Author: saqibkhan

XML Files

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

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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.

October 29, 2024

Binary Files

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.

October 29, 2024

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.
```
install.packages("xlsx")
```
Verify and Load the “xlsx” Package

Use the following command to verify and load the “xlsx” package.
```
# Verify the package is installed.
any(grepl("xlsx",installed.packages()))

# Load the library into R workspace.
library("xlsx")
```
When the script is run we get the following output.
```
[1] TRUE
Loading required package: rJava
Loading required package: methods
Loading required package: xlsxjars
```
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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	    43.25     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)
print(data)
```
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
```
October 29, 2024

CSV Files

In R, we can read data from files stored outside the R environment. We can also write data into files which will be stored and accessed by the operating system. R can read and write into various file formats like csv, excel, xml etc.

In this chapter we will learn to read data from a csv file and then write data into a csv file. The file should be present in current working directory so that R can read it. Of course we can also set our own directory and read files from there.

Getting and Setting the Working Directory

You can check which directory the R workspace is pointing to using the getwd() function. You can also set a new working directory using setwd()function.

# Get and print current working directory.
print(getwd())

# Set current working directory.
setwd("/web/com")

# Get and print current working directory.
print(getwd())

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

[1] "/web/com/1441086124_2016"
[1] "/web/com"

This result depends on your OS and your current directory where you are working.

Input as CSV File

The csv file is a text file in which the values in the columns are separated by a comma. Let’s consider the following data present in the file named input.csv.

You can create this file using windows notepad by copying and pasting this data. Save the file as input.csv using the save As All files(*.*) option in notepad.

id,name,salary,start_date,dept
1,Rick,623.3,2012-01-01,IT
2,Dan,515.2,2013-09-23,Operations
3,Michelle,611,2014-11-15,IT
4,Ryan,729,2014-05-11,HR
5,Gary,843.25,2015-03-27,Finance
6,Nina,578,2013-05-21,IT
7,Simon,632.8,2013-07-30,Operations
8,Guru,722.5,2014-06-17,Finance

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Reading a CSV File

Following is a simple example of read.csv() function to read a CSV file available in your current working directory −

data <- read.csv("input.csv")
print(data)

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

Analyzing the CSV File

By default the read.csv() function gives the output as a data frame. This can be easily checked as follows. Also we can check the number of columns and rows.

data <- read.csv("input.csv")

print(is.data.frame(data))
print(ncol(data))
print(nrow(data))

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

[1] TRUE
[1] 5
[1] 8

Once we read data in a data frame, we can apply all the functions applicable to data frames as explained in subsequent section.

Get the maximum salary

# Create a data frame.
data <- read.csv("input.csv")

# Get the max salary from data frame.
sal <- max(data$salary)
print(sal)

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

[1] 843.25

Get the details of the person with max salary

We can fetch rows meeting specific filter criteria similar to a SQL where clause.

# Create a data frame.
data <- read.csv("input.csv")

# Get the max salary from data frame.
sal <- max(data$salary)

# Get the person detail having max salary.
retval <- subset(data, salary == max(salary))
print(retval)

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

      id    name  salary  start_date    dept
5     NA    Gary  843.25  2015-03-27    Finance

Get all the people working in IT department

# Create a data frame.
data <- read.csv("input.csv")

retval <- subset( data, dept == "IT")
print(retval)

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

       id   name      salary   start_date   dept
1      1    Rick      623.3    2012-01-01   IT
3      3    Michelle  611.0    2014-11-15   IT
6      6    Nina      578.0    2013-05-21   IT

Get the persons in IT department whose salary is greater than 600

# Create a data frame.
data <- read.csv("input.csv")

info <- subset(data, salary > 600 & dept == "IT")
print(info)

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

       id   name      salary   start_date   dept
1      1    Rick      623.3    2012-01-01   IT
3      3    Michelle  611.0    2014-11-15   IT

Get the people who joined on or after 2014

# Create a data frame.
data <- read.csv("input.csv")

retval <- subset(data, as.Date(start_date) > as.Date("2014-01-01"))
print(retval)

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

       id   name     salary   start_date    dept
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
8      8    Guru     722.50   2014-06-17    Finance

Writing into a CSV File

R can create csv file form existing data frame. The write.csv() function is used to create the csv file. This file gets created in the working directory.

# Create a data frame.
data <- read.csv("input.csv")
retval <- subset(data, as.Date(start_date) > as.Date("2014-01-01"))

# Write filtered data into a new file.
write.csv(retval,"output.csv")
newdata <- read.csv("output.csv")
print(newdata)

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

  X      id   name      salary   start_date    dept
1 3      3    Michelle  611.00   2014-11-15    IT
2 4      4    Ryan      729.00   2014-05-11    HR
3 5     NA    Gary      843.25   2015-03-27    Finance
4 8      8    Guru      722.50   2014-06-17    Finance

Here the column X comes from the data set newper. This can be dropped using additional parameters while writing the file.

