One Year with R
R, and by R I mean R+tidyverse, is the world&#x27;s best graphing calculator attached to an OK scheme.<p>To which I mean R is a highly optimized, well-oiled machine if you&#x27;re using it for its highly-optimized, well-oiled purposes. I tend to have notebooks full of tiny fragments like this<p><pre><code>    dat_min %&gt;%
      group_by(ymd = make_date(year(date), month(date), day(date))) %&gt;%
      summarize(vol_btc=sum(vol_btc), vol_usdt=sum(vol_usdt), tradecount=sum(tradecount)) %&gt;%
      ungroup() %&gt;%
      pivot_longer(cols=c(-ymd)) %&gt;%
      ggplot(aes(ymd, value)) + 
      geom_line() +
      facet_grid(name ~ ., scales=&quot;free_y&quot;)
</code></pre>
It&#x27;s madness if you&#x27;re not familiar with the tidyverse, but 3 dozen fragments like this is enough to eviscerate a fresh data set. Almost any question you can dream of is a 3-20 line set of transforms away from a beautiful plot or analysis answering your question. <i>Very</i> notably, this includes some of the finest modeling tools available today.<p>Terseness here is a huge advantage as well because in many data analysis workflows you are rerunning that same 10 line snippet over and over, making small changes, adjusting to eventually visualize the thing you&#x27;re looking for perfectly. Having all of that in the same small block is ideal.<p>Finally, for the non-trivial number of folks in this specific scenario, the integration between Stan and R&#x2F;RStudio is top-notch and makes using both tools very pleasant.<p>You can replicate all of this in Python, but optimal Python&#x2F;Jupyter is still a far cry away from R&#x2F;RStudio for these specific sorts of tasks.