The R Consortium recently connected with Kylie Bemis, assistant teaching professor at the Khoury College of Computer Sciences at Northeastern University. She has a keen interest in statistical computing frameworks and techniques for analyzing intricate data, particularly focusing on datasets with complex correlation patterns or those that amalgamate data from various origins. 

Kylie created matter, an R package that offers adaptable data structures suitable for in-memory computing on both dense and sparse arrays, incorporating multiple features tailored for processing nonuniform signals, including mass spectra and various other types of spectral data. Recently, Kylie upgraded matter to version 2.4. Since our May 2023 discussion, Kylie has enhanced its signal processing capabilities, focusing on analytical tools like principal component analysis and dimension reduction algorithms, which are crucial for imaging and spectral data. A grant from the R Consortium supports this project.

We talked with you about matter in May 2023. You were providing support for matter and looking to improve the handling of larger data sets and sparse non-uniform signals. matter has been updated to version 2.4. What’s new?

Last time we spoke, I had already rewritten most of the matter infrastructure in C++ for better maintainability. Since then, my focus has been on enhancing our signal processing capabilities. This summer, I’ve been adding essential signal processing functions and basic analytical tools, which are particularly useful in fields dealing with spectra or various types of signals.

I’ve incorporated fundamental techniques like principal component analysis, non-negative matrix factorization, and partial least squares. I’ve also added several dimension reduction algorithms and a range of signal processing tools for both 1D and 2D signals. This includes smoothing algorithms for images and 1D signals and warping tools applicable to both. 

These enhancements are crucial for working with imaging and spectral data and include features like distance calculation and nearest neighbor search.

My aim has been to augment matter with robust signal processing tools, particularly for sparse and non-uniform signals. This is inspired by my experience in augmented reality (AR) and my desire to integrate tools similar to MATLAB’s Signal Processing Toolbox or SciPy in Python. As someone primarily analyzing mass spectrometry imaging data, I found these tools initially in my Cardinal package. I wanted to transfer them to a more appropriate platform, not specific to mass imaging, and reduce Cardinal’s reliance on compiled native code for easier version updates.

Additionally, I’ve been building a more robust testing infrastructure for these tools and documenting them thoroughly, including citations for the algorithms I used for key picking and smoothing techniques. This documentation details the implementation of various algorithms, such as guided filtering and nonlinear diffusion smoothing, citing the sources of these algorithms.

By providing support for non-uniform signal data, matter provides a back end to mass spectrometry imaging data. But working with large files is applicable in a lot of domains. What are some examples?

I deal with large files and data sets across various fields. Matter can be particularly impactful in areas dealing with signal, spectral, or imaging data. One field that comes to mind is remote sensing, where the imaging tools I’ve incorporated would be highly beneficial. That’s one key application area.

Another field is biomedical imaging, especially MRI data. For instance, a data format we often use for mass spectrometry imaging was originally developed for MRI – it’s called Analyze, and there’s a more recent variant known as NIfTI. This format is also supported in Cardinal for mass spec imaging data, but it’s primarily used in MRI and fMRI data analysis. While matter doesn’t directly offer plug-and-play functionality for MRI data, with some modifications, it could certainly be adapted for importing and processing MRI data stored in these formats.

We don’t have a specific function to read NIfTI files directly, but the structure of these files is quite similar to the mass imaging files we commonly work with. They consist of a binary file organized in a particular format, with a header that functions like a C or C++ struct, indicating where different image slices are stored. Understanding and interpreting this header, which is well-documented elsewhere, is key.

So, with some effort to read and attach the header file correctly, it’s entirely feasible to build a function for reading and importing MRI data. We’ve already done something similar with the Analyze format. Someone could definitely examine our approach and develop a method to handle MRI data effectively.

Previously, you indicated you wanted to improve R data frames and string support. You have a prototype data frame in the package already? What’s the schedule for improvements?

I’m currently evaluating how far we’ll expand certain features in our project. One of these features is supporting strings, which is already implemented. Regarding data frames, I believe there might be better solutions out there, but it’s quite simple to integrate our data with them. For instance, taking a vector or an array, whether a matter matrix or a matter vector, and inserting it into a data frame column works well, particularly with Bioconductor data frames.

