If I put a very small csv file in my GitHub directory so that it gets copied to /ocpu/github/username/projectname/www/ , will I be able to access the contents of the csv for use in a R function? I tried to ajax the file, but I get a 404 error even though I can see the csv file sitting in the www directory of my local server. I need to have the csv on the server as a static file rather than being uploaded by a function. Thanks
You should be able to access them like any other file. Can you post an example that shows what you are doing and what error you are getting?
That said, if you just want to use this data in your R functions, it is better to include it in the R package as an actual data file. Also see section 1.1.6 of Writing R Extensions. An example is the mapapp package, which includes a dataset called countryExData. Also see the live app.
Related
Im tring to create a shiny app that read and online onedrive xlsx file and show some things, but for the moment Im unable to read the onedrive xlsx file, I already explore the Microsoft365R and I can conect to my onedrive and I even can open the fil but... what it does is from r open a tab in chrome with the excel file.
I need the file in the local enviroment of r.. this its beacause the shiny app must be deploy in a web server, that every time the app runs it reads the update the file.
library(Microsfot365R)
odb <- get_business_onedrive()
odb$open_file("lcursos.xlsx")
Also this its a business account, so I also have to put the username and key to acces each file, that its beacause use the simple url doesnt work, it says Error 403 FORBIDEEN.
Any ideas?
Thank you so much!
Use the download_file() method to download the file to your local machine:
odb$download_file("lcursos.xlsx")
You can set the location of the download with the dest argument. Once it's downloaded, open it with the xls reader package of your choice. I suggest either openxlsx or readxl.
Note that if your file is password protected, your options are limited. See this question for possible solutions.
Similar questions, but not the question I have, were around loading a file that someone saved as somefilename.RData. I am trying to do something different.
What I am trying to do is load the actual .RData file that gets saved from an R session. The context is that I am using 2 different computers and am trying to download the .RData file from one computer and then load this same .RData file on a different computer in RStudio.
When I download the .RData file it shows up without the “.” (e.g., it shows up as RData). When I try to rename it “.RData”, Windows will not allow me to do so.
Is there a way to do what I am trying to do?
Thanks!
After playing around with this, I was able to load the file (even though it was called “RData“ and not called “.RData”, by using RStudio by going to Session > Load Workspace... and then navigating to that file. I had used File > Open File... which did not work
I'm currently doing a project that uses R to process some large csv files that are saved in my local directory linked to my repo.
So far, I managed to create the R project and commit and push R scripts into the repo with no problem.
However, the scripts read in the data from the csv files saved in my local directory, so the code goes in a form
df <- read.csv("mylocaldirectorylink")
However, this is not helpful if my partner and I working on the same project have to change that url to our own local directory every time we pull it off the repo. So I was thinking that maybe we can upload the csv files onto GitHub Repo and let the R script refer directly to the csv files online.
So my questions are:
Why can't I upload csv files onto GitHub? They keep saying that my file is too large.
If I can upload the csv files, how to I read the data from these csv files?
Firstly, it's generally a bad idea to store data on Github, especially if it's large. If you want to save it somewhere on the Internet, you can use, say, Dataverse, and then can access your data with URL (through the API), or Google Drive, as Jake Kaupp suggested.
Now back to your question. If your data doesn't change, I would just use not the absolute paths to CSV but relative ones. In other words, instead of
df<-read.csv("C:/folder/subfolder/data.csv")
I would use
df <- read.csv("../data.csv")
If you are working with R project, then the initial working directory is inside the folder of the project. You can check it with getwd(). This working directory changes as you move the R project. Just agree with your colleague that your data file should be in the same folder where the folder with R project is situated.
This is for a Python script.
You can track csv files by editing your .gitignore file.
**OR**
You can add csv files in your github repo, which can be used by others.
I did so by following steps:
Checkout the branch on github.com
Go to the folder where you want to keep csv files.
Here, you will see an option "Add file" in top right area as shown below:
Here you can upload csv files and commit the changes in same branch or by creating a new branch.
I have an iPad Pro as my primary computer, and am running R Studio from a cloud server for a class. I am trying to figure out how to import data sets, since my local working directory is on the server. I have been trying to download the package repmis, since I have been reading that that package allows for data set import from Dropbox. However, when I try to download the package, I get "Error:configuration failed for openssl" and a similar one for curl. I tried to install openssl but instead it says I need to install "deb" for ubuntu operating systems, but I can't find that in R Studio in the package database. (And I can't install curl without openssl either) Any suggestions?
If it's a relatively straightforward data set like a CSV, XML, JSON or even an .RData file you can use a Dropbox sharing URL to read it. Here's an example (it's a live URL) for reading in a CSV directly from a shared Dropbox link:
read.csv("https://www.dropbox.com/s/7xg5u0z1gtjcuol/mtcars.csv?dl=1")
The dl=1 won't be the default from a "share this link" copy (it'll probably be dl=0.
urla<- 'https://www.dropbox.com/s/fakecode/xyz.txt?raw=1'
bpdata<-read.table(urla, header=TRUE)
scode<-'https://www.dropbox.com/s/lp6fleehcsb3wi9/protoBP.R?raw=1'
source(scode)
readAndPlotZoomForYear(urla, 2019)
Basically I wanted to source code from a file in Dropbox and to use the read.table() function on a tab delineated data file. I found this could be done by replacing the string after the question mark with raw=1 in the Dropbox file links.
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 :)