I decided to try the jupyter notebooks for two reasons.
First is that I am a bit tired of programming in the RStudio RMarkdown notebooks where, I noticed, computational time is longer than direct input in the console – when it is not just simply not working. Also I want to go to something simpler and lighter to give a maximum of RAM for my actual scripts. For this latter, I actually switched to atom and packages like r-exec to directly send the commands to a basic R GUI. But the notebook format has its advantages when one want to quickly share a script and the figures along.
Second is that I am a missing python. The way of thinking is very different between python and R. Jupyter notebook with the magic commands like
%Rpush makes it very easy to combine both strength of the two languages.
So here is how I installed jupyter:
conda create -n jupyter python=3.7 source activate jupyter conda install jupyter conda install R=3.5.1 conda install -c conda-forge ipython-sql conda install cython conda install rpy2 conda install numpy conda install pandas conda install tzlocal conda install seaborn
Then in R terminal:
install.packages(c('repr', 'IRdisplay', 'evaluate', 'crayon', 'pbdZMQ', 'devtools', 'uuid', 'digest', 'httr', 'RJSONIO', 'Rcpp', 'R6', 'cli', 'fansi', 'rlang')) devtools::install_github('IRkernel/IRkernel', dependencies = T) IRkernel::installspec()