I am hoping to include a widget in my R shiny app where the user can select a folder destination for output files to be saved in. I've tried using the shinyDirChoose and shinyDirButton, and they work, but moving through the file directory is very slow. I'd like to have the speed of fileInput and the ability to select a folder, rather than a file, as the file input (even though it would really be used for output). Is there a MIME type for folders in Windows Explorer that I could specify in fileInput? Or is there a way to speed up shinyDirChoose?
Related
I am attempting to make a workflow as foolproof as possible using rmds and Git. Here, new rmd files and updates to old files are served via Git which is used by RStudio shell.
The issue is with different working directories between individual rmd files and the R console in RStudio. By default, the shell from which Git is used opens to the working directory of the R console. However, by default, rmd files work in the folder they are residing in, which is necessarily not the same as the R console working directory.
I am aware of the solution that sets the working directory of all cells within one rmd file (below), but this will not change the working directory of the R console, only chunks.
knitr::opts_knit$set(root.dir = "path/to/desired/wd")
Hence, I'd ask if anyone is aware of a way of forcing the R console working directory to match the rmd working directory. Ideally, this would be done within an rmd cell in a way that executing one cell would take care of matching the working directories automatically.
I'm providing a .zip with a .R file and a .xlsx file to some people
I need to make a code that can read this .xlsx file in any directory of any pc.
But as the directories vary from computer to computer, I couldn't find a solution.
IMPORTANT: I'm not using Rstudio for read this .R, so i just can use base functions
Using R - How do I search for a file/folder on all drives (hard drives as well as USB drives) This question don't solve my problem..
Take a look at the here package. When you load the library (library("here")) it sets "base" working directory and then you can use the package to construct relative file paths given that location. For example, if inside your .zip file you have an R script (e.g., My Data Analysis.R) that analyzes data that is kept within a folder called data you could read it in using, for example, read.csv(here("data", "my_csv_file.csv")) and it will construct the full appropriate file path no matter what computer it is on. Of course the file structure of the program needs to stay the same across programs.
Disclaimer: I am very new here.
I am trying to learn R via RStudio through a tutorial and very early have encountered an extremely frustrating issue: when I am trying to use the read.table function, the program consistently reads my files (written as "~/Desktop/R/FILENAME") as going through the path "C:/Users/Chris/Documents/Desktop/R/FILENAME". Note that the program is considering my Desktop folder to be through my documents folder, which is preventing me from reading any files. I have already set and re-set my working directory multiple times and even re-downloaded R and RStudio and I still encounter this error.
When I enter the entire file path instead of using the "~" shortcut, the program is successfully able to access the files, but I don't want to have to type out the full file path every single time I need to access a file.
Does anyone know how to fix this issue? Is there any further internal issue with how my computer is viewing the desktop in relation to my other files?
I've attached a pic.
Best,
Chris L.
The ~ will tell R to look in your default directory, which in Windows is your Documents folder, this is why you are getting this error. You can change the default directory in the RStudio settings or your R profile. It just depends on how you want to set up your project. For example:
Put all the files in the working directory (getwd() will tell you the working directory for the project). Then you can just call the files with the filename, and you will get tab completion (awesome!). You can change the working directory with setwd(), but remember to use the full path not just ~/XX. This might be the easiest for you if you want to minimise typing.
If you use a lot of scripts, or work on multiple computers or cross-platform, the above solution isn't quite as good. In this situation, you can keep all your files in a base directory, and then in your script use the file.path function to construct the paths:
base_dir <- 'C:/Desktop/R/'
read.table(file.path(base_dir, "FILENAME"))
I actually keep the base_dir assignemnt as a code snippet in RStudio, so I can easily insert it into scripts and know explicitly what is going on, as opposed to configuring it in RStudio or R profile. There is a conditional in the code snippet which detects the platform and assigns the directory correctly.
When R reports "cannot open the connection" it means either of two things:
The file does not exist at that location - you can verify whether the file is there by pasting the full path echoed back in the error message into windows file manager. Sometimes the error is as simple as an extra subdirectory. (This seems to be the problem with your current code - Windows Desktop is never nested in Documents).
