Write function to load set of predefined paths or files - r

I have recently made my first R package with specific tools for processing a large set of data that I am working with. In this project, there are several paths and files that I have to call and access at various points.
Is it possible to write functions that, when called will load a set of predefined paths or data to my global environment?
For example, the function
load_foo_paths()
would return
foo_path_1 <- "path/to/foo/1/"
foo_path_2 <- "path/to/foo/2/"
And the function
load_foo_data()
would return
foo_data_1 <- read.csv("foo_data_1.csv")
foo_data_2 <- read.csv("foo_data_2.csv")
How would I go about doing something like this?
Thanks


Maybe you can adapt the following to your use case:
loadHist <- function() {
HistFile <- c(".Rhistory", ".Rsession")
ruser <- Sys.getenv("R_USER") # C:\cygwin64\home\xxxx
whome <- Sys.getenv("HOME") # C:\cygwin64\home\xxxx
uprofile <- Sys.getenv("USERPROFILE") # C:\Users\xxxx
# Setting up History Paths (to .Rhistory)
hP1 <- c(getwd(), ruser, whome, uprofile)
hP2 <- c(file.path(hP1, HistFile[1]))
fe <- file.exists(hP2) # file.exists(file.path(getwd(), HistFile[1]))
# Load first find
fen = length(fe); i=1
while (i <= fen) {
if(fe[i]) {
cat('\nLoaded history from:\n',hP2[i],'\n', sep='')
try(utils::loadhistory(file=hP2[i]))
break
}
i = i + 1
}
#cat('\nDone!\n')
}

Related

Can I prevent subfunctions from returning to R environment after running custom function?

Setup:
Say I have two R functions, x() and y().
# Defining function x
# Simple, but what it does is not really important.
x <- function(input)
{output <- input * 10
return(output)}
x() is contained within an .R file and stored in the same directory as y(), but within a different file.
# Defining function y;
# What's important is that Function y's output depends on function x
y <- function(variable){
source('x.R')
output <- x(input = variable)/0.5
return(output)
}
When y() is defined in R, the environment populates with y() only, like such:
However, after we actually run y()...
# Demonstrating that it works
> y(100)
[1] 2000
the environment populates with x as well, like such:
Question:
Can I add code within y to prevent x from populating the R environment after it has ran? I've built a function that's dependent upon several source files which I don't want to keep in the environment after the function has run. I'd like to avoid unnecessarily crowding the R environment when people use the primary function, but adding a simple rm(SubFunctionName) has not worked and I haven't found any other threads on the topic. Any ideas? Thanks for your time!

1) Replace the source line with the following to cause it to be sourced into the local environment.
source('x.R', local = TRUE)
2) Another possibility is to write y like this so that x.R is only read when y.R is sourced rather than each time y is called.
y <- local({
source('x.R', local = TRUE)
function(variable) x(input = variable) / 0.5
})
3) If you don't mind having x defined in y.R then y.R could be written as follows. Note that this eliminates having any source statements in the code separating the file processing and code.
y <- function(variable) {
x <- function(input) input * 10
x(input = variable) / 0.5
}
4) Yet another possibility for separating the file processing and code is to remove the source statement from y and read x.R and y.R into the same local environment so that outside of e they can only be accessed via e. In that case they can both be removed by removing e.
e <- local({
source("x.R", local = TRUE)
source("y.R", local = TRUE)
environment()
})
# test
ls(e)
## [1] "x" "y"
e$y(3)
## [1] 60
4a) A variation of this having similar advantages but being even shorter is:
e <- new.env()
source("x.R", local = e)
source("y.R", local = e)
# test
ls(e)
## [1] "x" "y"
e$y(3)
## [1] 60
5) Yet another approach is to use the CRAN modules package or the klmr/modules package referenced in its README.

