R - Excel File


Microsoft Excel is the most widely used spreadsheet program which stores data in the .xls or .xlsx format. R can read directly from these files using some excel specific packages. Few such packages are - XLConnect, xlsx, gdata etc. We will be using xlsx package. R can also write into excel file using this package.

Install xlsx Package

You can use the following command in the R console to install the "xlsx" package. It may ask to install some additional packages on which this package is dependent. Follow the same command with required package name to install the additional packages.


Verify and Load the "xlsx" Package

Use the following command to verify and load the "xlsx" package.

# Verify the package is installed.

# Load the library into R workspace.

When the script is run we get the following output.

[1] TRUE
Loading required package: rJava
Loading required package: methods
Loading required package: xlsxjars

Input as xlsx File

Open Microsoft excel. Copy and paste the following data in the work sheet named as sheet1.

id	name      salary    start_date	dept
1	Rick	  623.3	    1/1/2012	IT
2	Dan       515.2     9/23/2013   Operations
3	Michelle  611	    11/15/2014	IT
4	Ryan	  729	    5/11/2014	HR
5	Gary	  843.25    3/27/2015	Finance
6	Nina	  578       5/21/2013	IT
7	Simon	  632.8	    7/30/2013	Operations
8	Guru	  722.5	    6/17/2014	Finance

Also copy and paste the following data to another worksheet and rename this worksheet to "city".

name	 city
Rick	 Seattle
Dan      Tampa
Michelle Chicago
Ryan	 Seattle
Gary	 Houston
Nina	 Boston
Simon	 Mumbai
Guru	 Dallas

Save the Excel file as "input.xlsx". You should save it in the current working directory of the R workspace.

Reading the Excel File

The input.xlsx is read by using the read.xlsx() function as shown below. The result is stored as a data frame in the R environment.

# Read the first worksheet in the file input.xlsx.
data <- read.xlsx("input.xlsx", sheetIndex = 1)

When we execute the above code, it produces the following result −

      id,   name,    salary,   start_date,     dept
1      1    Rick     623.30    2012-01-01      IT
2      2    Dan      515.20    2013-09-23      Operations
3      3    Michelle 611.00    2014-11-15      IT
4      4    Ryan     729.00    2014-05-11      HR
5     NA    Gary     843.25    2015-03-27      Finance
6      6    Nina     578.00    2013-05-21      IT
7      7    Simon    632.80    2013-07-30      Operations
8      8    Guru     722.50    2014-06-17      Finance