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Commit e7392378 authored by Martin Hustoles's avatar Martin Hustoles
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bug fix

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......@@ -10,3 +10,5 @@ library(learnr)
install.packages(readxl)
install.packages("readxl")
library("readxl")
install.packages("knitr")
install.packages("knitr")
DCProject.Rmd
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− 2
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......@@ -18,6 +18,9 @@ runtime: shiny_prerendered
```{r setup, include=FALSE}
library(learnr)
library(readxl)
library(knitr)
library(dplyr)
current_dir = getwd()
data <- read_excel(file.path(current_dir, "Datensatz.xlsx"))
......@@ -237,10 +240,12 @@ Für die 2. Hypothese können wir die Lineare Regression und die Koeffizienz ber
```{r}
daten <- data.frame(
Jahr = c(1998:2021),
Frauen = c(as.numeric(unlist(data[-1 ,9]))),
Männer = c(as.numeric(unlist(data[-1 ,8])))
Frauen = c(as.numeric(unlist(data[2:25 ,9]))),
Männer = c(as.numeric(unlist(data[2:25 ,8])))
)
kable(daten$Frauen)
daten$Frauen_Anteil <- daten$Frauen / (daten$Frauen + daten$Männer) * 100
daten$Männer_Anteil <- daten$Männer / (daten$Frauen + daten$Männer) * 100
......
DCProject.html
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− 17
View file @ e7392378
......@@ -647,31 +647,373 @@ class="section level3">
</div>
<div id="section-datenanalyse" class="section level2">
<h2>Datenanalyse</h2>
<p>Hier ist uner tatsächlicher Datensatz:</p>
<pre class="r"><code>print(data)</code></pre>
<pre><code>## # A tibble: 31 × 10
## Semester Deutsche ...3 ...4 Ausländer ...6 ...7 Insgesamt ...9 ...10
## &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt;
## 1 &lt;NA&gt; männlich weibli… Insg… männlich weib… Insg… männlich weib… Insg…
## 2 WS 1998/99 907403 727254 1634… 92321 73673 1659… 999724 8009… 1800…
## 3 WS 1999/00 872178 723246 1595… 95460 79605 1750… 967638 8028… 1770…
## 4 WS 2000/01 870016 741820 1611… 99906 87121 1870… 969922 8289… 1798…
## 5 WS 2001/02 887462 774628 1662… 107831 98410 2062… 995293 8730… 1868…
## 6 WS 2002/03 903218 808567 1711… 117205 1098… 2270… 1020423 9183… 1938…
## 7 WS 2003/04 935718 837611 1773… 125826 1203… 2461… 1061544 9579… 2019…
## 8 WS 2004/05 901979 814795 1716… 124220 1221… 2463… 1026199 9369… 1963…
## 9 WS 2005/06 912696 824712 1737… 124447 1239… 2483… 1037143 9486… 1985…
## 10 WS 2006/07 909740 822934 1732… 122923 1234… 2463… 1032663 9463… 1979…
## # ℹ 21 more rows</code></pre>
<p><em>Information: Die hier dargestellten Tabellen beinhalten zur
Veranschaulichung jeweils nur die ersten 10 Zeilen. Unser Datensatz
besteht aus 24 Zeilen.</em></p>
<p>Hier ist uner tatsächlicher Datensatz aus der Excel-Datei:</p>
<pre class="r"><code>kable(head(data, 10))</code></pre>
<table style="width:100%;">
<colgroup>
<col width="11%" />
<col width="9%" />
<col width="9%" />
<col width="10%" />
<col width="10%" />
<col width="9%" />
<col width="10%" />
<col width="10%" />
<col width="9%" />
<col width="10%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Semester</th>
<th align="left">Deutsche</th>
<th align="left">…3</th>
<th align="left">…4</th>
<th align="left">Ausländer</th>
<th align="left">…6</th>
<th align="left">…7</th>
<th align="left">Insgesamt</th>
<th align="left">…9</th>
<th align="left">…10</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">NA</td>
