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| <h1>The Babyname In USA</h1> | |
| <ul> | |
| <li><strong>Author</strong>: <a href="https://github.com/jianchongsu/">Jianchong Su</a> | |
| – I know you want math to show you are a statistician, e.g. \( f(k)={n \choose k}p^{k}(1-p)^{n-k} \)</li> | |
| </ul> | |
| <h2>Starter</h2> | |
| <p>On my 25th birthday,I strolled in the gitHub.I find a research about the babynames from the 1880 to 2008,the author list a list about a thousand babynames which are used most.He wants to see the 1000 names include the percent the whole the country babynames.</p> | |
| <p>It is interesting to find the 1000 boynames decreasing slower than the girls'.To the 2008,the 1000 boynames still include at least 80% the whole boynames in the state.But the girls' has decreased to about 68%.<br/> | |
| I want to find the most used babynames in the 2008.</p> | |
| <pre><code>## year name percent sex | |
| ## 1 1880 John 0.08154 boy | |
| ## 2 1880 William 0.08051 boy | |
| ## 3 1880 James 0.05006 boy | |
| ## 4 1880 Charles 0.04517 boy | |
| ## 5 1880 George 0.04329 boy | |
| ## 6 1880 Frank 0.02738 boy | |
| </code></pre> | |
| <pre><code class="r">btop2008 = boy2008[order(boy2008$percent, decreasing = T), ] | |
| head(btop2008, 10) | |
| </code></pre> | |
| <pre><code>## year name percent sex | |
| ## 128001 2008 Jacob 0.010355 boy | |
| ## 128002 2008 Michael 0.009437 boy | |
| ## 128003 2008 Ethan 0.009301 boy | |
| ## 128004 2008 Joshua 0.008799 boy | |
| ## 128005 2008 Daniel 0.008702 boy | |
| ## 128006 2008 Alexander 0.008566 boy | |
| ## 128007 2008 Anthony 0.008442 boy | |
| ## 128008 2008 William 0.008438 boy | |
| ## 128009 2008 Christopher 0.008268 boy | |
| ## 128010 2008 Matthew 0.008061 boy | |
| </code></pre> | |
| <p>So,Jacob is the champion.There is a boy named Jacob in one hundred boys.The other most used boynames in 2008 are Michael,Ethan,Joshua,Daniel and Alexander.<br/> | |
| It is quite funny in the hot US TV series <em>LOST</em>,Jacob ,Michael,Ethan and Daniel all there.</p> | |
| <p>Maybe when make the TV series story,the maker has pay the attention the research.<br/> | |
| Maybe the most babynames research is quite useful for our life!<br/> | |
| How sex the finding!<br/> | |
| Next to the girls</p> | |
| <pre><code class="r">gtop2008 = girl2008[order(girl2008$percent, decreasing = T), ] | |
| head(gtop2008, 10) | |
| </code></pre> | |
| <pre><code>## year name percent sex | |
| ## 257001 2008 Emma 0.009043 girl | |
| ## 257002 2008 Isabella 0.008941 girl | |
| ## 257003 2008 Emily 0.008377 girl | |
| ## 257004 2008 Madison 0.008199 girl | |
| ## 257005 2008 Ava 0.008198 girl | |
| ## 257006 2008 Olivia 0.008196 girl | |
| ## 257007 2008 Sophia 0.007729 girl | |
| ## 257008 2008 Abigail 0.007250 girl | |
| ## 257009 2008 Elizabeth 0.005748 girl | |
| ## 257010 2008 Chloe 0.005692 girl | |
| </code></pre> | |
| <p>The most used girlnames are Emma,Isabella,Emily,Madison and Ava.</p> | |
| <p>Haha,i thought the “Jack” and “Rose” will win the games.<br/> | |
| Now i will find the order the “Jack"and"Rose”.</p> | |
| <pre><code class="r">which(btop2008$name == "Jack") | |
| </code></pre> | |
| <pre><code>## [1] 39 | |
| </code></pre> | |
| <pre><code class="r">btop2008[which(btop2008$name == "Jack"), ] | |
| </code></pre> | |
