R Language - Adding timestamp to console output - r

I'm using sink() for logging purposes for running R-Scripts, which works fine.
*R> sink(file = paste(Log_Path, FileName), append = TRUE, type = c("output"), split = TRUE)*
I'm now doing performance tests and needing to find out how long certain parts of the R-Script runs, without adding tons of print statements.
This solution works, via in RGui Interface:
R> updatePrompt <- function(...) {options(prompt=paste(Sys.time(),"> ")); return(TRUE)}
R> addTaskCallback(updatePrompt)
However, The time prompts doesn't propagate back into the Console stream of sink() when running in the R-Server.
Suggestions?
I've explored txtStart , but not sure if that's what I need.
Is there a different package or a option to set to set the timestamp in the prompt in the sink() console output?
Thanks for any help...

The prompt is not part of stdout, which is why it doesn't make it to the sink. Why don't you just print from your callback? For example:
make_timing_fun <- function() {
time.start <- proc.time()
function(...) {
new.time <- proc.time()
print(new.time - time.start)
time.start <<- new.time
TRUE
}
}
addTaskCallback(make_timing_fun()) # note parens used to generate actual function
Note this times the time between statements completing, so if you're just waiting around the console doing nothing that will be part of the time as well.

I did try that originally, but I tried it again. and received same results:
Snippet of saved console output from log file:
> startdate <- as.vector(input_data2)
> input_data3 <- stop_date
> stopdate <- as.vector(input_data3)
.
Was hoping for this:
2014-01-03 09:07:57 > startdate <- as.vector(input_data2)
2014-01-03 09:07:57 > input_data3 <- stop_date
2014-01-03 09:07:57 > stopdate <- as.vector(input_data3)
.

Related

how to properly close connection so I won't get "Error in file(con, "r") : all connections are in use" when using "readlines" and "tryCatch"

