End of Year thoughts

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Sometimes it’s worth making New Year resolutions… A year ago I made one for 2017 to start an R blog using RMarkdown and Jekyll static sites. At the time, I didn’t even know git that well, had no clue what static sites are and was mostly oblivious to the rich and vibrant R community on Twitter. Fast-forward one year and… the picture couldn’t be any more different! I’d like to share my thoughts on writing this blog (and data science blog in general) and how it taught me about getting stuff done. But I can’t write all this without mentioning a very special group of people – #rstats community! – that is now a big part of everything that gets published here.


I’m a list-person. Totally. I love ticking off boxes, feeling that I’ve achieved a lot every day. At the same time, working full time in London (meaning work + non-negligible commute) while having two small children (2 and 4 years old) leaves me with very little spare time (usually in the evenings). Fortunately, my work as a data scientist and this blog give me lots of opportunities to solve problems and deliver something tangible in defined time frames. Here are some rules that I’ve learned in the last months and years that I try to apply whenever I can in my data projects:

  1. stay focused and clear – know what you want to achieve and by when. Be ambitious, yet realistic (lists and internal deadlines really help!).

  2. Learn to recognise when you’re delving into the literature that is irrelevant to the project (just because it’s interesting or may be useful in the future) and leave it for when the project is finished or when it can be directly applied to it.

  3. If you want to explore an “alternative solution” (for whatever reason: it may be more efficient, elegant, creative, etc.), give yourself a time limit by the end of which you’ll drop it and look for other alternatives (if you still aren’t any closer to achieving your goal). It’s surprising and embarrassing how many times I got stuck at something thinking it’s “the best way”, just to discover later better and quicker solutions.

  4. If you’re trying to figure out how to do something (using trial & error + Stack Overflow + Google) and you’re not successful after 30 minutes, post your question on Twitter/Stack Overflow. Seriously, not only are you likely to get the correct answer within 5-30 minutes, but also you’ll learn several different ways to approach your problem. Also, you’ll save yourself a significant amount of time and frustration…

  5. I said that before, but… don’t be a perfectionist. Your learning is never finished and there will be always something you could add or do better if you only knew about X, Y, Z at the time. So let it go and get stuff out. People’s feedback can only help and point you in the right direction once your project has been published. Also, being part of R community can guarantee you that this feedback will never be cruel or personal.

  6. If things take longer than you’d like to, is there a way to break them up? Writing two shorter posts instead of one? Presenting a method/package rather than a finished analysis?

  7. The last but definitely not the least: no number of ticked-off boxes on the to-do list will ever replace relationships. Now even if I have a plan to finish a project in the evening, but my partner comes home late and we hadn’t had a chance to catch up yet, I drop the project and spend the evening chatting with him. For some people it’s a no-brainer but I had to learn that data projects cannot compromise relationships that have been and will be supporting us for life.


That’s probably the most important lesson of 2017 – the R community is the most supportive and open community I’ve ever seen and been part of. Something absolutely critical that they made me realise is that the best and most popular R community members are so (not only) because they have great skills and produce great work, consistently. They are so because they spend a considerable amount of time and energy on acknowledging other people’s great work.

There are some people that particularly contributed to this view this year and I want to help them here for that:


2017 was absolutely mind-blowing for me. I started this year not being at all confident in my coding or data science skills, I’m finishing it writing a semi-successful R blog, co-organizing R-ladies meetups in London, getting invited to give DS talks at meetups and conferences and definitely feeling that I’m part of R community. Most importantly, my learning rate has gone through the roof and it doesn’t look it will be stopping anytime soon 🙂 Thanks everyone for this year, it wouldn’t happen without you!

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