How to prevent from overwriting the file? - r

I'm looking for a way to prevent R from overwriting files during the session. The more general solution then better.
Currently I got bunch of functions called e.g.: safe.save, safe.png, safe.write.table which are implemented more or less as
safe.smth <- function(..., file) {
if (file.exists(file))
stop("File exists!")
else
smth(..., file=file)
}
It works, but only if I got control over execution. If some (not mine) function created file I can't stop it from overwrite.
Another way is to set read only flag on files, which also top R from overwriting existing files. But this has drawbacks as well (e.g.: you don't know which files needs to be protected).
Or write one-liner:
protect <- function(p) if (file.exists(p)) stop("File exsits!") else p
and use it always when providing filename.
Is there a way to force this behaviour session wide? Some kind of global setting for connections? Maybe only for subset of functions (graphics devices, file-created connections, etc)? Maybe some system specific solution?
The following could be used as test case:
test <- function(i) {
try(write.table(i, "test_001.csv"))
try(writeLines(as.character(i), "test_002.txt"))
try({png("test_003.png");plot(i);dev.off()})
try({pdf("test_004.pdf");plot(i);dev.off()})
try(save(i, file="test_005.RData"))
try({f<-file("test_006.txt", "w");cat(as.character(i), file=f);close(f)})
}
test(1)
magic_incantations() # or magic_incantations(test(2)), etc.
test(2) # should fail on all writes (to test set read-only to files from first call)

The conventional way to avoid clobbering data files isn't to look for OS hacks, but to use filenames and directories that are special for your session.
session.dir <- tempdir()
...
write.table(i, file.path(session.dir,"test_001.csv"))
writeLines(as.character(i), file.path(session.dir,"test_002.txt"))
...
or
session.pid <- Sys.getpid()
...
write.table(i, paste0("test_001.",session.pid,".csv"))
writeLines(as.character(i), paste0("test_002.",session.pid,".txt"))
...

