From 07cb0efaef92269873ba5d97a1fd1ee0c90384fd Mon Sep 17 00:00:00 2001
From: Vy <quynh.thanh.vy.nguyen@studium.uni-hamburg.de>
Date: Tue, 20 Dec 2022 17:59:30 +0100
Subject: [PATCH] Erste Schritte zur Abgabe 1
---
demo.final.Rmd | 31 ++++++++++
demo.final.html | 57 ++++++++++--------
demo.final.md | 48 ++++++++++-----
.../figure-html/unnamed-chunk-3-1.png | Bin 0 -> 4614 bytes
4 files changed, 97 insertions(+), 39 deletions(-)
create mode 100644 demo.final_files/figure-html/unnamed-chunk-3-1.png
diff --git a/demo.final.Rmd b/demo.final.Rmd
index d99fe96..83e058c 100644
--- a/demo.final.Rmd
+++ b/demo.final.Rmd
@@ -15,4 +15,35 @@ output:
knitr::opts_chunk$set(echo = TRUE)
library(sozoeko1)
```
+# Thema 1
+
+Wir wollen etwas analysieren:
+
+* x
+* y
+* z
+
+Dabei nutzen wir die Methode **OLS**.
+
+# Verwendeter Datensatz
+Wir verwendet hier die Daten von Paket gfc.
+```{r}
+summary(gfc)
+```
+# Analyse
+Zur Analyse definieren wir eine neue Variable:
+```{r}
+gfc$gdpegdpu <- gfc$GDPE/gfc$GDPU
+```
+Diese Variable ist interessant, weil:
+1. Item 1
+2. Item 2
+3. Item 3
+ + Item 3a
+ + Item 3b
+
+# Plot
+```{r}
+plot (gfc$gdpegdpu)
+```
diff --git a/demo.final.html b/demo.final.html
index 9d6dd33..33abd19 100644
--- a/demo.final.html
+++ b/demo.final.html
@@ -1493,31 +1493,40 @@ border-radius: 0px;
<hr />
-<div id="r-markdown" class="section level2" number="0.1">
-<h2><span class="header-section-number">0.1</span> R Markdown</h2>
-<p>This is an R Markdown document. Markdown is a simple formatting
-syntax for authoring HTML, PDF, and MS Word documents. For more details
-on using R Markdown see <a href="http://rmarkdown.rstudio.com" class="uri">http://rmarkdown.rstudio.com</a>.</p>
-<p>When you click the <strong>Knit</strong> button a document will be
-generated that includes both content as well as the output of any
-embedded R code chunks within the document. You can embed an R code
-chunk like this:</p>
-<pre class="r"><code>summary(cars)</code></pre>
-<pre><code>## speed dist
-## Min. : 4.0 Min. : 2.00
-## 1st Qu.:12.0 1st Qu.: 26.00
-## Median :15.0 Median : 36.00
-## Mean :15.4 Mean : 42.98
-## 3rd Qu.:19.0 3rd Qu.: 56.00
-## Max. :25.0 Max. :120.00</code></pre>
+<div id="thema-1" class="section level1" number="1">
+<h1><span class="header-section-number">1</span> Thema 1</h1>
+<p>Wir wollen etwas analysieren:</p>
+<ul>
+<li>x</li>
+<li>y</li>
+<li>z</li>
+</ul>
+<p>Dabei nutzen wir die Methode <strong>OLS</strong>.</p>
</div>
-<div id="including-plots" class="section level2" number="0.2">
-<h2><span class="header-section-number">0.2</span> Including Plots</h2>
-<p>You can also embed plots, for example:</p>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<p>Note that the <code>echo = FALSE</code> parameter was added to the
-code chunk to prevent printing of the R code that generated the
-plot.</p>
+<div id="verwendeter-datensatz" class="section level1" number="2">
+<h1><span class="header-section-number">2</span> Verwendeter
+Datensatz</h1>
+<p>Wir verwendet hier die Daten von Paket gfc.</p>
+<pre class="r"><code>summary(gfc)</code></pre>
+<pre><code>## quarter GDPE GDPU
+## Length:60 Min. : 86.70 Min. : 80.4
+## Class :character 1st Qu.: 93.95 1st Qu.: 93.2
+## Mode :character Median :102.95 Median :103.0
+## Mean :101.86 Mean :102.6
+## 3rd Qu.:109.47 3rd Qu.:114.9
+## Max. :115.70 Max. :119.5</code></pre>
+</div>
+<div id="analyse" class="section level1" number="3">
+<h1><span class="header-section-number">3</span> Analyse</h1>
+<p>Zur Analyse definieren wir eine neue Variable:</p>
+<pre class="r"><code>gfc$gdpegdpu <- gfc$GDPE/gfc$GDPU</code></pre>
+<p>Diese Variable ist interessant, weil: 1. Item 1 2. Item 2 3. Item 3 +
+Item 3a + Item 3b</p>
+</div>
+<div id="plot" class="section level1" number="4">
+<h1><span class="header-section-number">4</span> Plot</h1>
+<pre class="r"><code>plot (gfc$gdpegdpu)</code></pre>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
diff --git a/demo.final.md b/demo.final.md
index c7b26b6..d5b271d 100644
--- a/demo.final.md
+++ b/demo.final.md
@@ -12,32 +12,50 @@ output:
---
+# Thema 1
-## R Markdown
+Wir wollen etwas analysieren:
-This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
+* x
+* y
+* z
-When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
+Dabei nutzen wir die Methode **OLS**.
+# Verwendeter Datensatz
+Wir verwendet hier die Daten von Paket gfc.
```r
-summary(cars)
+summary(gfc)
```
```
-## speed dist
-## Min. : 4.0 Min. : 2.00
-## 1st Qu.:12.0 1st Qu.: 26.00
-## Median :15.0 Median : 36.00
-## Mean :15.4 Mean : 42.98
-## 3rd Qu.:19.0 3rd Qu.: 56.00
-## Max. :25.0 Max. :120.00
+## quarter GDPE GDPU
+## Length:60 Min. : 86.70 Min. : 80.4
+## Class :character 1st Qu.: 93.95 1st Qu.: 93.2
+## Mode :character Median :102.95 Median :103.0
+## Mean :101.86 Mean :102.6
+## 3rd Qu.:109.47 3rd Qu.:114.9
+## Max. :115.70 Max. :119.5
```
+# Analyse
+Zur Analyse definieren wir eine neue Variable:
-## Including Plots
+```r
+gfc$gdpegdpu <- gfc$GDPE/gfc$GDPU
+```
+Diese Variable ist interessant, weil:
+1. Item 1
+2. Item 2
+3. Item 3
+ + Item 3a
+ + Item 3b
+
+# Plot
-You can also embed plots, for example:
+```r
+plot (gfc$gdpegdpu)
+```
-<!-- -->
+<!-- -->
-Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.
diff --git a/demo.final_files/figure-html/unnamed-chunk-3-1.png b/demo.final_files/figure-html/unnamed-chunk-3-1.png
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--
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