Test interaction with users in R package

I am developing an R package and one of the function implements interaction with users through standard input via readline. I now wonder how to test the behavior of this function, preferably with testthat library.
It seems test_that function assumes the answer is "" for user-input. I wish I could test the behavior conditional of various answers users may type in.
Below is a small example code. In the actual development, the marryme function is defined in a separate file and exported to the namespace.
devtools::test() gets me an error on the last line because the answer never becomes yes. I would like to test if the function correctly returns true when user types "y".
library(testthat)
test_that("input", {
marryme <- function() {
ans <- readline("will you marry me? (y/n) > ")
return(ans == "y")
}
expect_false(marryme()) # this is good
expect_true(marryme()) # this is no good
})

Use readLines() with a custom connection
By using readLines() instead of readline(), you can define the connection, which allows you to customize it using global options.
There are two steps that you need to do:
set a default option in your package in zzz.R that points to stdin:
.onAttach <- function(libname, pkgname){
options(mypkg.connection = stdin())
}
In your function, change readline to readLines(n = 1) and set the connection in readLines() to getOption("mypkg.connection")
Example
Based on your MWE:
library(testthat)
options(mypkg.connection = stdin())
marryme <- function() {
cat("will you marry me? (y/n) > ")
ans <- readLines(con = getOption("mypkg.connection"), n = 1)
cat("\n")
return(ans == "y")
}
test_that("input", {
f <- file()
options(mypkg.connection = f)
ans <- paste(c("n", "y"), collapse = "\n") # set this to the number of tests you want to run
write(ans, f)
expect_false(marryme()) # this is good
expect_true(marryme()) # this is no good
# reset connection
options(mypkg.connection = stdin())
# close the file
close(f)
})
#> will you marry me? (y/n) >
#> will you marry me? (y/n) >

How to add variables to several functions in R and run them in the command line

I have a script that is composed of several functions. A summarised example of my script looks like that
>Test.R
massive.process_1 <- function() {
seed(123)
x <- do_something()
save(x, '/home/Result1.RData')
}
massive.process_2 <- function() {
seed(4)
x <- do_something()
save(x, '/home/Result2.RData')
}
massive.process_1()
massive.process_2()
I have to execute this script but instead of 2 _massive.processs_I need to run 100 of them but changing the seed value and the name of the data saved in each step. I can do it manually, adding 100 massive.process functions but I would like to know if is there any way to put it on a script to avoid typing 100 functions?
Many thanks
My bash file to run it is the following:
#!/bin/bash
echo Started analysis at: `date`
rfile="Test.R"
Rscript $rfile
echo Finished analysis at: `date`

Adding to Dennis's answer...
to change the filename you can use "paste".
massive.process <- function(i) {
seed(i)
x <- do_something()
outname = paste("/home/Result", i, ".RData", sep="")
save(x, outname)
x
}
for (i in 1:100){
massive.process(i);
}
or
X = lapply(1:100, massive.process)
If you use the list approach, to access the ith x, just use X[i]
another way to write the lapply loop is with an anonymous function. This might make more clear what's going on.
X = lapply(1:100, function(i){
massive.process(i)
})
The previous notation is the same, just more compact.

Why not adding the seed as parameter to the functions?
massive.process <- function(seedValue) {...}
And it would probably a good idea to implement the loop in R instead of using a shell script.

Customizing R profile [duplicate]

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Closed 10 years ago.
I have always found startup profile files of other people both useful and instructive about the language. Moreover, while I have some customization for Bash and Vim, I have nothing for R.
For example, one thing I always wanted is different colors for input and output text in a window terminal, and maybe even syntax highlighting.

Here is mine. It won't help you with the coloring but I get that from ESS and Emacs...
options("width"=160) # wide display with multiple monitors
options("digits.secs"=3) # show sub-second time stamps
r <- getOption("repos") # hard code the US repo for CRAN
r["CRAN"] <- "http://cran.us.r-project.org"
options(repos = r)
rm(r)
## put something this is your .Rprofile to customize the defaults
setHook(packageEvent("grDevices", "onLoad"),
function(...) grDevices::X11.options(width=8, height=8,
xpos=0, pointsize=10,
#type="nbcairo")) # Cairo device
#type="cairo")) # other Cairo dev
type="xlib")) # old default
## from the AER book by Zeileis and Kleiber
options(prompt="R> ", digits=4, show.signif.stars=FALSE)
options("pdfviewer"="okular") # on Linux, use okular as the pdf viewer

