I am trying to export a dataframe from R to excel. I am using the 'writexl' package but it does not seem to work.
The code is as following:
install.packages('writexl')
library(writexl)
write_xlsx(data_frame, "H:\\folder1.xlsx")
There does not seem to be any error produced and the code appears to have run, however when I look in 'folder1' the data_frame is not there.
Is there anything I am doing incorrectly?
I've found the openxlsx package to be easier to use than the xlsx package. It also doesn't have a java dependency. The main command for directly writing a data frame to an Excel file is write.xlsx. You can also create worksheets, do lots of fancy formatting and write multiple tables to a worksheet (see the vignettes here for some examples), but start with write.xlsx for the basic creation of Excel files.
I have a 174603 rows and 178 column dataframe, which I'm importing to Excel using openxlsx::saveWorkbook, (Using this package to obtain the aforementioned format of cells, with colors, header styles and so on). But the process is extremely slow, (depending on the amount of memory used by the machine it can take from 7 to 17 minutes!!) and I need a way to reduce this significantly (Doesn't need to be seconds, but anything bellow 5 min would be OK)
I've already searched other questions but they all seem to focus either in exporting to R (I have no problem with this) or writing non-formatted files to R (using write.csv and other options of the like)
Apparently I can't use xlsx package because of the settings on my computer (industrial computer, Check comments on This question)
Any suggestions regarding packages or other functionalities inside this package to make this run faster would be highly appreciated.
This question has some time ,but I had the same problem as you and came up with a solution worth mentioning.
There is package called writexl that has implemented a way to export a data frame to Excel using the C library libxlsxwriter. You can export to excel using the next code:
library(writexl)
writexl::write_xlsx(df, "Excel.xlsx",format_headers = TRUE)
The parameter format_headers only apply centered and bold titles, but I had edited the C code of the its source in github writexl library made by ropensci.
You can download it or clone it. Inside src folder you can edit write_xlsx.c file.
For example in the part that he is inserting the header format
//how to format headers (bold + center)
lxw_format * title = workbook_add_format(workbook);
format_set_bold(title);
format_set_align(title, LXW_ALIGN_CENTER);
you can add this lines to add background color to the header
format_set_pattern (title, LXW_PATTERN_SOLID);
format_set_bg_color(title, 0x8DC4E4);
There are lots of formating you can do searching in the libxlsxwriter library
When you have finished editing that file and given you have the source code in a folder called writexl, you can build and install the edited package by
shell("R CMD build writexl")
install.packages("writexl_1.2.tar.gz", repos = NULL)
Exporting again using the first chunk of code will generate the Excel with formats and faster than any other library I know about.
Hope this helps.
Have you tried ;
write.table(GroupsAlldata, file = 'Groupsalldata.txt')
in order to obtain it in txt format.
Then on Excel, you can simply transfer you can 'text to column' to put your data into a table
good luck
I am using the topmodel package in R with the huagrahuma dataset that comes with the package.
I would like to bring all these variables into excel, edit those as per my requirement & then use the base in R.
package & data: https://rdrr.io/cran/topmodel/man/huagrahuma.html
You can save datasets using a command like write.csv() or one of the writing functions from the readr, readxl or xlsx packages (for example). Using:
?write.csv()
Will show you how to use the function. Once you have it saved as a .csv file on your computer, you can open it with Excel and do what you need with it.
Edit: Following G5W's comment below, you could try extracting elements of this list and saving those, depending on what you actually want to change. To be honest, with a list structure, you are better off changing the data in R, using any of the apply family of functions, or the purrr package. R is much better than Excel for transforming/tidying data, so why not use it? :-)
I want to export a dataset in the MASS package to SPSS for further investigation. I'm looking for the EuStockMarkets data set in the package.
As described in http://www.statmethods.net/input/exportingdata.html, I did:
library(foreign)
write.foreign(EuStockMarkets, "c:/mydata.txt", "c:/mydata.sps", package="SPSS")
I got a text file but the sps file is not a valid SPSS file. I'm really looking for a way to export the dataset to something that a SPSS can open.
As Thomas has mentioned in the comments, write.foreign doesn't generate native SPSS datafiles (.sav). What it does generate is the data in a comma delimited format (the .txt file) and a basic syntax file for reading that data into SPSS (the .sps file). The EuStockMarkets data object class is multivariate time series (mts) so when it's exported the metadata is lost and the resulting .sps file, lacking variable names, throws an error when you try to run it in SPSS. To get around this you can export it as a data frame instead:
write.foreign(as.data.frame(EuStockMarkets), "c:/mydata.txt", "c:/mydata.sps", package="SPSS")
Now you just need to open mydata.sps as a syntax file (NOT as a datafile) in SPSS and run it to read in the datafile.
