A binary file is a file that contains information stored only in form of bits and bytes.(0’s and 1’s). They are not human readable as the bytes in it translate to characters and symbols which contain many other non-printable characters. Attempting to read a binary file using any text editor will show characters like Ø and ð. The binary file has to be read by specific programs to be useable. For example, the binary file of a Microsoft Word program can be read to a human readable form only by the Word program. Which indicates that, besides the human readable text, there is a lot more information like formatting of characters and page numbers etc., which are also stored along with alphanumeric characters. And finally a binary file is a continuous sequence of bytes. The line break we see in a text file is a character joining first line to the next. Sometimes, the data generated by other programs are required to be processed by R as a binary file. Also R is required to create binary files which can be shared with other programs. R has two functions WriteBin() and readBin() to create and read binary files. Syntax writeBin(object, con) readBin(con, what, n ) Following is the description of the parameters used − con is the connection object to read or write the binary file. object is the binary file which to be written. what is the mode like character, integer etc. representing the bytes to be read. n is the number of bytes to read from the binary file. Example We consider the R inbuilt data "mtcars". First we create a csv file from it and convert it to a binary file and store it as a OS file. Next we read this binary file created into R. Writing the Binary File We read the data frame "mtcars" as a csv file and then write it as a binary file to the OS. # Read the "mtcars" data frame as a csv file and store only the columns "cyl", "am" and "gear". write.table(mtcars, file = "mtcars.csv",row.names = FALSE, na = "", col.names = TRUE, sep = ",") # Store 5 records from the csv file as a new data frame. new.mtcars <- read.table("mtcars.csv",sep = ",",header = TRUE,nrows = 5) # Create a connection object to write the binary file using mode "wb". write.filename = file("/web/com/binmtcars.dat", "wb") # Write the column names of the data frame to the connection object. writeBin(colnames(new.mtcars), write.filename) # Write the records in each of the column to the file. writeBin(c(new.mtcars$cyl,new.mtcars$am,new.mtcars$gear), write.filename) # Close the file for writing so that it can be read by other program. close(write.filename) Reading the Binary File The binary file created above stores all the data as continuous bytes. So we will read it by choosing appropriate values of column names as well as the column values. # Create a connection object to read the file in binary mode using "rb". read.filename <- file("/web/com/binmtcars.dat", "rb") # First read the column names. n = 3 as we have 3 columns. column.names <- readBin(read.filename, character(), n = 3) # Next read the column values. n = 18 as we have 3 column names and 15 values. read.filename <- file("/web/com/binmtcars.dat", "rb") bindata <- readBin(read.filename, integer(), n = 18) # Print the data. print(bindata) # Read the values from 4th byte to 8th byte which represents "cyl". cyldata = bindata[4:8] print(cyldata) # Read the values form 9th byte to 13th byte which represents "am". amdata = bindata[9:13] print(amdata) # Read the values form 9th byte to 13th byte which represents "gear". geardata = bindata[14:18] print(geardata) # Combine all the read values to a dat frame. finaldata = cbind(cyldata, amdata, geardata) colnames(finaldata) = column.names print(finaldata) When we execute the above code, it produces the following result and chart − [1] 7108963 1728081249 7496037 6 6 4 [7] 6 8 1 1 1 0 [13] 0 4 4 4 3 3 [1] 6 6 4 6 8 [1] 1 1 1 0 0 [1] 4 4 4 3 3 cyl am gear [1,] 6 1 4 [2,] 6 1 4 [3,] 4 1 4 [4,] 6 0 3 [5,] 8 0 3 As we can see, we got the original data back by reading the binary file in R.

XML is a file format which shares both the file format and the data on the World Wide Web, intranets, and elsewhere using standard ASCII text. It stands for Extensible Markup Language (XML). Similar to HTML it contains markup tags. But unlike HTML where the markup tag describes structure of the page, in xml the markup tags describe the meaning of the data contained into he file.

