It’s Valentine’s day, making this the most romantic time of the year. But actually, already 2018 was a year full of love here at STATWORX: many of my STATWORX colleagues got engaged. And so we began to wonder – some fearful, some hopeful – who will be next? Therefore, today we’re going to tackle this question in the only true way: with data science!
In this blog we will explore the plotly library for python and R. We show how plotly is structured and use the LA Metro Bike dataset as an example to create interactive plots.
In this blog we will explore the Bagging algorithm and a computational more efficient variant thereof, Subagging. With minor modifications these algorithms are also known as Random Forest and are widely applied here at STATWORX, in industry and academia.
In the last 23 days I presented one function each day from the helfRlein package we created here at STATWORX. I hope you found some of the functions useful and had some fun discovering new ways of doing things with R! Since today is Christmas, only one thing remains to say: To see all functions you can either check out …
This little helper adds functionality to the base R function
strsplit – hence the same name!
This little helper does the same thing as the "Find in files" search within RStudio.
This little helper returns indices of recurring patterns. It works with numbers as well as with characters.
This little helper replaces non-standard characters (such as the German umlaut "ä") with their standard equivalents (in this case "ae").
This little helper frees your memory from unused objects. Well, basically it just calls
gc() a few times.
With this little helper you can load functions directly from an URL (e.g. from GitHub).