Rather than exporting it, use the STATS GET R extension command. It will take a specified data frame from an R workspace/dataset and convert it into a Statistics dataset. You need the R Essentials for Statistics and the extension command, which are available via the SPSS Community site (www.ibm.com/developerworks/spssdevcentral)
I'm not trying to answer a question that has been answered. I just think there is something else to complement for other users looking for this.
On your SPSS window, you just need to find the first line of code and edit it. It should be something like this:
"file-name.txt"
You need to find the folder path where you're keeping your file:
"C:\Users\DELL\Google Drive\Folder-With-Your-File"
Then you just need to add this path to your file's name:
"C:\Users\DELL\Google Drive\Folder-With-Your-File\file-name.txt"
Otherwise SPSS will not recognize the .txt file.
Sorry if I'm repeating some information here, I just wanted to make it easier to understand.
I suppose that EuStockMarkets is a (labelled) data frame.
This should work and even keep the variable and value labels:
require(sjlabelled)
write_spss(EuStockMarkets, "mydata.sav")
Or you try rio:
rio::export(EuStockMarkets, "mydata.sav")
Please can someone help me on the best way to import an excel 2007 (.xlsx) file into R. I have tried several methods and none seems to work. I have upgraded to 2.13.1, windows XP, xlsx 0.3.0, I don't know why the error keeps coming up. I tried:
AB<-read.xlsx("C:/AB_DNA_Tag_Numbers.xlsx","DNA_Tag_Numbers")
OR
AB<-read.xlsx("C:/AB_DNA_Tag_Numbers.xlsx",1)
but I get the error:
Error in .jnew("java/io/FileInputStream", file) :
java.io.FileNotFoundException: C:\AB_DNA_Tag_Numbers.xlsx (The system cannot find the file specified)
Thank you.
For a solution that is free of fiddly external dependencies*, there is now readxl:
The readxl package makes it easy to get data out of Excel and into R.
Compared to many of the existing packages (e.g. gdata, xlsx,
xlsReadWrite) readxl has no external dependencies so it's easy to
install and use on all operating systems. It is designed to work with
tabular data stored in a single sheet.
Readxl supports both the legacy .xls format and the modern xml-based
.xlsx format. .xls support is made possible the with libxls C library,
which abstracts away many of the complexities of the underlying binary
format. To parse .xlsx, we use the RapidXML C++ library.
It can be installed like so:
install.packages("readxl") # CRAN version
or
devtools::install_github("hadley/readxl") # development version
Usage
library(readxl)
# read_excel reads both xls and xlsx files
read_excel("my-old-spreadsheet.xls")
read_excel("my-new-spreadsheet.xlsx")
# Specify sheet with a number or name
read_excel("my-spreadsheet.xls", sheet = "data")
read_excel("my-spreadsheet.xls", sheet = 2)
# If NAs are represented by something other than blank cells,
# set the na argument
read_excel("my-spreadsheet.xls", na = "NA")
* not strictly true, it requires the Rcpp package, which in turn requires Rtools (for Windows) or Xcode (for OSX), which are dependencies external to R. But they don't require any fiddling with paths, etc., so that's an advantage over Java and Perl dependencies.
Update There is now the rexcel package. This promises to get Excel formatting, functions and many other kinds of information from the Excel file and into R.
You may also want to try the XLConnect package. I've had better luck with it than xlsx (plus it can read .xls files too).
library(XLConnect)
theData <- readWorksheet(loadWorkbook("C:/AB_DNA_Tag_Numbers.xlsx"),sheet=1)
also, if you are having trouble with your file not being found, try selecting it with file.choose().
I would definitely try the read.xls function in the gdata package, which is considerably more mature than the xlsx package. It may require Perl ...
Update
As the Answer below is now somewhat outdated, I'd just draw attention to the readxl package. If the Excel sheet is well formatted/lain out then I would now use readxl to read from the workbook. If sheets are poorly formatted/lain out then I would still export to CSV and then handle the problems in R either via read.csv() or plain old readLines().
Original
My preferred way is to save individual Excel sheets in comma separated value (CSV) files. On Windows, these files are associated with Excel so you don't loose the double-click-open-in-Excel "feature".
CSV files can be read into R using read.csv(), or, if you are in a location or using a computer set up with some European settings (where , is used as the decimal place), using read.csv2().