# Create a data frame.
data <- read.csv("input.csv")
retval <- subset(data, as.Date(start_date) > as.Date("2014-01-01"))

# Write filtered data into a new file.
write.csv(retval,"output.csv", row.names = FALSE)
newdata <- read.csv("output.csv")
print(newdata)

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

      id    name      salary   start_date    dept
1      3    Michelle  611.00   2014-11-15    IT
2      4    Ryan      729.00   2014-05-11    HR
3     NA    Gary      843.25   2015-03-27    Finance
4      8    Guru      722.50   2014-06-17    Finance

October 29, 2024

Data Reshaping

Data Reshaping in R is about changing the way data is organized into rows and columns. Most of the time data processing in R is done by taking the input data as a data frame. It is easy to extract data from the rows and columns of a data frame but there are situations when we need the data frame in a format that is different from format in which we received it. R has many functions to split, merge and change the rows to columns and vice-versa in a data frame.

Joining Columns and Rows in a Data Frame

We can join multiple vectors to create a data frame using the cbind()function. Also we can merge two data frames using rbind() function.

# Create vector objects.
city <- c("Tampa","Seattle","Hartford","Denver")
state <- c("FL","WA","CT","CO")
zipcode <- c(33602,98104,06161,80294)

# Combine above three vectors into one data frame.
addresses <- cbind(city,state,zipcode)

# Print a header.
cat("# # # # The First data frame\n") 

# Print the data frame.
print(addresses)

# Create another data frame with similar columns
new.address <- data.frame(
   city = c("Lowry","Charlotte"),
   state = c("CO","FL"),
   zipcode = c("80230","33949"),
   stringsAsFactors = FALSE
)

# Print a header.
cat("# # # The Second data frame\n") 

# Print the data frame.
print(new.address)

# Combine rows form both the data frames.
all.addresses <- rbind(addresses,new.address)

# Print a header.
cat("# # # The combined data frame\n") 

# Print the result.
print(all.addresses)

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

# # # # The First data frame
 city       state zipcode[1,] "Tampa"    "FL"  "33602"
[2,] "Seattle"  "WA"  "98104"
[3,] "Hartford" "CT"   "6161" 
[4,] "Denver"   "CO"  "80294"

# # # The Second data frame
   city       state   zipcode1      Lowry      CO      80230
2      Charlotte  FL      33949

# # # The combined data frame
   city      state zipcode1      Tampa     FL    33602
2      Seattle   WA    98104
3      Hartford  CT     6161
4      Denver    CO    80294
5      Lowry     CO    80230
6     Charlotte  FL    33949

Merging Data Frames

We can merge two data frames by using the merge() function. The data frames must have same column names on which the merging happens.

In the example below, we consider the data sets about Diabetes in Pima Indian Women available in the library names “MASS”. we merge the two data sets based on the values of blood pressure(“bp”) and body mass index(“bmi”). On choosing these two columns for merging, the records where values of these two variables match in both data sets are combined together to form a single data frame.

library(MASS)
merged.Pima <- merge(x = Pima.te, y = Pima.tr,
   by.x = c("bp", "bmi"),
   by.y = c("bp", "bmi")
)
print(merged.Pima)
nrow(merged.Pima)

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

   bp  bmi npreg.x glu.x skin.x ped.x age.x type.x npreg.y glu.y skin.y ped.y
1  60 33.8       1   117     23 0.466    27     No       2   125     20 0.088
2  64 29.7       2    75     24 0.370    33     No       2   100     23 0.368
3  64 31.2       5   189     33 0.583    29    Yes       3   158     13 0.295
4  64 33.2       4   117     27 0.230    24     No       1    96     27 0.289
5  66 38.1       3   115     39 0.150    28     No       1   114     36 0.289
6  68 38.5       2   100     25 0.324    26     No       7   129     49 0.439
7  70 27.4       1   116     28 0.204    21     No       0   124     20 0.254
8  70 33.1       4    91     32 0.446    22     No       9   123     44 0.374
9  70 35.4       9   124     33 0.282    34     No       6   134     23 0.542
10 72 25.6       1   157     21 0.123    24     No       4    99     17 0.294
11 72 37.7       5    95     33 0.370    27     No       6   103     32 0.324
12 74 25.9       9   134     33 0.460    81     No       8   126     38 0.162
13 74 25.9       1    95     21 0.673    36     No       8   126     38 0.162
14 78 27.6       5    88     30 0.258    37     No       6   125     31 0.565
15 78 27.6      10   122     31 0.512    45     No       6   125     31 0.565
16 78 39.4       2   112     50 0.175    24     No       4   112     40 0.236
17 88 34.5       1   117     24 0.403    40    Yes       4   127     11 0.598
   age.y type.y
1     31     No
2     21     No
3     24     No
4     21     No
5     21     No
6     43    Yes
7     36    Yes
8     40     No
9     29    Yes
10    28     No
11    55     No
12    39     No
13    39     No
14    49    Yes
15    49    Yes
16    38     No
17    28     No
[1] 17

