Before you can begin any statistical analysis, you will need to learn to work with external data files so that you can import your data. R is able to read the comma-separated values (CSV), tab-delimited, and data interchange format (DIF) file formats, which are some of the standard file formats commonly used to transfer data between statistical and database packages.  With the help of an add-on package called foreign, it is possible to import a wider range of file types.

Whether you have personally recorded your data on paper or in a spreadsheet, or received a data file from someone else, this chapter will explain how to get your data into R.

You will learn how to:

• enter your data by typing the values in directly
• import plain text files, including the CSV, tab-delimited, and DIF file formats
• import Excelâ files by first converting them to the CSV format
• import a dataset stored in a file type specific to another software package such as an SPSS or Stata data file
• work with relative file paths
• export a dataset to a CSV or tab-delimited file

Entering Data Directly

If you have a small dataset that is not already recorded in electronic form, you may want to input your data into R directly.

Consider the dataset shown in Table 2-1, which gives some data for four U.K. supermarkets chains. It is representative of a typical dataset in which the columns represent variables and each row holds one observation.

To enter a dataset into R, the first step is to create a vector of data values for each variable using the c function, as explained under “Vectors” in Chapter 1. So, for the supermarkets data, input the four variables:

> Chain<-c("Morrisons", "Asda", "Tesco", "Sainsburys")
> Stores<-c(439, NA, 2715, 934)
> Sales.Area<-c(12261, NA, 36722, 19108)
> Market.Share<-c(12.3, 16.9, 30.3, 16.5)

The vectors should all have the same length, meaning that they should contain the same number of values. Where a data value is missing, enter the characters NA in its place. Remember to put quotation marks around non-numeric values, as shown for the Chain variable.

Once you have created vectors for each of the variables, use the data.frame function to combine them to form a data frame:

> supermarkets<-data.frame(Chain, Stores, Sales.Area, Market.Share)

You can check the dataset has been entered correctly by entering its name:

> supermarkets

        Chain   Stores   Sales.Area   Market.Share
1   Morrisons      439        12261           12.3
2        Asda       NA           NA           16.9
3       Tesco     2715        36722           30.3
4  Sainsburys      934        19108           16.5

After you have created the data frame, the individual vectors still exist in the workspace as separate objects from the data frame. To avoid any confusion, you can delete them with the rm function:

> rm(Chain, Stores, Sales.Area, Market.Share)

Importing Plain Text Files

The simplest way to transfer data to R is in a plain text file, sometimes called a flat text file. These are files that consist of plain text with no additional formatting and can be read by plain text editors such as Microsoft Notepad, TextEdit (for Mac users), or gedit (for Linux users). There are several standard formats for storing spreadsheet data in text files, which use symbols to indicate the layout of the data. These include:

• Comma-separated values or comma-delimited (.csv) files
• Tab-delimited (.txt) files
• Data interchange format (.dif) files

These file formats are useful because they can be read by all of the popular statistical and database software packages, allowing easy transfer of datasets between them.

The following subsections explain how you can import datasets from these standard file formats into R.

>dataset1<-read.csv("C:/folder/filename.csv")

>dataset1<-read.delim("C:/folder/filename.txt")

>dataset1<-read.csv("C:/folder/filename.csv", header=F)

> dataset1<-read.csv("C:/folder/filename.csv", header=F, col.names=c("Name1", "Name2", "Name3"))

>dataset1<-read.csv("C:/folder/filename.csv", na.strings=".")

>dataset1<-read.DIF("C:/folder/filename.dif")

>dataset1<-read.DIF("C:/folder/filename.dif", header=T)

>dataset1<-read.DIF("C:/folder/filename.dif", na.string="NULL")

>dataset1<-read.DIF("C:/folder/filename.dif", transpose=T)

>dataset1<-read.table("C:/folder/supermarkets.txt", sep="/", header=T)

dataset1<-read.table("C:/folder/filename.txt", sep="/", header=T, na.strings="NULL")

Importing Excel Files

The simplest way to import a Microsoft Excel file is to save your Excel file as a CSV file, which you can then import, as explained earlier in this chapter under “CSV and Tab-Delimited Files.”

First open your file in Excel and ensure that the data is arranged correctly within the spreadsheet, with one variable per column and one observation per row. If the dataset includes variable names, then these should be placed in the first row of the spreadsheet. Otherwise, the data values should begin on the first row. Figure 2-5 shows how the supermarkets data looks when correctly arranged in an Excel file.