I hate to type the full words 'head', 'summary', 'names' every time, so I use aliases.
You can put aliases into your .Rprofile file, but you have to use the full path to the function (e.g. utils::head) otherwise it won't work.
# aliases
s <- base::summary
h <- utils::head
n <- base::names
EDIT: to answer your question, you can use the colorout package to have different colors in the terminal. Cool! :-)

options(stringsAsFactors=FALSE)
Although I don't actually have that in my .Rprofile, because it might breaks my coauthors' code, I wish it was the default. Why?
1) Character vectors use less memory (but only barely);
2) More importantly, we would avoid problems such as:
> x <- factor(c("a","b","c"))
> x
[1] a b c
Levels: a b c
> x <- c(x, "d")
> x
[1] "1" "2" "3" "d"
and
> x <- factor(c("a","b","c"))
> x[1:2] <- c("c", "d")
Warning message:
In `[<-.factor`(`*tmp*`, 1:2, value = c("c", "d")) :
invalid factor level, NAs generated
Factors are great when you need them (e.g. implementing ordering in graphs) but a nuisance most of the time.

I like saving my R command history and having it available each time I run R:
In the shell or .bashrc:
export R_HISTFILE=~/.Rhistory
in .Rprofile:
.Last <- function() {
if (!any(commandArgs()=='--no-readline') && interactive()){
require(utils)
try(savehistory(Sys.getenv("R_HISTFILE")))
}
}

Here are two functions I find handy for working with windows.
The first converts the \s to /.
.repath <- function() {
cat('Paste windows file path and hit RETURN twice')
x <- scan(what = "")
xa <- gsub('\\\\', '/', x)
writeClipboard(paste(xa, collapse=" "))
cat('Here\'s your de-windowsified path. (It\'s also on the clipboard.)\n', xa, '\n')
}
The second opens the working directory in a new explorer window.
getw <- function() {
suppressWarnings(shell(paste("explorer", gsub('/', '\\\\', getwd()))))
}

Here's mine. I always use the main cran repository, and have code to make it easy to source in-development package code.
.First <- function() {
library(graphics)
options("repos" = c(CRAN = "http://cran.r-project.org/"))
options("device" = "quartz")
}
packages <- list(
"describedisplay" = "~/ggobi/describedisplay",
"linval" = "~/ggobi/linval",
"ggplot2" = "~/documents/ggplot/ggplot",
"qtpaint" = "~/documents/cranvas/qtpaint",
"tourr" = "~/documents/tour/tourr",
"tourrgui" = "~/documents/tour/tourr-gui",
"prodplot" = "~/documents/categorical-grammar"
)
l <- function(pkg) {
pkg <- tolower(deparse(substitute(pkg)))
if (is.null(packages[[pkg]])) {
path <- file.path("~/documents", pkg, pkg)
} else {
path <- packages[pkg]
}
source(file.path(path, "load.r"))
}
test <- function(path) {
path <- deparse(substitute(path))
source(file.path("~/documents", path, path, "test.r"))
}

I've got this, more dynamic trick to use full terminal width, which tries to read from the COLUMNS environment variable (on Linux):
tryCatch(
{options(
width = as.integer(Sys.getenv("COLUMNS")))},
error = function(err) {
write("Can't get your terminal width. Put ``export COLUMNS'' in your \
.bashrc. Or something. Setting width to 120 chars",
stderr());
options(width=120)}
)
This way R will use the full width even as you resize your terminal window.

Most of my personal functions and loaded libraries are in the Rfunctions.r script
source("c:\\data\\rprojects\\functions\\Rfunctions.r")
.First <- function(){
cat("\n Rrrr! The statistics program for Pirates !\n\n")
}
.Last <- function(){
cat("\n Rrrr! Avast Ye, YO HO!\n\n")
}
#===============================================================
# Tinn-R: necessary packages
#===============================================================
library(utils)
necessary = c('svIDE', 'svIO', 'svSocket', 'R2HTML')
if(!all(necessary %in% installed.packages()[, 'Package']))
install.packages(c('SciViews', 'R2HTML'), dep = T)
options(IDE = 'C:/Tinn-R/bin/Tinn-R.exe')
options(use.DDE = T)
library(svIDE)
library(svIO)
library(svSocket)
library(R2HTML)
guiDDEInstall()
shell(paste("mkdir C:\\data\\rplots\\plottemp", gsub('-','',Sys.Date()), sep=""))
pldir <- paste("C:\\data\\rplots\\plottemp", gsub('-','',Sys.Date()), sep="")
plot.str <-c('savePlot(paste(pldir,script,"\\BeachSurveyFreq.pdf",sep=""),type="pdf")')

