
## 8 MY 2.802 2.784 0.018
## 9 VN 3.428 2.414 1.014
## 10 PH 3.019 2.092 0.927
## 11 TH 1.976 1.839 0.137
## 12 AU 1.118 1.788 -0.670
## 13 NL 1.624 1.665 -0.041