<td align="left">männlich</td>
<td align="left">weiblich</td>
<td align="left">Insgesamt</td>
<td align="left">männlich</td>
<td align="left">weiblich</td>
<td align="left">Insgesamt</td>
<td align="left">männlich</td>
<td align="left">weiblich</td>
<td align="left">Insgesamt</td>
</tr>
<tr class="even">
<td align="left">WS 1998/99</td>
<td align="left">907403</td>
<td align="left">727254</td>
<td align="left">1634657</td>
<td align="left">92321</td>
<td align="left">73673</td>
<td align="left">165994</td>
<td align="left">999724</td>
<td align="left">800927</td>
<td align="left">1800651</td>
</tr>
<tr class="odd">
<td align="left">WS 1999/00</td>
<td align="left">872178</td>
<td align="left">723246</td>
<td align="left">1595424</td>
<td align="left">95460</td>
<td align="left">79605</td>
<td align="left">175065</td>
<td align="left">967638</td>
<td align="left">802851</td>
<td align="left">1770489</td>
</tr>
<tr class="even">
<td align="left">WS 2000/01</td>
<td align="left">870016</td>
<td align="left">741820</td>
<td align="left">1611836</td>
<td align="left">99906</td>
<td align="left">87121</td>
<td align="left">187027</td>
<td align="left">969922</td>
<td align="left">828941</td>
<td align="left">1798863</td>
</tr>
<tr class="odd">
<td align="left">WS 2001/02</td>
<td align="left">887462</td>
<td align="left">774628</td>
<td align="left">1662090</td>
<td align="left">107831</td>
<td align="left">98410</td>
<td align="left">206241</td>
<td align="left">995293</td>
<td align="left">873038</td>
<td align="left">1868331</td>
</tr>
<tr class="even">
<td align="left">WS 2002/03</td>
<td align="left">903218</td>
<td align="left">808567</td>
<td align="left">1711785</td>
<td align="left">117205</td>
<td align="left">109821</td>
<td align="left">227026</td>
<td align="left">1020423</td>
<td align="left">918388</td>
<td align="left">1938811</td>
</tr>
<tr class="odd">
<td align="left">WS 2003/04</td>
<td align="left">935718</td>
<td align="left">837611</td>
<td align="left">1773329</td>
<td align="left">125826</td>
<td align="left">120310</td>
<td align="left">246136</td>
<td align="left">1061544</td>
<td align="left">957921</td>
<td align="left">2019465</td>
</tr>
<tr class="even">
<td align="left">WS 2004/05</td>
<td align="left">901979</td>
<td align="left">814795</td>
<td align="left">1716774</td>
<td align="left">124220</td>
<td align="left">122114</td>
<td align="left">246334</td>
<td align="left">1026199</td>
<td align="left">936909</td>
<td align="left">1963108</td>
</tr>
<tr class="odd">
<td align="left">WS 2005/06</td>
<td align="left">912696</td>
<td align="left">824712</td>
<td align="left">1737408</td>
<td align="left">124447</td>
<td align="left">123910</td>
<td align="left">248357</td>
<td align="left">1037143</td>
<td align="left">948622</td>
<td align="left">1985765</td>
</tr>
<tr class="even">
<td align="left">WS 2006/07</td>
<td align="left">909740</td>
<td align="left">822934</td>
<td align="left">1732674</td>
<td align="left">122923</td>
<td align="left">123446</td>
<td align="left">246369</td>
<td align="left">1032663</td>
<td align="left">946380</td>
<td align="left">1979043</td>
</tr>
</tbody>
</table>
<div id="section-eigenschaften-der-daten" class="section level3">
<h3>Eigenschaften der Daten</h3>
<p>Nun können wir Eigenschaften wie Mittelwert, Meidan, Varianz und die
Standartabweichung berechnen. Dazu nutzen wir die 8. und 9. Spalte.