Related

downloading data and saving data to a folder in batches

I have 200,000 links that I am trying to download, I have tried downloading it all in one go but I ran into memory issues.
I am trying to create a function which will download 1000 links at a time and save them in a folder.
Packages:
library(dplyr)
library(purrr)
library(edgarWebR)
A small sample of the data is as follows:
Data 1:
urls_to_parse <- c("https://www.sec.gov/Archives/edgar/data/1750/000104746918004978/a2236183z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746917004528/a2232622z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746916014299/a2228768z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746915006136/a2225345z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746914006243/a2220733z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746913007797/a2216052z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746912007300/a2210166z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746911006302/a2204709z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746910006500/a2199382z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746909006783/a2193700z10-k.htm"
)
I then apply the following function to download these 10 links
parsed_files <- map(urls_to_parse, possibly(parse_filing, otherwise = NA))
Which stores it as a nice list, I can then apply names(parsed_files) <- urls_to_parse to name the lists as the links from where they were downloading them from. I can also use output <- plyr::ldply(parsed_files, data.frame) to store everything in a nice data frame.
Using the below data, how could I create batches to download the data in say batches of 10?
What I have currently:
start = 1
end = 100
output <- NULL
output_fin <- NULL
for(i in start:end){
output[[i]] <- map(urls_to_parse[[i]], possibly(parse_filing, otherwise = NA))
names(output) <- urls_to_parse[start:end]
save(output_fin, file = paste0("C:/Users/Downloads/data/",i, "output.RData"))
}
I am sure there is a better way using a function, since this code breaks for some of the results.
More data: - 100 links
urls_to_parse <- c("https://www.sec.gov/Archives/edgar/data/1750/000104746918004978/a2236183z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746917004528/a2232622z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746916014299/a2228768z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746915006136/a2225345z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746914006243/a2220733z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746913007797/a2216052z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746912007300/a2210166z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746911006302/a2204709z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746910006500/a2199382z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746909006783/a2193700z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746908008126/a2186742z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000110465907055173/a07-18543_110k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000110465906047248/a06-15961_110k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000110465905033688/a05-12324_110k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746904023905/a2140220z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746903028005/a2116671z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000091205702033450/a2087919z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095012310108231/c61492e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095015208010514/n48172e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095013707018659/c22309e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095013707000193/c11187e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095013406000594/c01109e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000120677405000032/d16006.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000120677404000013/d13773.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000104746903001075/a2097401z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000091205702001614/a2067550z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/319126/000115752308008030/a5800571.htm",
"https://www.sec.gov/Archives/edgar/data/319126/000115752307009801/a5515869.htm",
"https://www.sec.gov/Archives/edgar/data/319126/000115752306009238/a5227919.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046908000102/alpharmainc_10k.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046907000017/alo10k2006.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046906000027/alo10k2005.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046905000021/alo10k2004final.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046904000058/alo10k2003master.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046903000001/alo10k.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046902000004/alo10k2001.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046901500003/alo.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000000620118000009/a10k123117.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000119312517051216/d286458d10k.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000119312516474605/d78287d10k.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000119312515061145/d829913d10k.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000000620114000004/aagaa10k-20131231.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000620113000023/amr-10kx20121231.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000119312512063516/d259681d10k.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095012311014726/d78201e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000620110000006/ar123109.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000620109000009/ar120810k.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000451508000014/ar022010k.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013407003888/d43815e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013406003715/d33303e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013405003726/d22731e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013404002668/d12953e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000104746903013301/a2108197z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/65695/000095013407003823/h42902e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/65695/000095012906002343/h31028e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/65695/000095012905002955/h22337e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000156459018005085/cece-10k_20171231.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000156459017004264/cece-10k_20161231.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000156459016015157/cece-10k_20151231.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312515095828/d864880d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312514098407/d661608d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312513109153/d444138d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312512119293/d293768d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312511067373/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312510069639/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312509055504/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312508058939/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312507071909/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312506068031/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312505077739/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312504052176/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000110465910047121/a10-16705_110k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000114420409046933/v159572_10k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000110465906060737/a06-19311_110k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000104746905022854/a2162888z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000104746904028585/a2143353z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000104746903031974/a2119476z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000143774918010388/avx20180331_10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916317000028/avx-20170331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916316000079/avx-20160331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916315000024/avx-20150331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916314000035/avx-20140331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916313000022/avx-20130331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916312000024/avxform10kfy12.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916311000013/avxform10kfy11.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916310000020/avxform10kfy10.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916309000117/form10kfy09.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916308000192/form10qq1fy09.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916308000101/form10kfy08.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916307000122/form10kfy07.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916306000102/avxfy06form10-k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916305000094/fy0510k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916304000091/fy0410k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916303000020/fy0310k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916302000007/r10k-0302.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462218000018/pnw2017123110-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462217000010/pnw2016123110-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462216000087/pnw2015123110-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462215000013/pnw12311410-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000110465914012068/a13-25897_110k.htm"
)
Looping over to do batch job as you showed is a bad idea. If you have a 1000s of files to be downloaded, how do you recover from errors?
The performance is not solely depend on your computer's configuration, but the network performance is crucial.
Here are couple of suggestions.
Option 1
partition all URLs in to batches to be able to download them parallelly. The number of files to be downloaded could be equal to number of cores in your computer. Look at this question; reading multiple files quickly in R
store these batches in a queue objects - For ex: using a package like https://cran.r-project.org/web/packages/dequer/dequer.pdf
pop the queue and use the batch of URLs in your parallel file download function.
Use a retryable file download function like in -- HTTP error 400 in R, error handling, How to retry instead of forcing to stop?
Once the queue is completed, move to the next partition.
wrap the whole operation in a retryable loop. For example; How to retry a statement on error?
Why do I use a queue? Because you could retry on error easily.
A pseudo code
file_url_partitions <- partion_as_batches(all_urls, batch_size)
attempts = 3
while( file_url_partitions is not empty && attempt <= 3 ) {
batch = file_url_partitions.pop()
tryCatch({
download_parallel(batch)
}, some_exception = function(se) {
file_url_partitions.push(batch)
attemp = attempt+1
})
}
Note: I don't have access to R studio/environment now hence no way to try.
Option 2
Download files separately using a download manager/similar and use downloaded files.
Some useful resources:
https://www.r-bloggers.com/r-with-parallel-computing-from-user-perspectives/
http://adv-r.had.co.nz/beyond-exception-handling.html

If statement for directory starting with specific character

I have a script in which I call R and depending on the directory I specify I want it to carry out a different process. One directory starts with L and the other with S. I have numerous directories that either start with L or S and they all end differently.
I specify the directory in bash and run a script like so:
./script L_dir
or
./script S_dir
So within my R script I have it set up as such:
args <- commandArgs(TRUE)
img_dir <- args[1]
if(img_dir == "^L*"){
do_process_1
} else {
do_process_2
}
Everything works fine except that no matter what directory I specify, the process called will always be do_process_2.
I have looked at this question and tried to adapt it but can't get it to work.
After changing my code to
if(grepl("^LM*", img_dir)){
do_process_1
} else {
do_process_2
}
it worked. Be careful if you change it to the above and it still carries out process_2. This may be because what you are looking for, in my case ^L*, may also be in your second directory name i.e. dir_L = LMNOP, dir_S = STUVLJH. But once i specified ^LM* it did what i wanted it to do.

