R: How to use append = TRUE but also delete existing file - r

I have a for loop that loops through files and creates a giant CSV file by appending all the different data frames.
For this to work I have used
append= TRUE
However, since I used that, if i run the loop again, it just appends the same thing to the file before.
I was wondering if there is a way I can tell the code to delete any file with that name before running the loop, so it does not append to old data.
This is the write.table code I have right now
write.table(dat, "data.csv", append = TRUE, sep = ",", col.names=!file.exists("data.csv"))

How about this in front of the loop?
if (file.exists("[yourfilename]"){
unlink("[yourfilename]")
}

Related

Creating objects from all .xlsx documents in working directory

I am trying to create objects from all files in working directory with name of the original file. I tried to go the following way, but couldn't solve appearing problems.
# - SETTING WD
getwd()
setwd("PATH TO THE FILE")
library(readxl)
# - CREATING OBJECTS
file_objects <- list.files()
xlsx_objects <- unlist(grep(".xlsx",file_objects,value = T))
for (i in xlsx_objects) {
xlsx_objects[i] <- read_xlsx(xlsx_objects[i], header = T)
}
I tried to paste [i]item from "xlsx_objects" with path to WD but it only created a list of files names from docs in WD.
I also find information, that read.csv can read only one file at the time, but I guess that it should be the case with for loop, right? It is reading only one file at the time.
Using lapply (as described in this forum) I was able to get the data in the environment, but argument header didn't work, I lost names of my docs in that object which does not have desired structure. I am though looking for having these files in separated objects without calling every document exclusively.
IIUC, you could do something like:
files = list.files("PATH TO THE FILE", full.names = T, pattern = 'xlsx')
list_files = map(files, readxl::read_excel)
(You can't use read.csv to read excel files)
Also I recommend reading about R Projects so you don't have to use setwd() ever again, which makes your code harder to reproduce down the pipeline

R renaming file extension

I have tried looking at File extension renaming in R and using the script without any luck. My question is very much the same.
I have a bunch of files with the a file extension that I want to change. I have used the following code but cannot get the last step to work.
I know similar questions have been asked before but I'm simply stuck and therefore reaching out anyway.
startingDir<-"/Users/anders/Documents/Juni 2019/DATA"
endDir<-"/Users/anders/Documents/Juni 2019/DATA/formatted"
#List over files in startingDir with the extension .zipwblibcurl that I want to replace
old_files<-list.files(startingDir,pattern = "\\.zipwblibcurl")
#View(old_files)
#Renaming the file extension and making a new list i R changing the file extension from .zipwblibcurl to .zip
new_files <- gsub(".zipwblibcurl", ".zip", old_files)
#View(new_files)
#Replacing the old files in the startingDir. Eventually I would like to move them to newDir. For simplicity I have just tried as in the other post without any luck:...
file.rename( old_files, new_files)
After running file.rename I get the output FALSE for every entry.
The full answer here, including comment from #StephaneLaurent: make sure that you have full.names = TRUE inside the list.files(); otherwise the path to the file will not be captured, just the file name.
Full working snippet:
old = list.files(startingDir,
pattern = "\\.zipwblibcurl",
full.names = TRUE) #
# replace the file names
new <- gsub(".zipwblibcurl", ".zip", old )
# Rename old files names to the new file names
file.rename(old, new)
Like #StéphaneLaurent said, it's most likely that R tries to look in the current working directory for the files and can't find them. You can correct this by adding
file.rename(paste(startingDir, old_files, sep = "/"), paste(newDir, new_files, sep = "/"))

How to read a csv file or load an excel workbook by ignoring some characters in the file path?