If the file exists at the location, then R does not have permission to access the folder. This requires changing Windows folder permissions to grant R read and write permission to the folder.
In windows, if you launch RStudio from the folder you consider the "project workspace home", then all path references can use the dot as "relative to workspace home", e.g. "./data/inputfile.csv"
I am using the R shiny package to build a web interface for my executable program. The web interface provides user input and shows output.
On the server background, the R script formats user inputs and saves them to a local input file. Then R calls the system command to run the executable program.
My concern is that if multiple users run the web app at the same time, it is possible that the input file generated by the first user will be overwritten by the second user's input before it is read by the executable program.
One way to solve the conflict is to ask R to create a temporary folder and generate/run the input file under that folder for each user. But I'd like to know whether there is a better or automatic way to resolve this potential conflict with shiny. For example, if use shiny fileInputs, the uploaded files are automatically stored in a temporary folder.
Update
Thanks for the advice.#Symbolix and #Mike Wise
I read the persistent data storage article before but I don't think it is exactly what I wanted. Maybe my understanding is not correct. I end up with creating a temporary folder and run my executable from there.
I have a .Rmd which I use to report on data quality in a number of different r projects. It would then split the data to remove subsets with missing data, and interpolate missing results where appropriate. It would do this via a write.csv command to a file path in the form of "./Cleansed_data/"
To make an example
open rstudio
go to the rhs 'project' menu , and select and make a new
project wherever you'd like
go to the lhs 'new script' drop down and
select 'new .Rmd'
change the output to .pdf and hit ok
in the last r
chunk include write.csv(mtcars, file = "mtcars.csv")
hit the 'knit
pdf' button, save the report as "writeFile.Rmd" to your project working directory, and
let it run.
Previously I moved this .Rmd from place to place, however now I would like to built it into an internal package. I have included it (as the documentation indicates to) into inst/rmd within the package directory.
In order to do this build or open any package you have access to
add the file to inst/rmd (create it if this doesn't exist)
rebuild the package
I then rebuild the package and open a new project. I load my new package and attempt to run the document via the render command using the system.file command to locate the .rmd like so
rmarkdown::render(input = system.file("rmd/writeFile.Rmd", package="MyPackage"),
output_file = "writeFile.pdf", output_dir = "./Cars/)
This will render the report from the package build into the folder from output_dir, however, there are a number of pitfalls here. First, if I omit the output_dir argument, the report will render into the package library, usually located in the libraries r installation in the c drive. This is however fixable.
What I can't get around is that when the .Rmd hits the write.csv() then (I believe) the .Rmd is being rendered in the package environment at the time, the working directory of which is the package library folder, not the current project directory.
The Questions
How can I inform the template in the package what the current working directory is for the rstudio project? I'm vaguely aware there might be a rstudio api package? I have nearly no understanding of what it is though, or if this would provide a solution.
If this is either outright impossible or just potentially a very bad idea how can I modify the workflow to successfully retrieve a number of r object outputs into the environment or the working directory, on the call to the report, without having to modify the report for each different project? Further, why specifically is this approach such a bad plan?
In order to close this off:
I have selected to keep the .Rmd included in the package. The .Rmd need to move and be versioned with the package as that holds the functions they use to run.
In order to meet my requirements I style the documents to grab the working directory via the rstudio api in the form.
write.csv(mtcars, file = paste0(rstudioapi::getActiveProject(), "mtcars.csv"))
Having tested #CL's answer, this also runs and is not dependant on Rstudio as an IDE, however I know that these documents will
Always be accessed via the rstudio IDE
Always be accessed from within a specific project
I fear (though have not tested) that there would be the potential for other impacts from setting the working directory for the file to be artificially booted into a different WD. Potentially this could be things like child documents I might want to include later, or other code that might need to be relevant to the file path of the package installation, not the project. In this way I think (If I interpreted Yuhui correctly) the r doc is still the centre of it's own universe. It just writes it's data into another one :)