These posts are general blog posts on R and they are also published on R bloggers. Consequently, the language is in English.

celebRation 2020

The year 2020 marks the 20th anniversary of the release of R version 1.0.0! To celebrate this, we are inviting the community of R users and developers for a two-day celebRation workshop/mini-conference on February 28-29th 2020 in Copenhagen. HUGOMORE42 We kick off on 28th February with hands-on workshops on two hot topics, namely data visualization using contemporary ggplot2 and extending R with C++ using the Rcpp package.

Building a Shiny app to show the impact of vaccines

Debates about vaccines are ongoing in many countries and the debate has reblossomed in Denmark after we’ve had five recent occurrences of measels. While that is nothing compared to the measles outbreak currently ravaging Japan it is still enough to worry the health authorities that it might result in an epidemic. Here we’ll use Shiny to create an app that shows the impact of contagious diseases and the influence of vaccination.

World Cup prediction winners

Predicting the outcome of the different teams in the FIFA World Cup has been of great interest to the general public, and predicting the outcome has also attracted quite some attention in the R community. The World Cup has ended and by now, everyone knows that France managed to take home the trophy that slipped through their fingers when they hosted the UEFA Euro 2016 championship. But who won the more important competition of predicting the outcome?

Generating codebooks in R

A codebook is a technical document that provides an overview of and information about the variables in a dataset. The codebook ensures that the statistician has the complete background information necessary to undertake the analysis, and a codebook documents the data to make sure that the data is well understood and reusable in the future. Here we will show how to create codebooks in R using the dataMaid packages.

dataMaid: Your personal assistant for cleaning up the data cleaning process

As data analysts, we all have tasks that we enjoy more than others. Some like the exploratory analysis steps, some like statistical computing, while others enjoy visualizing and communicating the results of their analyses. But we have yet to meet a data analyst that is passionate about data cleaning, even though everyone is very much aware of the importance of a thorough, well-documented data cleaning. This first step of virtually any data analysis process is often unavoidable and key for smooth sailing through the rest of the data analysis.