In the former case, a minimal upgrade set is sought: the fewest possible version bumps to achieve a solution. If present, the user library is where packages will be installed to. When you start an R session in a packrat project directory, R will only look for packages in your private library; and anytime you install or remove a package, those changes will be made to your private library. There are a lot of scenarios where that might happen. Detecting which version of R a package was built with The following code will show which packages are installed, their version numbers, and which version of R they were built with. If you are suggesting that we allow conda install or conda update to cause an unsatisfiable environment without the use of a --force command No, certainly not. When you want to call a package, use library PackageNameHere.
The good news is that my larger solver fix will take care of this. I could probably use some combination of the information from old. The current update does a better job of ensuring satisfiability of the current environment but I don't think it pulls in every package---just the ones affected by the current operation. If this happens, users will experience problems like the ones described previously. In code you, can use installed. So you can either do that very infrequently, suffer with old versions in the middle, and experience great pain at update.
Also, keep in mind that you don't need to create a new deployment package every month as an update group is not related to a deployment package. Unfortunately, sometimes that is exactly what happens. To find the libraries for your user, you can run. I spent a bunch of time trying to debug it and reading the documentation, but in the end the solution was that I needed to update the purrr package on that machine. The thing is the users do not use most of the packages and now and then they ask me to update a package say fields they'd use. Depending on your installation, R will make a new directory for packages anyway, so you either have to relink your old set or reinstall them. If you only want to update a single package, the best way to do it is using install.
This is of course a very reasonable thing to do, and given the limitations of the current solver architecture that function is necessary. It is true that if a user performs a conda install B, it will fail to get B. I used Gavin's tip of skipping base packages which are not actually installable , and coded up a solution which also skips up-to-date packages. Here I am trying to update all, specifying defaults as the channel. We call this directory your private package library or just private library.
Upgrading installed Bioconductor packages Some versions of R support more than one version of Bioconductor. Same for python packages see next point. In general, there are no issues with subminor version upgrades, like going from 3. There is thus a higher premium on knowing that packages are from the same release, and that all packages are current within the release. On the other hand, when I do my own personal package development, or when I've taught R classes, I always update to the most recent version of R and update all the packages.
We don't check the modqueue very often. One day I was using a computer that I update much less frequently, and I got an error. Users of older R and Bioconductor must update their installation to take advantage of new features and to access packages that have been added to Bioconductor since the last release. The create worked fine and I believe the environment has satisfied dependencies to start. Package lazyeval is out of date 0.
See the example below the c function creates an array of strings. Or admit that maintaining your system is a normal ongoing. This is the start up code for R. And this is what happens with Continuous Integration - by integrating every day, the pain of integration almost vanishes. Even when things are deprecated, they tend to stay that way for a good while before they ever get removed, so updating is more likely to generate warnings than errors. If there is no user library, then packages will be installed to the site library, assuming the user has the correct permissions to do so.
Each time you update anything you run the risk of the code breaking or worse the results of the analysis changing in some subtle way. And of course the claim might even be true. About Packages in R Packages are collections of R functions, data, and compiled code in a well-defined format. When deleting update groups and deployment package, I would always first delete the update group. This post describes this idea in the context of software development: Given a general mandate for at least some level of backwards compatibility among package maintainers, I've seen very few bugs introduced by updating. Ok scrap that, what happens if I try to specify all those packages manually? First, we don't want to bump into bugs that have already been fixed. If so please file an issue; it shouldn't.