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diff --git a/404.html b/404.html
@@ -23,7 +23,7 @@
 <meta name="author" content="HSIEH, JI-LUNG" />
 
 
-<meta name="date" content="2024-03-17" />
+<meta name="date" content="2024-03-24" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1" />
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diff --git a/R23_join_twdemo_ref.md b/R23_join_twdemo_ref.md
@@ -50,12 +50,9 @@ raw %>% head
 
 ### 清理資料 {#moi_clean}
 
-我們之前在談資料的「觀察、統計、和二維表格」三種型態時，曾經談到統計型態和二維表格型態間的差異。當時所提到的「統計型態」，也就是每個變項欄恰好是我們所認知的單一變項（如每一個變項欄恰是人口統計變項的年齡、性別、教育程度、數量），會有助於進行統計分析，也就是tidy型態的資料。相較之下，上述的表格是把資料攤成二維的型態，每一個變項是某個年齡層的某種性別的某種婚姻狀況，包含了三個人口統計變項，是方便一般大眾讀的，但不是適合進行統計的tidy型態。
-這類的資料tidyverse的相關套件把它稱為tidy form。遵守tidy
-form形式的資料是，每一個欄恰好一個變項。例如在內政部開放資料「15歲以上現住人口按性別、年齡、婚姻狀況及教育程度分」中，每個變數（年齡、婚姻狀況、教育程度、人口數等等）均各自為一個欄上的變項。
+我們之前在談資料的「觀察、統計、和二維表格」三種型態時，曾經談到統計型態和二維表格型態間的差異。當時所提到的「統計型態」，也就是每個變項欄恰好是我們所認知的單一變項（如每一個變項欄恰是人口統計變項的年齡、性別、教育程度、數量），會有助於進行統計分析，也就是tidy型態的資料。相較之下，上述的表格是把資料攤成二維的型態，每一個變項是某個年齡層的某種性別的某種婚姻狀況，包含了三個人口統計變項，是方便一般大眾讀的，但不是適合進行統計的tidy型態。 這類的資料tidyverse的相關套件把它稱為tidy form。遵守tidy form形式的資料是，每一個欄恰好一個變項。例如在內政部開放資料「15歲以上現住人口按性別、年齡、婚姻狀況及教育程度分」中，每個變數（年齡、婚姻狀況、教育程度、人口數等等）均各自為一個欄上的變項。
 
--   [15歲以上現住人口按性別、年齡、婚姻狀況及教育程度分 \|
-    政府資料開放平臺 (data.gov.tw)](https://data.gov.tw/dataset/32944)
+-   [15歲以上現住人口按性別、年齡、婚姻狀況及教育程度分 \| 政府資料開放平臺 (data.gov.tw)](https://data.gov.tw/dataset/32944)
 
 接下來，我要把表格型態的資料轉為tidy型態資料。原本的資料是這樣的型態。
 
@@ -85,9 +82,7 @@ tidy_data <- raw %>%
 之後，我使用`tidyr::separate()`函式將`key`切成四個變項，分別為`married`、`ageLower`、`ageUpper`、`gender`。
 
 -   `separate()`有一個參數是`remove=T`（預設值），意思是說，當把`key`變項切割為四個變項後，預設把key變項給丟棄；但如果未來你還會用到`key`變項的話，你可以把`remove`改為`FALSE`，代表切割完後，還保留`key`變項。
--   `tidyr::separate()`：Given either regular expression or a vector of
-    character positions, separate() turns a single character column into
-    multiple columns.
+-   `tidyr::separate()`：Given either regular expression or a vector of character positions, separate() turns a single character column into multiple columns.
 
