# Importing Data

Importing data into **R** is fairly simple. For Stata and Systat, use the **foreign **package. For SPSS and SAS I would recommend the **Hmisc** package for ease and functionality. See the **Quick-R **section on **packages**, for information on obtaining and installing the these packages. Example of importing data are provided below.

## From A Comma Delimited Text File

`# first row contains variable names, comma is separator `

# assign the variable *id* to row names

# note the / instead of \ on mswindows systems

mydata <- read.table("c:/mydata.csv", header=TRUE,

sep=",", row.names="id")

## From Excel

One of the best ways to read an Excel file is to export it to a comma delimited file and import it using the method above. Alternatively you can use the **xlsx** package to access Excel files. The first row should contain variable/column names.

```
# read in the first worksheet from the workbook
```

*myexcel.xlsx*

# first row contains variable names

library(xlsx)

mydata <- read.xlsx("c:/myexcel.xlsx", 1)

# read in the worksheet named *mysheet*

mydata <- read.xlsx("c:/myexcel.xlsx", sheetName = "mysheet")

## From SPSS

`# save SPSS dataset in trasport format`

get file='c:\mydata.sav'.

export outfile='c:\mydata.por'.

# in R

library(Hmisc)

mydata <- spss.get("c:/mydata.por", use.value.labels=TRUE)

# last option converts value labels to R factors

## From SAS

`# save SAS dataset in trasport format`

libname out xport 'c:/mydata.xpt';

data out.mydata;

set sasuser.mydata;

run;

# in R

library(Hmisc)

mydata <- sasxport.get("c:/mydata.xpt")

# character variables are converted to R factors

## From Stata

`# input Stata file`

library(foreign)

mydata <- read.dta("c:/mydata.dta")

## From systat

`# input Systat file`

library(foreign)

mydata <- read.systat("c:/mydata.dta")

## To practice

To practice importing in R try this interactive course: Importing Data in R (Part 1).