I have a list of URLs (more than 4000) from a specific domain (pixilink.com) and what I want to do is to figure out if the provided domain is a picture or a video. To do this, I used the solutions provided here: How to write trycatch in R and Check whether a website provides photo or video based on a pattern in its URL and wrote the code shown below:
#Function to get the value of initial_mode from the URL
urlmode <- function(x){
mycontent <- readLines(x)
mypos <- grep("initial_mode = ", mycontent)
if(grepl("0", mycontent[mypos])){
return("picture")
} else if(grepl("tour", mycontent[mypos])){
return("video")
} else{
return(NA)
}
}
Also, in order to prevent having error for URLs that don't exist, I used the code below:
readUrl <- function(url) {
out <- tryCatch(
{
readLines(con=url, warn=FALSE)
return(1)
},
error=function(cond) {
return(NA)
},
warning=function(cond) {
return(NA)
},
finally={
message( url)
}
)
return(out)
}
Finally, I separated the list of URLs and pass it into the functions (here for instance, I used 1000 values from URL list) described above:
a <- subset(new_df, new_df$host=="www.pixilink.com")
vec <- a[['V']]
vec <- vec[1:1000] # only chose first 1000 rows
tt <- numeric(length(vec)) # checking validity of url
for (i in 1:length(vec)){
tt[i] <- readUrl(vec[i])
print(i)
}
g <- data.frame(vec,tt)
g2 <- g[which(!is.na(g$tt)),] #only valid url
dd <- numeric(nrow(g2))
for (j in 1:nrow(g2)){
dd[j] <- urlmode(g2[j,1])
}
Final <- cbind(g2,dd)
Final <- left_join(g, Final, by = c("vec" = "vec"))
I ran this code on a sample list of URLs with 100, URLs and it worked; however, after I ran it on whole list of URLs, it returned an error. Here is the error : Error in textConnection("rval", "w", local = TRUE) : all connections are in use Error in textConnection("rval", "w", local = TRUE) : all connections are in use
And after this even for sample URLs (100 samples that I tested before) I ran the code and got this error message : Error in file(con, "r") : all connections are in use
I also tried closeAllConnection after each recalling each function in the loop, but it didn't work.
Can anyone explain what this error is about? is it related to the number of requests we can have from the website? what's the solution?
So, my guess as to why this is happening is because you're not closing the connections that you're opening via tryCatch() and via urlmode() through the use of readLines(). I was unsure of how urlmode() was going to be used in your previous post so it had made it as simple as I could (and in hindsight, that was badly done, my apologies). So I took the liberty of rewriting urlmode() to try and make it a little bit more robust for what appears to be a more expansive task at hand.
I think the comments in the code should help, so take a look below:
#Updated URL mode function with better
#URL checking, connection handling,
#and "mode" investigation
urlmode <- function(x){
#Check if URL is good to go
if(!httr::http_error(x)){
#Test cases
#x <- "www.pixilink.com/3"
#x <- "https://www.pixilink.com/93320"
#x <- "https://www.pixilink.com/93313"
#Then since there are redirect shenanigans
#Get the actual URL the input points to
#It should just be the input URL if there is
#no redirection
#This is important as this also takes care of
#checking whether http or https need to be prefixed
#in case the input URL is supplied without those
#(this can cause problems for url() below)
myx <- httr::HEAD(x)$url
#Then check for what the default mode is
mycon <- url(myx)
open(mycon, "r")
mycontent <- readLines(mycon)
mypos <- grep("initial_mode = ", mycontent)
#Close the connection since it's no longer
#necessary
close(mycon)
#Some URLs with weird formats can return
#empty on this one since they don't
#follow the expected format.
#See for example: "https://www.pixilink.com/clients/899/#3"
#which is actually
#redirected from "https://www.pixilink.com/3"
#After that, evaluate what's at mypos, and always
#return the actual URL
#along with the result
if(!purrr::is_empty(mypos)){
#mystr<- stringr::str_extract(mycontent[mypos], "(?<=initial_mode\\s\\=).*")
mystr <- stringr::str_extract(mycontent[mypos], "(?<=\').*(?=\')")
return(c(myx, mystr))
#return(mystr)
#So once all that is done, check if the line at mypos
#contains a 0 (picture), tour (video)
#if(grepl("0", mycontent[mypos])){
# return(c(myx, "picture"))
#return("picture")
#} else if(grepl("tour", mycontent[mypos])){
# return(c(myx, "video"))
#return("video")
#}
} else{
#Valid URL but not interpretable
return(c(myx, "uninterpretable"))
#return("uninterpretable")
}
} else{
#Straight up invalid URL
#No myx variable to return here
#Just x
return(c(x, "invalid"))
#return("invalid")
}
}
#--------
#Sample code execution
library(purrr)
library(parallel)
library(future.apply)
library(httr)
library(stringr)
library(progressr)
library(progress)
#All future + progressr related stuff
#learned courtesy
#https://stackoverflow.com/a/62946400/9494044
#Setting up parallelized execution
no_cores <- parallel::detectCores()
#The above setup will ensure ALL cores
#are put to use
clust <- parallel::makeCluster(no_cores)
future::plan(cluster, workers = clust)
#Progress bar for sanity checking
progressr::handlers(progressr::handler_progress(format="[:bar] :percent :eta :message"))
#Website's base URL
baseurl <- "https://www.pixilink.com"
#Using future_lapply() to recursively apply urlmode()
#to a sequence of the URLs on pixilink in parallel
#and storing the results in sitetype
#Using a future chunk size of 10
#Everything is wrapped in with_progress() to enable the
#progress bar
#
range <- 93310:93350
#range <- 1:10000
progressr::with_progress({
myprog <- progressr::progressor(along = range)
sitetype <- do.call(rbind, future_lapply(range, function(b, x){
myprog() ##Progress bar signaller
myurl <- paste0(b, "/", x)
cat("\n", myurl, " ")
myret <- urlmode(myurl)
cat(myret, "\n")
return(c(myurl, myret))
}, b = baseurl, future.chunk.size = 10))
})
#Converting into a proper data.frame
#and assigning column names
sitetype <- data.frame(sitetype)
names(sitetype) <- c("given_url", "actual_url", "mode")
#A bit of wrangling to tidy up the mode column
sitetype$mode <- stringr::str_replace(sitetype$mode, "0", "picture")
head(sitetype)
# given_url actual_url mode
# 1 https://www.pixilink.com/93310 https://www.pixilink.com/93310 invalid
# 2 https://www.pixilink.com/93311 https://www.pixilink.com/93311 invalid
# 3 https://www.pixilink.com/93312 https://www.pixilink.com/93312 floorplan2d
# 4 https://www.pixilink.com/93313 https://www.pixilink.com/93313 picture
# 5 https://www.pixilink.com/93314 https://www.pixilink.com/93314 floorplan2d
# 6 https://www.pixilink.com/93315 https://www.pixilink.com/93315 tour
unique(sitetype$mode)
# [1] "invalid" "floorplan2d" "picture" "tour"
#--------
Basically, urlmode() now opens and closes connections only when necessary, checks for URL validity, URL redirection, and also "intelligently" extracts the value assigned to initial_mode. With the help of future.lapply(), and the progress bar from the progressr package, this can now be applied quite conveniently in parallel to as many pixilink.com/<integer> URLs as desired. With a bit of wrangling thereafter, the results can be presented very tidily as a data.frame as shown.
As an example, I've demonstrated this for a small range in the code above. Note the commented out 1:10000 range in the code in this context: I let this code run the last couple of hours over this (hopefully sufficiently) large range of URLs to check for errors and problems. I can attest that I encountered no errors (only the regular warnings In readLines(mycon) : incomplete final line found on 'https://www.pixilink.com/93334'). For proof, I have the data from all 10000 URLs written to a CSV file that I can provide upon request (I don't fancy uploading that to pastebin or elsewhere unnecessarily). Due to oversight on my part, I forgot to benchmark that run, but I suppose I could do that later if performance metrics are desired/would be considered interesting.
For your purposes, I believe you can simply take the entire code snippet below and run it verbatim (or with modifications) by just changing the range assignment right before the with_progress(do.call(...)) step to a range of your liking. I believe this approach is simpler and does away with having to deal with multiple functions and such (and no tryCatch() messes to deal with).

