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<h1 id="R-Programming-for-beginners"><a href="#R-Programming-for-beginners" class="headerlink" title="R Programming for beginners"></a>R Programming for beginners</h1><p>File - new R Script<br>comments: #</p>
<h2 id="Create-a-value-vector"><a href="#Create-a-value-vector" class="headerlink" title="Create a value/vector"></a>Create a value/vector</h2><figure class="highlight r"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div><div class="line">12</div><div class="line">13</div><div class="line">14</div><div class="line">15</div><div class="line">16</div><div class="line">17</div><div class="line">18</div><div class="line">19</div></pre></td><td class="code"><pre><div class="line"><span class="number">1</span>+<span class="number">1</span> => [<span class="number">1</span>] <span class="number">2</span></div><div class="line"></div><div class="line">x = <span class="number">1</span> + <span class="number">1</span> ; </div><div class="line"></div><div class="line">=> [<span class="number">1</span>] <span class="number">2</span></div><div class="line"></div><div class="line">x = <span class="number">1</span> + <span class="number">1</span> => nothing</div><div class="line"></div><div class="line">grades = c(<span class="number">100</span>,<span class="number">90</span>,<span class="number">85</span>,<span class="number">95</span>); grades => [<span class="number">1</span>] (<span class="number">100</span>,<span class="number">90</span>,<span class="number">85</span>,<span class="number">95</span>)</div><div class="line">grades + <span class="number">5</span>=></div><div class="line"></div><div class="line"> - <span class="keyword">function</span>: class(X) => data types ofunction X(Numeric/Character)</div><div class="line"></div><div class="line"> - y = c(<span class="number">5</span>,<span class="number">10</span>,<span class="string">"15"</span>,<span class="number">20</span>) ; y is not numerial anymore though only one element is char, now y is chr and change into [<span class="number">1</span>]<span class="string">"5"</span>,<span class="string">"10"</span>,<span class="string">"15"</span>,<span class="string">"20"</span></div><div class="line"> - <span class="keyword">function</span>: char to numeric： y = as.numeric(y) => [<span class="number">1</span>] <span class="number">5</span> <span class="number">10</span> <span class="number">15</span> <span class="number">20</span></div><div class="line"> - <span class="keyword">function</span>: numeric to char as.character</div><div class="line"></div><div class="line"> - Trouble <span class="number">1</span>: run previous results neccessary; solution: <span class="comment">#comments the variable</span></div><div class="line"> - Trouble <span class="number">2</span>: select rows <span class="keyword">in</span> code=> run only what you want</div></pre></td></tr></table></figure>
<figure class="highlight r"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div><div class="line">12</div><div class="line">13</div><div class="line">14</div><div class="line">15</div><div class="line">16</div><div class="line">17</div><div class="line">18</div><div class="line">19</div><div class="line">20</div><div class="line">21</div><div class="line">22</div><div class="line">23</div><div class="line">24</div></pre></td><td class="code"><pre><div class="line"></div><div class="line"><span class="keyword">function</span> table()& summarize() to calculate the frequency of Vector/Matrix</div><div class="line"></div><div class="line">Print some positions of V: </div><div class="line">Print some positions of V: </div><div class="line">V[<span class="number">1</span>,<span class="number">2</span>] correct into V[c(<span class="number">1</span>,<span class="number">2</span>)]</div><div class="line">V[-<span class="number">2</span>] is OK，but not V[-<span class="number">1</span>,-<span class="number">2</span>], it prints positions except positon <span class="number">2</span></div><div class="line"></div><div class="line"><span class="keyword">function</span> sequence: <span class="number">1</span>:<span class="number">10</span>; seq(<span class="number">1</span>,<span class="number">10</span>);seq(to = <span class="number">10</span>, from = <span class="number">1</span>);seq(<span class="number">10</span>,<span class="number">1</span>);seq(from = <span class="number">1</span>, to = <span class="number">10</span>, by = +-<span class="number">2</span>)</div><div class="line"></div><div class="line"><span class="keyword">function</span> <span class="keyword">repeat</span>: rep(<span class="string">"HI"</span>,<span class="number">10</span>)</div><div class="line"> // will not interfere with embedded <a href=<span class="string">"#voila2"</span>>tags</a>.