taskscheduleR doesn't load into work space - r

I am working in R. I figured out how to use the taskscheduleR package to run a script at a specific time when I am away from my computer. I am wondering if there is a way to have that code populate my workspace. I know I can save a workspace using this strategy, but I would like it to be loaded into my working environment as well. Utilizing a 'load()' function within the automated script doesn't seem to work. Anyone have any tips?

Related

[R][package] Load all functions in a package's NAMESPACE

Tl;dr
I have a complete NAMESPACE file for the package I am building. I want to execute all the importFrom(x,y) clauses in that file to get the set of necessary functions I use in the package. Is there an automated way to do that?
Full question
I'm currently working on building a package that in turns depends on a bunch of other packages. Think: both Hmisc and dplyr.
The other contributors to the package don't really like using devtools::build() every 5 minutes when they debug, which I can understand. They want to be able to toggle a dev_mode boolean which, when set to true, loads all dependencies and sources all scripts in the R/ and data-raw/ folders, so that all functions and data objects are loaded in memory. I don't necessarily think that's a perfectly clean solution, but they're technically my clients and it's a big help to them so please don't offer a frame challenge unless it's the most user-friendly solution imaginable.
The problem with this solution is that when I load two libraries whose namespace clash, functions in the package that would perfectly work otherwise start throwing errors. I thus need to improve my import system.
Thanks to thorough documentation (and the help of devtools::check()), my NAMESPACE is complete. I guess I could split it in pieces and eval() some well-chosen parts of it, but it sort of feels like I'm probably not the first person to encounter this difficulty. Is there an automated way to parse NAMESPACE and import the proper functions from the proper packages ?
Thanks !

How do I save R code using code in R Studio

I am looking for a way to 'auto save' my code each time i run it. I figure the best way to achieve this would be to write code in my models which will overwrite and save the file which is open. I have been experimenting with:
rstudioapi::documentSave(rstudioapi::getActiveDocumentContext()$id)
However, I have not had any success.
Good Morning All,
I worked out the issue. The model I am working on generates tables, which are produced with View(). This means when the code is finished running the script is not the tab displayed.
documentSave()
Will only save the script which is open. Therefore, it is advised that you should consider using
documentSaveAll()
Which will save all tabs open.

How run a script refering to an externaly stored script in R?

I am looking for a way to run a script with one part of the script stored in a separate file. Like a "normal" script but with one part of the script referring to an external script.
The script stored in a separate file will be generic to several scripts and will be regularly updated, and that is the reason for keeping this part of the script separate.
I have not found anything about how to solve this. Maybe someone else has a solution to this?
It seems you're looking for
source("[file location]")
Be aware that this will automatically run the entire script in that file location, so be aware of that with regards to which names you give objects in that script and the other script you're working with (e.g. if you open a dataframe in your current script and not in the external script, but you're working with that dataframe in the external script too, the name needs to be the same).
Alternatively, you could write the external script in a way that it is loaded into your workspace as a formula, so that you can refer to that formula in the 'current'script.

R is there a way to dynamically update a function as you are building it

I am very new to R and I am using RStudio. I am building a new "user-defined" function, which entails a huge amount of trial and error. Every time I make the slightest change to the function I need select the entire function and do crtl+Enter in order to "commit" the function to the workspace.
I am hoping there is a better way of doing it, perhaps in a separate window that automatically "commits" when I save.
I am coming from Matlab and am used to just saving the function after which it is already "committed".
Ctrl+Shift+P re-runs previously executed region, so you won't have to highlight your function again. so this will work unless you have executed something else in the interim.
If you wan to run some part of your code in RStudio you simply have to use Ctrl+Enter. If the code were run every time you saved it, it could have very bad effects. Imagine that you have a huge script that runs for a long time and uses much computer resources - this would lead you to killing R to stop the script every time you saved it!
What you could do is to save script in external file and than call it from your main script using source("some_directory/myscript.R").

Automatically "sourcing" function on change

While I am writing .R functions I constantly need to manually write source("funcname.r") to get the changes reflected in the workspace. I am sure it must be possible to do this automatically. So what I would like would be just to make changes in my function, save the function and be able to use the new function in R workspace without manually "sourcing" this function. How can I do that?
UPDATE: I know about selecting appropriate lines of code and pressing CTRL+R in R Editor (RGui) or using Notepad++ and executing the lines into R. But this approach has a disadvantage of making my workspace console "muddled". I would like to stop this practice if at all possible.
You can use R studio which has a source on save option.
If you are prepared to package your functions into a package, you may enjoy exploring Hadley's devtools package. This provides a suite of tools to write, test and document
packages.
https://github.com/hadley/devtools
This approach offer many advantages, but mainly reloading the package with a minimum of retyping.
You will still have to type load_all("yourpackage") but I find this small amount of typing is small beer compared to the advantages of devtools.
For additional information, including how to setup devtools, have a look at https://github.com/hadley/devtools/wiki/development
If you're using Eclipse + StatET, you can press CTRL+R+S, which saves your script and sources it. As close to automatic as I can get.
If you can get your text editor to run a system command after it saves the file, then you could use something like AutoIt (on Windows) or a batch script (on UNIX-derivative) to pass a call to source off to all running copies of R. But that's a heck of a lot of work for not much gain.
Still, I think it's much more likely to work being event-driven on the text editor end vs. having R constantly scan for updates (or somehow interface with the OS's update-event-messaging-system).
This is likely not possible (automatically detecting disc changes without intervention or running at least one line).
R needs to read into memory functions, so a change on the disc wouldn't be reflected in the workspace without reloading your functions.
If you are into developing R functions, some amount of messiness during your development process will be likely inevitable, but perhaps I could suggest that you try writing an R-package to house your functions?
This has the advantage of being able to robustly document your functions, using lazy loading so that you have access to your functions/datasets immediately without sourcing them.
Don't be afraid of making a package, it's easy with package.skeleton() and doesn't have to go on CRAN but could be for your own personal use without distribution! Just have fun!
Try to accept some messiness during development knowing you are working towards your goal and fighting the good fight of code organization and documentation!
We are only imperfect people, in an imperfect world, but we mean well!

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