I’m not entirely convinced that developing standalone, specialized data frame support in matter is necessary. It seems that other platforms, especially those like Bioconductor, are already making significant advancements in this area. For now, it seems sufficient that users can easily incorporate a matter vector or array into a data frame column. I’m hesitant to duplicate efforts or create overlapping functionalities with what’s already being done in this field.

What’s the best way for someone to try matter? How should someone get started?

Like any Bioconductor package, we offer a vignette on the Bioconductor website. This vignette provides a basic guide on how to start using our package, including creating matrices and arrays. It shows how these can serve as building blocks to construct larger matrices, arrays, and vectors. This is a straightforward way for users to begin.

Regarding the applicability of our package, it really depends on the specific data needs of the user. For instance, our package provides out-of-memory matrices and arrays. If that’s the primary requirement, then our package is certainly suitable. However, there are other packages, both in Bioconductor, like HDF5 array support, and on CRAN, such as big memory and FF, that also offer similar functionalities.

The real advantage of our package becomes apparent when dealing with specific data types. If you’re working with data formats like MRI, where you have a binary file and a clear understanding of its format, our package can be very helpful. It simplifies attaching different parts of the file to an R data structure.

Moreover, if your work involves signal data, particularly non-uniform signals like those in mass spectrometry or imaging data, our package becomes even more beneficial. Over the summer, I’ve added extensive support for preprocessing, dimension reduction, and other processes that are crucial for handling these types of data. So, in these scenarios, our package can be a valuable tool.

Anything else you would like to share about matter 2.0?

I’ve spent much of the summer working on improvements to the matter package, and it’s now in a good place, particularly regarding signal processing capabilities. These enhancements are largely aligned with the needs of mass spectrometry, an area I closely focus on. As new requirements emerge in mass spectrometry, I’ll look to add relevant features to matter, particularly in signal and image processing.

However, my current priority is updating the Cardinal package to support all these recent changes in matter. Ensuring that Cardinal is fully compatible with the new functionalities in matter is my next major goal, and I’m eager to get started on this as soon as possible.

About ISC Funded Projects

A major goal of the R Consortium is to strengthen and improve the infrastructure supporting the R Ecosystem. We seek to accomplish this by funding projects that will improve both technical infrastructure and social infrastructure.

The R Consortium recently talked with Natalia Andriychuk, Statistical Data Scientist at Pfizer and co-founder of the RTP R User Group (Research Triangle Park in Raleigh, North Carolina), to get details about her experience supporting the Pfizer R community and starting a local R user group. 

She started her R journey over 7 years ago, and since then, she has been passionate about open source development. She is a member of the Interactive Safety Graphics Task Force within the American Statistical Association Biopharmaceutical Safety Working Group, which is developing graphical tools for the drug safety community. 

Please share your background and involvement with the R community at Pfizer and beyond.

From 2015 to 2022, I worked at a CRO (Contract Research Organization) in various roles, where I discovered my passion for Data Science after being introduced to R, JavaScript, and D3 by my talented colleagues. I became a part of an amazing team where I learned valuable skills.

Later, when I began looking for new career opportunities, I knew that I wanted to focus on R. I sought a role that would deepen my R skills and further advance my R knowledge. This is how I came to join Pfizer in 2022 and became a part of the amazing team. I am a Statistical Data Scientist in the R Center of Excellence SWAT (Scientific Workflows and Analytic Tools) team.

The R Center of Excellence (CoE) supports various business lines at Pfizer. We provide technical expertise, develop training on R and associated tools, promote best practices, and build a community of R users within Pfizer. Our community currently consists of over 1,200 members. 

I will present Pfizer’s R CoE progress and initiatives during the R Consortium R Adoption Series Webinar on February 8th at 3:00 pm EST. 

My first introduction to the R community was through the posit::conf (previously known as rstudio::conf) in 2018. Attending the conference allowed me to witness the welcoming nature of the R community. Five years later, in 2023, I made it to the speakers’ list and presented at the posit::conf 2023. It was an incredible experience!