Here's from my ~/.Rprofile, designed for Mac and Linux.
These make errors easier to see.
options(showWarnCalls=T, showErrorCalls=T)
I hate the CRAN menu choice, so set to a good one.
options(repos=c("http://cran.cnr.Berkeley.edu","http://cran.stat.ucla.edu"))
More history!
Sys.setenv(R_HISTSIZE='100000')
The following is for running on Mac OSX from the terminal (which I greatly prefer to R.app because it's more stable, and you can organize your work by directory; also make sure to get a good ~/.inputrc). By default, you get an X11 display, which doesn't look as nice; this instead gives a quartz display same as the GUI. The if statement is supposed to catch the case when you're running R from the terminal on Mac.
f = pipe("uname")
if (.Platform$GUI == "X11" && readLines(f)=="Darwin") {
# http://www.rforge.net/CarbonEL/
library("grDevices")
library("CarbonEL")
options(device='quartz')
Sys.unsetenv("DISPLAY")
}
close(f); rm(f)
And preload a few libraries,
library(plyr)
library(stringr)
library(RColorBrewer)
if (file.exists("~/util.r")) {
source("~/util.r")
}
where util.r is a random bag of stuff I use, under flux.
Also, since other people were mentioning console width, here's how I do it.
if ( (numcol <-Sys.getenv("COLUMNS")) != "") {
numcol = as.integer(numcol)
options(width= numcol - 1)
} else if (system("stty -a &>/dev/null") == 0) {
# mac specific? probably bad in the R GUI too.
numcol = as.integer(sub(".* ([0-9]+) column.*", "\\1", system("stty -a", intern=T)[1]))
if (numcol > 0)
options(width= numcol - 1 )
}
rm(numcol)
This actually isn't in .Rprofile because you have to re-run it every time you resize the terminal window. I have it in util.r then I just source it as necessary.

Here are mine:
.First <- function () {
options(device="quartz")
}
.Last <- function () {
if (!any(commandArgs() == '--no-readline') && interactive()) {
require(utils)
try(savehistory(Sys.getenv("R_HISTFILE")))
}
}
# Slightly more flexible than as.Date
# my.as.Date("2009-01-01") == my.as.Date(2009, 1, 1) == as.Date("2009-01-01")
my.as.Date <- function (a, b=NULL, c=NULL, ...) {
if (class(a) != "character")
return (as.Date(sprintf("%d-%02d-%02d", a, b, c)))
else
return (as.Date(a))
}
# Some useful aliases
cd <- setwd
pwd <- getwd
lss <- dir
asd <- my.as.Date # examples: asd("2009-01-01") == asd(2009, 1, 1) == as.Date("2009-01-01")
last <- function (x, n=1, ...) tail(x, n=n, ...)
# Set proxy for all web requests
Sys.setenv(http_proxy="http://192.168.0.200:80/")
# Search RPATH for file <fn>. If found, return full path to it
search.path <- function(fn,
paths = strsplit(chartr("\\", "/", Sys.getenv("RPATH")), split =
switch(.Platform$OS.type, windows = ";", ":"))[[1]]) {
for(d in paths)
if (file.exists(f <- file.path(d, fn)))
return(f)
return(NULL)
}
# If loading in an environment that doesn't respect my RPATH environment
# variable, set it here
if (Sys.getenv("RPATH") == "") {
Sys.setenv(RPATH=file.path(path.expand("~"), "Library", "R", "source"))
}
# Load commonly used functions
if (interactive())
source(search.path("afazio.r"))
# If no R_HISTFILE environment variable, set default
if (Sys.getenv("R_HISTFILE") == "") {
Sys.setenv(R_HISTFILE=file.path("~", ".Rhistory"))
}
# Override q() to not save by default.
# Same as saying q("no")
q <- function (save="no", ...) {
quit(save=save, ...)
}
# ---------- My Environments ----------
#
# Rather than starting R from within different directories, I prefer to
# switch my "environment" easily with these functions. An "environment" is
# simply a directory that contains analysis of a particular topic.
# Example usage:
# > load.env("markets") # Load US equity markets analysis environment
# > # ... edit some .r files in my environment
# > reload() # Re-source .r/.R files in my environment
#
# On next startup of R, I will automatically be placed into the last
# environment I entered
# My current environment
.curr.env = NULL
# File contains name of the last environment I entered
.last.env.file = file.path(path.expand("~"), ".Rlastenv")
# Parent directory where all of my "environment"s are contained
.parent.env.dir = file.path(path.expand("~"), "Analysis")
# Create parent directory if it doesn't already exist
if (!file.exists(.parent.env.dir))
dir.create(.parent.env.dir)
load.env <- function (string, save=TRUE) {
# Load all .r/.R files in <.parent.env.dir>/<string>/
cd(file.path(.parent.env.dir, string))
for (file in lss()) {
if (substr(file, nchar(file)-1, nchar(file)+1) %in% c(".r", ".R"))
source(file)
}
.curr.env <<- string
# Save current environment name to file
if (save == TRUE) writeLines(.curr.env, .last.env.file)
# Let user know environment switch was successful
print (paste(" -- in ", string, " environment -- "))
}
# "reload" current environment.
reload <- resource <- function () {
if (!is.null(.curr.env))
load.env(.curr.env, save=FALSE)
else
print (" -- not in environment -- ")
}
# On startup, go straight to the environment I was last working in
if (interactive() && file.exists(.last.env.file)) {
load.env(readLines(.last.env.file))
}

