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<h1 class="title toc-ignore">FInal REport</h1>
<h4 class="author"><em>OwenYeh</em></h4>
<h4 class="date"><em>20170130 created</em></h4>
</div>
<div id="TOC">
<ul>
<li><a>輸/匯入資料</a></li>
<li><a>清除未知數</a></li>
<li><a>分組</a></li>
<li><a>準確度計算</a></li>
<li><a>探索沒上傳不存在的資料</a></li>
<li><a>修正匯出格式</a></li>
<li><a>寫出結論</a></li>
<li><a href="#references">references</a></li>
</ul>
</div>
<blockquote>
<p>Ralph Waldo Emerson : We have conquered the power, so we have the strength</p>
</blockquote>
<div class="section level2">
<h2>輸/匯入資料</h2>
<p>將來自“<a href="https://storage.googleapis.com/r_rookies/kaggle_titanic_train.csv" class="uri">https://storage.googleapis.com/r_rookies/kaggle_titanic_train.csv</a>” 匯入資料庫之中，並且命名為titanic</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">titanic <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"https://storage.googleapis.com/r_rookies/kaggle_titanic_train.csv"</span>)
<span class="kw">str</span>(titanic)</code></pre></div>
<pre><code>## 'data.frame': 891 obs. of 12 variables:
## $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : int 0 1 1 1 0 0 0 0 1 1 ...
## $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ...
## $ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
## $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : Factor w/ 148 levels "","A10","A14",..: 1 83 1 57 1 1 131 1 1 1 ...
## $ Embarked : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(titanic)</code></pre></div>
<pre><code>## PassengerId Survived Pclass
## Min. : 1.0 Min. :0.0000 Min. :1.000
## 1st Qu.:223.5 1st Qu.:0.0000 1st Qu.:2.000
## Median :446.0 Median :0.0000 Median :3.000
## Mean :446.0 Mean :0.3838 Mean :2.309
## 3rd Qu.:668.5 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :891.0 Max. :1.0000 Max. :3.000
##
## Name Sex Age
## Abbing, Mr. Anthony : 1 female:314 Min. : 0.42
## Abbott, Mr. Rossmore Edward : 1 male :577 1st Qu.:20.12
## Abbott, Mrs. Stanton (Rosa Hunt) : 1 Median :28.00
## Abelson, Mr. Samuel : 1 Mean :29.70
## Abelson, Mrs. Samuel (Hannah Wizosky): 1 3rd Qu.:38.00
## Adahl, Mr. Mauritz Nils Martin : 1 Max. :80.00
## (Other) :885 NA's :177
## SibSp Parch Ticket Fare
## Min. :0.000 Min. :0.0000 1601 : 7 Min. : 0.00
## 1st Qu.:0.000 1st Qu.:0.0000 347082 : 7 1st Qu.: 7.91
## Median :0.000 Median :0.0000 CA. 2343: 7 Median : 14.45
## Mean :0.523 Mean :0.3816 3101295 : 6 Mean : 32.20
## 3rd Qu.:1.000 3rd Qu.:0.0000 347088 : 6 3rd Qu.: 31.00
## Max. :8.000 Max. :6.0000 CA 2144 : 6 Max. :512.33
## (Other) :852
## Cabin Embarked
## :687 : 2
## B96 B98 : 4 C:168
## C23 C25 C27: 4 Q: 77
## G6 : 4 S:644
## C22 C26 : 3
## D : 3
## (Other) :186</code></pre>
<p><br /> ##將Titanic 的資料彙整成圖，觀察性別、年紀與社經地位</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(ggplot2)
<span class="kw">library</span>(plotly)
TT1<-<span class="kw">ggplot</span>(titanic, <span class="kw">aes</span>(Pclass)) +<span class="st"> </span>
<span class="st"> </span><span class="kw">geom_bar</span>(<span class="kw">aes</span>(<span class="dt">fill =</span> Sex))+
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"the quantity of Passenger classes vesus different kind of sexes"</span>)
<span class="kw">ggplotly</span>(TT1)</code></pre></div>
<p><div id="htmlwidget-53f1709503f24cae98f1" style="width:672px;height:480px;" class="plotly html-widget"></div>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">TT2<-<span class="kw">ggplot</span>(titanic, <span class="kw">aes</span>(<span class="dt">x =</span> Age)) +
<span class="st"> </span><span class="kw">geom_histogram</span>(<span class="kw">aes</span>(<span class="dt">fill =</span> Sex))+
