diff --git a/DCProject.Rmd b/DCProject.Rmd
index 8f1b7d37717565ad861a6da87dfabf5a39e11210..478c2099d2f49a7b15539ddcba75f7fbdead8abd 100644
--- a/DCProject.Rmd
+++ b/DCProject.Rmd
@@ -115,6 +115,7 @@ Größe des Datensatzes: 24 Zeilen und 10 Spalten
 1. Die Anzahl der Ausländischen Studenten in Deutschland hat sich seit dem WS 00/01 verdoppelt.  
 2. Der %-Anteil an studierenden Frauen (insgesamt) gegenüber studierenden Männern (insgesamt) hat sich seit dem WS 98/99 stetig erhöht.
 
+
 ## Datenaufbereitung
 ### Wie können Daten eingelesen werden
 Es können verschiedene Datenvormate eingelesen werden, z.B CSV, XLSX
@@ -187,6 +188,15 @@ Es können verschiedene Datenvormate eingelesen werden, z.B CSV, XLSX
     na="NA"
     ```
     
+```{r letter-c, echo=FALSE}
+question("Jetzt bist du dran! Angenommen du möchtest eine .CSV Datei einlesen die im selben Arbeitsverzeichnis liegt, in der Dezimalzahlen mit einem . getrennt werden und fehlende Werte mit NA ausgefüllt werden sollen. Wie gehst du vor?",
+  answer('daten_csv <- read.csv("daten.csv")'),
+  answer('daten_csv <- read.csv2(file.choose(), na = "NA")'),
+  answer('daten_csv <- read.csv("daten.csv", na = "NA")', correct = TRUE),
+  answer('daten_csv <- read.csv2("daten.csv", na = "NA")')
+)
+```
+    
 ### Wie können Inkosistenzen und Leerstellen behoben werden
 
 [Fälle mit fehlende Werten in R löschen](https://www.youtube.com/watch?v=SXg-8DAB6gc)
@@ -238,11 +248,19 @@ Jahre:
 selected_data <- data[ ,8:9]
 kable(head(selected_data, 10))
 ```
-
+```{r letter-b, echo=FALSE}
+question("Jetzt bist du dran! Wie müssten wir den Befehl umändern, um nur mit der Anzahl der Deutschen Männlichen und Weiblichen Studend:innen weiterzuarbeiten?",
+  answer("selected_data <- data[3:2,])"),
+  answer("selected_data <- data[5:9,]"),
+  answer("selected_data <- data[ ,2:3]", correct = TRUE),
+  answer("selected_data <- data[ ,1:2]")
+)
+```
 Wir teilen die Daten in männlich und weiblich. Zusätzlich werden die Spalten etwas konvertiert:
 ```{r}
 data_male <- as.numeric(unlist(selected_data[-1 ,1]))
 data_female <- as.numeric(unlist(selected_data[-1 ,2]))
+print(data_male)
 ```
 
 Und Berechnen:  
@@ -263,6 +281,7 @@ und die Standartabweichung:
 print(paste("Std. Abweichung male: ", sd(data_male), "Std. Abweichung female: ", sd(data_female)))
 ```
 
+
 ### Hypothesentest
 In der Datenbasis haben wir 2 Hypothesen aufgestellt:
 
@@ -324,6 +343,7 @@ abline(trend_m, col = "blue")
 Wie man sieht ist es tatsächlich so, dass der Frauenanteil stetig gestiegen ist, im vergleich zum Männeranteil.
 
 
+
 ## Teaminfos
 Wer hat was erstellt/implementiert:  
   
