# Using the xlsx package to create an Excel file

June 17, 2017
By

(This article was first published on R-exercises, and kindly contributed to R-bloggers)

Microsoft Excel is perhaps the most popular data anlysis tool out there. While arguably convenient, spreadsheet software is error prone and Excel code can be very hard to review and test.

After successfully completing this exercise set, you will be able to prepare a basic Excel document using just R (no need to touch Excel yourself), leaving behind a reproducible R-script.

Solutions are available here.

Exercise 1
Install and load the `xlsx` package, using the `dependencies = TRUE` option.

Exercise 2
Create an `xlsx` workbook object in your R workspace and call it `wb`.

Exercise 3
Create a sheet object in `wb` named `iris` assign it the name `sheet1` in your workspace.

Exercise 4
Write the built-in Iris `data.frame` to the iris sheet without row names. Hint: use the `addDataFrame()` function.

Now you can write your workbook anytime to your working directory using `saveWorkbook(wb, "filename.xlsx")`.

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• Learn some of the differences between working in Excel with regression modelling and R
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Exercise 5
Apply ‘freeze pane’ on the top row.

Exercise 6
Set width of columns 1 through 5 to 12, that is 84 pixels.

Exercise 7
Use `Font`, `CellBlock` and `CB.setFont` to make the header in bold.

Exercise 8
Using tapply generate a table with the mean of ‘petal width’ by species and write to a new sheet called `pw`, from row 2 down.

Exercise 9
Add a title in cell `A1` above the table, merge the cells of the first three columns.

Exercise 10
Save your workbook to your working directory and open using Excel. Go back to `R` and continue formatting and adding information to your workbook at will.

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