Diese enthalten die gesamte Anzahl der Männlichen und Weiblichen
Student:innen der Jahre:</p>
<pre class="r"><code>selected_data &lt;- data[ ,8:9]
kable(head(selected_data, 10))</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">Insgesamt</th>
<th align="left">…9</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">männlich</td>
<td align="left">weiblich</td>
</tr>
<tr class="even">
<td align="left">999724</td>
<td align="left">800927</td>
</tr>
<tr class="odd">
<td align="left">967638</td>
<td align="left">802851</td>
</tr>
<tr class="even">
<td align="left">969922</td>
<td align="left">828941</td>
</tr>
<tr class="odd">
<td align="left">995293</td>
<td align="left">873038</td>
</tr>
<tr class="even">
<td align="left">1020423</td>
<td align="left">918388</td>
</tr>
<tr class="odd">
<td align="left">1061544</td>
<td align="left">957921</td>
</tr>
<tr class="even">
<td align="left">1026199</td>
<td align="left">936909</td>
</tr>
<tr class="odd">
<td align="left">1037143</td>
<td align="left">948622</td>
</tr>
<tr class="even">
<td align="left">1032663</td>
<td align="left">946380</td>
</tr>
</tbody>
</table>
<p>Wir teilen die Daten in männlich und weiblich. Zusätzlich werden die
Spalten etwas konvertiert:</p>
<pre class="r"><code>data_male &lt;- as.numeric(unlist(selected_data[-1 ,1]))
data_female &lt;- as.numeric(unlist(selected_data[-1 ,2]))</code></pre>
<p>Und Berechnen:</p>
<pre class="r"><code>print(paste(&quot;Median male: &quot;, median(data_male), &quot;Median female: &quot;, median(data_female)))</code></pre>
<pre><code>## [1] &quot;Median male: NA Median female: NA&quot;</code></pre>
<pre class="r"><code>print(paste(&quot;Varianz male: &quot;, var(data_male), &quot;Varianz female: &quot;, var(data_female)))</code></pre>
<pre><code>## [1] &quot;Varianz male: NA Varianz female: NA&quot;</code></pre>
<pre class="r"><code>print(paste(&quot;Std. Abweichung male: &quot;, sd(data_male), &quot;Std. Abweichung female: &quot;, sd(data_female)))</code></pre>
<pre><code>## [1] &quot;Std. Abweichung male: NA Std. Abweichung female: NA&quot;</code></pre>
</div>
<div id="section-hypothesentest" class="section level3">
<h3>Hypothesentest</h3>
<p>In der Datenbasis haben wir 2 Hypothesen aufgestellt:</p>
<ul>
<li>Die Anzahl der Ausländischen Studenten in Deutschland hat sich seit
dem WS 00/01 verdoppelt.</li>
<li>Der %-Anteil an studierenden Frauen (insgesamt) gegenüber
studierenden Männern (insgesamt) hat sich seit dem WS 98/99 stetig
erhöht.</li>
</ul>
<p>Für die erste Hypothese müssen wir ledeglich die Summe der
Ausländischen Student:innen vom Wintersemester 2000 mit dem aktuellstem
Wintersemester vergleichen und schauen, ob der Wert vom Aktuellen
Semester größer oder gleich doppelt so groß ist wie vom altem
Wintersemester:</p>
<pre class="r"><code>old_ws &lt;- as.numeric(data[2,7])
new_ws &lt;- as.numeric(data[25,7])
faktor &lt;- 2
old_ws_double &lt;- old_ws * faktor
print(paste(&quot;Stimmt es, dass die Anzahl an ausländischen Student:innen sich seid dem WS 2000 mindestens verdoppelt hat: &quot;, new_ws &gt;= old_ws_double))</code></pre>
<pre><code>## [1] &quot;Stimmt es, dass die Anzahl an ausländischen Student:innen sich seid dem WS 2000 mindestens verdoppelt hat: TRUE&quot;</code></pre>
<p>Für die 2. Hypothese können wir die Lineare Regression und die
Koeffizienz berechnen.</p>
<pre class="r"><code>daten &lt;- data.frame(
Jahr = c(1998:2021),
Frauen = c(as.numeric(unlist(data[2:25 ,9]))),