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"the quantity of different ages humanbeings vesus different kind of sexes"</span>)
<span class="kw">ggplotly</span>(TT2)</code></pre></div>
<div id="htmlwidget-33ef85862be4a98c400b" style="width:672px;height:480px;" class="plotly html-widget"></div>
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<p><br /></p>
</div>
<div class="section level2">
<h2>清除未知數</h2>
<p>使程式碼簡潔化</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">titanic <-<span class="st"> </span>titanic[<span class="kw">complete.cases</span>(titanic), ]
titanic$Survived <-<span class="st"> </span><span class="kw">factor</span>(titanic$Survived)
titanic$Embarked <-<span class="st"> </span><span class="kw">as.character</span>(titanic$Embarked)
titanic$Embarked[titanic$Embarked ==<span class="st"> ""</span>] <-<span class="st"> "S"</span>
titanic$Embarked <-<span class="st"> </span><span class="kw">factor</span>(titanic$Embarked)</code></pre></div>
<p><br /></p>
</div>
<div class="section level2">
<h2>分組</h2>
<p>以70%的資料作為訓練駔，剩下的為測試組</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">n <-<span class="st"> </span><span class="kw">nrow</span>(titanic)
<span class="kw">set.seed</span>(<span class="dv">90</span>)
shuffled_titanic <-<span class="st"> </span>titanic[<span class="kw">sample</span>(n), ]
train_indices <-<span class="st"> </span><span class="dv">1</span>:<span class="kw">round</span>(<span class="fl">0.7</span> *<span class="st"> </span>n)
train <-<span class="st"> </span>shuffled_titanic[train_indices, ]
test_indices <-<span class="st"> </span>(<span class="kw">round</span>(<span class="fl">0.7</span> *<span class="st"> </span>n) +<span class="st"> </span><span class="dv">1</span>):n
test <-<span class="st"> </span>shuffled_titanic[test_indices, ]</code></pre></div>
<p><br /></p>
</div>
<div class="section level2">
<h2>準確度計算</h2>
<p>利用<code>rpart</code>的樹模型，並利用<code>confusion_matrix</code>去計算其準確度</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#install.packages("rpart")</span>
<span class="kw">library</span>(rpart)
tree_fit <-<span class="st"> </span><span class="kw">rpart</span>(Survived ~<span class="st"> </span>Pclass +<span class="st"> </span>Sex +<span class="st"> </span>Age +<span class="st"> </span>SibSp +<span class="st"> </span>Parch +<span class="st"> </span>Fare +<span class="st"> </span>Embarked, <span class="dt">data =</span> train, <span class="dt">method =</span> <span class="st">"class"</span>)
prediction <-<span class="st"> </span><span class="kw">predict</span>(tree_fit, test[, <span class="kw">c</span>(<span class="st">"Pclass"</span>, <span class="st">"Sex"</span>, <span class="st">"Age"</span>, <span class="st">"SibSp"</span>, <span class="st">"Parch"</span>, <span class="st">"Fare"</span>, <span class="st">"Embarked"</span>)], <span class="dt">type =</span> <span class="st">"class"</span>)
confusion_matrix <-<span class="st"> </span><span class="kw">table</span>(test$Survived, prediction)
accuracy <-<span class="st"> </span><span class="kw">sum</span>(<span class="kw">diag</span>(confusion_matrix)) /<span class="st"> </span><span class="kw">sum</span>(confusion_matrix)
accuracy</code></pre></div>
<pre><code>## [1] 0.817757</code></pre>
<p><br /></p>
</div>
<div class="section level2">
<h2>探索沒上傳不存在的資料</h2>
<p>輸入 to_predict</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">url <-<span class="st"> "https://storage.googleapis.com/py_ds_basic/kaggle_titanic_test.csv"</span>
to_predict <-<span class="st"> </span><span class="kw">read.csv</span>(url)
