<story><title>TensorFlow for R</title><url>https://github.com/rstudio/tensorflow</url></story><parent_chain><item><author>cs702</author><text>Great news for R users who largely have been left behind by the deep learning community.&lt;p&gt;The main downside to this is that we will now start seeing all sorts of experimental models from R users who know a lot more about statistics than about software engineering and therefore often write code that is not very elegant nor easy to understand.[1]&lt;p&gt;[1] Don&amp;#x27;t take it from me.  Quoting Hadley Wickham: &amp;quot;Much of the R code you’ll see in the wild is written in haste to solve a pressing problem. As a result, code is not very elegant, fast, or easy to understand.  Most users do not revise their code to address these shortcomings. Compared to other programming languages, the R community tends to be more focussed on results instead of processes. Knowledge of software engineering best practices is patchy.&amp;quot; &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;adv-r.had.co.nz&amp;#x2F;Introduction.html&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;adv-r.had.co.nz&amp;#x2F;Introduction.html&lt;/a&gt;</text></item></parent_chain><comment><author>Houshalter</author><text>I recently started writing some R code and found it much more elegant than previous code. R has a bunch of functions for handling data and statistics related stuff, that really reduces complexity. But also it has a lot nice iteration functions that make it possible to do a lot of stuff with a single line. I only needed to use a for loop once. I never needed to do anything complex with data structures or nested loops or branching code, everything seemed to reduce to a few functions. It&amp;#x27;s a really elegant language, at least for its specialized domain.</text></comment>
<story><title>TensorFlow for R</title><url>https://github.com/rstudio/tensorflow</url></story><parent_chain><item><author>cs702</author><text>Great news for R users who largely have been left behind by the deep learning community.&lt;p&gt;The main downside to this is that we will now start seeing all sorts of experimental models from R users who know a lot more about statistics than about software engineering and therefore often write code that is not very elegant nor easy to understand.[1]&lt;p&gt;[1] Don&amp;#x27;t take it from me.  Quoting Hadley Wickham: &amp;quot;Much of the R code you’ll see in the wild is written in haste to solve a pressing problem. As a result, code is not very elegant, fast, or easy to understand.  Most users do not revise their code to address these shortcomings. Compared to other programming languages, the R community tends to be more focussed on results instead of processes. Knowledge of software engineering best practices is patchy.&amp;quot; &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;adv-r.had.co.nz&amp;#x2F;Introduction.html&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;adv-r.had.co.nz&amp;#x2F;Introduction.html&lt;/a&gt;</text></item></parent_chain><comment><author>Noseshine</author><text>&lt;p&gt;&lt;pre&gt;&lt;code&gt;    &amp;gt;  code that is not very elegant nor easy to understand
&lt;/code&gt;&lt;/pre&gt;
That was my impression when I started learning R while learning statistics - again, but this time with an actual interest in the subject and not just for the grade :)&lt;p&gt;On the other hand, the whole R-learning experience, which was aided by having to do practical &amp;quot;statistical&amp;quot; things in the language and working with actual data, gave me a new perspective on programming, a much more data-oriented perspective if I may call it that.&lt;p&gt;Before (learning R and statistics) I was interested in the code and what it could do, afterwards (but only for those kinds of applications) I was interested &lt;i&gt;in the data&lt;/i&gt; and what I could do with it, the &amp;quot;how&amp;quot; was secondary. From that perspective I understood a lot of the (IMHO) &amp;quot;hacky&amp;quot; nature of R and some of its libraries.</text></comment>