I also follow several other avenues to connect with R community members. As the name suggests, I read R Weekly weekly and attend the Data Science Hangout led by Rachael Dempsey at Posit. Every Thursday, Rachael invites a data science community leader to be a featured guest and share their unique experiences with the audience. Fortunately, I was invited as a featured guest to one of the Posit Data Science Hangouts. I shared my experience organizing and hosting an internal R at Pfizer Hangout. 

Can you share your experience of starting the RTP (Research Triangle Park) R User Group?

Nicholas Masel and I co-organize the RTP R User Group in our area. We formed the RTP R User Group in 2023 and have held three meetings‌: meet-and-greet, social hour, and a posit::conf 2023 watch party. 

We hope to expand and increase attendance at our meetups in 2024. We currently have approximately 74 members who joined the online meetup group, and we look forward to meeting all of them in person moving forward. 

Can you share what the R community is like in the RTP area? 

Nicholas and I both work in the pharmaceutical industry, and thus far, our in-person user group meetings have predominantly included individuals from this field. However, we want to emphasize that our user group is open to everyone, regardless of industry or background. 

The RTP area has great potential for a thriving R User Group. We are surrounded by three major universities (University of North Carolina at Chapel Hill, Duke University, and North Carolina State University), the growing high-technology community and a notable concentration of life science companies. We anticipate attracting more students in the coming year, especially those studying biostatistics or statistics and using R in their coursework. We also look forward to welcoming individuals from various industries and backgrounds to foster a rich and collaborative R user community.

Please share about a project you are working on or have worked on using the R language. Goal/reason, result, anything interesting, especially related to the industry you work in?

I am an open source development advocate, believing in the transformative power of collaborative innovation and knowledge sharing. I am a member of the Interactive Safety Graphics (ISG) Task Force, part of the American Statistical Association Biopharmaceutical Safety Working Group. The group comprises volunteers from the pharmaceutical industry, regulatory agencies, and academia to develop creative and innovative interactive graphical tools following the open source paradigm. Our task force is developing a collection of R packages for clinical trial safety evaluation. The {safetyGraphics} package we developed provides an easy-to-use shiny interface for creating shareable safety graphics for any clinical study. 

{safetyGraphics} supports multiple chart typesincluding web-based interactive graphics using {htmlwidgets}

We are preparing to share three new interactive visualizations we developed in 2023 during the upcoming ASA-DIA Safety Working Group Quarterly Scientific Webinar – Q1 2024 on January 30 (11:00 – 12:30 EST). Participating in the ISG Task Force has been an invaluable experience that allowed me to learn from talented data scientists and expand my professional network. 

How do I Join?

R Consortium’s R User Group and Small Conference Support Program (RUGS) provides grants to help R groups organize, share information, and support each other worldwide. We have given grants over the past four years, encompassing over 65,000 members in 35 countries. We would like to include you! Cash grants and meetup.com accounts are awarded based on the intended use of the funds and the amount of money available to distribute.

Last year, Antonio Hegar of the R Glasgow user group shared the challenges of organizing an R user group in Glasgow. The group now regularly hosts events, attracting local R users and experts. Antonio shared with the R Consortium the group’s journey and anecdotes that have helped it to build momentum. He also shared his hopes for maintaining this momentum, with speakers lined up for the next three events.

R Glasgow will be hosting their first event this year titled “Fake it Until You Make it: How and Why to Simulate Data” on January 25, 2024. 

Antonio also discussed his work with R for his PhD research in data analysis for healthcare. He spoke about the ever-evolving nature of R and some of the new developments that have been useful for his research. 

What’s new with the Glasgow R User Group since we last talked?

As we discussed the last time, one of the most pressing issues we faced as a local R user group was our lack of engagement with the community. This is particularly interesting given that both Glasgow and Edinburgh have their own R user groups. Both cities are only an hour apart, yet we weren’t seeing the same level of engagement as other groups in the UK.

To address this issue, we have been strategizing and holding several meetings. To summarize, we discussed improving our marketing and engagement with our audience. We also decided to hold one final meeting at the end of the year.

Besides our internal meetings, we also hosted two R events. One of the group’s founders, Andrew Baxter, a postgraduate researcher at the University of Glasgow, has been instrumental in organizing these events. Because he works at the University of Glasgow, he has access to many resources, including physical venues and fellow academics, and this has been a major plus in facilitating our engagement.

