I just downloaded anaconda and downloaded their basic R package.
I also got some packages from anaconda, using the anaconda terminal commands that they provide on their website.
My question is -- when I am starting a new R session in r studio, do I still need to install.packages()? Can I just load the package?
When I press libary(rJava) for example -- the R command line doesn't say anything back on whether it was successful, that's why I'm not sure. Thanks.
if you have installed the R packages via the R command line then you can just directly load them. I would recommend that you use the command line rather than R studio.
Related
First time question asker.
I created an Excel/R tool that uses:
Excel VBA to create a CSV file with data for R,
Launch R using a Windows Shell,
Detects when R is finished running and then
Imports the results in a CSV file created by the R script.
Unfortunately, the R code does not work as programed with some package versions created after 3/1/2020, which creates problems for new users because they cannot just install the current package versions or users who want to use conflicting versions for other projects.
I have a solution for users who do not require newer versions of R for their other work; writing a script that installs all the packages and their dependents using the “versions” package. However, I think this approach will constrain users who want to use newer versions of R. **Is this a correct assumption? **
I thought the {checkpoint} package might offer a good solution. I can get it to run in well in RStudio by creating a RStudio project (where I ran the {checkpoint} package to install the 3/1/2020 versions of my packages). However, I have not found a way to run the R script from the Windows Shell. The R script does not seem to be able to access the packages installed in the RStudio project using {checkpoint}. Does anyone have ideas of how I can have Excel VBA launch the start of the R script in a way that it can assess the packages installed in the RStudio project by {checkpoint}? Perhaps there is a Windows Shell call for RStudio similar to the one I use now for R?
Here is the Windows Shell code I currently use for R in case it helps. It works with the versions approach but not the {checkpoint} approach.
rExeCall = "C:\Program Files\R\R-3.6.2\bin\Rscript.exe"
rExeOptns = " --no-environ --no-init-file --no-restore --verbose "
rscrpt=”Tool.R”
Shell (rExeCall & rExeOptns & rscrpt)
Thank you in advance for your help!
I had hoped that my current R shell code would work when I used {checkpoint} to install the correct versions of the packages in an RStudio project.
I tried specifying the .libPaths to the file location for the RStudio projection (per Running R script from PHP in VSCode not recognizing R packages) without success.
I have installed R at following location C:\E_Drive\ProgramFiles\R-3.4.3, so I think R environment installed at C:\E_Drive\ProgramFiles\R-3.4.3 will be used when I run the R console, right? Please correct me if I am wrong.
Now, I install a package using the R console.
Now, I download and install RStudio and from the R console of RStudio if I check whether that package is available or not then I see that it is available. I am wondering, how RStudio's R console reported that package is available, I didn't expect that since C:\E_Drive\ProgramFiles\R-3.4.3 is not on my PATH and in no way is linked RStudio with C:\E_Drive\ProgramFiles\R-3.4.3, so I thought RStudio would be referring its own R environment.
Can you please help me understand how my RStudio is referencing the C:\E_Drive\ProgramFiles\R-3.4.3 R environment.
R normally installs packages in the same directory tree as its own binary, but it can also install them elsewhere. On Windows, this generally happens because regular users often don't have write permission in the Program Files directory. The standard Windows installer also records R's location in the registry, so that it doesn't need to be on the PATH to be found by RStudio.
You can find out where Windows or RStudio found R by running R.home() within R. You can find out where R is finding packages in a particular session by running .libPaths().
I have been using ipython notebook to run some R scripts. Now the problem is I have two R versions on my Ubuntu 14.04.
One is R.3.2.2 at /home/MYNAME/anaconda2/bin/R, another one is the R which I need for R studio,
now the problem is I want to only use R.3.3.1 for my system as I need some advanced task to be done.
I use conda uninstall r, after running this, according to the print out, a lot of R related packages is removed, if i run conda uninstall r again, it said
Fetching package metadata .......
Using Anaconda Cloud api site https://api.anaconda.org
Solving package specifications: ..........
Error: no packages found to remove from environment: /home/MyName/anaconda2
but when I run Which R again, still it is the anaconda R, if I run R in the terminal, it is still R3.2.2, anyone knows how could I remove this anaconda R version?
You probably needed to run hash -r in your session (or rehash if using zsh) to update your executables on PATH for the which command. A new terminal session would also fix the problem.
you can try conda uninstall r-base, this will remove R and all of the R-library.
The questions also implies that one cannot use anaconda R with rstudio.
On Linux you can
export RSTUDIO_WHICH_R=/home/USER/anaconda3/bin/R
and add to .profile (d/o your distro) to use rstudio with anaconda R and packages
I feel pretty comfortable working with R, and I want to get into Python through Anaconda.
Upon trying the Jupyter Notebook and finding it has compatibility with R, I really want to use it.
I'm having problem installing the R packages that don't come in R-essentials; and mainly because I noticed it uses a different R installation than the one I had before. Not only is this a different R installation, but it also uses a previous version of R. In my local installation I have updated to 3.2.3 but in the Anaconda environment for Jupyter I got 3.1.
I also found a post to change the .libPaths variable to include the packages that I had already installed. Still, I see this as a potential problem because of the different R versions.
I wanted to know if I can update the R version that's used in Anaconda, or if I can point to the one that's installed locally.
Thank you.
You can install IRkernel in the normal R installation and then register the kernel: simply follow the instructions at http://irkernel.github.io/installation/
First let me preface this with the disclaimer that I'm new to R, but a longtime Python power user. Given that I love the conda ecosystem and the Jupyter notebook, I'm trying to set them up as my R development environment as well.
So using the instructions at: https://www.continuum.io/blog/developer/jupyter-and-conda-r I've set up a Jupyter Notbook that using an RKernel that should be hitting the installation of R installed in my Anaconda folder (I would think anyway).
Getting it setup was easy peasy and everything is working great for standard R stuff but my analysis requires some R libraries that are not available in r-essentials channel. No problem, I think I know how to install an R library. I go to "C:\Anaconda\R\bin\x64\Rgui.exe" and install rgdal, dismo, and some other packages. To check my work I looked in C:\Anaconda\R\library and there they are.
But when I run a jupyter notebook from the Anaconda command prompt. And start a new R notebook I get a "Error in library(dismo): there is no package called 'dismo'" Wait a sec, I run a ".libPaths()" from the notebook and it looks like its pointing
You can add .libPaths('path_where_your_packages_are') in a code cell at the beginning of your notebook to tell jupyter where your packages are. For me that was .libPaths('~/R/win-library/3.2') (work-around from discnerd who filed this issue on github).
To find out the path to your packages, just install a random package in R and wait for the location to be printed to the console.
More details (likely specific to my system/installations): When running .libPaths() in R, I got 2 locations: one for which admin rights were required for writing, and one for which admin rights were not required for writing. While packages installed through R land in the location where admin rights are not required, jupyter looks at the location where admin rights are required.
You can find out the path to your library with installed.packages()