<span class="kw">summary</span>(to_predict)</code></pre></div>
<pre><code>## PassengerId Pclass
## Min. : 892.0 Min. :1.000
## 1st Qu.: 996.2 1st Qu.:1.000
## Median :1100.5 Median :3.000
## Mean :1100.5 Mean :2.266
## 3rd Qu.:1204.8 3rd Qu.:3.000
## Max. :1309.0 Max. :3.000
##
## Name Sex
## Abbott, Master. Eugene Joseph : 1 female:152
## Abelseth, Miss. Karen Marie : 1 male :266
## Abelseth, Mr. Olaus Jorgensen : 1
## Abrahamsson, Mr. Abraham August Johannes : 1
## Abrahim, Mrs. Joseph (Sophie Halaut Easu): 1
## Aks, Master. Philip Frank : 1
## (Other) :412
## Age SibSp Parch Ticket
## Min. : 0.17 Min. :0.0000 Min. :0.0000 PC 17608: 5
## 1st Qu.:21.00 1st Qu.:0.0000 1st Qu.:0.0000 113503 : 4
## Median :27.00 Median :0.0000 Median :0.0000 CA. 2343: 4
## Mean :30.27 Mean :0.4474 Mean :0.3923 16966 : 3
## 3rd Qu.:39.00 3rd Qu.:1.0000 3rd Qu.:0.0000 220845 : 3
## Max. :76.00 Max. :8.0000 Max. :9.0000 347077 : 3
## NA's :86 (Other) :396
## Fare Cabin Embarked
## Min. : 0.000 :327 C:102
## 1st Qu.: 7.896 B57 B59 B63 B66: 3 Q: 46
## Median : 14.454 A34 : 2 S:270
## Mean : 35.627 B45 : 2
## 3rd Qu.: 31.500 C101 : 2
## Max. :512.329 C116 : 2
## NA's :1 (Other) : 80</code></pre>
<p>先將年齡用平均年齡填滿</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#install.packages("dplyr")</span>
<span class="kw">library</span>(dplyr)
<span class="co">#install.packages("magrittr")</span>
<span class="kw">library</span>(magrittr)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mean_age_by_Pclass <-<span class="st"> </span>to_predict %>%
<span class="st"> </span><span class="kw">group_by</span>(Pclass) %>%
<span class="st"> </span><span class="kw">summarise</span>(<span class="dt">mean_age =</span> <span class="kw">round</span>(<span class="kw">mean</span>(Age, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>)))
filter_1 <-<span class="st"> </span><span class="kw">is.na</span>(to_predict$Age) &<span class="st"> </span>to_predict$Pclass ==<span class="st"> </span><span class="dv">1</span>
filter_2 <-<span class="st"> </span><span class="kw">is.na</span>(to_predict$Age) &<span class="st"> </span>to_predict$Pclass ==<span class="st"> </span><span class="dv">2</span>
filter_3 <-<span class="st"> </span><span class="kw">is.na</span>(to_predict$Age) &<span class="st"> </span>to_predict$Pclass ==<span class="st"> </span><span class="dv">3</span>
mean_age_by_Pclass</code></pre></div>
<pre><code>## # A tibble: 3 × 2
## Pclass mean_age
## <int> <dbl>
## 1 1 41
## 2 2 29
## 3 3 24</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">to_predict[filter_1, ]$Age <-<span class="st"> </span><span class="dv">41</span>
to_predict[filter_2, ]$Age <-<span class="st"> </span><span class="dv">29</span>
to_predict[filter_3, ]$Age <-<span class="st"> </span><span class="dv">24</span></code></pre></div>
<p>再將FARE用其平均值填滿</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fare_mean <-<span class="st"> </span><span class="kw">mean</span>(to_predict$Fare, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>)
to_predict$Fare[<span class="kw">is.na</span>(to_predict$Fare)] <-<span class="st"> </span>fare_mean</code></pre></div>
<p>匯出一個結論</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(to_predict)</code></pre></div>
<pre><code>## PassengerId Pclass