Previously, I had been trying to do what other groups have done: finding random venues and hosting events there. However, this was not as effective as we had hoped.

From the discussions that we had, as well as listening to our audience, we learned that people who are interested in working with R have very specific wants and needs. If these needs are not being met, then it is unlikely that people will be attracted to the group, and as such, we had to reframe our approach to attracting people.

We recognized it is key to have a specific venue. We now hold the vast majority of our meetings at the University of Glasgow. This seems to be very appealing to people, as they enjoy the academic setting. Furthermore, the University of Glasgow is well known and respected, not just in Scotland but across the world, and this adds weight to the appeal, and the reputation helps to draw people in. 

The second thing that proved essential was consistency. Having a meeting for one month and then having a gap breaks the flow, and sends the wrong message to your audience. When people see that you are committed to what you want to do, they respond to that and are more likely to be engaged in the community.

We had a final meeting in December, and Andrew Baxter contacted Mike Smith, one of the local R Consortium representatives. He is based in Dublin, Ireland, but frequently travels back and forth to Scotland. He leveraged this network to recommend speakers and topics for the conference. This was particularly helpful in attracting people from industry, who are often interested in the latest developments in R. Mike has been a tremendous asset to the group since our meeting in December.

A venue, people on the inside of the industry, and a consistent schedule have been the three key components. Three speakers have been lined up for early 2024: one for January, February, and March.

We will not have much difficulty finding additional speakers based on the academic and industrial contacts. At most, we must determine who will speak on which topic and when they will be available, which is not difficult. Based on the current situation, it does not appear that we will have any trouble maintaining momentum and keeping the meetings going. 

What industry are you currently in?

I am a PhD student at Glasgow Caledonian University. My PhD research focuses on data science applied to health, specifically using machine learning to predict disease outcomes.

I am interested in understanding why some people who experience an acute illness, such as COVID-19, develop long-term health problems. In some countries, up to 10% of people who contract COVID-19 never fully recover. These individuals may experience permanent shortness of breath, headaches, brain fog, joint pain, and other symptoms.

I am currently researching how data science can be used to answer questions such as these, using large data sets from, for example, the NHS. R is the primary tool used for this research.

When we last spoke, I was in the second year of my PhD. I am now in my third and final year. I should be submitting my dissertation before the end of this year. Balancing my commitments to R, my PhD work, and other activities is challenging, but I managed to pull it off.

How do you use R for your work?

I extensively use R. One of R’s most beneficial aspects is that it’s constantly evolving and expanding. As a result, it is impossible to master everything. You do not master R; rather, you master certain R areas relevant to your research or area of expertise. In my research, I found several medical statistics and biostatistics packages extremely useful. I was aware of a few of them but unaware of how many there were.

For instance, consider the following brief instance of a task that I began working on yesterday. In the context of medical data, particularly when analyzing health conditions, it is common for individuals to have multiple health conditions that are often linked. This often makes it more difficult for doctors to treat and for individuals to recover fully. 

If I were to apply classical statistics using base R, this would be very time-consuming. However, I recently discovered that there are also medical statistical packages specifically designed for analyzing data for individuals with comorbidities. For example, if I wanted to analyze individuals suffering from diabetes, hypertension, cancer, obesity, or a combination of different diseases, I could do so using these packages.

In addition, it is possible to create a score that can be used to estimate the likelihood of a person who becomes ill and goes to the hospital, stays for a long time, or dies. It is possible to perform this task using regular statistics and programming in R, but it would be very tedious. In my case, I am working on a tight deadline and need to submit my work by a specific date. I believe the package I am speaking of is the comorbidity package in R. It was developed recently by researchers at the London School of Hygiene & Tropical Medicine and is an invaluable tool.

I work with NHS data through a third-party organization that controls it and allows me access to it. Last year in December, they provided me with brief training and taught me how to access their data on a DBS SQL server using SQL queries embedded in R code. 

