Marcin Kosiński

It’s been 2 weeks since the end of a Text Mining 2020.whyr.pl/hackathon/ at Why R?. This was a promotional event before the Why R? 2020 conference that aimed to promote knowledge related to text analyses. We prepared 4 various challenges so that teams were able to pick tasks that suit their skills best. At the beginning of this week we published videos presenting winning solutions on youtube.com/WhyRFoundation channel. If you are interested in the course of the event follow this blog post.

The fourth edition of Why R? Conference (2020.whyr.pl) is startin 2020-09-26. Below are informations on how to participate!

Why R 2020 Discussion Panels are scheduled for this weekend! Check out the post to find out about dates and descriptions of them for 2020.whyr.pl.

Data visualization for machine learning practitioners is the title of Why R? Keynote Kick Off by Julia Silge that starts today at 5pm UTC!. You can find all scheduled talks on our youtube.com/WhyRFoundation channel.

The fourth edition of Why R? Conference (2020.whyr.pl) is happening this week. Below are informations on how to participate!

This Thursday September 17 we are lauching another [whyr.pl/webinars/][http://whyr.pl/webinars/] entitled Me, Myself and my Rprofile

The fourth edition of Why R? Conference (2020.whyr.pl) is starting in 2 weeks! It was planned for September 24-27 2020 and happens remotely. Some pre-events, like Text Mining Hackathon start on 23rd (and lasts till the end of 24th). Have a look at this blog post to find out a big-picture agenda, proposed workshops and details around lectures. There are still seats on some workshops if you’re willing to participate. Simply visit the registration page 2020.whyr.pl/register/.

Why R? Webinars are back for Season 2! After over more than 20 000 views in past season we are eager to start a new set of R webinars! All videos from the past series can be watched on our youtube.com/WhyRFoundation channel. See this post to find out about upcoming webinars in September. The aim of webinars is to promote Why R? 2020 Conference (Remote) that is planned for 24-27 September 2020.

July 2nd (8:00pm UTC+2) is a date for the last Webinar at Why R? Foundation during season 1!. This week JD Long will talk about Taking friction out of R: helping drive data science adoption in organizations.

June 25th (8:00pm UTC+2) is a date for next Webinar at Why R? Foundation. This time we will have a chance to host Dr. Leon Eyrich Jessen who will present a In Silico Immunology: Neural Networks for Modelling Molecular Interactions using Tensorflow via Keras in R.

June 11th (8:00pm UTC+2) is a date for next fascinating Webinar at Why R? Foundation. This week we will have a pleasure to host Dr. Erin Hodgess who will present a talk entitled Using R with High Performance Tools on a Windows Laptop.

May 28th (8:00pm UTC+2) will bring another fascinating Webinar at Why R? Foundation. We will have a joint talk by Bernd Bischl, Florian Pfisterer and Martin Binder about Pipelines and AutoML with mlr3.

May 21st (8:00pm UTC+2) will bring next fascinating webinar at Why R? Foundation YouTube channel. We will have a pleasure to host Dr. Erin Ledell from H2O.ai. The topic of the talk is Scalable Automatic Machine Learning in R with H2O AutoML.

May 7th (8:00pm GMT+2) is the next date for a webinar at Why R? Foundation YouTube channel. We will have an amazing talk by Dr. Nina Zumel and Dr. John Mount from win-vector. The title of the webinar is Advanced Data Preparation for Supervised Machine Learning which corresponds to Practical Data Science with R book that Nina an John have written. Check out 40% discount code - mtpwhyr20 - that you can use on all their goods here.

April 23rdth (8:00pm GMT+2) is the next time we’ll be hosting a webinar at Why R? Foundation YouTube channel - https://youtube.com/c/WhyRFoundation This time we will have a joint presentation by Robin Lovelace and Jakub Nowosad (authors of Geocomputation with R). Jakub is an assistant professor in the Institute of Geoecology and Geoinformation at the Adam Mickiewicz University, Poznan, Poland. He is a computational geographer working at the intersection between geocomputation and the environmental sciences. Robin is associate professor at the Institute for Transport Studies (ITS) and Leeds Institute for Data Analytics (LIDA), University of Leeds, UK. His research focuses on geocomputation and reproducible data science for evidence-based policy-making.

Motivated by a successful turnaround of visits of Why R? 2019 keynote talks that uploaded to Why R? YouTube channel we decided to start Why R? Webinars series! We hope this will impact the growth of the R community.

Why R? conferences have been the hallmark of the Why R? Foundation (whyr.pl). Our goal has been to establish a series of international R-related events in Poland. After three years, we are happy to announce that our main event, the Why R? conference, has become one of the largest annual R conferences in Central Europe.

A month ago we closed Call for Papers for Why R? 2019 Conference. Today we are pleased to announce the agenda!

Two editions of Why R? conferences were a great networking success. We gather 200-300 R enthusiasts from Europe during each edition and were able to host more than 25 workshops together. The hunger for a bigger event led us to the 3rd edition!

We are pleased to announance upcoming Why R? 2018 conference that is going to happen in central-eastern Europe (Poland, Wroclaw) this July (2-5th). It is the last week for the call for papers! Submit your talk here.

This seems easy as it requires just to change x parameter to y in the plot specification. Well, there are some edge cases where R users might get a in trouble!

FSelectorRcpp - Rcpp (free of Java/Weka) implementation of FSelector entropy-based feature selection algorithms with a sparse matrix support, has finally arrived on CRAN after a year of development. It is also equipped with a parallel backend.