I'm writing a loop script which involves reading a file from a workbook (using the package XLConnect). The challenge is that the file names contain characters (representing time) that I want to ignore.
For example, here are 3 paths to those files:
G://User//Documents//daily_data//Op_Schedule_20160520_132025.xlsx
G://User//Documents//daily_data//Op_Schedule_20160521_142805.xlsx
G://User//Documents//daily_data//Op_Schedule_20160522_103052.xlsx
I need to import hundreds of those files. I can easily account for the character string representing the date (e.g. 20160522), but not the time.
Is there a way to tell R to ignore some characters located in the file path? Here is how I was thinking of writing my script (the "???" is where i need help). I know a loop is probably not the most efficient way, but i'm open to suggestions, should you have any:
require(XLConnect)
path= "G://User//Documents//daily_data//Op_Schedule_"
wd.seq = format(seq(as.Date("2014-01-01"),as.Date("2016-12-31"),"days"),format="%Y%m%d")
scheduleList = rep(list(matrix(1,1,1)),length(wd.seq))
for(i in 1:length(wd.seq)) {
wb = loadWorkbook(file= paste0(path,wd.seq[i],"???",".xlxs"))
scheduleList[[i]] = readWorksheet(wb,sheet='=SCHEDULE', header = TRUE)
}
`
Thanks for reading and suggestions, if any.
Mathieu
I don't know if this is helpful, but if you want to read all the files in a certain directory (which it seems to me is what you're after), you can read all the filenames into a list using the list.files() function, for example
fileList <- list.files(""G://User//Documents//daily_data//")
And then load the xlsx files looping through the list with a for loop
for(i in fileList) {
loadWorkbook(file = i)
}
I haven't used the XLConnect function before so that exact code probably doesn't work, but the loop will iterate through all the files in that directory and so you can construct your loading call using the i variable for the filename (it won't be an absolute path though, so you might need to use paste to add the first part of the filepath)
I realize there might be other files in the directory that are not excel files, you could use grepl to select only files containg "OP_Schedule_"
fileListClean <- fileList[grepl("Op_Schedule_",fileList)]
or perhaps only selecting .xlsx files in the directory:
fileListClean <- fileList[grepl(".xlsx",fileList)]
Edit to fit your reply:
Since you need to fit it to a sequence, you can do it as you did earlier:
wd.seq = format(seq(as.Date("2014-01-01"),as.Date("2016-12-31"),"days"),format="%Y%m%d")
wd.seq2 <- paste("Op_Schedule_", wd.seq, sep = "")
And then use grepl to only pick files starting with that extensions:
fileListClean <- fileList[grepl(paste(wd.seq2, collapse = "|"), fileList)]
Full disclosure: The last part i got from this SO answer: grep using a character vector with multiple patterns

Adding items to txt output in the same file from

I would like to printout to the same txt (outfile.txt) file items one after the other.
For instance, first I would like to print to outfile.txt a dataframe - u. Afterwards, a written message 'hello' and finally a summary of model.
How can I do it? Is sink(outfile.txt) is appropriate for this case?
It is generally a very bad idea to mix data in the same file. I advise against it in the strongest terms: it makes the data file next to unusable for other programs.
That said, most functions to save data have an append argument. You can set this to TRUE to append to an existing file rather than overwriting its contents. No need for sink.
Where you do need sink (or equivalent) is when you want to write contents formatted in the same way as it’s written on the console. This, for instance, is the case for summary.
Here’s an example similar to your requirements:
filename = 'test.txt'
write.table(head(cars), filename, quote = FALSE, col.names = NA)
cat('\nHello\n\n', file = filename, append = TRUE)
capture.output(print(summary(cars)), file = filename, append = TRUE)
Rather than sink, this uses capture.output, which is a convenience wrapper around sink.

Read, process and export analysis results from multiple .csv files in R

I have a bunch of CSV files and I would like to perform the same analysis (in R) on the data within each file. Firstly, I assume each file must be read into R (as opposed to running a function on the CSV and providing output, like a sed script).
What is the best way to input numerous CSV files to R, in order to perform the analysis and then output separate results for each input?
Thanks (btw I'm a complete R newbie)
You could go for Sean's option, but it's going to lead to several problems:
You'll end up with a lot of unrelated objects in the environment, with the same name as the file they belong to. This is a problem because...
For loops can be pretty slow, and because you've got this big pile of unrelated objects, you're going to have to rely on for loops over the filenames for each subsequent piece of analysis - otherwise, how the heck are you going to remember what the objects are named so that you can call them?
Calling objects by pasting their names in as strings - which you'll have to do, because, again, your only record of what the object is called is in this list of strings - is a real pain. Have you ever tried to call an object when you can't write its name in the code? I have, and it's horrifying.
A better way of doing it might be with lapply().
# List files
filelist <- list.files(pattern = "*.csv")
# Now we use lapply to perform a set of operations
# on each entry in the list of filenames.
to_dispose_of <- lapply(filelist, function(x) {
# Read in the file specified by 'x' - an entry in filelist
data.df <- read.csv(x, skip = 1, header = TRUE)
# Store the filename, minus .csv. This will be important later.
filename <- substr(x = x, start = 1, stop = (nchar(x)-4))
# Your analysis work goes here. You only have to write it out once
# to perform it on each individual file.
...
# Eventually you'll end up with a data frame or a vector of analysis
# to write out. Great! Since you've kept the value of x around,
# you can do that trivially
write.table(x = data_to_output,
file = paste0(filename, "_analysis.csv"),
sep = ",")
})
And done.
You can try the following codes by putting all csv files in the same directory.
names = list.files(pattern="*.csv") %csv file names
for(i in 1:length(names)){ assign(names[i],read.csv(names[i],skip=1, header=TRUE))}
Hope this helps !

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