Learning about very niche packages, which are very content-specific or topic-specific, is very useful for researchers like myself. Integrating different programming languages is also useful because they are all merging into one. Python, Julia, R, and Java have a lot of cross-fertilization and use between the different programming and software development packages. If R continues to streamline its services to integrate other packages, it will be a win-win situation for everyone.

What is the R Community like in Glasgow? What efforts are you putting in to keep your group inclusive for all participants?

We are not trying to cater to one specific level of expertise. The last meeting had a good mix of participants, including PhD students, undergraduates, people who have worked in finance and tech, software developers, and an individual from the R Consortium in Dublin, Ireland.

The group is open to everyone, and we are trying to mix participants with different needs, wants, and interests. It is understood that attendees will choose which events they would like to attend. Certain events will focus more on entry-level individuals beginning their R learning journey. For example, they are interested in learning what they can do with ggplot and the tidyverse.

Mid-level individuals, including graduate students, will also be targeted. A portion of these students are novices, but many are more experienced. They have a strong foundation in R and RStudio or Posit. However, they are now seeking to learn more advanced techniques, such as how to perform specific calculations. For instance, they may be working with quantitative or qualitative data and are now at the analysis stage of their research and wonder what to do next.

Finally, there are a small number of highly experienced programmers who are interested in learning more about integrating specific features into a package. They may want to know how to create their packages and launch them. They are also interested in learning about Shiny and Quarto and how they can use these tools for their businesses or companies. 

Most individuals fall into the beginner or intermediate levels, but there are a few who are highly advanced and still interested in attending. As a result, most of the talks will be geared toward individuals with intermediate-level experience. This will ensure that the material is not too advanced for beginners but also not too basic for advanced learners.

Can you tell us about a recent event that received a good response from the audience? 

Of the recent events that were particularly successful, I would like to highlight the one held in November last year. It was titled “Flex Dashboard: Displaying data with high impact using minimal code.” Erik Igelström, a researcher from the University of Glasgow, presented his use of R Shiny to display data from the Scottish government. The presentation was highly informative and demonstrated the potential of Shiny to present data in a user-friendly manner. 

The meeting was attended by a representative from R Software in Ireland, who provided us with a wealth of information about industry developments, including the latest trends and upcoming projects. As a result of this meeting, 2023 was the most productive year for our R meetup.

The preceding meetups were not entirely unproductive, but the most recent one, held in November last year, laid the groundwork for the current initiatives.

You have a Meetup titled “Fake it Until You Make it: How and Why to Simulate Data” on 25th January 2024.  Can you share more on the topic covered? Why this topic? 

Professor Lisa DeBruine will be presenting at this Meetup. She is a professor of psychology at the University of Glasgow in the School of Psychology and Neuroscience. She is a member of the UK Reproducibility Network and works in PsyTeachR. She has used the psych package extensively and many other good packages in R to conduct her psychological research. Her presentation will be on how to simulate data to prepare analyses for pre-registration.

As those who work with data know, it is sometimes counterproductive to work directly with the data itself. For example, if one is building a model, it is not advisable to use all of the data to build the model, especially if the data set is small. This is because there is a risk of over-fitting.

Generating dummy data for quantitative data is a well-known technique. However, generating dummy data for qualitative data is rare. This is because qualitative data is often unstructured and difficult to quantify. Professor Lisa DeBruine is an expert in generating dummy data for qualitative data.

SPSS is a popular statistical software package used by sociologists, anthropologists, and psychologists. However, R is a more powerful and flexible tool that can perform a wider range of analyses. Learning to use R and the psych package can greatly simplify the process of conducting factor analysis. Additionally, R can be used to perform calculations and analyses that are impossible in SPSS.

Our team is highly capable, and we have another team member who is particularly skilled in generating graphics and designing flyers. He has been responsible for creating the promotional material and has done an excellent job.

The R Consortium recently connected with Lampros Sp. Mouselimis, the creator of the ICESat2R package, discussing the ICESat-2 mission, a significant initiative in understanding the Earth’s surface dynamics. This NASA mission, utilizing the Advanced Topographic Laser Altimeter System (ATLAS), provides in-depth altimetry data, capturing Earth’s topography with unparalleled precision. 

