I exported very large csv format data via R, but since the the maximum raw of excel is 10480, I had to split it into several csv files in order to export it as csv, it's very inconvenient to handle them after that, is there any apropos way to export nearly 10 times of 10480 raw csv in just one file at the same time via R?
Thank you very much.
If you must use Excel with the data, you can keep the huge csv files and query them from within Excel. You could alternatively start building a database with the csv files and query the database from within Excel. This link might help you.
https://support.office.com/en-us/article/Use-Microsoft-Query-to-retrieve-external-data-42a2ea18-44d9-40b3-9c38-4c62f252da2e
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I have 2 excel files which have macros in it. The file extension ends with .xlsb and .xlsm. I want to read these files into R and do exactly what excel is doing with these files in terms of data inputs in R. What is the way to go about it?
For example: if the excel file calculates house prices in sheet 2 based on data input in sheet 1, how can the same results for house price calculation be obtained in R?
You might take a look at the R package RDCOMClient:
https://github.com/omegahat/RDCOMClient
Here is a nice example shown:
https://www.r-bloggers.com/2021/07/rdcomclient-read-and-write-excel-and-call-vba-macro-in-r/
I have multiple excel files in multiple directories that I am reading into R. However, I don't want to read in EVERY excel file; I only want to read in the most recent ones (for example, only the ones created in the last month). Is there a way to do this?
Currently I am using this to read in all of the excel files, which is working just fine:
filenames <- Sys.glob(file.path('(name of dir)', "19*", "Electrode*02.xlsx")) <br>
elecsheet <- do.call("cbind", lapply(filenames, read_excel))
Somewhere in this second line of code (I think), I need to tell R to look at the metadata and only read in the excel files that have been created since a certain date.
Thank you!
I have a file in Excel which has a column with Chinese simplified characters. When I open it in R from the corresponding CSV file I only get ?'s.
I'm afraid the problem is when exporting from Excel to CSV because when I open the CSV file on a text editor I also get ?'s.
How can I get around this?
The best way to secure your Chinese/Unicode characters is to read file from .xlsx:
library(readxl)
read_xlsx("yourfilepath.xlsx", col_types = "text")
If your file is too big to read from .xlsx, then the best way is to open Excel and split manually into multiple files.
(My experience with a laptop with 8GB RAM is to split files into 250,000 rows x 106 columns.)
If you need to read from .csv, your all windows settings/localization needs to be the same as your file, but even that does not guarantee the integrity of all your Unicode characters (eg. emojis).
(If you also need .csv for something else, then you can use the R function write.csv after you read data from .xlsx into R.)
I have a .csv dataset that gets dumped everyday which I use to generate a daily list for tracking participants using a R script. I would like to automate this R script, however in order to do so, I need to read in the .csv using Sys.Date().
The .csv dataset is named: DumpedList_2013-11-27 (The date will always be today's date).
I would like to import this into the script, like I would for .Rdata file.
load(paste('/srv/Data/Baseline2/baseline2_', Sys.Date(), '.Rdata',sep=''))
What is the equivalent of the command above for reading in .csv files?
I have tried load and read.csv commands, but get error messages:
data=read.csv('P:/DirectoryPath/DumpedList_',Sys.Date(),'.csv')
I also attempted to create todaydate=Sys.Date() and then used it to load the data, but error messages again. a=load(paste("P:/DirectoryPath/DumpedList_",todaydate,".csv"))
Any insight?
By default paste will separate with spaces, use paste0 to join strings together seamlessly:
read.csv(paste0('P:/DirectoryPath/DumpedList_',Sys.Date(),'.csv'))
I have a RMA normalized data ( from the CEL files ) and would like to write it into a file that I could open in excel but have some problems.
library(affy)
cel <- ReadAffy()
pre<-rma(cel)
write.table(pre, file="norm.txt", sep="\t")
write.table(pre, file="norma.txt")
The outut is arranged row-wise in the text file that is written using the above command and hence when exported to excel it is in a wrong form and many of the information is cut off as the maximum rows are used up .The output looks the following way :
GSM 133971.CEL 5.85302 3.54678 6.57648 9.45634
GSM 133972.CEL 4.65784 3.64578 3.54213 7.89566
GSM 133973.CEL 6.78543 3.54623 2.54345 7.89767
How to write it in a proper format from CEL files in R to a notepad or excel ?
You need to extract the values from the normalised probes using the exprs function. Something like:
write.csv(exprs(pre), file="output.csv", row.names=FALSE)
should do the trick.
I'm not totally clear about what the problem is, what do you mean by "proper format"? Excel will struggle with huge tables and doing your analysis in R with Bioconductor is likely a better way to go, you could then export a smaller results or summary table to excel.
Nevertheless, if you want the file written columnwise, you could try:
write.csv(t(pre),file="norm.txt")
But excel (at least used to) allow many more rows than columns.