sink(file = 'R.log', split=T)
options(scipen=5)
.ls.objects <- function (pos = 1, pattern, order.by = "Size", decreasing=TRUE, head = TRUE, n = 10) {
# based on postings by Petr Pikal and David Hinds to the r-help list in 2004
# modified by: Dirk Eddelbuettel (http://stackoverflow.com/questions/1358003/tricks-to- manage-the-available-memory-in-an-r-session)
# I then gave it a few tweaks (show size as megabytes and use defaults that I like)
# a data frame of the objects and their associated storage needs.
napply <- function(names, fn) sapply(names, function(x)
fn(get(x, pos = pos)))
names <- ls(pos = pos, pattern = pattern)
obj.class <- napply(names, function(x) as.character(class(x))[1])
obj.mode <- napply(names, mode)
obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
obj.size <- napply(names, object.size) / 10^6 # megabytes
obj.dim <- t(napply(names, function(x)
as.numeric(dim(x))[1:2]))
vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
obj.dim[vec, 1] <- napply(names, length)[vec]
out <- data.frame(obj.type, obj.size, obj.dim)
names(out) <- c("Type", "Size", "Rows", "Columns")
out <- out[order(out[[order.by]], decreasing=decreasing), ]
if (head)
out <- head(out, n)
out
}

Make data.frames display somewhat like 'head', only without having to type 'head'
print.data.frame <- function(df) {
if (nrow(df) > 10) {
base::print.data.frame(head(df, 5))
cat("----\n")
base::print.data.frame(tail(df, 5))
} else {
base::print.data.frame(df)
}
}
(From How to make 'head' be applied automatically to output? )

I often have a chain of debug calls I need to call and uncommenting them can be very tedious. With the help of the SO community, I went for the following solution and inserted this into my .Rprofile.site. # BROWSER is there for my Eclipse Tasks so that I have an overview of browser calls in the Task View window.
# turn debugging on or off
# place "browser(expr = isTRUE(getOption("debug"))) # BROWSER" in your function
# and turn debugging on or off by bugon() or bugoff()
bugon <- function() options("debug" = TRUE)
bugoff <- function() options("debug" = FALSE) #pun intended

Mine is not too fancy:
# So the mac gui can find latex
Sys.setenv("PATH" = paste(Sys.getenv("PATH"),"/usr/texbin",sep=":"))
#Use last(x) instead of x[length(x)], works on matrices too
last <- function(x) { tail(x, n = 1) }
#For tikzDevice caching
options( tikzMetricsDictionary='/Users/cameron/.tikzMetricsDictionary' )

setwd("C://path//to//my//prefered//working//directory")
library("ggplot2")
library("RMySQL")
library("foreign")
answer <- readline("What database would you like to connect to? ")
con <- dbConnect(MySQL(),user="root",password="mypass", dbname=answer)
I do a lot of work from mysql databases, so connecting right away is a godsend. I only wish there was a way of listing the avaialble databases so I wouldn't have to remember all the different names.