Mouselimis’ contribution, the ICESat2R package, is an R-based tool designed to streamline the analysis of ICESat-2 data. It simplifies accessing, processing, and visualizing the vast datasets generated by ATLAS, which emits 10,000 laser pulses per second to measure aspects like ice sheet elevation, sea ice thickness, and global vegetation biomass. This package enables users to analyze complex environmental changes such as ice-sheet elevation change, sea-ice freeboard, and vegetation canopy height more efficiently and accurately. The R Consortium funded this project. 

You built an R package called ICESat2R using the ICESat-2 satellite. Do you consider your ICESat2R project a success?

ICESat-2 R has 7,252 downloads, which, considering the smaller group of researchers who focus on using ICESat-2 data, qualifies it as a popular tool. It’s not as popular compared to some other remote sensing packages, but I believe it’s been a success based on two main points:

• Contribution to the R users community: I hope that the R programmers who use the IceSat2R R package are now able to process altimetry data without any issues, and, if any, then I’ll be able to resolve these by updating the code in the GitHub and CRAN repositories.

• Personal and Professional achievement: I applied for a grant to the R consortium, and my application was accepted. Moreover, I implemented the code by following the milestone timelines. Seeing a project through and providing it publicly is a success, I believe.

Who uses ICESat2R, and what are the main benefits? Any unique benefits compared to the Python and Julia interfaces?

The users of the ICESat2R package can be professionals, researchers, or R programming users in general. I assume that these users could be:

• Ice scientists, ecologists, and hydrologists (to name a few) who would be interested in the altimeter data to perform their research
• Public authorities or military personnel, who, for instance, would like to process data related to high-risk events such as floods
• Policy and decision-makers (the ICESat-2 data can be used, for instance, in resource management)
• R users that would like to “get their hands dirty” with altimeter data

I am aware of the Python and Julia interfaces, and to tell the truth, I looked at the authors’ code bases before implementing the code, mainly because I wanted to find out the exact source they used to download the ICESat-2 data. 

Based on the current implementation, I would say that the benefits of the ICESat2R package are the following:

• The R programming users can use NASA’s OpenAltimetry interface, which, as of December 2023, doesn’t require any credentials
• The R package includes 3 Vignettes (Articles) and detailed documentation (Reference) for the implemented code

What is an interesting example of using ICESat2R?

There are many examples where the ICESat2R package can be used. For instance, a potential use case would be to display differences between a Digital Elevation Model (Copernicus DEM) and land-ice-height ‘ICESat-2’ measurements. The next image shows the ICESat-2 land-ice-height in winter (green) and summer (orange) compared to a DEM,

More detailed explanations related to this use case exist in the Vignette ICESat-2 Atlas Products of the package.

Were there any issues using OpenAltimetry API (the “cyberinfrastructure platform for discovery, access, and visualization of data from NASA’s ICESat-2 mission”)? (NOTE: Currently, the OpenAltimetry API website appears to be down?)

At the beginning of October 2023, I was informed that the OpenAltimetry website (previously https://openaltimetry.org/) has migrated to https://openaltimetry.earthdatacloud.nasa.gov/. I then contacted the support of the  National Snow & Ice Data Center, which informed me about the migration of the API interface. 

Currently, I have an open issue in my Github repo related to this migration. Once the OpenAltimetry API becomes functional again, I’ll submit the updated version of the ICESat2R package to CRAN.

In your blog post for the copernicusDEM package, you showed a code snippet showing how it loads files, iterates over the files, and uses a for-loop to grab all the data. Can you provide something similar for ICESat2R?

Whenever I submit an R package to CRAN, I include one (or more) vignettes that explain the package’s functionality. Once the package is accepted, I also upload one of the vignettes to my personal blog. This was the case for the CopernicusDEM R package,

• Vignette
• Blog Post

but also for the ICESat2R package,

• Vignette 1
• Vignette 2
• Vignette 3
• Blog Post

The current version of IceSat2R on CRAN (https://CRAN.R-project.org/package=IceSat2R) is 1.04. Are you still actively supporting IceSat2R? Are you planning to add any major features?

Yes, I still actively support IceSat2R. I always respond to issues related to the package and fix potential bugs or errors. The NEWS page of the package includes the updates since the first upload of the code base to Github.