 此時我清理出來的資料大致如下：
 

diff --git a/R25_tidy_temoral_features.md b/R25_tidy_temoral_features.md
@@ -62,11 +62,19 @@ class(t)
 ```r
 ?strptime
 t1 <- strptime(t, "%Y-%m-%dT%H:%M:%SZ")
-raw %>% glimpse()
+clean %>% head # %>% View
 ```
 
 ```{.output}
-## function (length = 0L)
+## # A tibble: 6 × 7
+##   plink                  board pcontent poster ptitle ptime               ipaddr
+##   <chr>                  <chr> <chr>    <chr>  <chr>  <dttm>              <chr> 
+## 1 https://www.ptt.cc/bb… Hate… "\n\n韓… loveb… Re: [… 2019-04-12 02:21:14 83.22…
+## 2 https://www.ptt.cc/bb… Hate… "\n\n\n… ikr36… Re: [… 2019-04-12 02:13:45 114.4…
+## 3 https://www.ptt.cc/bb… Hate… "\n\n正… sunye… Re: [… 2019-04-12 02:10:18 118.1…
+## 4 https://www.ptt.cc/bb… Hate… "\n:\n\… rock7… Re: [… 2019-04-12 02:03:14 118.1…
+## 5 https://www.ptt.cc/bb… Hate… "\n\n我… btm97… Re: [… 2019-04-12 02:01:12 101.1…
+## 6 https://www.ptt.cc/bb… Hate… "\n\n\n… cblade [討論… 2019-04-12 01:55:06 180.2…
 ```
 
 ### Density plot along time

diff --git a/R42_read_json.md b/R42_read_json.md
@@ -309,7 +309,7 @@ raw$retVal[["0001"]]
 
 ## Case 1: Air-Quality (well-formatted )
 
-前往 [https://data.gov.tw/dataset/40448](https://data.gov.tw/dataset/40448，點擊)對JSON 檔案按右鍵，然後複製連結，例如 "[https://data.epa.gov.tw/api/v2/aqx_p\_432?api_key=e8dd42e6-9b8b-43f8-991e-b3dee723a52d&limit=1000&sort=ImportDate%20desc&format=JSON"。](https://data.epa.gov.tw/api/v2/aqx_p_432?api_key=e8dd42e6-9b8b-43f8-991e-b3dee723a52d&limit=1000&sort=ImportDate%20desc&format=JSON%22。) (但是，連結地址，特別是 `api_key=9be7b239-557b-4c10-9775-78cadfc555e9`，每次都會更改。所以你必須要自己嘗試)。
+前往 [https://data.gov.tw/dataset/40448](https://data.gov.tw/dataset/40448，點擊)對JSON 檔案按右鍵，然後複製連結，例如 "[https://data.epa.gov.tw/api/v2/aqx_p_432?api_key=e8dd42e6-9b8b-43f8-991e-b3dee723a52d&limit=1000&sort=ImportDate%20desc&format=JSON"。](https://data.epa.gov.tw/api/v2/aqx_p_432?api_key=e8dd42e6-9b8b-43f8-991e-b3dee723a52d&limit=1000&sort=ImportDate%20desc&format=JSON%22。) (但是，連結地址，特別是 `api_key=9be7b239-557b-4c10-9775-78cadfc555e9`，每次都會更改。所以你必須要自己嘗試)。
 
