am trying to write an R script that will access an Excel file that is stored on my company's Sharepoint page so that I can make a few calculations and plot the results. I've tried various ways to do this (download.file, RCurl getURL(), gdata), but I can't seem to figure out how to do this. The url is HTTPS and there should be a username and password required. I've gotten the closest with this code:
require(RCurl)
URL<-"https://companyname.sharepoint.com/sites/folder/_layouts/15/WopiFrame.aspx?sourcedoc={2DCC2ED7-1C13-4910-AFAD-4A9ACFF1C797}&file=myfile.xlsx&action=default'
f<-getURL(URL,verbose=T,ssl.verifyhost=F,ssl.verifypeer=F,userpwd="mylogin:mypw")
This seems to connect (although the username and password don't seem to matter) and returns
> f
[1] "<html><head><title>Object moved</title></head><body>\r\n<h2>Object moved to here.</h2>\r\n</body></html>\r\n"`
However, I'm not sure what to do at this point, or even if I'm on the right track. Any help will be greatly appreciated.
I use
library(readxl)
read_excel('//companySharepointSite/project/.../ExcelFilename.xlsx', 'Sheet1', skip=1)
Note, no https:, and sometimes I have to open the file first (i.e., cut and paste //companySharepointSite/project/.../ExcelFilename.xlsx into my browser's address bar)
I found that other answers did not work for me, perhaps because I am on a Mac, which obviously does not play as well with Microsoft products such as Sharepoint.
Ended up having to split it into two pieces: first download the Excel file to disk and then separately read that Excel file.
library(httr)
library(readxl)
# the URL of your sharepoint file
file_url <- "https://yoursharepointsite/Documents/yourfile.xlsx"
# save the excel file to disk
GET(file_url,
authenticate(active_directory_username, active_directory_password, "ntlm"),
write_disk("tempfile.xlsx", overwrite = TRUE))
# save to dataframe
df <- read_excel("tempfile.xlsx")
df
# remove excel file from disk
file.remove("tempfile.xlsx")
This gets the job done, though would be interested if anyone knows how to avoid the interim step of writing to disk.
N.B. Depending on your specific machine/network/Sharepoint configuration, you may also be able to just use authenticate(":",":","ntlm") per this answer.
I was unable to accomplish this using hints from answers above in R (I tried many approaches found on this site). However, just to highlight the response by #RyanBradley above and especially the response by #ZS27:
I instead had to use the OneDrive Desktop client (Windows) to allow me to sync the folder to my computer. Newer versions of SharePoint (like that found in MS Teams) have a sync button or feature in the document libraries / folders that interfaces with OneDrive.
This is the functional equivalent of mounting the folder as a network drive, so R interfaces with the file as if it was a part of your file system. Works for me.
You may need to map a network drive to the SharePoint library so that you can connect to it directly. Or if you don't want to map a network drive you could also place a shortcut to the folder in your startup folder.
Example file path:
\company_sharepoint_site\ssp\site_name\sub_site_name\library_name
Example start up folder location (Windows 10):
C:\Users\USER_NAME\AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Startup
Note direction of the slashes ("\" rather than "/") is important so that your file path is interpreted as a file location, not an internet browser location. By placing such a path in a network drive or as a shortcut in your startup folder your PC should connect to it when it boots.
# Load or install readxl
if(require(readxl) == FALSE){
install.packages("readxl")
if(require(readxl)== FALSE){stop("Unable to install and load readxl")}
}
# Define path to data
data_path <- "\\\\company_sharepoint_site\\ssp\\site_name\\sub_site_name\\library_name\\Example.xlsx"
# Pull data
df_employees <- read_xlsx(data_path)
I had a situation exactly like you. I want to access an excel file, available on an sharepoint site using R programming language.
I have also surfed many stuff in Internet and I didn't find anything relevant to my requirement.
Then, I have attempted the following thing:
I have made the sharepoint folder as a network drive folder, in my local system.
Then, I have accessed that excel file(in sharepoint site) from my machine without accessing web browser.
Hence, I have copied the network path, present in my system (it will be same as your sharepoint site, however it will not have https/http.
The site will start with "\" like the following: "\sharepoint.test.com\folder\path").
Launch RStudio and select Import Dataset option, under Environment section.
Choose 'From Excel'. 'Import Excel Data' form will be opened.
Under File/URL field: Paste the network path of sharepoint (copied from your machine).
Click Import, the excel file in Sharepoint will be imported in R successfully.
