2/27/2024 0 Comments Upgrade rstudio![]() This is something I noticed while working on. Rather, they're on the newest un-ignored version. If a user has chosen to ignore updating to the newest version of RStudio and then manually checks for updates, we currently tell them that they're on the newest version although that's not true. Dialog says you're on the newest version.Manually check for update using Help > Check for Updates.Upon RStudio startup, ignore the newest RStudio version update.For full reprodicibility you would need a container-based solution such as Docker or Singularity.# if ("$ENV) This is much better than custom solutions, and at least takes care of reproducibility on the same machine. Restoring the environment from the lock file would only require to have that renv.lock in the project directory and then running renv::restore() (assuming that external dependencies such as e.g. For the end user it really comes down to just calling a few renv command. That means different projects can have different package versions on the same machine, without that you have to fiddle with manually setting and changing the package library path. Renv is also project-specific so you can have multiple projects on the same machine, all with their own local package library rather than having everything installed in the system library. Renv takes care of properly tracking all that. It also will not download and install packages from Github or other non CRAN/Bioc repositories (I guess). The problem with your solution is that it does not track down the dependencies so it can well be that results will not be reproducible even on the same machine if in a new installation dependencies get updated. Basically, the tool goes through all R scripts in that project and scans for require() and library() commands, then collects the packages and its dependencies. In a nutshell, you make a snapshot of your active RProject which creates a so called renv.lock file which is a JSON file with all package versions and its dependencies. It would be much easier to use a dedicated package for handling package versions. I hope this will help someone in the future and save their time. Let's activate the newly create conda environment. Now, it is always a good practice ( recommoned here) to create a new conda environment, which will help to debug the package-specific and compatibility issues without disrupting the base environment. NOTE: You can undo this change by setting strict priority to the default channel as described here.Ĭheck whether an updated R version is added in the conda search space or not. We are going to install R through conda-forge and not through the default channel, so we need to set its priority over the default channel.Ĭonda config -set channel_priority strict ![]() Here are the commands that I had used to resolve this issue:Īn updated R version (>=4.0) is available through the conda-forge channel, so first add the channel. With the help of google, finally, I found the solution. In fact, the same channel (checked with conda info command) that I had set long back to install R3.2. ![]() Because here, they have clearly mentioned the R version but when I was trying to install R4.0 it was not going through the conda-forge channel, instead, it was redirected to a different channel. While trying other solutions I realized that it is something to do with conda channels that I am using. ![]() Therefore, again, I started communicating with google about updating R and tried the solutions described above. So, you see? it's not only about Seurat, and I can not simply mess up with my R environment, just to update the Seurat version, right? Because I am working on different projects and to deal with them I have already configured other R packages. But unfortunately, in my case, it was not an optimal choice. But today I started working on another dataset and there I encountered this error [[=4.0) in one environment and R3.2 in another. I was using Seurat v3.2 and I did most of my single-cell analysis using this version.
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