<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Ggplot on It was simple</title>
    <link>https://hansjoerg.me/tags/ggplot/</link>
    <description>Recent content in Ggplot on It was simple</description>
    <generator>Hugo -- gohugo.io</generator>
    <copyright>Hansjörg Plieninger</copyright>
    <lastBuildDate>Fri, 10 May 2019 00:00:00 +0000</lastBuildDate>
    
	<atom:link href="https://hansjoerg.me/tags/ggplot/index.xml" rel="self" type="application/rss+xml" />
    
    
    <item>
      <title>Regression Modeling With Proportion Data (Part 1)</title>
      <link>https://hansjoerg.me/2019/05/10/regression-modeling-with-proportion-data-part-1/</link>
      <pubDate>Fri, 10 May 2019 00:00:00 +0000</pubDate>
      
      <guid>https://hansjoerg.me/2019/05/10/regression-modeling-with-proportion-data-part-1/</guid>
      <description>Modeling Proportion Data Application: Handball-Bundesliga Setup Selected Variables  Initial Results for Beta Regression Illustrative Plot of Estimates Residuals  Model Comparisons Models Considered Model Performance  Prediction of Future Matches Resources   As a data scientist, one often encounters dependent variables that are proportions: for example, the number of successes divided by the number of attempts, party vote, proportion of money spent for something, or the attendance rate of public events.</description>
    </item>
    
    <item>
      <title>Durchschnittsalter in den Mannheimer Stadtteilen</title>
      <link>https://hansjoerg.me/2019/04/02/durchschnittsalter-mannheim/</link>
      <pubDate>Tue, 02 Apr 2019 00:00:00 +0000</pubDate>
      
      <guid>https://hansjoerg.me/2019/04/02/durchschnittsalter-mannheim/</guid>
      <description>Ich wollte schon immer einmal Geodaten plotten und heute bin ich endlich dazu gekommen das auszuprobieren. Das Plotten selbst ist eigentlich ganz einfach …
fortunes::fortune(&amp;quot;done it.&amp;quot;) #&amp;gt; #&amp;gt; It was simple, but you know, it&amp;#39;s always simple when you&amp;#39;ve done it. #&amp;gt; -- Simone Gabbriellini (after solving a problem with a trick suggested #&amp;gt; on the list) #&amp;gt; R-help (August 2005) Die Herausforderung besteht eher darin die Daten aufzubereiten, in diesem Fall die Polygone der Stadtteile von Mannheim.</description>
    </item>
    
    <item>
      <title>Plotting Many Groups With ggplot2</title>
      <link>https://hansjoerg.me/2019/02/15/plotting-many-groups-with-ggplot2/</link>
      <pubDate>Fri, 15 Feb 2019 00:00:00 +0000</pubDate>
      
      <guid>https://hansjoerg.me/2019/02/15/plotting-many-groups-with-ggplot2/</guid>
      <description>ggplot2 is a great R package and I use it almost everyday. When plotting data for different groups, one has different options to identify them, for example, by means of different colors or different shapes. However, with many groups, it often becomes very difficult or even impossible to discriminate between the groups. Herein, I will illustrate a solution to plot an intermediate number of groups with ggplot2.
First, I will use different colors to discriminate between the groups.</description>
    </item>
    
    <item>
      <title>Lowest Number of Red Cards in 40 Years</title>
      <link>https://hansjoerg.me/2018/07/17/web-scraping-world-cup/</link>
      <pubDate>Tue, 17 Jul 2018 00:00:00 +0000</pubDate>
      
      <guid>https://hansjoerg.me/2018/07/17/web-scraping-world-cup/</guid>
      <description>While watching a FIFA World Cup game, I suddenly had the impression that games got fairer over the years. Everybody remembers the headbutt of Zinedine Zidane, but I haven’t seen similar things in 2018. I always wanted to try out web scraping and this was the opportunity to do so.
https://media.giphy.com/media/9AuHkzLy26AmI/giphy.gif
 In this post, I will give a very brief intro to web scraping focusing mostly on scraping the World Cup data.</description>
    </item>
    
  </channel>
</rss>