I don’t plan to add any new features in the near future, but I’m open to pull requests in the Github repository if a user would like to include new functionality that could benefit the R programming community.

About ISC Funded Projects

A major goal of the R Consortium is to strengthen and improve the infrastructure supporting the R Ecosystem. We seek to accomplish this by funding projects that will improve both technical infrastructure and social infrastructure.

Recently, Kamil Sijko of the Warsaw R User Group discussed with the R Consortium his transition from academia to leading data science in the business sector. He noted the current dormancy of Warsaw’s R community and the eagerness to revive its dynamic, pre-COVID meetups. The group’s latest meeting explored new, interactive formats to engage its diverse membership better.

Please share about your background and involvement with the RUGS group.

During my early academic years at the University of Social Sciences in Warsaw, we explored several interesting projects, one of which was ‘webiR’ in 2009. This project was an attempt to blend R’s capabilities with web application development, which was not very common at the time. We developed webiR a few years before the advent of Shiny in 2012 with the idea of making R more accessible to non-technical users.

While webiR might not be widely remembered today, unlike the widely successful Shiny, it represented our early efforts to simplify data analysis. The application allowed users to choose survey questions they were interested in, and then it would automatically select suitable analyses through a set of heuristics. This approach aimed to eliminate the need for users to understand the underlying R functions, making data analysis more approachable.

Although webiR wasn’t a major success, it was a valuable learning experience and a stepping stone in exploring how R could be used innovatively, especially in web development. These kinds of exploratory projects contribute to the ongoing evolution and versatility of R, which we continue to see today.

Later, I transitioned to working at research institutes, including a government-funded Polish Educational Research Institute. Now, I’m in the business sector. I serve as the Head of Data Science at Transition Technologies Science, a company that operates in the medical industry. We collaborate with pharmaceutical companies, universities, and medical scientists. My role involves leveraging data science in various aspects of the medical field.

Can you share what the R community is like in Warsaw, Poland? 

The situation is dormant, but it’s good timing for a reboot. There have been no revised activities since the pandemic ended. Before COVID, though, this was a hot topic of discussion. There were frequent meetups, including Python and data science gatherings. These meetups were unique, and I found them slightly unconventional in a good way. For example, Python meetups often focused on deep learning and applications in risk management or insurance. 

But with R meetups, there was a broader spectrum of topics, often venturing far beyond conventional subjects. I found this diversity particularly refreshing, especially as many academics were involved, exploring a wide range of innovative applications.

One of the things that stood out was the involvement of women from the Warsaw University of Technology, who ran the ‘R Ladies’ in Warsaw. They organized numerous workshops, which were quite popular. These workshops offered an accessible entry point into data science for those looking to change careers. One interesting observation made was that R is often seen as more approachable as a first language for newcomers from different backgrounds.

We also have a strong scientific group in Warsaw led by Professor Biecek, a fervent advocate of R and leader of MI2.AI. His work in Explainable AI is cutting-edge, making us feel connected to a vibrant local scene. Another point raised was the curiosity about local technological developments, not just the global cutting-edge advancements.

I recall an initiative named ‘PoweR’ – a three-week crash course in data science that attracted about 500 participants. I didn’t participate myself, but it was impressive. Also, the fields of science like medicine, statistics, econometrics, spatial sciences, and humanities were highlighted. R is extremely popular in these areas, allowing for exploration of unique and diverse topics.

It’s clear there’s a strong desire to revive these meetups and initiatives, as they foster a unique learning environment and community spirit.

You had a Meetup on December 11th, 2023. Can you share more on the topic covered? Why this topic? 

In our recent meeting, we deviated from the usual format of workshops and lectures, opting for a more unique approach that we may not repeat. Instead, we engaged in a peer-to-peer discussion, which was feasible due to the small number of attendees. We focused on two main topics. The first was understanding what people miss most about our meetings, as I aim to incorporate these elements when I reboot them. The second topic was exploring future directions for our meetings.

We delved into the different types of participants attending our meetings. One group comprises those familiar with R and eager to learn about advanced techniques, for whom lectures are ideal. Another group includes individuals transitioning from other fields to data science. We also considered students, particularly those favoring Python over R, and I believe it’s important to dispel any misconceptions about career prospects in R.