Allowing (or circumventing) parallel write access to a file

I'm calling a Windows executable from multiple parallel R processes (via a system call within parSapply). This .exe (let's call it my.exe) is passed a filename as an argument, and processes this file (details are probably irrelevant). Unfortunately, my.exe creates a log file (in the same directory as my.exe) that it writes to while it runs, and, since the log file's name is fixed, subsequent R processes calling my.exe results in my.exe` throwing the error:
Cannot create result file "log.res".
Do you have write access in the current directory?
I've managed to work around this by creating multiple copies of the my.exe (as many as the number of cores in my cluster, i.e. 7). I can then ensure that each is only in use by a single R process at any one time, by passing to the cores a vector of 7 paths to .bat files, each of which repeatedly calls a given copy of my.exe.
Is there a more elegant way to deal with this issue, perhaps by having the processes create virtual instances of my.exe automagically? I don't require the log files.
Since this is an error thrown by the program and not by R, I suspect there might be no way to permit concurrent write access to the log file from the R side of things.
Ideally, I want to be doing something like this:
ff <- c('a', 'long', 'vector', 'of', 'file', 'paths') # abbreviated
parSapply(cl, ff, function(f) system(sprintf("my.exe %s", f)))
but instead I've resorted to doing (more or less) this (after copying my.exe to c:/1/, c:/2/, c:/3/, through c:/7/):
cat(paste('CALL C:/1/my.exe', ff[1:10], '/RUN=YES'), file='run1.bat', sep='\n')
cat(paste('CALL C:/2/my.exe', ff[11:20], '/RUN=YES'), file='run2.bat', sep='\n')
cat(paste('CALL C:/3/my.exe', ff[21:30], '/RUN=YES'), file='run3.bat', sep='\n')
cat(paste('CALL C:/4/my.exe', ff[31:40], '/RUN=YES'), file='run4.bat', sep='\n')
cat(paste('CALL C:/5/my.exe', ff[41:50], '/RUN=YES'), file='run5.bat', sep='\n')
cat(paste('CALL C:/6/my.exe', ff[51:60], '/RUN=YES'), file='run6.bat', sep='\n')
cat(paste('CALL C:/7/my.exe', ff[61:70], '/RUN=YES'), file='run7.bat', sep='\n')
parSapply(cl, c('run1.bat', 'run2.bat', 'run3.bat', 'run4.bat',
'run5.bat', 'run6.bat', 'run7.bat'), system)
(Above, instead of letting parSapply assign the 70 elements of ff to the various processes, I manually split them when creating the batch files, and then run the batch files in parallel.)
It sounds like your basic strategy is the only known solution to the problem, but I think it can be done more elegantly. For instance, you could avoid creating .BAT files by having each worker execute a different command line based on a worker ID. The worker ID could be assigned using:
# Assign worker ID's to each of the cluster workers
setid <- function(id) assign(".Worker.id", id, pos=globalenv())
clusterApply(cl, seq_along(cl), setid)
Also, you may want to automate the creation of the directories that contain "my.exe". I also prefer to use a symlink rather than a copy of the executable:
# Create directories containing a symlink to the real executable
exepath <- "C:/bin/my.exe" # Path to the real executable
pdir <- getwd() # Parent of the new executable directories
myexe <- file.path(pdir, sprintf("work_%d", seq_along(cl)), "my.exe")
for (x in myexe) {
dir.create(dirname(x), showWarnings=FALSE)
if (file.exists(x))
unlink(x)
file.symlink(exepath, x)
}
If symlinks don't fool "my.exe" into creating the log file in the desired directory, you could try using "file.copy" instead of "file.symlink".
Now you can run your parallel job using:
# Each worker executes a different symlink to the real executable
worker.fun <- function(f, myexe) {
system(sprintf("%s %s /RUN=YES", myexe[.Worker.id], f))
}
ff <- c('a', 'long', 'vector', 'of', 'file', 'paths')
parSapply(cl, ff, worker.fun, myexe)
You could also delete the directories that were created, but they don't use much space since symlinks are used, so it might be better to keep them, especially during debugging/testing.

