To be honest, I took days preparing for my curator week so I had collected information, made a plan and trying to learn to use twitter 🙈 Still, it did not work very smoothly. Only on my second day I learned (or rather, the very nice ex-curator @PipingHotData taught me) how to upload gifs and how to make threads. Anyway, I survived and I got some nice comments, so hopefully people enjoyed it.

First, what an honor to curate the twitter account of a community that I love and follow closely. Especial honor for it being on International Women’s Day!! This day I wanted to focus on people that have inspired me and especially to some #Latinx women. I find the R-Ladies community super enriching but I am normally not into posting myself, so that was a challenge. However, if I am serious about my professional and personal development, I feel that I have to go out of my comfort zone.

On the next days I shared some info that I do not want to forget (yes, I know, I shared it but that does not mean I have all those links on top of my head). Here is a small summary:

Putting your code in production:

I mainly talked about what it means and the struggles. From here, what I want to highligh are the package management techniques:

  • renv for day-to-day package management.

  • conda for multi-language (mostly Python with a bit of R) package management.

  • miniCRAN for package management in a whole company. Ensuring security and access to common libraries.

  • Containers: including Docker and Singularity.
    _ Very nice video to understand containers + R (by Elizabeth Stark, useR2018): https://t.co/2wI9JqEkDS?amp=1

Rmarkdown extensions:

This was a good one I think. I enlisted many Rmarkdown extensions and asked people to add more if they could. Not many were added but still, I loved the list. The full list is going into a separate collection (for now a Google Spreadsheet) where I can keep adding stuff.

Learning CSS:

I gave some suggestions but the ones from @yabellini were way better!

Under-appreciated R-packages:

This one was the one with more reactions. I got to hear about so many nice packages! Now looking forward to try them all! For now I just link the main tweet here but I am making a document collecting all the names.

Animations and Interactive plots:

  • I shared {plotly} with a special mention to the ggplotly() function that I use quite often to convert my ggplot objects to a plotly one.

  • {gganimate} is what I use and love to make animations.

  • {tweenr} can also be used for this purpose and seems to be smoother in some cases: https://blog.revolutionanalytics.com/2017/05/tweenr.html

  • {leaflet} for interactive maps.

  • {DT} for interactive tables. Although lately it seems that {gt} is getting some very nice cool features.

  • @loreabad6 suggested {tmap} for interactive maps and gifs.

  • {highcharter} seems to be better than {plotly} for time-series visualizations according to @Jack36161714.

Material to learn R Shiny:

I shared some material to learn/get deeper into R-Shiny and also got some nice suggestions.

Podcasts that might be interesting for the #rstats community:

I shared most of them but did get a couple of suggestions.

  • CoRecursive: coding stories
  • BioinfoChat about bioinformatics topics
  • The Bioinformatics and Beyond Podcast
  • The PolicyVizPodcast
  • Cautionary Tales by Tim Harford
  • Talking to strangers by Malcolm Gladwell
  • Casual Inference by two R-Ladies @LucyStats and @EpiEllie
  • Build a Career in Data Science

Tips to take full advantage of RStudio:

I retweeted a tip from @shannon000 about how to use rainbow parentheses in RStudio. This tool allows you to easily see which pair of parentheses belong together. As an answer, @thomas_mock shared many nicer tips that I collect here:

Bioinformatics packages:

  • I mentioned {DESeq2} for RNA-seq differential expression analysis.

  • {dada2} and {phyloseq} for analyzing 16S-sequencing data for microbiome research.

  • {ggtree} for phylogenetic trees.

  • {BentoBox} for making specialized genomics plots and multi-panel plots that I am dying to test.

  • @its_JPhilipp recommended also {Biostrings} for sequences.

  • @Nv_pyromelana recommended {vegan} for diversity data, {RMark} for mark recapture studies and {nlme} for random effects models.

  • @LukeAnderTroc recommended {Riverplot} for making custom sankey diagrams.

These are not the only topics I covered. However, these were my favorites, where more reactions or just valuable info was shared. It was really a great and even dare to say overwhelming week but I do think I enjoyed it a lot. I definitely want to curate again for this great community!