using R and debugging, I often might have a function with several arguments set by default.
e.g.
foo <- function(x=c(3,4,5), y= 'house', dateidx = '1990-01-01'){}
Often I just want to manually run through some lines in the function, while using the pre-set parameters. If the parameter list is long, I have to type or paste each argument to the console manually before stepping through the function.
x=c(3,4,5)
y= 'house'
dateidx = '1990-01-01'
It's ok if the list of arguments is small but if there is a long list of arguments, it gets tedious. Is there some way to just copy the whole set of arguments, paste to console, and do something like unlist, so that all the arguments are passed to the console as if I manually passed each one?
p.s. I'm weakly familiar with the debug tool, but sometimes I find it easier and faster to just troubleshoot lines quickly and manually as above.
There is no easy pre-existing way to do this--mainly because this a problem solved by the debugger.
One could imagine hacking something together that might parse these parameters with a regex and set them automatically--or something like that. However, the effort would be much better spent learning how to use the debugger.
It should be quite quick to test the part of the code you are interested in with the debugger if you learn how to use it. RStudio has a visual debugger. Using this, you can simply mark the command you are interested in testing with a breakpoint and run the script. The script will run until it reaches the breakpoint, then stop there so you can inspect what is happening.
Related
I have just started to learn to code on R, so I apologize for the very simple question. I understand it is best to type your code in as a Script so you can edit and save it. However, when I try to make an object in the script section, it does not work. If I make an object in the console, R saves the object and it appears in my environment. I am typing in a very simple code to try a quick exercise on rolling dice:
die <- 1:6
But it only works in the console and not when typed as a script. Any help/explanation appreciated!
Essentially, you interact with R environment differently when running an .R script via RScript.exe or via console with R.exe, Rterm, etc. and in GUI IDEs like RGui or RStudio. (This applies to any programming language with interactive compilers not just R).
The script does save thedie object in R environment but only during the run or lifetime of that script (i.e., from beginning to end of code lines). Your code line is simply an assignment of object. You do nothing with it. Apply some function, output results, and other actions in that script to see.
On the console, the R environment persists interactively until you quit it with q(). So assigned objects remains for lifetime of your console session. After assigning, you can afterwards apply function, output results, or other actions in line by line calls.
Ultimately, scripts gathers all line by line code in advance of run for automated execution without relying on user to supply lines. Imagine running 1,000 lines of code with nested if/then or for/while loops, apply functions on console! Therefore, have all your R coding needs summarily handled in scripts.
It is always better to have the script, as you say, you can save edit correct, without having to rewrite the code to change a variable or number.
I recommend using Rstudio, it is very practical and will help you to program more efficiently and allows you to see, among other things, the different objects that you have created.
Despite numerous searches, I can't seem to find a clear explanation as to what "Source on Save" means in RStudio.
I have tried ?source and the explanation there isn't clear, either.
As far as I can tell, it seems to run the script when I hit Save, but I don't understand the relevance/significance of it.
In simple terms, what exactly does Source on Save do and why would/should I use it?
This is kind of a shortcut to save and execute your code. You type something, save the script and it will be automatically sourced.
Very useful for short scripts but very annoying for time consuming longer scripts.
So sourcing is basically running each line of your file.
EDIT:
SO thinking of a scenario where this might be useful...
You developing a function which you will later put into a package... So you write this function already in an extra file but execute the function for testing in the command line...
Normally, you have to execute the whole function again, when you changed something. While using "Source on Save" the function will be executed and you can use Ctrl + 2 to jump into command line and test the function directly.
Since I am working with R, my datasets are much bigger. But I am remembering starting coding in python and vi, I updated my setting in a way to execute the code on save, since these little scripts where done in less then 10 seconds...
So maybe it is just not standard to work with small datasets... But I can still recommend it, for development, to use only 10% of a normal dataset. It will speed up the graphics creation and a lot of other things as well. Test it with the complete dataset every now and then.
Thanks for all your advice. My remaining question is this:
Can I replace column name 'sulphate' in the following statement ...
dataclean <- datatable$sulfate[!datanas]
.... with a reference to a parameter 'pollutant', which may or may not have a value of 'sulfate'?
When you attach values to arguments, they appear as they would be objects in your workspace. But the environment is not workspace but that of the function.
So in your case, directory would be a character string and it would work. For the first time. Your working directory is now changed and you need to revert back to the previous one for the function to work again. This can get pretty messy so what I like to do is just refer to raw files by full path. See ?list.files for more info.
