&gt; prototyping data-driven applications, Python would probably be better<p>I would disagree. Python&#x27;s libraries are really reimplementing R in Python (Mainly Pandas). I find R to be very flexible and especially in the last 5 years with Hadley Wickham&#x27;s libraries things are concise and very powerful.<p>Please look at dplyr and see how this new way fo doing R works. Especially with piping with %&gt;%. <a href="https:&#x2F;&#x2F;cran.rstudio.com&#x2F;web&#x2F;packages&#x2F;dplyr&#x2F;vignettes&#x2F;introduction.html" rel="nofollow">https:&#x2F;&#x2F;cran.rstudio.com&#x2F;web&#x2F;packages&#x2F;dplyr&#x2F;vignettes&#x2F;introd...</a><p>Code in R can look like this beautiful code (If you don&#x27;t code in R and I would expect anyone can see what is happening) This is why I disagree that prototyping in Python would be better.:<p>flights %&gt;%
  group_by(year, month, day) %&gt;%<p><pre><code>  select(arr_delay, dep_delay) 

  summarise(

    arr = mean(arr_delay, na.rm = TRUE),

    dep = mean(dep_delay, na.rm = TRUE)) %&gt;%

  filter(arr &gt; 30 | dep &gt; 30)

</code></pre>
Python has .pipe but I find it strange it goes to the new line before the items.<p>Python Code: 
&gt;&gt;&gt; (df.pipe(h)<p>...    .pipe(g, arg1=a)<p>...    .pipe((f, &#x27;arg2&#x27;), arg1=a, arg3=c)<p>...  )