I have started to use R programming language and I have couple of questions bothering me. I have some background in the shell programming which is pretty easy to learn and use in my opinion. However, I have observed that R language is not so straighforward as it could be.
For example, I have a file called tumor.bam in my working directory. In shell programming I can save it and other .bam files into variable FILE and use it simply by typing;
FILE=./tumor.bam
$FILE
If I want to extract the body of filename and use it somewhere else, I can type;
${FILE%.bam}.bai
My question is: is there same kind of shortcut to handle filenames in the R language? Is there any simple way to perform similar actions in R? I must deal with hundreds of different files and this kind of shortcut would be more than favourable.
Thanks for your help in advance!
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I'm a newbie and have searched Stack, the internet, everywhere I can think of.. But I cannot figure out why when I use write.csv() in R it doesn't actually save it as a csv file on my computer. All I want is to get a .csv file of my work from RStudio to Tableau and I've spent a week trying to figure it out. Many of the answers I have read use too much coding "lingo" and I cannot translate it because I'm just a beginner. Would be so so thankful for any help.
Here is the code I'm using:
""write.csv(daily_steps2,"C:\daily_steps2.csv", row.names = TRUE)""
I put the double quotes around the code because it seems like that's what I'm supposed to do here? IDK, but I don't have those when I run the function. There is no error when I run this, it just doesn't show up as a .csv on my computer. It runs but actually does nothing. Thank you so much for any help.
In my opinion, the simplest way would be to save the file to the same folder that rstudio is running in, and use the rstudio gui. It should be write.csv(daily_steps, "./daily_steps.csv") (no quotes around the function), and then on the tab in the bottom right of rstudio, you can select files, and it should be there. Then you can use the graphic user interface to move it to your desktop in a way analogous to what you would do in MS word.
Quick fix is to use double slashes or forward slash for Windows paths. (Also, since row.names=TRUE is the default, there is no need to specify)
write.csv(daily_steps2, "C:\\daily_steps2.csv")
write.csv(daily_steps2, "C:/daily_steps2.csv")
However, consider the OS-agnostic file.path() and avoid issues of folder separators in file paths including forward slash (used on Unix systems like Mac and Linux) or backslash (used on Windows systems).
write.csv(daily_steps2, file.path("C:", "daily_steps2.csv"))
Another benefit is that because of this functional form to path expression, you can pass dynamic file or folder names without paste.
I'm no R-programmer (because of the problem I started learning it), I'm using Python, In a forcasting task I got a dataset signalList.rdata of a pheomenen called partial discharge.
I tried some commands to load, open and view, Hardly got a glimps
my_data <- get(load('C:/Users/Zack-PC/Desktop/Study/Data Sets/pdCluster/signalList.Rdata'))
but, since i lack deep knowledge about R, I wanted to convert it into a csv file, or any type that I can deal with in python.
or, explore it and copy-paste manually.
so, i'm asking for any solution whether using R or Python or any tool to get what's in the .rdata file.
Have you managed to load the data successfully into your working environment?
If so, write.csv is the function you are looking for.
If not,
setwd("C:/Users/Zack-PC/Desktop/Study/Data Sets/pdCluster/")
signalList <- load("signalList.Rdata")
write.csv(signalList, "signalList.csv")
should do the trick.
If you would like to remove signalList from your working directory,
rm(signalList)
will accomplish this.
Note: changing your working directory isn't necessary, it just makes it easier to read in a comment I feel. You may also specify another path for saving your csv to within the second argument of write.csv.
I would like to perform a line-by-line review of code written using RStudio.
I have two questions:
How do I export the script file as a PDF/text file?
How do I make sure that the exported script file includes the line numbers?
Thanks!
** Update: Considering that I wasn't trying to write a report straight from the R/RStudio interface, I realized I could easily open and print the code using Notepad ++. So, here's to remembering a software that most folks probably use for their coding anyway.
I found the answer to a similar question that I had for writing a script to a text file here https://statisticsglobe.com/r-save-all-console-input-output-to-file and wanted to share for others facing the same dilemma. Unfortunately, this method does not write out the line numbers though.
# Writing currently opened R script to file
fout = "filpath/filename.txt"
cat(readChar(rstudioapi::getSourceEditorContext()$path,
file.info(rstudioapi::getSourceEditorContext()$path)$size), file = fout)
Have you ever heard about Knitr?, and also look at this question.
I'm currently implementing a tool in R and I got stucked with a problem. I looked already in the forums and didn't found anything.
I have many .csv files, which are somehow correlated with each other. The problem is I don't know yet how (this depends on the input of the user of the tool). Now I would like to read in a csv-file, that contains an arbitrary function f, e.g. f: a=b+max(c,d), and then the inputs, e.g. a="Final Sheet", b="Sheet1", c="Sheet2", d="Sheet3". (Maybe I didn't explained it very well, then I will upload a picture).
Now my question is, can I somehow read that csv file in, such that I can later use the function f in the programm? (Of course the given function has to be common in R).
I hope you understand my problem and I would appreciate any help or idea!!
I would not combine data files with R source. Much easier to keep them separate. You put your functions in separate script files and then source() them as needed, and load your data with read.csv() etc.
"Keep It Simple" :-)
I am sure there's a contorted way of reading in the source code of a function from a text file and then eval() it somehow -- but I am not sure it would be worth the effort.
I am trying to use R to analyze large DNA sequence files (fastq files, several gigabytes each), but the standard R interface to these files (ShortRead) has to read the entire file at once. This doesn't fit in memory, so it causes an error. Is there any way that I can read a few (thousand) lines at a time, stuff them into an in-memory file, and then use ShortRead to read from that in-memory file?
I'm looking for something like Perl's IO::Scalar, for R.
I don’t know much about R, but have you had a look at the mmap package?
It looks like ShortRead is soon to add a "FastqStreamer" class that does what I want.
Well, I don't know about readFastq accepting something other than a file...
But if it can, for other functions, you can use the R function pipe() to open a unix connection, then you could do this with a combination of unix commands head and tail and some pipes.
For example, to get lines 90 to 100, you use this:
head file.txt -n 100 | tail -n 10
So you can just read the file in chunks.
If you have to, you can always use these unix utilities to create a temporary file, then read that in with shortRead. It's a pain but if it can only take a file, at least it works.
Incidentally, the answer to generally how to do an in-memory file in R (like Perl's IO::Scalar) is the textConnection function. Sadly though, the ShortRead package cannot handle textConnection objects as inputs, so while the idea that I expressed in the question of reading a file in small chunks into in-memory files which are then parsed bit by bit is certainly possible for many applications, but not for may particular application since ShortRead does not like textConnections. So the solution is the FastqStreamer class described above.