To ensure a smooth file conversion, check the following:

• There are no empty cells above or to the left of the data grid
• There are no merged cells
• There is no more than one row of column headers
• There is no formatting such as bold, italic or colored text, cell borders or background colors
• Where data values are missing, the cell is left empty
• There are no commas in large numbers (e.g., 1324157 is acceptable but 1,324,157 is not)
• If exponential (scientific) notation is used, the format is correct (e.g., 0.00312 can be expressed as 3.12e-3 or 3.12E-3)
• There are no currency, unit, or percent symbols in numeric variables (symbols in categorical variables or in the variable names are fine)
• The minus sign is used to indicate negative numbers (e.g., -5) and not brackets (parentheses) or red text
• The workbook has only one worksheet

When the data is prepared, save the spreadsheet as a CSV file by selecting Save As from the File menu. When the Save As dialog box appears, select the .csv file type from the Save As Type field, as shown in Figure 2-6. Then save the file as usual. Excel will give you a warning that all formatting will be lost, which you can accept.

The CSV file is now ready for you to import with the read.csv function, as explained previously in this chapter (see “CSV and Tab-Delimited Files”).

If you do not have access to Excel, you can use an add-on package such as xlsx or xlsReadWrite to import Excel files directly. See Appendix B for more details on using add-on packages.

Importing Files from Other Software

Sometimes you may need to import a dataset that is saved in a file format specific to another statistical package, such as an SPSS or Stata data file.

If you have access to the software, the simplest solution is to open the file using the software and convert the file to the CSV file format using the Save As or Export option, which is usually found in the File menu. Once the file is in CSV format, you can import it with the read.csv function (see “CSV and Tab-Delimited Files” earlier in this chapter).

If you are not able to convert the file, then you can use an add-on package called foreign, which allows you to directly import data from files types produced by some of the popular statistical software packages.

Add-on packages are covered in greater detail in Appendix A. For now, you just need to know that an add-on package contains additional functions that are not part of the standard R installation. To use the functions within the foreign package, you must first load the package.

To load the foreign package, select Load Package from the Packages menu. When the list of packages appears, select “foreign” and press OK. Once the package has loaded, all of the functions within it will be available for you to use for the duration of the session.

Table 2-2 lists some of the functions available for importing foreign file types.

For example, to import a Stata data file, use the command:

>dataset1<-read.dta("C:/folder/filename.dta")

You may need to use additional arguments to ensure the file is imported correctly. For further information on using the functions in the foreign package, use the help function or refer to the package documentation available from the R project website at cran.r-project.org/web/packages/foreign/foreign.pdf.

Using Relative File Paths

So far, we have only used absolute file paths to describe the location of a data file. An absolute file path gives the full address of the file, which in the Windows environment begins with a drive name such as C:/.

You can also use relative file paths, which describe the location of the file in relation to the working directory. The working directory is the directory in which R is set to look when given relative file paths. This is useful if you need to import or export a large number of files and don't want to type the full file path each time. To see which is the current working directory, use the command:

> getwd()

If you are using a fresh installation of R for Windows, the working directory will be your Documents folder, and R will output something like this:

[1] "C:/Users/Username/Documents"

For Mac and Linux users, the default working directory will be your home directory and will look something like this:

[1] "/Users/Username"

So, to import a CSV file that has the absolute file path:

C:/Users/Username/Documents/Data/filename.csv

you could use the command:

> read.csv("Data/filename.csv")

To change the working directory to wherever you prefer to store your data files, use the setwd function, as shown below. The change is applied for the remainder of the session:

> setwd("C:/folder/subfolder")

Exporting Datasets

R allows you to export datasets from the R workspace to the CSV and tab-delimited file formats.

To export a data frame named dataset to a CSV file, use the write.csv function:

> write.csv(dataset, "filename.csv")

For example, to export the Puromycin dataset to a file named puromycin_data.csv, use the command:

> write.csv(Puromycin, "puromycin_data.csv")

This command creates the file and saves it to your working directory (see the preceding section, “Using Relative File Paths,” for how to find and set the working directory). To save the file somewhere other than in the working directory, enter the full path for the file:

> write.csv(dataset, "C:/folder/filename.csv")

If a file with your chosen name already exists in the specified location, R overwrites the original file without giving a warning. You should check the files in the destination folder beforehand to make sure that you are not overwriting anything important.

The write.table function allows you to export data to a wider range of file formats, including tab-delimited files. Use the sep argument to specify which character should be used to separate the values. To export a dataset to a tab-delimited file, set the sep argument to " " (which denotes the tab symbol):

> write.table(dataset, "filename.txt", sep="	")

By default, the write.csv and write.table functions create an extra column in the file containing the observation numbers. To prevent this, set the row.names argument to F:

> write.csv(dataset, "filename.csv", row.names=F)

With the write.table function, you can also prevent variable names being placed in the first line of the file with the col.names argument:

> write.table(dataset, "filename.txt", sep="	", col.names=F)

Summary

You should now be able to get your data into R, whether by entering it directly or by importing it from an external data file. You should also understand how to use relative file paths and be able to export a dataset to an external file.

This table summarizes the most import commands covered in this chapter.

Now that you have learned how to get your dataset into R, we can move on to Chapter 3, which explains how to prepare your dataset for analysis.