R - Connect Scripts via Pipes

I have a number of R scripts that I would like to chain together using a UNIX-style pipeline. Each script would take as input a data frame and provide a data frame as output. For example, I am imagining something like this that would run in R's batch mode.
cat raw-input.Rds | step1.R | step2.R | step3.R | step4.R > result.Rds
Any thoughts on how this could be done?
Writing executable scripts is not the hard part, what is tricky is how to make the scripts read from files and/or pipes. I wrote a somewhat general function here: https://stackoverflow.com/a/15785789/1201032
Here is an example where the I/O takes the form of csv files:
Your step?.R files should look like this:
#!/usr/bin/Rscript
OpenRead <- function(arg) {
if (arg %in% c("-", "/dev/stdin")) {
file("stdin", open = "r")
} else if (grepl("^/dev/fd/", arg)) {
fifo(arg, open = "r")
} else {
file(arg, open = "r")
}
}
args <- commandArgs(TRUE)
file <- args[1]
fh.in <- OpenRead(file)
df.in <- read.csv(fh.in)
close(fh.in)
# do something
df.out <- df.in
# print output
write.csv(df.out, file = stdout(), row.names = FALSE, quote = FALSE)
and your csv input file should look like:
col1,col2
a,1
b,2
Now this should work:
cat in.csv | ./step1.R - | ./step2.R -
The - are annoying but necessary. Also make sure to run something like chmod +x ./step?.R to make your scripts executables. Finally, you could store them (and without extension) inside a directory that you add to your PATH, so you will be able to run it like this:
cat in.csv | step1 - | step2 -
Why on earth you want to cram your workflow into pipes when you have the whole R environment available is beyond me.
Make a main.r containing the following:
source("step1.r")
source("step2.r")
source("step3.r")
source("step4.r")
That's it. You don't have to convert the output of each step into a serialised format; instead you can just leave all your R objects (datasets, fitted models, predicted values, lattice/ggplot graphics, etc) as they are, ready for the next step to process. If memory is a problem, you can rm any unneeded objects at the end of each step; alternatively, each step can work with an environment which it deletes when done, first exporting any required objects to the global environment.
If modular code is desired, you can recast your workflow as follows. Encapsulate the work done by each file into one or more functions. Then call these functions in your main.r with the appropriate arguments.
source("step1.r") # defines step1_read_input, step1_f2
source("step2.r") # defines step2_f2
source("step3.r") # defines step3_f1, step3_f2, step3_f3
source("step4.r") # defines step4_write_output
step1_read_input(...)
step1_f2(...)
....
step4write_output(...)
You'll need to add a line at the top of each script to read in from stdin. Via this answer:
in_data <- readLines(file("stdin"),1)
You'll also need to write the output of each script to stdout().

Robust cross-platform method of moving a directory

What is the most robust method to move an entire directory from say /tmp/RtmpK4k1Ju/oldname to /home/jeroen/newname? The easiest way is file.rename however this doesn't always work, for example when from and to are on different disks. In that case the entire directory needs to be recursively copied.
Here is something I came up with, however it's a bit involved, and I'm not sure it will work cross-platform. Is there a better way?
dir.move <- function(from, to){
stopifnot(!file.exists(to));
if(file.rename(from, to)){
return(TRUE)
}
stopifnot(dir.create(to, recursive=TRUE));
setwd(from)
if(all(file.copy(list.files(all.files=TRUE, include.dirs=TRUE), to, recursive=TRUE))){
#success!
unlink(from, recursive=TRUE);
return(TRUE)
}
#fail!
unlink(to, recursive=TRUE);
stop("Failed to move ", from, " to ", to);
}
I think file.copy shall be sufficient.
file.copy(from, to, overwrite = recursive, recursive = FALSE,
copy.mode = TRUE)
From ?file.copy:
from, to: character vectors, containing file names or paths. For
‘file.copy’ and ‘file.symlink’ ‘to’ can alternatively
be the path to a single existing directory.
and:
recursive: logical. If ‘to’ is a directory, should directories in
‘from’ be copied (and their contents)? (Like ‘cp -R’ on
POSIX OSes.)
From the description about recursive we know from can have directories. Therefore in your above code listing all files before copy is unnecessary. And just remember the to directory would be the parent of the copied from. For example, after file.copy("dir_a/", "new_dir/", recursive = T), there'd be a dir_a under new_dir.
Your code have done the deletion part pretty well. unlink has a nice recursive option, which file.remove doesn't.
unlink(x, recursive = FALSE, force = FALSE)
Why not just invoke the system directly:
> system('mv /tmp/RtmpK4k1Ju/oldname /home/jeroen/newname')

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