</div><div class="line"> </div><div class="line"><span class="literal">NA</span> means sth does not exist</div><div class="line"></div><div class="line"><span class="keyword">function</span> 把一大组数归纳总结频率table(),转成表as.data.frame(table())，在表里加一些colomn比如Order:cbind.data.frame</div><div class="line"></div><div class="line"><span class="keyword">function</span> 排序 order()升序，order(-)降序</div><div class="line"></div><div class="line"> - Remember <span class="number">4</span>: Vector[c(<span class="number">1</span>,<span class="number">2</span>,<span class="number">3.</span>..)]获取数值，而matrix$..来获取！ 注意！</div><div class="line"> - Remember <span class="number">5</span>: Vector直接 mean(Vector)就好！</div><div class="line"> - Remember <span class="number">1</span>: Variable[<span class="number">1</span>] not V[<span class="number">0</span>] prints the first one</div><div class="line"> - Remember <span class="number">2</span>: Variable<span class="string">'s name should be combined without space between</span></div><div class="line"> - Remember 3: Factors can not be allowed to compare value (=<>)</div></pre></td></tr></table></figure>
<h2 id="Metrix"><a href="#Metrix" class="headerlink" title="Metrix:"></a>Metrix:</h2><figure class="highlight"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div><div class="line">12</div><div class="line">13</div><div class="line">14</div><div class="line">15</div><div class="line">16</div><div class="line">17</div><div class="line">18</div><div class="line">19</div><div class="line">20</div><div class="line">21</div><div class="line">22</div><div class="line">23</div><div class="line">24</div><div class="line">25</div><div class="line">26</div></pre></td><td class="code"><pre><div class="line"></div><div class="line">function data.frame(v1,v2) , if length ofunction v1 is larger than v2 then v2's vector in metrix will be repeated to fill the space.</div><div class="line"></div><div class="line">add another colomn to metrix: function cbind(data.frame(v1,v2), v3)</div><div class="line">1. 先按照要求造个vector,再作为一个colomn加上去！！！！</div><div class="line"></div><div class="line">function colnames/rownames(M) = c("","") to change the names</div><div class="line"></div><div class="line">function M[A,B] Row 1st, colomn 2nd show numbr on that position, A or B can be left empty to show the full colomn/row, B can be 1:2 or c(1,2) both OK!</div><div class="line"></div><div class="line">function: View(metrix) -> show the table </div><div class="line"></div><div class="line">function length, nrow, ncol, head() tail() names() #head rows, </div><div class="line"></div><div class="line">Sum Fuction: sum(data[,5]); sum(data$population)</div><div class="line"></div><div class="line"> - function 不是countif和count，是nrow()! : X[X$region==1,] Return all information on states located in only 1(region ==1). nrow()to count . </div><div class="line">Add conditions on colomn show only colomn: X[X$region==1,".."or 1 ]</div><div class="line"></div><div class="line"> - function data[data$murder==max(data$murder), "state"] return the state ofunction largest murder</div><div class="line"> </div><div class="line">function: T/function matrix: data[data$murder, 1]==max(data$murder) not data[data$murder==max(data$murder), ]</div><div class="line"></div><div class="line">function:ifelse()ifelse(data$illiteracy<1, "low","high")</div><div class="line"></div><div class="line">function 提取table的字/标题 names(table), 提取table的数字 numeric(table)</div></pre></td></tr></table></figure>
