I’ve managed to post my first ever tweet on Twitter yesterday on the R project I’ve done recently on rare disease drugs. I’m not used to posting anything on social media as I almost always prefer to stay low-profile, rather than the opposite, and this may not be a good thing especially if I’m trying to transition into a different job field. So I did it finally…
One of the reasons that I’ve finally decided to post on Twitter is that I’ve heard about the active and friendly R community on Twitter and thought if I could get any feedbacks on my short piece of work in R then that’ll be helpful for me to see if I’ve missed or done anything incorrectly since there’s not really a mentor person around that is able to do this at the moment (I’ve dreadfully taken the self-learning route, rather than attending data science bootcamps or get “another” degree, which is something I’m not really fancied at doing again, after already having MPhil and PhD already…). Luckily, I did get one helpful response and with a small number of likes so perhaps I’m not doing it entirely wrong (hopefully). My future plans will likely be trying to do a #TidyTuesday data visualisation and post whenever I can to learn and grow.
I’ve also recently edited my rare disease drug projects in R and Python to add summaries of findings from these two projects so it’s easier for anyone to read, especially if there’s not really much time to go through long threads of codes in GitHub. I’m also currently working on another project in the rare diseases series on phenotypes associated with rare diseases from Orphanet. My plan at the moment is to use Python to clean the data (I’ve tried to load it in RStudio via a URL with XML file, it’s >4000 rows and taking quite a long time to run on my laptop, so will stick to Python on Anaconda as it has loaded a lot faster than RStudio, then perhaps once it’s cleaned, I’ll re-import it back into RStudio for analysis and visualisations).