Stephen Turner's post on .Rprofiles has several useful aliases and starter functions.
I find myself using his ht and hh often.
#ht==headtail, i.e., show the first and last 10 items of an object
ht <- function(d) rbind(head(d,10),tail(d,10))
# Show the first 5 rows and first 5 columns of a data frame or matrix
hh <- function(d) d[1:5,1:5]

Here's mine, including some of the mentioned ideas.
Two things you might want to look at:
.set.width() / w() update your print width to the one of the terminal. Unfortunately I did not find a way to do this automatically on terminal resize - R documentation mentions this is done by some R interpreters.
history is saved every time together with a timestamp and the working directory
.
.set.width <- function() {
cols <- as.integer(Sys.getenv("COLUMNS"))
if (is.na(cols) || cols > 10000 || cols < 10)
options(width=100)
options(width=cols)
}
.First <- function() {
options(digits.secs=3) # show sub-second time stamps
options(max.print=1000) # do not print more than 1000 lines
options("report" = c(CRAN="http://cran.at.r-project.org"))
options(prompt="R> ", digits=4, show.signif.stars=FALSE)
}
# aliases
w <- .set.width
.Last <- function() {
if (!any(commandArgs()=='--no-readline') && interactive()){
timestamp(,prefix=paste("##------ [",getwd(),"] ",sep=""))
try(savehistory("~/.Rhistory"))
}
}

I use the following to get cacheSweave (or pgfSweave) to work with the "Compile PDF" button in RStudio:
library(cacheSweave)
assignInNamespace("RweaveLatex", cacheSweave::cacheSweaveDriver, "utils")

Mine includes options(menu.graphics=FALSE) because I like to Disable/suppress tcltk popup for CRAN mirror selection in R.

Here's mine. Nothing too innovative. Thoughts on why particular choices:
I went with setting a default for stringsAsFactors because I find
it extremely draining to pass it as an argument each time I read a CSV in. That said, it has already caused me some minor vexation when using code written on my usual computer on a computer which did not have my .Rprofile. I'm keeping it, though, as the troubles it has caused pale in comparison to the troubles not having it set everyday used to cause.
If you don't load the utils package before options(error=recover), it cannot find recover when placed inside an interactive() block.
I used .db for my dropbox setting rather than options(dropbox=...) because I use it all the time inside file.path and it saves much typing. The leading . keeps it from appearing with ls().
Without further ado:
if(interactive()) {
options(stringsAsFactors=FALSE)
options(max.print=50)
options(repos="http://cran.mirrors.hoobly.com")
}
.db <- "~/Dropbox"
# `=` <- function(...) stop("Assignment by = disabled, use <- instead")
options(BingMapsKey="blahblahblah") # Used by taRifx.geo::geocode()
.First <- function() {
if(interactive()) {
require(functional)
require(taRifx)
require(taRifx.geo)
require(ggplot2)
require(foreign)
require(R.utils)
require(stringr)
require(reshape2)
require(devtools)
require(codetools)
require(testthat)
require(utils)
options(error=recover)
}
}

Here's a little snippet for use exporting tables to LaTeX. It changes all the column names to math mode for the many reports I write. The rest of my .Rprofile is pretty standard and mostly covered above.
# Puts $dollar signs in front and behind all column names col_{sub} -> $col_{sub}$
amscols<-function(x){
colnames(x) <- paste("$", colnames(x), "$", sep = "")
x
}

I set my lattice color theme in my profile. Here are two other tweaks I use:
# Display working directory in the titlebar
# Note: This causes demo(graphics) to fail
utils::setWindowTitle(base::getwd())
utils::assignInNamespace("setwd",function(dir) {.Internal(setwd(dir));setWindowTitle(base::getwd())},"base")
# Don't print more than 1000 lines
options(max.print=2000)

