Touring the tidyverse: tidyeval

Another stop on the journey through the tidyverse and this time it’s one of the most interesting and mysterious corners (I think) of the entire suite of tidyverse packages - tidy evaluation. My goal for the talk was to introduce ideas behind tidy evaluation in an interactive manner. To do that, I’ve used mostly exactly the same approach as Lionel Henry (main contributor to rlang that hosts tidy evaluation) outlined in this WIP book creatively called “Tidy evaluation”. [Read More]

Touring the tidyverse: purrr

Another R meetup, another talk about tidyverse. This time I’ve talked about purrr. I really like this package, but I do have a feeling that I didn’t present in the best light possible. I certainly could have prepared a couple more examples that showcase why this package is so awesome and what kind of workflow it allows to create. To pat myself on the back a bit, it was a good idea to anticipate that it would take a bit longer than I’ve planned and to put “interesting” parts of purrr first. [Read More]

Touring the tidyverse: dplyr

On 25th of July, 2018, I’ve given a talk with the topic “Touring the tidyverse: dplyr”. It was a second installment of the series of talks about tidyverse. R markdown format of the presentation is here, code I’ve used during presentation is here. Over time I’m planning to cover most of the packages in tidyverse. Let me know if anything is not clear about this talk or any other in the series. [Read More]

Touring the tidyverse: tidyr

This Thursday I’ve given a talk at Berlin R-Users Group. The topic was “Touring the tidyverse: tidy data + tidyr”. The idea is that this will become a series of talks where each consequent talk is going to present one (or couple) of packages from the tidyverse. tidyr to me seems like a good choice for the first talk since concept of tidy data is so central to all of the packages. [Read More]

Buying a car data sceintist way

Recently I’ve been thinking about what car to buy. Searching the web is one way, but I wanted to do it in a more “data” way, so in this series I’ll show how I’ve extracted data about ~19.000 used cars in Berlin to find out what models/manufacturers seem to hold the price for longer. In my case I’ve decided to use this as an excuse to practice some of the skills that I’ve been dying to try for a long time - web scrapping with R. [Read More]