Ensure that the file should not have html language as input (lie %20 and all) and Backslash should be used as separator in the URL.
While importing the file, provide the input of the folder name exactly, as you see.
For example:
Sharepoint.microsoft.com - Sharepoint's Domain
Department name - name of the Folder
Project name - name of the folder
Sample.xlsx - name of the file
So, your URL to import dataset should be:
"\Sharepoint.microsoft.com\Department name\Project name\Sample.xlsx".
Thank you!
Try using the link in this format:
http://site/_layouts/download.aspx?SourceUrl=url-of-document-in-library
If above doesn't work try this syntax [note slash directions]:
"\\gov.sharepoint.com#SSL/DavWWWRoot/sites/SomePath/SomePath/SomePath/SomeFile"
See this for more info about syntax and what's going on:
Connect to a site via SSL/DavWWWRoot not usual URL? Why does this make a difference?
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.
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"
does anyone of you have an idea of how to upload an R file (saved in the R environment) onto an FTP server?
It would be perfect to export the file directly as .csv or .json file, so I already tried some solutions like:
write.csv2(my.data, file = url("ftp://user:password#ftpserver.net/mydata.csv"))
or
write_json(my.data, path = url("ftp://user:password#ftpserver.net/folder/mydata.csv"))
but, unfortunately, in both cases R gives me the error:
"can only open URLs for reading"
which is kind of sad, but makes sense anyway.
Ok, after that I tried to work with the RCurl library - like this:
library("RCurl")
userpwd <- "user:password"
ftpUpload(I(my.data), "ftp://ftpserver.net/folder/mydata.csv", userpwd=userpwd, asText = T)
which doesn't work properly as well. Somehow this command brings R to a "fatal error" so that my session gets terminated - and oddly enough an empty mydata.csv file gets uploaded onto the FTP server.
To be honest, I have no clue of how to deal with that.
Does anyone of you have a smart idea?
Would a temp file bring a solution? My ideal is, to avoid storing data locally on my laptop but instead saving (and exporting it) directly onto the ftp server.
Many, many thanks in advance!!
I am writing a script that's Requires Data Which is in my computer folder.
But eventually this script will be used in another computer, by another person.
I can't tell him to change all the paths to the data in the script.
How i Connect the data that's in my folder without writing the specific path
Like:"C:\Users\Dima\Desktop\NewData\..."
The best way of making your code shareable depends upon your use case.
As Carl Witthoft pointed out, most code should be encapsulated in functions. These functions can then be packaged into packages and easily redistributed on other peoples's machines. Writing packages is easier than you think.
For one off analyses, scripts are appropriate. How you make them user-independent depends on who your users are. If your are sharing the script with colleagues, try to keep your data on a network drive, then the link to the data will be the same for everyone. If you are sharing your script with the world, then keep your data on the internet, and the link to the data will be a hyperlink, again, the same for everyone.
If you are sharing your script with a few people who don't have access to a common drive, and you can't put your data on the internet, then some directory manipulation is acceptable.
Change your working directory to the root of where your project files are.
setwd("c:/Users/Dima/My Project")
Then you can reference the location of the data using relative paths.
data_file <- "Data/My data file.csv"
my_data <- read.csv(data_file)
Assuming that you keep the directory structure within your project the same, then you only need to change the call to setwd on each machine.
Also note that the special location "~" refers to your user home directory. Try
normalizePath("~")
That way, if you keep your project in that location, you can avoid reference to "Dima" entirely.
First I should say that a lot of this is over my head, so I apologize in advance for using incorrect terminology and potentially asking an unclear question. I'm doing my best.
Also, I saw ThisPost; is RCurl the tool I want to use for this task?
Every day for 4 months I'll be analyzing new data, and generating .csv files and .png's that need to be uploaded to a web site so that other team members will be checking. I've (nearly) automated all of the data collecting, data downloading, analysis, and file saving. The analysis is carried out in R, and R saves the files. Currently I use Filezilla to manually upload the new files to the website. Is there a way to use R to upload the files to the web site, so that I don't have to open Filezilla and drag+drop files?
It'd be nice to run my R-code and walk away, knowing that once it finishes running, the newly saved files will be automatically be put on the website.
Thanks for any help!
You didn't specify which protocol you use to upload your files using FileZilla. I assume it is ftp. If so, you can use the ftpUpload function of RCurl:
library(RCurl)
ftpUpload("yourfile", "ftp://ftp.yourserver.foo/yourfile",
userpwd="username:passwd")
RCurl also had methods for scp and should also support sftp using ftpUpload.