You can read a xml file in R using the "XML" package. This package can be installed using following command.

install.packages("XML")

Input Data

Create a XMl file by copying the below data into a text editor like notepad. Save the file with a .xml extension and choosing the file type as all files(*.*).

<RECORDS>
   <EMPLOYEE>
      <ID>1</ID>
      <NAME>Rick</NAME>
      <SALARY>623.3</SALARY>
      <STARTDATE>1/1/2012</STARTDATE>
      <DEPT>IT</DEPT>
   </EMPLOYEE>
 
   <EMPLOYEE>
      <ID>2</ID>
      <NAME>Dan</NAME>
      <SALARY>515.2</SALARY>
      <STARTDATE>9/23/2013</STARTDATE>
      <DEPT>Operations</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>3</ID>
      <NAME>Michelle</NAME>
      <SALARY>611</SALARY>
      <STARTDATE>11/15/2014</STARTDATE>
      <DEPT>IT</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>4</ID>
      <NAME>Ryan</NAME>
      <SALARY>729</SALARY>
      <STARTDATE>5/11/2014</STARTDATE>
      <DEPT>HR</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>5</ID>
      <NAME>Gary</NAME>
      <SALARY>843.25</SALARY>
      <STARTDATE>3/27/2015</STARTDATE>
      <DEPT>Finance</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>6</ID>
      <NAME>Nina</NAME>
      <SALARY>578</SALARY>
      <STARTDATE>5/21/2013</STARTDATE>
      <DEPT>IT</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>7</ID>
      <NAME>Simon</NAME>
      <SALARY>632.8</SALARY>
      <STARTDATE>7/30/2013</STARTDATE>
      <DEPT>Operations</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>8</ID>
      <NAME>Guru</NAME>
      <SALARY>722.5</SALARY>
      <STARTDATE>6/17/2014</STARTDATE>
      <DEPT>Finance</DEPT>
   </EMPLOYEE>
 
</RECORDS>

Reading XML File

The xml file is read by R using the function xmlParse(). It is stored as a list in R.

# Load the package required to read XML files.
library("XML")

# Also load the other required package.
library("methods")

# Give the input file name to the function.
result <- xmlParse(file = "input.xml")

# Print the result.
print(result)

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

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

Get Number of Nodes Present in XML File

# Load the packages required to read XML files.
library("XML")
library("methods")

# Give the input file name to the function.
result <- xmlParse(file = "input.xml")

# Exract the root node form the xml file.
rootnode <- xmlRoot(result)

# Find number of nodes in the root.
rootsize <- xmlSize(rootnode)

# Print the result.
print(rootsize)

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

output
[1] 8

Details of the First Node

Let's look at the first record of the parsed file. It will give us an idea of the various elements present in the top level node.

# Load the packages required to read XML files.
library("XML")
library("methods")

# Give the input file name to the function.
result <- xmlParse(file = "input.xml")

# Exract the root node form the xml file.
rootnode <- xmlRoot(result)

# Print the result.
print(rootnode[1])

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

$EMPLOYEE
   1
   Rick
   623.3
   1/1/2012
   IT
 

attr(,"class")
[1] "XMLInternalNodeList" "XMLNodeList" 

Get Different Elements of a Node

# Load the packages required to read XML files.
library("XML")
library("methods")

# Give the input file name to the function.
result <- xmlParse(file = "input.xml")

# Exract the root node form the xml file.
rootnode <- xmlRoot(result)

# Get the first element of the first node.
print(rootnode[[1]][[1]])

# Get the fifth element of the first node.
print(rootnode[[1]][[5]])

# Get the second element of the third node.
print(rootnode[[3]][[2]])

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

1 
IT 
Michelle 

XML to Data Frame

To handle the data effectively in large files we read the data in the xml file as a data frame. Then process the data frame for data analysis.

# Load the packages required to read XML files.
library("XML")
library("methods")

# Convert the input xml file to a data frame.
xmldataframe <- xmlToDataFrame("input.xml")
print(xmldataframe)

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

      ID    NAME     SALARY    STARTDATE       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

As the data is now available as a dataframe we can use data frame related function to read and manipulate the file.