Dear All,
I would be grateful for advice on 2 questions.
1. How to connect inferior modes / ESS processes to run on a specified docker
container?
Within Org mode source blocks, I can use the :dir
/docker:container-name/:/home/ header argument to run commands on docker
containers and it seems to work well.
However, when I edit the source block C-c ' - and ESS has to connect to an R
process - how can I have the process connected to the specified project docker
container? Extending this - how about native .R files?
2. How can I enable flymake in ESS?
Going through the documentation, I set the following and did not receive an
error, but there is no indication of flymake working, and I think I have the
syntax wrong. For example, I am able to create a variable name starting with a
number and there is no error indication. So in my modeline - I can see
flymake:!.
(setq ess-use-flymake '("lintr::default_linters()"))
(setq ess-use-flymake "lintr::default_linters()")
Here are the 2 errors in the flymake logfile :
Warning [flymake stock_analysis_completed.R]: Disabling backend
flymake-proc-legacy-flymake because (error Can’t find a suitable init function)
Error [ess-r-flymake *ess-r-flymake*]: ‘lintr‘ package not installed
Warning [flymake *Org Src README.org<DSB-101-R>[ R ]*]: Disabling backend
ess-r-flymake because (wrong-type-argument sequencep 1)
Warning [flymake *Org Src README.org<DSB-101-R>[ R ]*]: Disabling backend
flymake-proc-legacy-flymake because (error Can’t find a suitable init function)
-----------------------------------------------
Some additional background may help with context for the docker question:
I have setup a bunch of docker images with a long list of common packages used
in data science : https://github.com/shrysr/sr-ds-docker.
In a nutshell : there is an OS image, and then an image with R packages built
on the OS image, and then separate rstudio server and shiny server images built
on the R packages layer. This is actually a convenient starting point for
anybody venturing in this direction. I would be happy for feedback, and it is a
work in progress as always.
The shiny server works as expected. The idea is to standardise my working
environment and have my data science projects to be reproducible. My current
plan is to use a dedicated container for each individual project, and then
extent these efforts into CI/CD and other things.
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