<h2 id="FOR-LOOP-提取colomn-创建结果为colomn（各个添加）"><a href="#FOR-LOOP-提取colomn-创建结果为colomn（各个添加）" class="headerlink" title="FOR LOOP + 提取colomn + 创建结果为colomn（各个添加）"></a>FOR LOOP + 提取colomn + 创建结果为colomn（各个添加）</h2><figure class="highlight r"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div></pre></td><td class="code"><pre><div class="line"></div><div class="line">Create a new column “Age.Range” </div><div class="line">age = hw1$Age</div><div class="line">Age.Range = <span class="string">""</span></div><div class="line"><span class="keyword">for</span>( i <span class="keyword">in</span> <span class="number">1</span>:length(age)){<span class="keyword">if</span> (age[i] < <span class="number">20</span>){Age.Range[i]=<span class="string">"Below 20 years"</span>} <span class="keyword">else</span> <span class="keyword">if</span>(<span class="number">25</span>> age[i]){Age.Range[i] = <span class="string">"[20-25) years"</span>}<span class="keyword">else</span> <span class="keyword">if</span>(<span class="number">30</span> > age[i]){Age.Range[i] = <span class="string">"[25-30) years"</span>} </div><div class="line"><span class="keyword">else</span> <span class="keyword">if</span>(<span class="number">30</span>< age[i]){Age.Range[i] = <span class="string">"Above 30 years"</span>}}</div><div class="line">Age.Range</div></pre></td></tr></table></figure>
<h2 id="import-outside-data-source"><a href="#import-outside-data-source" class="headerlink" title="import outside data source"></a>import outside data source</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div></pre></td><td class="code"><pre><div class="line">change/get working directory</div><div class="line">function getwd(); setwd(<span class="string">"D:/AW/MSBA&Programming合集/R"</span>); </div><div class="line"></div><div class="line">open <span class="keyword">and</span> save file: function data= read.csv(<span class="string">"statedata.csv"</span>)];</div><div class="line">{% endcodeblock %}</div></pre></td></tr></table></figure>
<h2 id="CODES"><a href="#CODES" class="headerlink" title="CODES"></a>CODES</h2><figure class="highlight"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div><div class="line">12</div><div class="line">13</div><div class="line">14</div><div class="line">15</div><div class="line">16</div><div class="line">17</div><div class="line">18</div><div class="line">19</div><div class="line">20</div><div class="line">21</div><div class="line">22</div><div class="line">23</div><div class="line">24</div><div class="line">25</div><div class="line">26</div><div class="line">27</div><div class="line">28</div><div class="line">29</div><div class="line">30</div><div class="line">31</div><div class="line">32</div><div class="line">33</div><div class="line">34</div><div class="line">35</div><div class="line">36</div><div class="line">37</div><div class="line">38</div><div class="line">39</div><div class="line">40</div><div class="line">41</div><div class="line">42</div><div class="line">43</div><div class="line">44</div><div class="line">45</div><div class="line">46</div><div class="line">47</div><div class="line">48</div><div class="line">49</div><div class="line">50</div><div class="line">51</div><div class="line">52</div><div class="line">53</div><div class="line">54</div><div class="line">55</div><div class="line">56</div><div class="line">57</div><div class="line">58</div><div class="line">59</div><div class="line">60</div><div class="line">61</div><div class="line">62</div><div class="line">63</div><div class="line">64</div><div class="line">65</div><div class="line">66</div><div class="line">67</div><div class="line">68</div><div class="line">69</div></pre></td><td class="code"><pre><div class="line">{% codeblock lang:R %}</div><div class="line">[</div><div class="line"></div><div class="line">x = 1+1</div><div class="line">#x</div><div class="line"></div><div class="line"> </div><div class="line">grades = c(100,90, 85, 95)</div><div class="line"># grades</div><div class="line"></div><div class="line">classes = c("DS", "ss")</div><div class="line"># classes</div><div class="line"></div><div class="line">class(classes)</div><div class="line"></div><div class="line">grades + 5</div><div class="line"></div><div class="line">y = c(5,10,"15",20)</div><div class="line">y = as.numeric(y)</div><div class="line">#y</div><div class="line"></div><div class="line">#y[1]</div><div class="line"></div><div class="line">#grades[c(1,2,4)]</div><div class="line"></div><div class="line">#grades[-3,-4]</div><div class="line"></div><div class="line">Metrix1 = cbind(data.frame(y,classes),grades)</div><div class="line">#View(Metrix1)</div><div class="line"></div><div class="line"></div><div class="line">rownames(Metrix1) = c("stu1","stu2","stu3","stu4")</div><div class="line">View(Metrix1)</div><div class="line"></div><div class="line">nrow(Metrix1)</div><div class="line"></div><div class="line">2:10</div><div class="line">seq(1,10)</div><div class="line">seq(to = 10, from = 1)</div><div class="line">seq(10,1) </div><div class="line">seq(from = 1, to = 10, by = 2)</div><div class="line"></div><div class="line">rep("HI",10)</div><div class="line"></div><div class="line">data= read.csv("statedata.csv")</div><div class="line">View(data)</div><div class="line"></div><div class="line">tail(data)</div><div class="line"></div><div class="line">sum(data[,5])</div><div class="line"></div><div class="line">sum(data$population)</div><div class="line"></div><div class="line">nrow(data[data$region== 1 ,])</div><div class="line"></div><div class="line">data[data$illiteracy<0.7, "state"]</div><div class="line"></div><div class="line"></div><div class="line">#data[data$murder==max(data$murder) or data$murder==min(data$murder), "state" ]</div><div class="line"></div><div class="line">data[data$murder==max(data$murder) and data$murder==min(data$murder) ,1]</div><div class="line"></div><div class="line">data[data$murder, 1]==max(data$murder)</div><div class="line"></div><div class="line">data$illiteracyCategory = ifelse(data$illiteracy<1, "low","high")</div><div class="line"></div><div class="line">data[data$illiteracyCategory=="low",] ];</div><div class="line"></div><div class="line">{% endcodeblock %}</div></pre></td></tr></table></figure>
<h2 id="conditional-Loops-in-R-—-Stupid-guy"><a href="#conditional-Loops-in-R-—-Stupid-guy" class="headerlink" title="conditional Loops in R — Stupid guy"></a>conditional Loops in R — Stupid guy</h2><p>Age.Range = if(hw1$Age < 20){“Below 20 years”} else{<br> if(25> hw1$Age >= 20){“[20-25) years”}<br> else{<br> if(30 > hw1$Age >= 25){“[25-30) years”}<br> else{“Above 30 years”}}}<br>错了！为啥？？？</p>
<ol>
<li>因为elseif 应该是 else if </li>
<li>else if 最后一项也是，不能是else 要写清条件</li>
</ol>
<h2 id="R-Analysis-for-most-popular-clothes-On-Stylenanda-Shop"><a href="#R-Analysis-for-most-popular-clothes-On-Stylenanda-Shop" class="headerlink" title="R Analysis for most popular clothes On Stylenanda Shop"></a>R Analysis for most popular clothes On Stylenanda Shop</h2><h3 id="Beginning"><a href="#Beginning" class="headerlink" title="Beginning"></a>Beginning</h3><p> So today we gonna come through some simple data analysis to find the most popular clothes ofunction famous brand “Stylenanda”. (Data from <a href="http://en.stylenanda.com/" target="_blank" rel="external">http://en.stylenanda.com/</a>)</p>
<h3 id="Step-1-catch-info"><a href="#Step-1-catch-info" class="headerlink" title="Step 1: catch info"></a>Step 1: catch info</h3><p>All the informations we need are the name price, sales and comments ofunction all the clothes.</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div></pre></td><td class="code"><pre><div class="line">Library(rvest)</div><div class="line">Library(xml2)</div><div class="line">> <span class="keyword">as</span><-seq(<span class="number">60</span>,<span class="number">420</span>,<span class="number">60</span>) </div><div class="line">> <span class="keyword">for</span>(s <span class="keyword">in</span> <span class="keyword">as</span>){ </div><div class="line">moda<-read_html(<span class="string">"Stylenandas=60&q=%C1%AC%D2%C2%C8%B9&sort=s&style=g&from=sn_1_brand-qp&active=2&industryCatId=50025135&type=pc#J_Filter"</span>,encoding=<span class="string">"GBK"</span>) +dress_name<-html_nodes(moda,<span class="string">'.productTitle a'</span>)%>%html_text</div><div class="line">dress_name<-html_nodes(stylenanda,<span class="string">'.productTitle a'</span>)%>%html_text </div><div class="line">dress_price<-html_nodes(stylenanda,<span class="string">'.productPrice em'</span>)%>%html_text sale_number<-html_nodes(stylenanda,<span class="string">'.productStatus em'</span>)%>%html_text</div><div class="line">> the_comment<-html_nodes(stylenanda,<span class="string">'.productStatus a'</span>)%>%html_text </div><div class="line"></div><div class="line">+ }</div><div class="line">> dress<-data.frame(dressname,dressprice,salenumber,thecomment)</div></pre></td></tr></table></figure>