Additionally, we discussed members of the open source community around Warsaw, recognizing their contributions during events like hackathons. Another interesting aspect was the companies’ involvement, not just in recruitment but also in sharing their work with the community.

An unaddressed yet intriguing aspect was attendees transitioning within the data science field, seeking insights into new companies and trends. I also want to focus more on social interactions beyond just having pizza and experiment with ideas like speed dating or extended interactions with lecture presenters.

Lastly, we considered the language of our meetings. Operating in Poland, we debated whether to conduct some sessions in English, stream them, or post them on YouTube to reach a broader audience. I’m excited to experiment with these ideas, which could significantly enhance our meetings.

Who is the target audience for attending this event? 

Up to this point, our focus has primarily been on individuals who are already interested in R and seeking to deepen their knowledge with expert insights. That’s been our main audience. The other significant group consists of those completely new to the field who are looking to be introduced to data science through R. These are the two main types of participants we usually have.

We aim to be more inclusive; of course, there’s the ‘R Ladies’ initiative. The ‘R Ladies’ essentially engage in the same activities as the rest of our groups, but they cater to a different audience. The content and structure of their sessions are similar to what we offer to other participants. Still, they focus on creating an inclusive environment for women interested in data science and R.

Any techniques you recommend using for planning for or during the event? (Github, zoom, other) Can these techniques be used to make your group more inclusive to people that are unable to attend physical events in the future?   

There were various opinions, but one perspective really resonated with me. COVID took away our in-person meetups, and although there was an attempt to transition them to a virtual environment, it wasn’t the same. We miss face-to-face interactions and being in the same physical space together. That’s something special.

There were instances where, despite people already gathering in the room, we had to announce that the expert wouldn’t be able to come and would instead join via Zoom. This often led to disappointment, with some attendees leaving the room immediately, as they weren’t interested in a virtual presentation. After all, there’s plenty of similar material available online.

One comment struck me: even though we could have experts from RStudio (now posit) or other places speak to us from across the ocean about their latest developments, this information is already accessible on platforms like YouTube. The experience is likely to be similar. In terms of using Zoom or similar virtual platforms, we’re leaning towards not pursuing that path for future meetups.

We would like to get to know you more on the personal side. Can you please tell me about yourself? For example, hobbies/interests or anything you want to share about yourself.

A fun fact about me is my deep involvement in an initiative focused on teaching children creative computer skills. I’ve found it incredibly rewarding to help kids learn how to use technology creatively. It’s a lot of fun, both for me and the children. For instance, I recently prepared workshops on creating Electronic Dance Music (EDM). These workshops cover aspects like sampling and looping. I find this work enjoyable and immensely fulfilling, as it combines my passion for technology with the joy of teaching and engaging with children.

Additionally, in my work with CoderDojo, I’ve had the opportunity to engage children in programming projects, including a special focus on encouraging a group of girls. We utilized ‘Kodu Game Lab‘ for these sessions, a platform that offers a more immersive, video game-like environment for coding. This platform enabled the children to learn programming concepts in a playful manner, such as coding a robot to follow or avoid objects and even creating their own simple games.

A key moment came when the girls highlighted a significant limitation: the lack of relatable characters in the games, noting the predominance of robots and other figures but a conspicuous absence of princesses or characters they could identify with. This feedback was invaluable and led us to adapt our approach. We creatively worked around this limitation by incorporating an object—a ‘tag’—which we collectively imagined as a princess needing rescue. This improvisation turned into a unique game by the end of the day.

This experience was not just fun but also enlightening, underscoring the importance of CoderDojo’s approach in offering unique insights into how different groups perceive technology. It highlighted the need to understand and address diverse perspectives and requirements in technology, especially when introducing young minds to the world of programming.

How do I Join?

R Consortium’s R User Group and Small Conference Support Program (RUGS) provides grants to help R groups organize, share information, and support each other worldwide. We have given grants over the past four years, encompassing over 65,000 members in 35 countries. We would like to include you! Cash grants and meetup.com accounts are awarded based on the intended use of the funds and the amount of money available to distribute.