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) >

Get all R code which is run when running

Suppose I have a bunch of R code in a script and I want to log all the R code which is run from the .GlobalEnv to a flat file or a database together with the errors and warning messages.
I could write a simple logme function as follows or make it a bit more complex to also fetch the errors by changing options(error = mylogginfunction)
mylogfile <- tempfile()
logme <- function(x){
mode <- "at"
if(!file.exists(mylogfile)){
mode <- "wt"
}
myconn <- file(mylogfile, mode)
writeLines(x, myconn)
close(myconn)
invisible()
}
logme(sprintf("%s: started some yadayada, ", Sys.time()))
x <- 10
x * 7
logme(sprintf("%s: done with yadayada", Sys.time()))
## Get the log
cat(readLines(mylogfile))
The log prints out:
2015-05-14 17:24:31: started some yadayada, 2015-05-14 17:24:31: done with yadayada
But what I would like to have is that the logfile writes down the expressions which were executed without me having to write a wrapper around each statement.
I would like the log to look like.
2015-05-14 17:24:31: started some yadayada, x <- 10, x * 7 2015-05-14 17:24:31: done with yadayada
So my question is, how do I fetch what is being executed by R so that I can store the executed expressions in a log/database. And without having to write a function call before each expression (as in myhandler(x <- 10); myhandler(x * 10)).
Any help on this?
For catching input commands you could use addTaskCallback
mylogfile <- tempfile()
addTaskCallback(
function(...) {
expr <- deparse(as.expression(...)[[1]]) # it could handled better...
cat(expr, file=mylogfile, append=TRUE, sep="\n")
# or cat(sprintf("[%s] %s", Sys.time(), expr),...) if you want timestamps
TRUE
}
,name="logger"
)
x <- 10
x * 7
removeTaskCallback("logger")
Then result is:
cat(readLines(mylogfile), sep="\n")
... addTaskCallback definition ...
x <- 10
x * 7
But what you get is parsed expression, which means that line
x+1;b<-7;b==2
will be logged as
x + 1
b <- 7
b == 2
In addition:
output will not be logged, in particular message or warning shown in console
in case of error logging will not be triggered, so you need separate function to handle it
This is probably to simple to work in every case, but you can try with this:
Define myhandler as:
myhandler <- function(x, file = stdout()) {
expr <- substitute(x)
for(e_line in as.list(expr)) {
cat( file = file, as.character(Sys.time()), capture.output(e_line), "\n")
eval(e_line, envir = parent.frame())
}
}
Use it with your code inside the brackets:
myhandler({
a <- 1
a <- a + 1
print(a)
})
Result:
# 2015-05-14 18:46:34 `{`
# 2015-05-14 18:46:34 a <- 1
# 2015-05-14 18:46:34 a <- a + 1
# 2015-05-14 18:46:34 print(a)
# [1] 2
I confess that I don't really get what "to have the running expressions in the same process available as where the R commands are run" means when we chatted a bit in the comments. However, I expanded what I had in mind. You can create a logGenerator.R file with the following lines:
logGenerator<-function(sourcefile,log) {
..zz <- file(log, open = "at")
sink(..zz)
sink(..zz, type = "message")
on.exit({
sink(type="message")
sink()
close(..zz)
})
..x<-parse(sourcefile)
for (..i in 1:length(..x)) {
cat(as.character(Sys.time()),"\n")
cat(as.character(..x[..i]),"\n")
..y<-eval(..x[..i])
}
}
This function takes as arguments the source file and the log file names. This script will take an R file and will log the time at which each instruction is executed. Then it records the expression on the same log file. Every output directed to the stdout() and the error messages are directed to the log file. You obviously don't have to modify in any way your source file.

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.

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