<h3 id="Step-2-Integrate-and-Standardize-our-data"><a href="#Step-2-Integrate-and-Standardize-our-data" class="headerlink" title="Step 2: Integrate and Standardize our data"></a>Step 2: Integrate and Standardize our data</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div></pre></td><td class="code"><pre><div class="line">> library(plyr) </div><div class="line">> Unify variable name</div><div class="line">dress1<rename(dress1,c(dress_name=<span class="string">"dressname"</span>,dress_price=<span class="string">"dressprice"</span>,sale_number=<span class="string">"salenumber"</span>,the_comment=<span class="string">"thecomment"</span>) </div><div class="line">> Modadress <-rbind(dress,dress1)</div></pre></td></tr></table></figure>
<p>We got the output for “Modadress$dressprice” , new column for the dress price</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div></pre></td><td class="code"><pre><div class="line">[<span class="number">1</span>] ￥<span class="number">399.00</span> ￥<span class="number">299.50</span> ￥<span class="number">749.00</span> ￥<span class="number">699.00</span> ￥<span class="number">112.00</span> ￥<span class="number">306.00</span> ￥<span class="number">298.00</span> ￥<span class="number">649.00</span></div><div class="line"> [<span class="number">9</span>] ￥<span class="number">299.00</span> ￥<span class="number">279.00</span> ￥<span class="number">249.00</span> ￥<span class="number">299.50</span> ￥<span class="number">374.50</span> ￥<span class="number">324.50</span> ￥<span class="number">274.50</span> ￥<span class="number">324.50</span></div><div class="line"> [<span class="number">17</span>] ￥<span class="number">349.50</span> ￥<span class="number">199.00</span> ￥<span class="number">349.50</span> ￥<span class="number">699.00</span> ￥<span class="number">279.00</span> ￥<span class="number">349.00</span> ￥<span class="number">749.00</span> ￥<span class="number">199.00</span></div><div class="line"> [<span class="number">25</span>] ￥<span class="number">299.50</span> ￥<span class="number">324.50</span> ￥<span class="number">349.50</span> ￥<span class="number">349.50</span> ￥<span class="number">349.50</span> ￥<span class="number">324.50</span> ￥<span class="number">324.50</span> ￥<span class="number">299.0</span></div></pre></td></tr></table></figure>
<p>The number ofunction dress sales :</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div></pre></td><td class="code"><pre><div class="line">[<span class="number">1</span>] <span class="number">1198</span>笔 <span class="number">1394</span>笔 <span class="number">0</span>笔 <span class="number">0</span>笔 <span class="number">1</span>笔 <span class="number">0</span>笔 <span class="number">1934</span>笔 <span class="number">64</span>笔 <span class="number">836</span>笔 <span class="number">1848</span>笔 <span class="number">1203</span>笔</div><div class="line"> [<span class="number">12</span>] <span class="number">566</span>笔 <span class="number">573</span>笔 <span class="number">1380</span>笔 <span class="number">1808</span>笔 <span class="number">609</span>笔 <span class="number">1776</span>笔 <span class="number">1621</span>笔 <span class="number">1162</span>笔 <span class="number">348</span>笔 <span class="number">470</span>笔 <span class="number">397</span>笔 </div><div class="line"> [<span class="number">23</span>] <span class="number">446</span>笔 <span class="number">364</span>笔 <span class="number">468</span>笔 <span class="number">579</span>笔 <span class="number">666</span>笔 <span class="number">362</span>笔 <span class="number">776</span>笔 <span class="number">770</span>笔 <span class="number">617</span>笔 <span class="number">611</span>笔 <span class="number">791</span>笔 </div><div class="line"> [<span class="number">34</span>] <span class="number">154</span>笔 <span class="number">781</span>笔 <span class="number">275</span>笔 <span class="number">1118</span>笔 <span class="number">688</span>笔 <span class="number">916</span>笔 <span class="number">772</span>笔 <span class="number">569</span>笔 <span class="number">55</span>笔 <span class="number">114</span>笔 <span class="number">927</span>笔 </div><div class="line"> [<span class="number">45</span>] <span