These functions have sensible defaults that makes reading appropriately formatted files simple. Just keep any labels for samples or variables in the first row or column.
Added benefits of storing files in CSV are that as the files are plain text they can be passed around very easily and you can be confident they will open anywhere; one doesn't need Excel to look at or edit the data.
Example 2012:
library("xlsx")
FirstTable <- read.xlsx("MyExcelFile.xlsx", 1 , stringsAsFactors=F)
SecondTable <- read.xlsx("MyExcelFile.xlsx", 2 , stringsAsFactors=F)
I would try 'xlsx' package for it is easy to handle and seems mature enough
worked fine for me and did not need any additionals like Perl or whatever
Example 2015:
library("readxl")
FirstTable <- read_excel("MyExcelFile.xlsx", 1)
SecondTable <- read_excel("MyExcelFile.xlsx", 2)
nowadays I use readxl and have made good experience with it.
no extra stuff needed
good performance
This new package looks nice http://cran.r-project.org/web/packages/openxlsx/openxlsx.pdf
It doesn't require rJava and is using 'Rcpp' for speed.
If you are running into the same problem and R is giving you an error -- could not find function ".jnew" -- Just install the library rJava. Or if you have it already just run the line library(rJava). That should be the problem.
Also, it should be clear to everybody that csv and txt files are easier to work with, but life is not easy and sometimes you just have to open an xlsx.
For me the openxlx package worked in the easiest way.
install.packages("openxlsx")
library(openxlsx)
rawData<-read.xlsx("your.xlsx");
I recently discovered Schaun Wheeler's function for importing excel files into R after realising that the xlxs package hadn't been updated for R 3.1.0.
https://gist.github.com/schaunwheeler/5825002
The file name needs to have the ".xlsx" extension and the file can't be open when you run the function.
This function is really useful for accessing other peoples work. The main advantages over using the read.csv function are when
Importing multiple excel files
Importing large files
Files that are updated regularly
Using the read.csv function requires manual opening and saving of each Excel document which is time consuming and very boring. Using Schaun's function to automate the workflow is therefore a massive help.
Big props to Schaun for this solution.
What's your operating system? What version of R are you running: 32-bit or 64-bit? What version of Java do you have installed?
I had a similar error when I first started using the read.xlsx() function and discovered that my issue (which may or may not be related to yours; at a minimum, this response should be viewed as "try this, too") was related to the incompatability of .xlsx pacakge with 64-bit Java. I'm fairly certain that the .xlsx package requires 32-bit Java.
Use 32-bit R and make sure that 32-bit Java is installed. This may address your issue.
You have checked that R is actually able to find the file, e.g. file.exists("C:/AB_DNA_Tag_Numbers.xlsx") ? – Ben Bolker Aug 14 '11 at 23:05
Above comment should've solved your problem:
require("xlsx")
read.xlsx("filepath/filename.xlsx",1)
should work fine after that.
I have tried very hard on all the answers above. However, they did not actually help because I used a mac. The rio library has this import function which can basically import any type of data file into Rstudio, even those file using languages other than English!
Try codes below:
library(rio)
AB <- import("C:/AB_DNA_Tag_Numbers.xlsx")
AB <- AB[,1]
Hope this help.
For more detailed reference: https://cran.r-project.org/web/packages/rio/vignettes/rio.html
You may be able to keep multiple tabs and more formatting information if you export to an OpenDocument Spreadsheet file (ods) or an older Excel format and import it with the ODS reader or the Excel reader you mentioned above.
As stated by many here, I am writing the same thing but with an additional point!
At first we need to make sure that our R Studio has these two packages installed:
"readxl"
"XLConnect"
In order to load a package in R you can use the below function:
install.packages("readxl/XLConnect")
library(XLConnect)
search()
search will display the list of current packages being available in your R Studio.
Now another catch, even though you might have these two packages but still you may encounter problem while reading "xlsx" file and the error could be like "error: more columns than column name"
To solve this issue you can simply resave your excel sheet "xlsx" in to
"CSV (Comma delimited)"
and your life will be super easy....
Have fun!!
The installation of xlsx package require rJava and xlsxjars. Indirectly they require the specific (32 or 64 bit) java runtime environment on the system.
Pro of read.xlsx: In the same package there are read.xlsx and write.xlsx
Con: Very low speed
As suggested, the easy way is to save in .csv format from excel.
Simple benchmark on a 5800x15 dataset (median)
read.xlsx: >10000ms
read_xlsx: 70ms
read.csv: 15ms