How To Download ((EXCLUSIVE)) Rmd File From Rstudio Cloud
Gregory Muench
Hi. I'm looking to illustrate using RStudio Cloud as a data cleaning interface for people in companies where IT won't let them install RStudio. Relatively easy to get data in, how do I get final data out in different formats? write.xlsx() from openxlsx and export() from rio both produce the error below, although write.csv() works OK. Any advice please?
I read the first part of James' question as "how do I download a data set from R Studio Cloud to my computer", which I haven't figured out how to do either. The Files pane has "upload"; how do you do the corresponding "download"?
I'm working on a project in R Studio Cloud (Google Data Analytics program case studies), and I haven't been able to figure out how to export my project as a .csv to my desktop or to Google Drive (either is fine, I can work with either). I read up on the Guru99 article on how to export CSVs from RStudio, but being that I'm using the Cloud version, the file path for exporting is unrecognized by RStudio.
You can't export a project but you can export data (in tabular format) as a CSV, just save the file in your project's folder, select the file in the "files" pane, make click on the "More" button and select "Export.."
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The file will get downloaded from RStudio Cloud into your local drive
I am sure this has been asked before but from what I have read and tried I haven't been able to find anything that works so far. I am using RStudio Cloud, and I successfully created a csv. I used:
write.csv(t.res,file = "t.res.csv" to get the csv to appear in the Files pane in the lower right corner. When I export it, it does not export as a csv, just some generic file that my computer is unable to open. When I export the entire project I just get the script and not a zip file. Does anyone have any tips here? Not sure what I am doing wrong, I just want to be able to access the new csv outside of RStudio Cloud.
I am planning to run a course that requires students to access (private) repositories on GitHub from RStudio Cloud. As I understand, from August 13 GitHub will no longer accept account passwords as a mean of authentication. This will interrupt my intended workflow mid-course. Is there an alternative, safe and working solution for authenticating GitHub operations from RStudio Cloud?
I think the recommended solution from GitHub is to use a Personal Access Token (PAT) instead of a password for authentication. These Personal Access Tokens can be managed in your GitHub account settings.
The other option would be to configure an SSH key for your GitHub account. You can save this SSH key in the home directory for your project. If you've not used SSH keys before, this might be a little tricky, so I would probably consider using the PAT. Also note: one downside here is that on RStudio Cloud home directories are not shared between projects, so you would need to configure the SSH key on each project where you want to push/pull from GitHub.
Thanks Andy. Yes, I could sort that out on my local installation but am unsure how to proceed in the cloud. Saving credentials in project directories in not an option since it will leave me with access to all the student's credentials.
For the 2019-2020 academic year, we used rstudio.cloud, a cloud instance of R that is designed for collaboration and teaching. At the time, rstudio.cloud was in pre-release status and was free, so the trade-off was the potential for some bugs, but with no cost attached.
From the students' perspective, they can login, see the workspace I have added them to and create their own duplicate of any project I have created in the workspace. Their projects are visible to them and me (as the instructor) but once a project has been duplicated by the student, their copy is independent from the original - material the instructor adds later is not copied into project duplicates.
All of this is very useful, but is worth noting that this additional layer of interaction means that students who are completely new to R and RStudio will have to learn to manage and navigate the rstudio.cloud interface (duplicating projects etc.) at the same time as learning R and R Studio. It can be a lot to take in and definitely has an additional cognitive cost at the outset.
If everyone were already using R, then transitioning to a cloud version for ease of deployment might be an easier sell. But then the university might want to install RStudio server edition, so everything stays in-house and the data are stored on their own servers (GDPR compliance). After all, if you are going to pay for a tool like RStudio, you want to be able to use it for more than just teaching.
a bit off topic today. I am just starting to use R studio cloud from a supercomputer to analyze my single cell and have recently experienced issue with installing packages. Not sure whether anyone have any experience installing packages in R studio cloud and could suggest solutions. I have been searching for solutions, but none of them worked for me. A HPC team have granted firewall access to -project.org/src/contrib. Here is the error!
Always add sessionInfo() when you are asking for help with problems related to R packages. If you're using the basic RStudio cloud plan, it limits you to 1GB of RAM, which is near useless. You'd be better off running RStudio Server on a computer and accessing it using a browser.
RStudio.cloud is convenient, but it can be slow and it is not designed to be able to handle larger datasets, more complicated analysis, or fancier graphics. Over the course of the semester, you should wean yourself off of RStudio.cloud and install all these things locally. This is also important if you want to customize fonts, since RStudio.cloud has extremely limited support for fonts other than Helvetica.
Cloud server instances with GPUs are available from services like Amazon EC2 and Google Compute Engine. You can use RStudio Server on these instances, making the development experience nearly identical to working locally.
The EC2 instance is by default configured to allow access to SSH and HTTP traffic from all IP addresses on the internet, whereas it would be more desirable to restrict this to IP addresses that you know you will access the server from (this can however be challenging if you plan on accessing the server from a variety of public networks).
To get the workshop materials from GitHub into RStudio Cloud, create a new project directly from a git repository. In Your Workspace, create a new project: Click on the down arrow next to New Project in the blue button, then select New Project from a Git Repository. Provide the address of the workshop repository (the GitHub URL for the workshop). This will copy all of the files from this repository into your new project.
For all options above, you may need to install additional packages. Information on required packages may be sent ahead of time, may be listed in the workshop repository, or you may be asked to install additional packages during the workshop. For more detailed instructions, see Installing and Loading Packages from Danielle Navarro, and troubleshooting R package installs on Quest and Quest Analytics.
While installing tidyverse, if you are prompted with a yes or no question about compiling or installing from source, choose NO. Tidyverse is actually a large set of packages, so it can take a long time to install. Installation problems are common. If you run into issues, you can request a consultation for help, or come a little early to the workshop to get help from the instructor or a TA. You will see lots of messages, some of them in red text, while the packages are installing. This does not necessarily mean there is anything wrong.
If you get an error or different message than what you see above, the package may not have installed correctly. You can request a consultation for help or, for in-person workshops, come a little early to the workshop to get help from the instructor or a TA.
Text and figures are licensed under Creative Commons Attribution CC BY-NC 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
If you have a Mac, install the latest release from the newest R-x.x.x.pkg link (or a legacy version if you have an older operating system). After you install R, you should also install XQuartz to be able to use some visualisation packages.
You can install the LaTeX typesetting system to produce PDF reports from RStudio. Without this additional installation, you will be able to produce reports in HTML but not PDF. To generate PDF reports, you will additionally need:
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