class="number">471</span>笔 <span class="number">750</span>笔 <span class="number">1117</span>笔 <span class="number">734</span>笔 <span class="number">798</span>笔 <span class="number">208</span>笔 <span class="number">620</span>笔 <span class="number">492</span>笔 <span class="number">298</span>笔 <span class="number">160</span>笔 <span class="number">846</span>笔 </div><div class="line"> [<span class="number">56</span>] <span class="number">902</span>笔 <span class="number">109</span>笔 <span class="number">345</span>笔 <span class="number">802</span>笔 <span class="number">101</span>笔 <span class="number">688</span>笔 <span class="number">912</span>笔 <span class="number">774</span>笔 <span class="number">569</span>笔 <span class="number">55</span>笔 <span class="number">115</span>笔</div></pre></td></tr></table></figure>
<p>Then we need to filter and delete the unnecessary info </p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div></pre></td><td class="code"><pre><div class="line">Modadress$dressprice<-gsub(<span class="string">"￥"</span>,<span class="string">""</span>,Modadress$dressprice)</div><div class="line">Modadress$salenumber<-gsub(<span class="string">"笔"</span>,<span class="string">""</span>,Modadress$salenumber)</div></pre></td></tr></table></figure>
<p>Find their properties</p>
<figure class="highlight"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div></pre></td><td class="code"><pre><div class="line">> class(modadress$salenumber)</div><div class="line">[1] "factor"</div><div class="line">>class(modadress$thecomment)</div><div class="line">[1] "factor" </div><div class="line">> class(dressprice) </div><div class="line">[1] "character"</div></pre></td></tr></table></figure>
<p>Change strings into number format:</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div></pre></td><td class="code"><pre><div class="line">> Modadress$dressprice<-<span class="keyword">as</span>.numeric(modadress$dressprice) </div><div class="line">> Modadress$salenumber<-<span class="keyword">as</span>.numeric(modadress$salenumber)</div><div class="line">> Modadress$thecomment<-<span class="keyword">as</span>.numeric(modadress$thecomment)</div></pre></td></tr></table></figure>
<h3 id="Step3-Summarize-and-Visualize-data"><a href="#Step3-Summarize-and-Visualize-data" class="headerlink" title="Step3: Summarize and Visualize data"></a>Step3: Summarize and Visualize data</h3><p>Summarize data:</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div></pre></td><td class="code"><pre><div class="line">group_by(Modadress,priceleve)%>%summarise(price=mean(dressprice,na.rm=T),number=mean(salenumber,na.rm=T),comment=mean(thecomment,na.rm=T))</div><div class="line"><span class="comment"># A tibble: 2 x 4</span></div><div class="line"> priceleve price number comment</div><div class="line"> <fctr> <dbl> <dbl> <dbl></div><div class="line"><span class="number">1</span> high <span class="number">722.5484</span> <span class="number">117.6774</span> <span class="number">39.90323</span></div><div class="line"><span class="number">2</span> normal <span class="number">316.2674</span> <span class="number">703.0562</span> <span class="number">422.12360</span></div></pre></td></tr></table></figure>
<p>Visualize data in chart</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div></pre></td><td class="code"><pre><div class="line">ggplot(Modadress,aes(x=dressprice,y=salenumber,color=thecomment))+geom_point()+scale_x_continuous(expand=c(<span class="number">0</span>,<span class="number">0</span>),breaks=c(<span class="number">100</span>,<span class="number">200</span>,<span class="number">300</span>,<span class="number">400</span>,<span class="number">500</span>,<span class="number">600</span>,<span class="number">700</span>,<span class="number">800</span>,<span class="number">900</span>,<span class="number">1000</span>,<span class="number">1100</span>),labels=c(<span class="number">100</span>,<span class="number">200</span>,<span class="number">300</span>,<span class="number">400</span>,<span class="number">500</span>,<span class="number">600</span>,<span class="number">700</span>,<span class="number">800</span>,<span class="number">900</span>,<span class="number">1000</span>,<span class="number">1100</span>))</div></pre></td></tr></table></figure>
