With the parameter SheetNames you can set the names of the data sheets (visible at the bottom of Excel, not included in the screenshot). Here, you could replace data with c('data', 'data2'). You can save several dataframes in one Excel file by including the names of the objects at the first position.

This is what the resulting Excel file looks like on my machine. WriteXLS(data, ExcelFileName = 'data.xlsx', I tried a few packages for writing Excel files and I find this one the most convenient to use. In this case, you can use the Write.XLS() function from the Write.XLS package.

Maybe some colleagues only work with Excel (because you still not managed to convince them switching to R) or you want to use Excel for annotating your dataset with a spreadsheet editor. You might come into a situation where you want to export your dataset to an Excel file. The only advantage of save() really is that you can save several objects into one file - but in the end it might be better to have one file for one object. Also, it is more similar to the behavior of all the other “reading functions” like read.table(): for these, you also have to assign the result to a variable. This might mean more typing but it also has the advantage that you can choose a new name for the variable to integrate it in into the rest of the new script more smoothly. When we use readRDS(), we have to assign the result of the reading process to a variable. But this also means that you have to “remember” the names of the previously used objects when using load(). When we use load(), we do not assign the result of the loading process to a variable because the original names of the objects are used. So, you might ask “why should I use saveRDS() instead of save()”? Actually, I like saveRDS() better - for one specific reason that you might not have noticed in the calls above. The difference between save() and saveRDS()

In the scope of this post, let’s suppose that the calculation above took veeeery long and you absolutely don’t want to run it everytime.

If they don’t, you can just run your pre-processing code every time you are getting back to analyzing the dataset. data$ <- is.na(data$gender) | data$gender = 'Unknown'Īs I wrote above: Saving the current state of your dataset in R makes sense when all the preparations take a lot of time. Here, I’m assigning a new column data$ which is TRUE whenever data$gender is 'Unknown' or NA. Just for the sake of simulating a real workflow, I will do some very light data manipulation. We now have a dataset with over 50,000 rows (you can scroll through the first 6 of them in the box above) and 14 variables in our global environment (the ‘workspace’).

Load ezdata crack