I have an environment variable R_USER_WORKSPACE which points to the top directory of my packages. In .Rprofile I define a function devlib which sets the working directory (so that data() works) and sources all .R files in the R subdirectory. It is quite similar to Hadley's l() function above.
devlib <- function(pkg) {
setwd(file.path(Sys.getenv("R_USER_WORKSPACE", "."), deparse(substitute(pkg)), "dev"))
sapply(list.files("R", pattern=".r$", ignore.case=TRUE, full.names=TRUE), source)
invisible(NULL)
}
.First <- function() {
setwd(Sys.getenv("R_USER_WORKSPACE", "."))
options("repos" = c(CRAN = "http://mirrors.softliste.de/cran/", CRANextra="http://www.stats.ox.ac.uk/pub/RWin"))
}
.Last <- function() update.packages(ask="graphics")

I found two functions really necessary: First when I have set debug() on several functions and I have resolved the bug, so I want to undebug() all functions - not one by one. The undebug_all() function added as the accepted answer here is the best.
Second, when I have defined many functions and I am looking for a specific variable name, it's hard to find it within all results of the the ls(), including the function names. The lsnofun() function posted here is really good.

How to get R script file name when a function in it is called?

I have several R script files, such as f1.R, f2.R, f3.R.
I have another function called AddSignal(signal), which adds a signal vector to a list. Functions in f1.R, f2.R, etc. may call this AddSignal() function.
Now what I want is that, in function AddSignal(), besides doing the add signal part, it also records which function in which R file made the call. For example, I'd like to know function ff1() in f1.R added signal sig1.
Is there a way to do that?
In fact, using sys.call(), I can know which function (for example, ff1()) called AddSignal(). But I don't know which R file that ff1() is in. I can see a hard way of doing it, is to scan all .R files and then store a mapping of file names and function names. But I'd like to see whether there is an easier way of doing it.
Thanks.

What I would do is create a lookup table which maps a function to the .R file it is in. You have to recreate this table every time you add, remove, or move a function, but I think it would be preferable to regenerating the table every time you want to find the source file of a function. So here is my take on create such a table:
library(plyr)
functionsFromRfile = function(filename) {
# Get all functions from a source file. Create new enviroment
# source the functions into them and use ls() to extract names.
e = new.env()
source(filename, local = e)
return(ls(envir = e))
}
# This assumes you are in the directory with your R code,
# and that all files need to be included. You can also
# make this list manually ofcourse
Rfiles = list.files(".", pattern = ".R")
# Get a list of functions for each .R file
lutFunc2sourcefile = ldply(Rfiles, function(x) {
return(data.frame(fname = x, func = functionsFromRfile(x)))
})
For one of my own packages this leads to:
> head(lutFunc2sourcefile)
fname func
1 autofitVariogram.r autofitVariogram
2 autoKrige.cv.r autoKrige.cv
3 autoKrige.cv.r checkIfautokrige.cv
4 autoKrige.cv.r compare.cv
5 autoKrige.cv.r cv.compare.bubble
6 autoKrige.cv.r cv.compare.ggplot
You can use the lookup table to performing the mapping using the function name obtained from sys.call.
EDIT: In view of your comment on non-function code, this code uses parse, which does not evaluate the code. It searches through the output of parse, and weeds out the functions, and should not evaluate any code or return code that is not a function. I haven't tested it exhaustively, give it a try.
library(plyr)
Rfiles = list.files(".", pattern = "(.R|.r)")
lutFunc2sourcefile = ldply(Rfiles, function(fname) {
f = parse(fname)
functions = sapply(strsplit(as.character(f), "="), function(l) {
if(grepl("^function", sub(' ', '', l[2]))) {
return(l[1])
} else {
return(NA)
}
})
return(data.frame(fname, func = functions))
})
# Remove lines with func = NA
lutFunc2sourcefile = lutFunc2sourcefile[!is.na(lutFunc2sourcefile$func),]

you could modify AddSignal(signal) to
AddSignal(signal, filename=NULL){
...
...
return(list(signal=signal,filename=filename))}

fn <- commandArgs()[4]
filename <- strsplit(fn, "=")[[1]][2]
cat(filename)

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