<p><img src="images/13.jpg" width="500"></p>
<p>From the picture, a lot ofunction clothes fall into the range ofunction 200RMB-400RMB, fewer cost more than 650RMB and none cost 400RMB-650RMB.</p>
<p>Then in order to figure out the sales ofunction clothes falling into each range, we need to recode the price at first and add “pricelevel” variable after that. </p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div></pre></td><td class="code"><pre><div class="line">Modadress<-within(Modadres,{</div><div class="line">pricelevel<-<span class="string">"NA"</span></div><div class="line">pricelevel[dressprice<<span class="number">450</span>]<-<span class="string">"normal"</span></div><div class="line">pricelevel[dressprice><span class="number">500</span>]<-<span class="string">"high"</span></div><div class="line">})</div></pre></td></tr></table></figure>
<p>Calculate the sales ofunction clothes falling into each range.</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div></pre></td><td class="code"><pre><div class="line">total<-tapply(Modadress$salenumber,Modadress$pricelevel,sum)</div><div class="line">> total</div><div class="line"> high normal </div><div class="line"> <span class="number">3648</span> <span class="number">62572</span></div></pre></td></tr></table></figure>
<p>Differences between two levels ofunction sales are obvious. Now we shall draw into picture to show clearly.</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div></pre></td><td class="code"><pre><div class="line">z<-c(<span class="string">"high"</span>,<span class="string">"normal"</span>)</div><div class="line">chart<-data.frame(z,total)</div><div class="line">> wtp<-ggplot(chart,aes(x=z,y=total))+geom_bar(stat=<span class="string">"identity"</span>,fill=<span class="string">"lightblue"</span>,width=<span class="number">0.5</span>)+geom_text(aes(label=total),vjust=<span class="number">-0.2</span>,colour=<span class="string">"black"</span>,size=<span class="number">8</span>)</div><div class="line">> wtp+xlab(<span class="string">"price"</span>)+ylab(<span class="string">"sales"</span>)</div></pre></td></tr></table></figure>
<p><img src="images/14.jpg" width="500"></p>
<h3 id="Step-4-Get-the-data-we-what-and-rule-out-others"><a href="#Step-4-Get-the-data-we-what-and-rule-out-others" class="headerlink" title="Step 4: Get the data we what and rule out others"></a>Step 4: Get the data we what and rule out others</h3><p>At first we need to specify the meaning ofunction “Popolar clothers”. The standard I refer to is comment volume and sales volume.</p>
<p>According to the figure above, the lighter the color ofunction the blue dot, the larger the comment volume. </p>
<p>Set 1500 as dividing line and choose the best-selling products.</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div></pre></td><td class="code"><pre><div class="line">filter(Modadress,thecomment><span class="number">1500</span>)%>%arrange(desc(salenumber))</div><div class="line"> dressname dressprice salenumber thecomment priceleve</div></pre></td></tr></table></figure>
<p>Output:</p>
<p>1 Self-Tie Collar Button-Down Shirt 279 1848 1557 normal<br>2 Check Pattern Buttoned Back Shirt 199 1621 1822 normal<br>3 Pre-Damaged Knit Cardigan 249 1203 1647 normal<br>4 Extended Sleeve Lettering Print Hoodie 399 1198 2059 normal</p>
<p>Thus we got Top4 for most popular clothes:</p>
<ol>
<li><p>Self-Tie Collar Button-Down Shirt</p>
<p><img src="images/17.JPG"></p>
</li>
<li><p>Check Pattern Buttoned Back Shirt </p>
<p><img src="images/18.JPG"></p>
</li>
<li><p>Pre-Damaged Knit Cardigan </p>
<p><img src="images/19.JPG"></p>
</li>
<li><p>Extended Sleeve Lettering Print Hoodie</p>
</li>
</ol>
<p><img src="images/20.JPG"></p>
<h5 id="Come-on-Just-follow-this-instruction-and-have-a-try-Use-R-to-find-any-info-about-items-ofunction-your-favorite-brand"><a href="#Come-on-Just-follow-this-instruction-and-have-a-try-Use-R-to-find-any-info-about-items-ofunction-your-favorite-brand" class="headerlink" title="Come on! Just follow this instruction and have a try. Use R to find any info about items ofunction your favorite brand."></a>Come on! Just follow this instruction and have a try. Use R to find any info about items ofunction your favorite brand.</h5>
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