Normally, machine learning is all about Python, but recently [Minna no R](http://www.amazon.co.jp/%E3%81%BF%E3%82%93%E3%81%AA%E3%81 % AER-% E3% 83% 87% E3% 83% BC% E3% 82% BF% E5% 88% 86% E6% 9E% 90% E3% 81% A8% E7% B5% B1% E8% A8% 88% E8% A7% A3% E6% 9E% 90% E3% 81% AE% E6% 96% B0% E3% 81% 97% E3% 81% 84% E6% 95% 99% E7% A7% 91% The book E6% 9B% B8-Jared-P-Lander / dp / 4839955212) was a hot topic, and I thought I would study R once in a while. I bought the Kindle version of R for Everyone, which is the original work of Minna no R [^ 1] ], Then I started to build the environment for R.
I'm not very familiar with R, but in the case of Ubuntu, you can enter it with ʻapt-get. I used to do this, but when I install other libraries, I don't like it because the build usually runs when I try to put something that isn't in the repository with R'package.install. So I remembered that Anaconda, which is always indebted to me in Python, recently supported R [^ 2], and I actually tried it.
[^ 1]: This is cheaper
$ conda create -n R python=3
$ conda install -n R -c r r
only this. To run it, either enable this conda environment or hit the directly installed R.
You can also use Anaconda's repository in addition to installing the library with package.install.
$ conda install -n R -c r r-ggplot2
I installed the official deb file and started it, but I got angry when R was not found. The Linux version seems to use the result of which R, so I could deal with it by putting the R link of conda in / usr / local / bin / R for the time being.
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