| <pre><code>## year name percent sex | |
| ## 128039 2008 Jack 0.004547 boy | |
| </code></pre> | |
| <pre><code class="r">which(gtop2008$name == "Rose") | |
| </code></pre> | |
| <pre><code>## [1] 344 | |
| </code></pre> | |
| <pre><code class="r">gtop2008[which(gtop2008$name == "Rose"), ] | |
| </code></pre> | |
| <pre><code>## year name percent sex | |
| ## 257344 2008 Rose 0.000465 girl | |
| </code></pre> | |
| <p>Wooe.“Jack” ranks the 39,“Rose” is far away from the “Jack”,she ranks the 334.</p> | |
| <p>Ok,let us do it again in the 1880.</p> | |
| <pre><code class="r">baby1880 = babyname[babyname[, 1] == "1880", ] | |
| boy1880 = baby1880[baby1880$sex == "boy", ] | |
| girl1880 = baby1880[baby1880$sex == "girl", ] | |
| btop1880 = boy1880[order(boy1880$percent, decreasing = T), ] | |
| head(btop1880, 10) | |
| </code></pre> | |
| <pre><code>## year name percent sex | |
| ## 1 1880 John 0.08154 boy | |
| ## 2 1880 William 0.08051 boy | |
| ## 3 1880 James 0.05006 boy | |
| ## 4 1880 Charles 0.04517 boy | |
| ## 5 1880 George 0.04329 boy | |
| ## 6 1880 Frank 0.02738 boy | |
| ## 7 1880 Joseph 0.02223 boy | |
| ## 8 1880 Thomas 0.02140 boy | |
| ## 9 1880 Henry 0.02064 boy | |
| ## 10 1880 Robert 0.02040 boy | |
| </code></pre> | |
| <pre><code class="r">gtop1880 = girl1880[order(girl2008$percent, decreasing = T), ] | |
| head(gtop1880, 10) | |
| </code></pre> | |
| <pre><code>## year name percent sex | |
| ## 129001 1880 Mary 0.07238 girl | |
| ## 129002 1880 Anna 0.02668 girl | |
| ## 129003 1880 Emma 0.02052 girl | |
| ## 129004 1880 Elizabeth 0.01987 girl | |
| ## 129005 1880 Minnie 0.01789 girl | |
| ## 129006 1880 Margaret 0.01617 girl | |
| ## 129007 1880 Ida 0.01508 girl | |
| ## 129008 1880 Alice 0.01449 girl | |
| ## 129009 1880 Bertha 0.01352 girl | |
| ## 129010 1880 Sarah 0.01320 girl | |
| </code></pre> | |
| <p>John,James,Charles are still in the <em>LOST</em>.<br/> | |
| Where are the “Jack” and “Rose”?</p> | |
| <pre><code class="r">which(btop1880$name == "Jack") | |
| </code></pre> | |
| <pre><code>## [1] 77 | |
| </code></pre> | |
| <pre><code class="r">btop2008[which(btop1880$name == "Jack"), ] | |
| </code></pre> | |
| <pre><code>## year name percent sex | |
| ## 128077 2008 Nathaniel 0.002679 boy | |
| </code></pre> | |
| <pre><code class="r">which(gtop1880$name == "Rose") | |
| </code></pre> | |
| <pre><code>## [1] 30 | |
| </code></pre> | |
| <pre><code class="r">btop2008[which(gtop1880$name == "Rose"), ] | |
| </code></pre> | |
| <pre><code>## year name percent sex | |
| ## 128030 2008 Gavin 0.005414 boy | |
| </code></pre> | |
| <p>####Jack is 77,and Rose is 30.Compare to 2008,Jack is 39,Rose is 334.<br/> | |
| “Rose” had made a huge change in the history of girlnames.She also made the farther distance to Jack.What a pity about <em>Titanic</em>!</p> | |
| <pre><code class="r"># Investigate the percentage of all names that the top 1000 baby names | |
| # occupies | |
| ofall <- ddply(all, .(year, sex), sump) | |
| qplot(year, percent, data = ofall, colour = sex, geom = "line") + ylim(0, 1) | |
| </code></pre> | |
| <p><img src="data:image/png;base64,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" alt="plot of chunk birthplot"/> </p> | |
| </body> | |
| </html> | |
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