diff --git a/DCProject.html b/DCProject.html
index 0a55f4e2ab3db0c511ce0fe261eb9920c63dd425..55e3b76623b90641c5a5e272015c017c5352fef2 100644
--- a/DCProject.html
+++ b/DCProject.html
@@ -592,7 +592,7 @@ erhöht.</li>
 <div id="section-wie-können-daten-eingelesen-werden"
 class="section level3">
 <h3>Wie können Daten eingelesen werden</h3>
-<p>Es können verschieden Datenvormate eingelesen werden z.B CSV,
+<p>Es können verschiedene Datenvormate eingelesen werden, z.B CSV,
 XLSX</p>
 <ol style="list-style-type: decimal">
 <li><p>Daten aus einer CSV-Datei einlesen</p>
@@ -642,6 +642,14 @@ können</p>
 <p>hier gibt es aber nicht die Argumente wie bei csv auser</p>
 <pre><code>na=&quot;NA&quot;</code></pre></li>
 </ol>
+<div class="panel panel-default tutorial-question-container">
+<div data-label="letter-c" class="tutorial-question panel-body">
+<div id="letter-c-answer_container" class="shiny-html-output"></div>
+<div id="letter-c-message_container" class="shiny-html-output"></div>
+<div id="letter-c-action_button_container" class="shiny-html-output"></div>
+<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
+</div>
+</div>
 </div>
 <div
 id="section-wie-können-inkosistenzen-und-leerstellen-behoben-werden"
@@ -654,7 +662,7 @@ Fehlerhafte Werte stehen.</p>
 <p>Im Beispiel hier werden alle Zeilen mit na werten vom dataframe daten
 entfernt und im dataframe gesaueberte_daten abgelegt</p>
 <pre><code>gesaueberte_daten &lt;- na.omit(daten)</code></pre>
-<p>Um fehlende Werte direkt bei der Berechnung zu behandel kann na.rm
+<p>Um fehlende Werte direkt bei der Berechnung zu behandeln kann na.rm
 benutzt werden. Wenn es auf TRUE gesetzt ist überspringt es NA werte.
 Folgende Funktionen können na.rm nutzen</p>
 <pre><code>colSums()
@@ -893,10 +901,22 @@ kable(head(selected_data, 10))</code></pre>
 </tr>
 </tbody>
 </table>
+<div class="panel panel-default tutorial-question-container">
+<div data-label="letter-b" class="tutorial-question panel-body">
+<div id="letter-b-answer_container" class="shiny-html-output"></div>
+<div id="letter-b-message_container" class="shiny-html-output"></div>
+<div id="letter-b-action_button_container" class="shiny-html-output"></div>
+<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
+</div>
+</div>
 <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>
+data_female &lt;- as.numeric(unlist(selected_data[-1 ,2]))
+print(data_male)</code></pre>
+<pre><code>##  [1]  999724  967638  969922  995293 1020423 1061544 1026199 1037143 1032663
+## [10] 1014761 1057806 1106450 1157485 1255372 1314017 1371640 1408534 1434126
+## [19] 1453625 1464643 1465978 1464867 1476366 1466282</code></pre>
 <p>Und Berechnen:<br />
 Den Mittelwert:</p>
 <pre class="r"><code>print(paste(&quot;Mittelwert male: &quot;, mean(data_male), &quot;Mittelwert female: &quot;, mean(data_female)))</code></pre>
@@ -1066,7 +1086,6 @@ library(learnr)
 library(readxl)
 library(knitr)
 library(dplyr)
-library(shiny)
 
 
 current_dir = getwd()
@@ -1092,6 +1111,70 @@ session$onSessionEnded(function() {
         learnr:::event_trigger(session, "session_stop")
       })
 </script>
+ 
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+"character")), answers = list(structure(list(id = "lnr_ans_171b41d", 
+    option = "daten_csv <- read.csv(\"daten.csv\")", value = "daten_csv <- read.csv(\"daten.csv\")", 
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+    label = structure("daten_csv &lt;- read.csv(“daten.csv”, na = “NA”)", html = TRUE, class = c("html", 
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+)), session = session)
+</script>
+
+
+<script type="application/shiny-prerendered" data-context="server">
+learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "letter-b", question = structure("Jetzt bist du dran! Wie müssten wir den Befehl umändern, um nur mit der Anzahl der Deutschen Männlichen und Weiblichen Studend:innen weiterzuarbeiten?", html = TRUE, class = c("html", 
+"character")), answers = list(structure(list(id = "lnr_ans_37ebccb", 
+    option = "selected_data <- data[3:2,])", value = "selected_data <- data[3:2,])", 
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+    option = "selected_data <- data[ ,1:2]", value = "selected_data <- data[ ,1:2]", 
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+    "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
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+)), session = session)
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