Importing a big xlsx file into R?

I stumbled on this question when someone sent me (yet another) Excel file to analyze. This one isn't even that big but for whatever reason I was running into a similar error:

java.lang.OutOfMemoryError: GC overhead limit exceeded

Based on comment by @DirkEddelbuettel in a previous answer I installed the openxlsx package (http://cran.r-project.org/web/packages/openxlsx/). and then ran:

library("openxlsx")mydf <- read.xlsx("BigExcelFile.xlsx", sheet = 1, startRow = 2, colNames = TRUE)

It was just what I was looking for. Easy to use and wicked fast. It's my new BFF. Thanks for the tip @DirkEddelbuettel!

BTW, I don't want to poach this answer from Dirk E, so if he posts an answer, please accept it rather than mine!

options(java.parameters = "-Xmx2048m")  ## memory set to 2 GBlibrary(XLConnect)

allow for more memory using "options" before any java component is loaded. Then load XLConnect library (it uses java).

That's it. Start reading in data with readWorksheet .... and so on.:)

I do agree with @orville jackson response & it really helped me too.

Inline to the answer provided by @orville jackson. here is the detailed description of how you can use openxlsx for reading and writing big files.

When data size is small, R has many packages and functions which can be utilized as per your requirement.

write.xlsx, write.xlsx2, XLconnect also do the work but these are sometimes slow as compare to openxlsx.

So, if you are dealing with the large data sets and came across java errors. I would suggest to have a look of "openxlsx" which is really awesome and reduce the time by 1/12th.

I've tested all and finally i was really impressed with the performance of openxlsx capabilities.

Here are the steps for writing multiple datasets into multiple sheets.

install.packages("openxlsx")library("openxlsx")start.time <- Sys.time()# Creating large data framex <- as.data.frame(matrix(1:4000000,200000,20))y <- as.data.frame(matrix(1:4000000,200000,20))z <- as.data.frame(matrix(1:4000000,200000,20))# Creating a workbookwb <- createWorkbook("Example.xlsx")Sys.setenv("R_ZIPCMD" = "C:/Rtools/bin/zip.exe") ## path to zip.exe

Sys.setenv("R_ZIPCMD" = "C:/Rtools/bin/zip.exe") has to be static as it takes reference of some utility from Rtools.

Note: Incase Rtools is not installed on your system, please install it first for smooth experience. here is the link for your reference: (choose appropriate version)https://cran.r-project.org/bin/windows/Rtools/

check the options as per link below (need to select all the check box while installation)https://cloud.githubusercontent.com/assets/7400673/12230758/99fb2202-b8a6-11e5-82e6-836159440831.png

# Adding a worksheets : parameters for addWorksheet are 1. Workbook Name 2. Sheet NameaddWorksheet(wb, "Sheet 1")addWorksheet(wb, "Sheet 2")addWorksheet(wb, "Sheet 3")# Writing data in to respetive sheets: parameters for writeData are 1. Workbook Name 2. Sheet index/ sheet name 3. dataframe namewriteData(wb, 1, x)# incase you would like to write sheet with filter available for ease of access you can pass the parameter withFilter = TRUE in writeData function.writeData(wb, 2, x = y, withFilter = TRUE)## Similarly writeDataTable is another way for representing your data with table formatting:writeDataTable(wb, 3, z)saveWorkbook(wb, file = "Example.xlsx", overwrite = TRUE)end.time <- Sys.time()time.taken <- end.time - start.timetime.taken

openxlsx package is really good for reading and writing huge data from/ in excel files and has lots of options for custom formatting within excel.

The interesting fact is that we don’t have to bother about java heap memory here.