Have you ever tried to return a NULL with the ifelse() function? This function is a simple vectorized workflow for conditional statements. However, one can’t just return a NULL value as a result of this evaluation. Check a tricky workaround solution in this post.

At Thursday (12.01.2017) we had a chance to attend the first TriCity R Users Group (Pomerania, Poland) meeting. The meetup was unexpectedly very successful! The success can be measured in the time attendees spent on ardently comments and questions after each of 2 great presentations. After every 20-25 min long presentation we could observe 30 min long lively discussion! It is amazing that questions lasted longer than presentations. Is it thanks to the climate? Is it due to the nature of a Pomeranian community? Perhaps this is due to excellent organization? In this post I present summary of the meeting, I describe presentations and reveal organizers’ identity.

The image processing and the computer vision have gained a significant interest in last 2 decades. The image analysis can be used to detect items or people on images and videos. It is widely used in the medicine to detect cancer tissues and to improve brain, lungs and heart diseases diagnostic. The computer automation enabled analyzing terabytes of an image data, based on which we improve our life status and get insights for business decisions. In this post I present basic operations that can be applied to a simple image, all thanks to imager package by which I am truly impressed. I also present a quick entropy approach to the image binarization, which applied to images on a greyscale transforms them to the binarized black-and-white output.

The often demand in the biostatistical research is to group patients depending on explanatory variables that are continuous. In some cases the requirement is to test overall survival of the subjects that suffer on a mutation in specific gene and have high expression (over expression) in other given gene. To visualize differences in the Kaplan-Meier estimates of survival curves between groups, first the discretization of continuous variable is performed. Problems caused by categorization of continuous variables are known and widely spread (Harrel, 2015), but in this case there appear a simplification requirement for the discretization. In this post I present the maxstat(maximally selected rank statistics) statistic to determine the optimal cutpoint for continuous variables, which was provided it in the survminer package by Alboukadel Kassambara kassambara.

Data Science is sometimes a stressful and responsible job… so we spontaneously organize mini R quizzes in our R team at the company!

What is R? I was asked at the end of my presentation on the 10th Cracow R Users Meetup that was held last Friday (30.09.2016). I felt strange but absolutely confirmed that R is the language of Data Science and is designed to performed the statistical data analysis. Later I found out that few of listeners came to the meetup to listen more about Docker than R, as my topic was Rocker - explanation and motivation for Docker containers usage in applications development. In this post I present the overview of my presentation. If you are not familiar with using Dockers in R applications development, then this is a must read for you!

As R users we mostly perform analysis, produce reports and create interactive shiny applications. Those are rather one-time performances. Sometimes, however, the R developer enters the world of the real software development, where R applications should be distributed and maintained on many machines. Then one really appreciates the value of a proper applications monitoring. In this post I present my last package called RZabbix - the R interface to the Zabbix API data.

Machine and statistical learning wizards are becoming more eager to perform analysis with Spark ML library if this is only possible. It’s trendy, posh, spicy and gives the feeling of doing state of the art machine learning and being up to date with the newest computational trends. It is even more sexy and powerful when computations can be performed on the extraordinarily enormous computation cluster - let’s say 100 machines on YARN hadoop cluster makes you the real data cruncher! In this post I present sparklyr package (by RStudio), the connector that will transform you from a regular R user, to the supa! data scientist that can invoke Scala code to perform machine learning algorithms on YARN cluster just from RStudio! Moreover, I present how I have extended the interface to K-means procedure, so that now it is also possible to compute cost for that model, which might be beneficial in determining the number of clusters in segmentation problems. Thought about learnig Scala? Leave it - user sparklyr!

Text mining is a new challenge for machine wandering practitioners. The increased interest in the text mining is caused by an augmentation of internet users and by rapid growth of the internet data which is said that in 80% is a text data. Extracting information from articles, news, posts and comments have became a desirable skill but what is even more needful are tools for text mining models diagnostics and visualizations. Such visualizations enable to better understand the insight from a model and provides an easy interface for presenting your research results to greater audience. In this post I present the Latent Dirichlet Allocation text mining model for text classification into topics and a great LDAvis package for interactive visualizations of topic models. All this on R-Bloggers posts!

Have you ever suffered because of the impossibility of reproducing graphs, tables or analysis’ results in R? Have you ever bothered yourself for not being able to share R objects (i.e., plots or final analysis models) within your reports, posters or articles? Or maybe simply you have too many objects you can’t manage to store in a convenient and handy way? Now you can share partial results of analysis, provide hooks to valuable R objects within articles, manage analysis’ results and restore objects’ pedigree with archivist package and its extension archivist.github, allautomatically through GitHub without closing RStudio. If you are tired of archiving results by yourself, then read how you can became an R Hero with the archivist.github package power.

Have you ever encountered R packages versioning issues when one application required different dependent packages versions than other? Have you ever got stuck with your project because of wrong pre-installed software versions on machine on which you should run your code? Or maybe you had heavy adventures with installing R software on a new machine because you couldn’t recall all installation steps like; what have I done 2 years ago that RCurl works on my local machine but I can’t install it now on my virtual machine with Windows? Or maybe installation of your R project on new machine was easy but admin couldn’t manage with this process, as he’s not regular R user? If you ever find it problematic to move your R applications to other machines, then this Docker guid post is for you!

Getting users feedback is always a pleasant moment. In most cases in World of Open Source we are creating tools and applications for people and we love to hear that someone thinks our (generally pet) project is useful. Mostly this moment is nicer than any paycheck. Check this post to find out how to provide easy interface for collecting the user feedback!