For your second question, your best bet is to refer to a certain level within the variable, is to do
x[, pollutant]
It is convenient to add drop = FALSE argument there, in order to keep the what I'm assuming is a data.frame.
You could improve your function by also implementing the datatable argument. That way you have all the objects bundled together nicely.
The most important thing to note here would be "debugging". You should learn to use at least browser(). This function will stop the execution of your function at the very step where it was called. This enables you, in the R console, to inspect elements in the function and run code to see what's going. This way you can speed up the development of code, at least initially when you usually haven't internalized all the data structures and paradigms yet.
In Julia, a lot of the Base and closer related functions are also written in pure Julia, and the code is easily avaible. One can skim through the repository or the local downloaded files, and see how the function is written/implemented. But I think there is allready some built in method that does that for you, so you can write in REPL or Jupyter Notebook something like:
#code functioninquestion()
and get something like:
functioninquestion(input::Type)
some calculations
return
end
without pagingh throug the code.
I just don't remember the method or call. I have read the Reflection/Introspection section of the Manual but I cannot seem to be able to use anything there. I've tried methods, methodswith, code_lowered, expand and cannot seem to make them give what I want-
This is not currently supported but probably will be in the future.
Though this may not be what the OP is looking for, #less is very convenient to read the underlying code (so I very often use it). For example,
julia> #less 1 + 2
gives
+(x::Int, y::Int) = box(Int,add_int(unbox(Int,x),unbox(Int,y)))
which corresponds to the line given by
julia> #which 1 + 2
+(x::Int64, y::Int64) at int.jl:8
#edit functioninquestion() will open up your editor to the location of the method given.
It probably wouldn't be to hard to take the same information used by #edit and use it to open the file and skip to the method definition, and then display it directly in the REPL (or Jupyter).
EDIT: While I was answering, somebody else mentioned #less, which seems to do exactly what you want already.
There is now another tool for this, https://github.com/timholy/CodeTracking.jl. It is part of Revise.jl (and works better when also using Revise). It should work inside Jupyter and with functions defined in the REPL, unlike #edit/#less.
I've an R script, that takes commandline arguments, where the top line is:
#!/usr/bin/Rscript --slave
I wanted to interrupt execution in a function (so I can interactively use the data variables that have been loaded by that point to work out the next bit of code I need to write). I added this inside the function in question:
browser()
but it gets ignored. A bit of searching suggests it might be because the program is running in non-interactive mode. But even more searching has not tracked down how I switch the script out non-interactive mode so that browser() will work. Something like a browser_yes_I_really_mean_it() function.
P.S. I want to avoid altering the rest of the script if at all possible. My current approach is to copy and paste the code chunks, needed to prepare the data, into an interactive session; but as the script gets more and more complex this is getting more and more unreasonable.
UPDATE: for anyone else with the same question, it appears the answer to the actual question is that it is impossible. Once you start R in a non-interactive mode the die is cast. The given answers are therefore workarounds: either you hack your code (remembering to unhack it afterwards), or you refactor to make debugging easier. (This comment is not intended as a criticism of the answers; the suggested refactoring makes the code cleaner anyway.)
Can you just fire up R and source the file instead?
R
source("script.R")
Following mdsumner's answer, I edited my script like this:
if(!exists("argv")){
argv=commandArgs(TRUE)
if(length(argv)!=4)usage_and_exit()
}else{
if(length(argv)!=4){
stop("Must set argv as a 4 element vector. E.g. argv=c(...)")
}
}
Then no other change was needed, and I was able to do:
R
> argv=c('a','b','c','d')
> source("script.R")
In addition to the previous answer, I'd create a toplevel function (e.g. doStuff) which performs the analysis you want to perform in batch. The function takes the cmd line options as input. In the batch script you source the script that contains this function and call it. In this way you can easily run the function in interactive mode and use e.g. browser().
In some cases, the suggested solution (workaround) may not work - for example, when the R code needs to be run as a part of an existing bash script. For those cases, I suggest to write in your R script into the bash script using here document:
#!/bin/bash
R --interactive << EOT
# R code starts here
argv=c('a','b','c','d')
print(interactive())
# Rest of script contents
quit("no")
# R code ends here
EOT
This way, print(interactive()) above will yield TRUE.
Sidenote: Make sure to avoid the $ character in your R code, as this would not be processed correctly - for example, retrieve a column from a data.frame() by using df[["X1"]] instead of df$X1.