 
 ```r
@@ -384,7 +384,7 @@ glimpse(df)
 ### Combining all
 
 -   UVI Open data: <https://data.gov.tw/dataset/6076>
--   [https://data.epa.gov.tw/api/v2/uv_s\_01?api_key=e8dd42e6-9b8b-43f8-991e-b3dee723a52d&limit=1000&sort=publishtime desc&format=JSON](https://data.epa.gov.tw/api/v2/uv_s_01?api_key=e8dd42e6-9b8b-43f8-991e-b3dee723a52d&limit=1000&sort=publishtime%20desc&format=JSON)
+-   [https://data.epa.gov.tw/api/v2/uv_s_01?api_key=e8dd42e6-9b8b-43f8-991e-b3dee723a52d&limit=1000&sort=publishtime desc&format=JSON](https://data.epa.gov.tw/api/v2/uv_s_01?api_key=e8dd42e6-9b8b-43f8-991e-b3dee723a52d&limit=1000&sort=publishtime%20desc&format=JSON)
 
 #### Get from web api
 

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diff --git a/Z2_Exploring_data_Visually_files/figure-html/eda-central-trend-1.png b/Z2_Exploring_data_Visually_files/figure-html/eda-central-trend-1.png
diff --git a/amount.html b/amount.html
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diff --git a/crawler-overview.html b/crawler-overview.html
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diff --git a/ggplot.html b/ggplot.html
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diff --git a/html-parser.html b/html-parser.html
@@ -23,7 +23,7 @@
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diff --git a/index.html b/index.html
@@ -23,7 +23,7 @@
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@@ -784,15 +784,15 @@ <h1>
 <div id="header">
 <h1 class="title">R for Data Journalism</h1>
 <p class="author"><em>HSIEH, JI-LUNG</em></p>
-<p class="date"><em>2024-03-17</em></p>
+<p class="date"><em>2024-03-24</em></p>
 </div>
 <div id="about" class="section level1 unnumbered hasAnchor">
 <h1>About<a href="index.html#about" class="anchor-section" aria-label="Anchor link to header"></a></h1>
 <p>這本書是寫給臺大新聞所「<strong>新聞資料分析與視覺化</strong>」課程使用。該課程並重三個面向的訓練：程式語言、視覺化、資料新聞。學生必須先能夠熟練地使用R語言來操作、讀取、清理、視覺化資料；然後以產製新聞為課程目標，了解資料要如何清理，以及選擇適合的視覺化的方法來強化新聞敘事，並避免視覺化方式引起讀者對新聞的理解謬誤。準此，本書分為幾個部分，包含PART I介紹程式語言基礎；PART II則以國際或國內新聞為個案，來介紹資料獲取（爬蟲）、清理、合併、篩選、轉換；PART III則著重如何用資料視覺化來強化敘事。</p>
 <p>本書所沿用的資料分析與視覺化案例均為國內、國外的新聞案例如各國產假支薪等級、居住正義、空氣污染、人口議題、COVID-19、資源區域分佈不均、選舉與公投、運輸交通等相關議題的新聞。並大量採用紐約時報挑選作為數據理解與視覺化推廣的「<a href="https://www.nytimes.com/column/whats-going-on-in-this-graph">What’s going on in this graph?</a>」系列新聞，包含美國不同年代各年齡層的淨資產來做視覺化案例。在視覺化教材的設計上，本書大量參考紐時「<a href="https://www.nytimes.com/column/whats-going-on-in-this-graph">What’s going on in this graph?</a>」的分類與<span class="citation">(<a href="#ref-wilke2019fundamentals">Wilke 2019</a>)</span>所著「<a href="https://clauswilke.com/dataviz/">Fundamentals of Data Visualization</a>」一書的內容安排，強調利用資料視覺化方法來呈現新聞數據中的數量、分佈、比例、趨勢等，並均換用國內或紐時的相關資料新聞案例做範例，以利中文讀者的理解。</p>
 <p><strong>學習路徑</strong></p>
-<div class="grViz html-widget html-fill-item" id="htmlwidget-2db1c4bba43f2459a86d" style="width:672px;height:480px;"></div>