Männer = c(as.numeric(unlist(data[2:25 ,8])))
)
kable(daten$Frauen)</code></pre>
<table>
<thead>
<tr class="header">
<th align="right">x</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">800927</td>
</tr>
<tr class="even">
<td align="right">802851</td>
</tr>
<tr class="odd">
<td align="right">828941</td>
</tr>
<tr class="even">
<td align="right">873038</td>
</tr>
<tr class="odd">
<td align="right">918388</td>
</tr>
<tr class="even">
<td align="right">957921</td>
</tr>
<tr class="odd">
<td align="right">936909</td>
</tr>
<tr class="even">
<td align="right">948622</td>
</tr>
<tr class="odd">
<td align="right">946380</td>
</tr>
<tr class="even">
<td align="right">926644</td>
</tr>
<tr class="odd">
<td align="right">967501</td>
</tr>
<tr class="even">
<td align="right">1014728</td>
</tr>
<tr class="odd">
<td align="right">1059809</td>
</tr>
<tr class="even">
<td align="right">1125602</td>
</tr>
<tr class="odd">
<td align="right">1185392</td>
</tr>
<tr class="even">
<td align="right">1245241</td>
</tr>
<tr class="odd">
<td align="right">1290376</td>
</tr>
<tr class="even">
<td align="right">1323673</td>
</tr>
<tr class="odd">
<td align="right">1353385</td>
</tr>
<tr class="even">
<td align="right">1380335</td>
</tr>
<tr class="odd">
<td align="right">1402244</td>
</tr>
<tr class="even">
<td align="right">1426182</td>
</tr>
<tr class="odd">
<td align="right">1467779</td>
</tr>
<tr class="even">
<td align="right">1475633</td>
</tr>
</tbody>
</table>
<pre class="r"><code>daten$Frauen_Anteil &lt;- daten$Frauen / (daten$Frauen + daten$Männer) * 100
daten$Männer_Anteil &lt;- daten$Männer / (daten$Frauen + daten$Männer) * 100
trend_f &lt;- lm(Frauen_Anteil ~ Jahr, data = daten)
trend_m &lt;- lm(Männer_Anteil ~ Jahr, data = daten)
print(coef(trend_f))</code></pre>
<pre><code>## (Intercept) Jahr
## -257.9118506 0.1520867</code></pre>
<pre class="r"><code>print(coef(trend_m))</code></pre>
<pre><code>## (Intercept) Jahr
## 357.9118506 -0.1520867</code></pre>
</div>
</div>
<div id="section-ergebnispräsentation" class="section level2">
<h2>Ergebnispräsentation</h2>
<p>In der Datenanalyse haben wir bei der 2. Hypothese geschaut, ob die
Anzahl an Frauen im vergleich zu Männern stetig erhöt hat. Hierzu eine
Visualisierung:</p>
<pre class="r"><code>plot(daten$Jahr, daten$Frauen_Anteil, xlab = &quot;Jahr&quot;, ylab = &quot;% Anteil Frauen&quot;, main = &quot;Trend der Frauenanteile&quot;)
abline(trend_f, col = &quot;red&quot;)</code></pre>
<p><img src="DCProject_files/figure-html/unnamed-chunk-7-1.png" width="624" /></p>
<pre class="r"><code>plot(daten$Jahr, daten$Männer_Anteil, xlab = &quot;Jahr&quot;, ylab = &quot;% Anteil Männer&quot;, main = &quot;Trend der Männeranteile&quot;)
abline(trend_m, col = &quot;blue&quot;)</code></pre>
<p><img src="DCProject_files/figure-html/unnamed-chunk-8-1.png" width="624" /></p>
<p>Wie man sieht ist es tatsächlich so, dass der Frauenanteil stetig
gestiegen ist, im vergleich zum Männeranteil.</p>
</div>
<div id="section-teaminfos" class="section level2">
<h2>Teaminfos</h2>
......@@ -683,6 +1025,9 @@ class="section level3">
<script type="application/shiny-prerendered" data-context="server-start">
library(learnr)
library(readxl)
library(knitr)
library(dplyr)
current_dir = getwd()
data <- read_excel(file.path(current_dir, "Datensatz.xlsx"))
......@@ -715,7 +1060,7 @@ session$onSessionEnded(function() {
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