Find top level functions in R file - r

I come from a python background and am trying to get up to speed with R, so please bear with me
I have an R file - util.R with the following lines
util.add <- function(a,b) a + b
util.sub <- function(a,b) {
a - b
}
I need to write a function that returns the following:
findFunctions('path/util.R')
[1] "util.add" "util.sub"


I think sourcing will be necessary, but you don't need to clutter you global environment. I tested this locally and it seems to work:
find_functions = function(file) {
search_env = new.env()
source(file = file, local = search_env)
objects = ls(envir = search_env)
functions = objects[sapply(ls(envir = search_env), FUN = function(x) {
is.function(get(x, envir = search_env))
})]
return(functions)
}

Related

identical() but for environments/R6 in base R?

If I can run code before and after a user runs some code, how can I detect which variables were set or changed using base R? I can do this using identical() for non-environment objects. But is there a base-R solution for environments, including R6 classes?
Here's a solution using identical() which fails for envs/R6:
# Copy of initial vars
this_frame = sys.frame()
start_vars = ls()
start_copy = lapply(start_vars, get, envir = this_frame )
names(start_copy) = start_vars
# (user code here)
# Assess what's new and what's changed
end_vars = ls()
new_vars = end_vars[end_vars %in% start_vars == FALSE]
old_vars = end_vars[end_vars %in% start_vars == TRUE]
changed_vars = old_vars[sapply(old_vars, function(x) identical(get(x, envir = this_frame), start_copy[[x]])) == FALSE]
I'm writing a package that lets users run code in a separate session. I'd like to return only objects that were changed.

This solution detects changes in an environment, sub-environments, and R6-classes.
General approach
run start_state = env_as_list() on sys.frame()which stores everything in a list and recursively converts all environments/R6 and sub-environments/R6 to list.
Let the user manipulate stuff
Run end_state = env_as_list() and use identical() to detect changes between start_state and end_state.
env_as_list = function(env) {
rapply(
object = as.list(env, all.names = TRUE),
f = function(x) {
if ("R6" %in% class(x)) {
# R6 to list without recursion
x = as.list(x, all.names = TRUE)
x$.__enclos_env__$self = NULL
x$.__enclos_env__$super = NULL
env_as_list(x)
} else if (is.environment(x)) {
env_as_list(x)
} else {
stop("Impossible to get here")
}
},
classes = c("environment", "R6"),
how = "replace"
)
}
Demonstration
Let's test it: let's fill globalenv() with a some stuff to begin with:
R6_class = R6::R6Class("Testing", list(a = 1))
my_R6 = R6_class$new()
my_env = new.env()
my_env$sub_env = new.env()
my_env$sub_env$some_value = 2
my_regular = rnorm(5)
Snapshot time!
start_state = env_as_list(sys.frame())
Let the user play:
my_R6$a = 99 # Change R6
new_regular = 3 # new var
my_env$sub_env$some_value = 99 # Change sub-environment
Snapshot again!
end_state = env_as_list(sys.frame())
end_state$start_state = NULL # don't include this
Did nothing change?
> identical(start_state, end_state))
# FALSE
Which variables changed?
> is_same = lapply(names(end_state), function(x) identical(start_state[[x]], end_state[[x]]))
> names(end_state)[is_same == FALSE]
# "my_env" "new_regular" "my_R6"
Bonus
You can also use this to compute the size of an environment, including all R6 and sub-environments. Simply:
object.size(env_as_list(globalenv()))

Converting long code file to separate files

I have two quite long R files with many function definitions and some comments irregularly between them. Is there any R function to move each function definition to a separate R file? It would be quite nice to name the new file with function name :).

Content of the file (let's call it tuttifrutti.R) with all the functions could be something like this:
fun1 <- function(x) {
# comment
x * x
}
fun2 <- function(x) {
# comment
x/2
}
And then you can collect all functions and print them to samename file.
localenv <- new.env()
source("tuttifrutti.R", local = localenv)
sapply(localenv, print)
sapply(names(localenv), FUN = function(x, en) {
dump(x, file = sprintf("%s.R", x), envir = en)
}, en = localenv)