## Min. : 892.0 Min. :1.000
## 1st Qu.: 996.2 1st Qu.:1.000
## Median :1100.5 Median :3.000
## Mean :1100.5 Mean :2.266
## 3rd Qu.:1204.8 3rd Qu.:3.000
## Max. :1309.0 Max. :3.000
##
## Name Sex
## Abbott, Master. Eugene Joseph : 1 female:152
## Abelseth, Miss. Karen Marie : 1 male :266
## Abelseth, Mr. Olaus Jorgensen : 1
## Abrahamsson, Mr. Abraham August Johannes : 1
## Abrahim, Mrs. Joseph (Sophie Halaut Easu): 1
## Aks, Master. Philip Frank : 1
## (Other) :412
## Age SibSp Parch Ticket
## Min. : 0.17 Min. :0.0000 Min. :0.0000 PC 17608: 5
## 1st Qu.:23.00 1st Qu.:0.0000 1st Qu.:0.0000 113503 : 4
## Median :25.00 Median :0.0000 Median :0.0000 CA. 2343: 4
## Mean :29.41 Mean :0.4474 Mean :0.3923 16966 : 3
## 3rd Qu.:36.38 3rd Qu.:1.0000 3rd Qu.:0.0000 220845 : 3
## Max. :76.00 Max. :8.0000 Max. :9.0000 347077 : 3
## (Other) :396
## Fare Cabin Embarked
## Min. : 0.000 :327 C:102
## 1st Qu.: 7.896 B57 B59 B63 B66: 3 Q: 46
## Median : 14.454 A34 : 2 S:270
## Mean : 35.627 B45 : 2
## 3rd Qu.: 31.500 C101 : 2
## Max. :512.329 C116 : 2
## (Other) : 80</code></pre>
<p><strong>to_predict的NA已經清空</strong></p>
</div>
<div class="section level2">
<h2>修正匯出格式</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">predicted <-<span class="st"> </span><span class="kw">predict</span>(tree_fit, <span class="dt">newdata =</span> to_predict[, <span class="kw">c</span>(<span class="st">"Pclass"</span>, <span class="st">"Sex"</span>, <span class="st">"Age"</span>, <span class="st">"SibSp"</span>, <span class="st">"Parch"</span>, <span class="st">"Fare"</span>, <span class="st">"Embarked"</span>)])
result <-<span class="st"> </span><span class="kw">data.frame</span>(to_predict[, <span class="st">"PassengerId"</span>], predicted)
<span class="kw">names</span>(result) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"PassengerId"</span>, <span class="st">"Survived"</span>)
<span class="kw">head</span>(result, <span class="dt">n =</span> <span class="dv">10</span>)</code></pre></div>
<pre><code>## PassengerId Survived NA
## 1 892 0.8888889 0.1111111
## 2 893 0.7333333 0.2666667
## 3 894 0.8888889 0.1111111
## 4 895 0.8888889 0.1111111
## 5 896 0.7333333 0.2666667
## 6 897 0.8888889 0.1111111
## 7 898 0.3571429 0.6428571
## 8 899 0.8888889 0.1111111
## 9 900 0.3571429 0.6428571
## 10 901 0.8888889 0.1111111</code></pre>
</div>
<div class="section level2">
<h2>寫出結論</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">write.csv</span>(result, <span class="dt">file =</span> <span class="st">"result_of_the_Tiatanic_report.csv"</span>, <span class="dt">row.names =</span> <span class="ot">FALSE</span>)</code></pre></div>
<div class="figure">
<img src="https://4.bp.blogspot.com/-OCLkiSwee-Q/WI8an8LOPgI/AAAAAAAAAT4/_IajLvnpyPc7T27cG3nblqJ_-BYaDA3ywCLcB/s1600/FINAL1.png" alt="The Result and the rank" />
<p class="caption">The Result and the rank</p>
</div>
</div>
<div id="references" class="section level1">
<h1>references</h1>
<p>台大資工系統訓練班 R 程式設計班的教學專案– <a href="https://yaojenkuo.github.io/r_programming/" class="uri">https://yaojenkuo.github.io/r_programming/</a></p>
<p>R Markdown Cheat Sheet– <a href="http://www.rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf" class="uri">http://www.rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf</a></p>
<p>DataCampRmd– <a href="https://github.com/dspim/DataCampRmd/blob/master/index.md" class="uri">https://github.com/dspim/DataCampRmd/blob/master/index.md</a></p>
<div class="figure">
<img src="http://www.migflug.com/jetflights/wp-content/uploads/2015/03/reditt-com-mig-25-foxbat.jpg" alt="what message does R handle and created for you when it’s correctly used looks like:" />
<p class="caption">what message does R handle and created for you when it’s correctly used looks like:</p>
</div>
</div>
</div>
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$('tr.header').parent('thead').parent('table').addClass('table table-condensed');
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