<story><title>RStudio is now Posit</title><url>https://posit.co/</url></story><parent_chain><item><author>bscphil</author><text>&amp;gt; acknowledges the shitshow that is the python library&amp;#x2F;package&amp;#x2F;environment management&lt;p&gt;I&amp;#x27;m puzzled by this and wonder if you can provide some examples. The scientists I know tend to have incredibly disorganized R code, with a bunch of hard-coded paths and a single global environment in their home directory that all their R packages get installed to. Even stuff that seems critically important like reproducible science can be much harder than you&amp;#x27;d expect in a lot of fields because questions like &amp;quot;what version of the libraries did you use&amp;quot; has to be answered (if it can be answered at all) by looking at the references in the paper.&lt;p&gt;Whereas in Python, I don&amp;#x27;t know how things could be any simpler. Creating an individualized environment for your project is one command. Installing packages that only live inside that environment is one `pip install` away. Most scientific work is not &amp;quot;distributed&amp;quot; in the sense of having users, but if you do ship a product to users, Python gives you the option of either relying on distribution provided packages (my preferred approach most of the time) or shipping a single binary created with something like PyInstaller.</text></item><item><author>toddm</author><text>From Hadley&amp;#x27;s video blurb, the ringer is when he states &amp;quot;things just work&amp;quot; in the R environment vis-à-vis python (where he tactfully yet implicitly acknowledges the shitshow that is the python library&amp;#x2F;package&amp;#x2F;environment management).&lt;p&gt;Kudos to the R community and supporters for providing a great and useful platform!</text></item></parent_chain><comment><author>Lyngbakr</author><text>I&amp;#x27;ve also seen my fair share of garbage R code and I think Gordon Shotwell&amp;#x27;s comment that &amp;quot;There really are no production languages – only production engineers&amp;quot; speaks to this.[0] A big problem in the scientific community is that scientists aren&amp;#x27;t trained to write code like production engineers. I don&amp;#x27;t see it necessarily as being an issue that is endemic to R, though.&lt;p&gt;Packrat[1] — an RStudio package — can be used to easily avoid the library versioning issues you describe. The problem isn&amp;#x27;t that the tooling isn&amp;#x27;t there or that it isn&amp;#x27;t easy to use. It&amp;#x27;s that some folks simply don&amp;#x27;t use it and are perhaps oblivious as to &amp;#x2F;why&amp;#x2F; they should even use it, anyway.&lt;p&gt;[0] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;shotwell.ca&amp;#x2F;posts&amp;#x2F;2019-12-30-why-i-use-r&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;shotwell.ca&amp;#x2F;posts&amp;#x2F;2019-12-30-why-i-use-r&amp;#x2F;&lt;/a&gt;
[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;rstudio.github.io&amp;#x2F;packrat&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;rstudio.github.io&amp;#x2F;packrat&amp;#x2F;&lt;/a&gt;</text></comment>
<story><title>RStudio is now Posit</title><url>https://posit.co/</url></story><parent_chain><item><author>bscphil</author><text>&amp;gt; acknowledges the shitshow that is the python library&amp;#x2F;package&amp;#x2F;environment management&lt;p&gt;I&amp;#x27;m puzzled by this and wonder if you can provide some examples. The scientists I know tend to have incredibly disorganized R code, with a bunch of hard-coded paths and a single global environment in their home directory that all their R packages get installed to. Even stuff that seems critically important like reproducible science can be much harder than you&amp;#x27;d expect in a lot of fields because questions like &amp;quot;what version of the libraries did you use&amp;quot; has to be answered (if it can be answered at all) by looking at the references in the paper.&lt;p&gt;Whereas in Python, I don&amp;#x27;t know how things could be any simpler. Creating an individualized environment for your project is one command. Installing packages that only live inside that environment is one `pip install` away. Most scientific work is not &amp;quot;distributed&amp;quot; in the sense of having users, but if you do ship a product to users, Python gives you the option of either relying on distribution provided packages (my preferred approach most of the time) or shipping a single binary created with something like PyInstaller.</text></item><item><author>toddm</author><text>From Hadley&amp;#x27;s video blurb, the ringer is when he states &amp;quot;things just work&amp;quot; in the R environment vis-à-vis python (where he tactfully yet implicitly acknowledges the shitshow that is the python library&amp;#x2F;package&amp;#x2F;environment management).&lt;p&gt;Kudos to the R community and supporters for providing a great and useful platform!</text></item></parent_chain><comment><author>goosedragons</author><text>I think that has more to do with the fact that most scientists are not trained programmers! Plus a lot of data analysis work doesn&amp;#x27;t lend itself to the same style of programming IMO.&lt;p&gt;While there could be more effort in getting things like library versions out there a lot of journals don&amp;#x27;t care so there&amp;#x27;s no pressure on scientists to provide it.</text></comment>