Pass Object (DF) to Custom Function Stored in Script

How do I pass a pre-existing object, such as a DF, to my custom functions, given the setup I have (see below)?
Alternatively, do I need to set up my custom functions differently?
My functions reside in a series of *.R scripts.
I source the functions in my .Rprofile:
.env$fxShortName <- function(){
source("C:\\path\\to\\scriptFile.R")
}
Not-Quite Solutions:
1) Defining the Function Manually || It works w/ the obvious drawback that I need to manually load my functions each time.
2) Rscript + commandArgs || This works if I define the DF within the function, like this:
#foo.R
a <- data.frame(a = c(1))
b <- data.frame(b = c(1))
args <- commandArgs(trailingOnly = TRUE)
print(args)
data.frame.name <- args[1]
print(colnames(get(data.frame.name)))
Rscript creates a new R instance, though, so it doesn't see my pre-existing DF. At least, it doesn't find it out-of-the-box.
3) Function w/ substitute, match.call, etc. || I've adopted %>>% to set up auto-updated views of certain DFs, so I tried modifying the setup that works in that case. For %>>% I have this code in my .Rprofile:
.env$`%>>%` <- function(expr, x) {
x <- substitute(x)
call <- match.call()[-1]
fun <- function() {NULL}
body(fun) <- call$expr
makeActiveBinding(sym = deparse(x), fun = fun, env = parent.frame())
invisible(NULL)
}
This type of setup works with DFs from my current session. However, I prefer the structure offered by keeping my custom scripts separate from my .Rprofile.
4) get() & mget() || This seemed promising, but I don't understand it enough to definitively say whether or not it will help. And, yes, I did RTFM.
Reproducible Example:
myfx(head, preExistingDF)
Sample Code:
myfx <- function(expr, x) {
x <- substitute(x)
call <- match.call()[-1]
fun <- function() {NULL}
body(fun) <- call$expr
print(body(fun))}
Put the sample code in a script. Add the following code to your .Rprofile:
.env$mySamplefx <- function(){
source("C:\\path\\to\\myfx.R")
}
Then try it after adding the code directly to your .Rprofile.

Use of environment variables in R

I am trying to understand the reducer.R code taken from the following website.
http://www.thecloudavenue.com/2013/10/mapreduce-programming-in-r-using-hadoop.html
This code is using for Hadoop Streaming using R.
I have given the code below:
#! /usr/bin/env Rscript
# reducer.R - Wordcount program in R
# script for Reducer (R-Hadoop integration)
trimWhiteSpace <- function(line) gsub("(^ +)|( +$)", "", line)
splitLine <- function(line) {
val <- unlist(strsplit(line, "\t"))
list(word = val[1], count = as.integer(val[2]))
}
env <- new.env(hash = TRUE)
con <- file("stdin", open = "r")
while (length(line <- readLines(con, n = 1, warn = FALSE)) > 0) {
line <- trimWhiteSpace(line)
split <- splitLine(line)
word <- split$word
count <- split$count
if (exists(word, envir = env, inherits = FALSE)) {
oldcount <- get(word, envir = env)
assign(word, oldcount + count, envir = env)
}
else assign(word, count, envir = env)
}
close(con)
for (w in ls(env, all = TRUE))
cat(w, "\t", get(w, envir = env), "\n", sep = "")
Could someone explain the significance of the use of the following new.env command and the subsequent use of the env in the code:
env <- new.env(hash = TRUE)
Why is this required? What happens if this is not included in the code?
Update 06/05/2014
I tried writing another version of this code without having a new environment defined and have given the code as follows:
#! /usr/bin/env Rscript
current_word <- ""
current_count <- 0
word <- ""
con <- file("stdin", open = "r")
while (length(line <- readLines(con, n = 1, warn = FALSE)) > 0)
{
line1 <- gsub("(^ +)|( +$)", "", line)
word <- unlist(strsplit(line1, "[[:space:]]+"))[[1]]
count <- as.numeric(unlist(strsplit(line1, "[[:space:]]+"))[[2]])
if (current_word == word) {
current_count = current_count + count
} else
{
if(current_word != "")
{
cat(current_word,'\t', current_count,'\n')
}
current_count = count
current_word = word
}
}
if (current_word == word)
{
cat(current_word,'\t', current_count,'\n')
}
close(con)
This code gives the same output as the one with a new environment defined.
Question: Does using new environment provide any advantages from a Hadoop standpoint? Is there a reason for using it in this specific case?
Thank you.

Your question is related with environment in R, example code for make new environment in R
> my.env <- new.env()
> my.env
<environment: 0x114a9d940>
> ls(my.env)
character(0)
> assign("a", 999, envir=my.env)
> my.env$foo = "This is the variable foo."
> ls(my.env)
[1] "a" "foo"
I think this article can help you http://www.r-bloggers.com/environments-in-r/
or press
?environment
for more help
Like on code that you give, the author make a new environmnt.
env <- new.env(hash = TRUE)
when he want to assign value they defined the environment
assign(word, oldcount + count, envir = env)
And for the question "What happens if this is not included in the code?" I think you can find the answer on the link that I already provided
About the advantages using new env in R is already answered in this link
so the reason is in this case you will play with the large of dataset, when you passing your dataset to the function, R will make a copy your dataset and then the return data will overwrite the old dataset. But if you passing env, R will directly process that env without copying large dataset.

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.

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