In this blogpost, we will come back to the subject of the German Elections. We will try to show, mostly visually, the changes in election results during the 21st century. Thus, we will use data from the elections in 2002 to the last ones in 2017. The main focus will be mapping the results of the parties represented in the current Bundestag (German Parliament) during this time span. Let’s start our coding.
This post will talk about multiple linear regression in the context of machine learning. Linear regression is one of the simplest and most used approaches for supervised learning. This tutorial will try to help you in how to use the linear regression algorithm. I am also new to the machine learning approach, but I’m very interested in this area given the predictive ability that you can gain from this. Let’s hope I can help you.
In R missing values are usually, but not always, represented by letters NA. How to deal with missing values is very important in the data analytics world. Missing data can be sometimes tricky while analyzing a data frame, since it should be handled correctly for our statistical analysis. Before diving into more complex details about missing data, the first question that should be asked in any exploratory data analysis is: Do I have missing values in my database?
Sometimes, before we start to explore our data, we need to put them together. For instance, we might have them stored in different data frames and we have to join variables from two or more data frames in one. This post will talk about the different functions we can use to achieve that goal. We will be using the dplyr package to combine different data frames.
Firstly, we will show examples related to what is called mutating joins.
This post talks about making interactive visualizations in R with leaflet(). In this example, I’ll map the USA locations of two of the biggest coffee chains, Starbucks and Dunkin’ Donuts. This package allows us to map data and play interactively with it. For instance, we can zoom in or zoom out to augment or diminish map details, respectively. We can add markers that signal the position of our data in the map and move the mouse cursor over to get information about it.
This post will explore with R one of the simplest approaches to predict a response of a quantitative nature. This approach is called Linear Regression.
I will use a simple Linear Regression to study whether there is any relationship between the gross domestic product (gdp) per capita of each state in the USA and its tuition costs. Therefore, our predictor will be the gdp per capita per state and our response will be the tuition costs per state.
As the title of this post implies we will analyze, using the statistical programming language R, the German Federal Election which took place on 24 September of 2017. It will not be an exhaustive analysis of the results. I’m only interested in visualizing the share of the vote that each party represented in the Parliament (i.e. Bundestag) received in each one of the 16 States of Germany.
In order to make this visualization possible in R, loading the respective packages is the first step.