-<script type="application/json" data-for="htmlwidget-2db1c4bba43f2459a86d">{"x":{"diagram":"\ndigraph G {\n  fontname=\"Helvetica,Arial,sans-serif\"\n  graph [layout = dot, rankdir=TD]\n  node [shape = rect,  height=0, fontname=\"Helvetica\", width=2]\n  node [style = filled, fillcolor=\"honeydew1\"]\n  edge [fontname=\"Courier\", splines=false, weight=2]\n\n  # define nodes\n  rbasic  [label = \"R Basic\"]\n  matleave[label = \"Filter & Select data\n(Maternal leave)\"]\n  tptheft [label = \"Pivot Analysis\n(Taipei Theft)\"]\n  \n  subgraph cluster_1{\n    label=\"base to dplyr\"\n    style=filled; fillcolor=\"lightgrey\";\n    dplyr_ml  [label = \"Filter & Select data with dplyr\n(Maternal leave)\"]\n    dplyr_tp  [label = \"Pivot Analysis with dplyr\n(Taipei Theft)\"]\n  }\n  \n  # define edge\n  node [fillcolor=\"azure1\"]\n    rbasic -> matleave -> tptheft -> {dplyr_tp, dplyr_ml} -> dplyr \n  node [fillcolor=\"gold\"]\n    dplyr -> {join, ggplot, textmining, scraper}\n  node [fillcolor=\"lightgrey\"]\n    join -> DB\n  node [fillcolor=\"yellow1\"]\n    ggplot -> temporal -> geospatial\n  node [fillcolor=\"skyblue\"]\n    scraper -> json -> htmlparser\n  node [fillcolor=\"pink1\"]\n    textmining -> doclevel -> wordlevel -> keyness -> POS -> sentiment\n  \n  # define path over edge\n  edge [constraint=false, penwidth=3, color=\"#ff000033\", weight=1, splines=curved]\n  rbasic -> matleave -> tptheft -> dplyr_ml\n  dplyr_ml -> dplyr_tp -> dplyr -> join -> ggplot\n  ggplot -> temporal -> geospatial -> textmining\n  textmining -> doclevel -> wordlevel -> keyness -> scraper\n}\n","config":{"engine":"dot","options":null}},"evals":[],"jsHooks":[]}</script>
+<div class="grViz html-widget html-fill-item" id="htmlwidget-aa696927c88eeb1fe24b" style="width:672px;height:480px;"></div>
+<script type="application/json" data-for="htmlwidget-aa696927c88eeb1fe24b">{"x":{"diagram":"\ndigraph G {\n  fontname=\"Helvetica,Arial,sans-serif\"\n  graph [layout = dot, rankdir=TD]\n  node [shape = rect,  height=0, fontname=\"Helvetica\", width=2]\n  node [style = filled, fillcolor=\"honeydew1\"]\n  edge [fontname=\"Courier\", splines=false, weight=2]\n\n  # define nodes\n  rbasic  [label = \"R Basic\"]\n  matleave[label = \"Filter & Select data\n(Maternal leave)\"]\n  tptheft [label = \"Pivot Analysis\n(Taipei Theft)\"]\n  \n  subgraph cluster_1{\n    label=\"base to dplyr\"\n    style=filled; fillcolor=\"lightgrey\";\n    dplyr_ml  [label = \"Filter & Select data with dplyr\n(Maternal leave)\"]\n    dplyr_tp  [label = \"Pivot Analysis with dplyr\n(Taipei Theft)\"]\n  }\n  \n  # define edge\n  node [fillcolor=\"azure1\"]\n    rbasic -> matleave -> tptheft -> {dplyr_tp, dplyr_ml} -> dplyr \n  node [fillcolor=\"gold\"]\n    dplyr -> {join, ggplot, textmining, scraper}\n  node [fillcolor=\"lightgrey\"]\n    join -> DB\n  node [fillcolor=\"yellow1\"]\n    ggplot -> temporal -> geospatial\n  node [fillcolor=\"skyblue\"]\n    scraper -> json -> htmlparser\n  node [fillcolor=\"pink1\"]\n    textmining -> doclevel -> wordlevel -> keyness -> POS -> sentiment\n  \n  # define path over edge\n  edge [constraint=false, penwidth=3, color=\"#ff000033\", weight=1, splines=curved]\n  rbasic -> matleave -> tptheft -> dplyr_ml\n  dplyr_ml -> dplyr_tp -> dplyr -> join -> ggplot\n  ggplot -> temporal -> geospatial -> textmining\n  textmining -> doclevel -> wordlevel -> keyness -> scraper\n}\n","config":{"engine":"dot","options":null}},"evals":[],"jsHooks":[]}</script>
 
 </div>
 <h3>References</h3>