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Commit c65cf51c authored by Bartke Simon's avatar Bartke Simon
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Am Bericht weitergearbeitet.

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...@@ -33,7 +33,20 @@ summary(gfc) ...@@ -33,7 +33,20 @@ summary(gfc)
``` ```
## Analyse ## Analyse
Wir erstellen wir folgende neue Variable Wir erstellen wir folgende neue Variable:
```{r}
gfc$gdpegdpu <- gfc$GDPE/gfc$GDPU
```
Diese Variable ist interessant, weil:
1. a
2. b
3. und so weiter
```{r}
plot(gfc$gdpegdpu)
```
......
...@@ -1540,7 +1540,11 @@ Datensatz</h2> ...@@ -1540,7 +1540,11 @@ Datensatz</h2>
</div> </div>
<div id="analyse" class="section level2" number="1.2"> <div id="analyse" class="section level2" number="1.2">
<h2><span class="header-section-number">1.2</span> Analyse</h2> <h2><span class="header-section-number">1.2</span> Analyse</h2>
<p>Wir erstellen wir folgende neue Variable</p> <p>Wir erstellen wir folgende neue Variable:</p>
<pre class="r"><code>gfc$gdpegdpu &lt;- gfc$GDPE/gfc$GDPU</code></pre>
<p>Diese Variable ist interessant, weil: 1. a 2. b 3. und so weiter</p>
<pre class="r"><code>plot(gfc$gdpegdpu)</code></pre>
<p><img 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" /><!-- --></p>
</div> </div>
</div> </div>
   
......
...@@ -41,7 +41,24 @@ summary(gfc) ...@@ -41,7 +41,24 @@ summary(gfc)
``` ```
## Analyse ## Analyse
Wir erstellen wir folgende neue Variable Wir erstellen wir folgende neue Variable:
```r
gfc$gdpegdpu <- gfc$GDPE/gfc$GDPU
```
Diese Variable ist interessant, weil:
1. a
2. b
3. und so weiter
```r
plot(gfc$gdpegdpu)
```
![](demo_final_files/figure-html/unnamed-chunk-2-1.png)<!-- -->
......
demo_final_files/figure-html/unnamed-chunk-2-1.png

10.9 KiB

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