(This article was first published on

There are number of ways in importing data into R, and several formats are available,**Analysis with R**, and kindly contributed to R-bloggers)- From Excel to R
- From SPSS to R
- From Stata to R, and more here

In this post, I'm going to talk about importing common data format that we often encounter, such as Excel, or Text data. Most of the data are saved in MS Excel, and the best way to import this is to save this as in CSV format, below is the procedure:

- Open your Excel data
- Go to
**File > Save As**or press**Ctrl+Shift+S** - Name this with anything you want, say
**Data**. Then before clicking**Save**, make sure to change the**File Format**to*Comma Delimited Text*and better set the directory to**My Documents**folder, for Windows. - When saved, this file will have a name
**Data.csv**.

The argument

Now in some cases, data are saved in Text (.txt) format. And to import this, we use the

**header = TRUE**tells R that the first row of the data are the labels of every column. If set to**FALSE**, means the first row of the data are not the labels, but are considered as data points.Now in some cases, data are saved in Text (.txt) format. And to import this, we use the

**read.table**function. Consider the data below, and say this is saved as**Data1.txt**in**My Documents**folder (for Windows).To import this to R, simply run

Or apply

**read.table**directly to the data

There are times, however, when the Text data are saved in the internet, here is an example. To import this to R, of course, make sure of the internet connection first. Next, copy the URL of the data and assign this to any variable name, then apply

**read.table**. Try the codes below,Please comment below if you have any problems.

To

**leave a comment**for the author, please follow the link and comment on his blog:**Analysis with R**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...