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Melting and Casting

One of the most interesting aspects of R programming is about changing the shape of the data in multiple steps to get a desired shape. The functions used to do this are called melt() and cast().

We consider the dataset called ships present in the library called “MASS”.

library(MASS)
print(ships)

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

     type year   period   service   incidents
1     A   60     60        127         0
2     A   60     75         63         0
3     A   65     60       1095         3
4     A   65     75       1095         4
5     A   70     60       1512         6
.............
.............
8     A   75     75       2244         11
9     B   60     60      44882         39
10    B   60     75      17176         29
11    B   65     60      28609         58
............
............
17    C   60     60      1179          1
18    C   60     75       552          1
19    C   65     60       781          0
............
............

Melt the Data

Now we melt the data to organize it, converting all columns other than type and year into multiple rows.

molten.ships <- melt(ships, id = c("type","year"))
print(molten.ships)

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

      type year  variable  value
1      A   60    period      60
2      A   60    period      75
3      A   65    period      60
4      A   65    period      75
............
............
9      B   60    period      60
10     B   60    period      75
11     B   65    period      60
12     B   65    period      75
13     B   70    period      60
...........
...........
41     A   60    service    127
42     A   60    service     63
43     A   65    service   1095
...........
...........
70     D   70    service   1208
71     D   75    service      0
72     D   75    service   2051
73     E   60    service     45
74     E   60    service      0
75     E   65    service    789
...........
...........
101    C   70    incidents    6
102    C   70    incidents    2
103    C   75    incidents    0
104    C   75    incidents    1
105    D   60    incidents    0
106    D   60    incidents    0
...........
...........

Cast the Molten Data

We can cast the molten data into a new form where the aggregate of each type of ship for each year is created. It is done using the cast() function.

recasted.ship <- cast(molten.ships, type+year~variable,sum)
print(recasted.ship)

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

     type year  period  service  incidents
1     A   60    135       190      0
2     A   65    135      2190      7
3     A   70    135      4865     24
4     A   75    135      2244     11
5     B   60    135     62058     68
6     B   65    135     48979    111
7     B   70    135     20163     56
8     B   75    135      7117     18
9     C   60    135      1731      2
10    C   65    135      1457      1
11    C   70    135      2731      8
12    C   75    135       274      1
13    D   60    135       356      0
14    D   65    135       480      0
15    D   70    135      1557     13
16    D   75    135      2051      4
17    E   60    135        45      0
18    E   65    135      1226     14
19    E   70    135      3318     17
20    E   75    135       542      1

October 29, 2024

Packages

R packages are a collection of R functions, complied code and sample data. They are stored under a directory called “library” in the R environment. By default, R installs a set of packages during installation. More packages are added later, when they are needed for some specific purpose. When we start the R console, only the default packages are available by default. Other packages which are already installed have to be loaded explicitly to be used by the R program that is going to use them.

All the packages available in R language are listed at R Packages.

Below is a list of commands to be used to check, verify and use the R packages.

Check Available R Packages

Get library locations containing R packages

.libPaths()

When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.

[2] "C:/Program Files/R/R-3.2.2/library"

Get the list of all the packages installed

library()

When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.

Packages in library ‘C:/Program Files/R/R-3.2.2/library’:

base                    The R Base Package
boot                    Bootstrap Functions (Originally by Angelo Canty
                    for S)class                   Functions for Classification
cluster                 "Finding Groups in Data": Cluster Analysis
                    Extended Rousseeuw et al.codetools               Code Analysis Tools for R
compiler                The R Compiler Package
datasets                The R Datasets Package
foreign                 Read Data Stored by 'Minitab', 'S', 'SAS',
                    'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', ...graphics                The R Graphics Package
grDevices               The R Graphics Devices and Support for Colours
                    and Fontsgrid                    The Grid Graphics Package
KernSmooth              Functions for Kernel Smoothing Supporting Wand
                    &amp; Jones (1995)lattice                 Trellis Graphics for R
MASS                    Support Functions and Datasets for Venables and
                    Ripley's MASSMatrix                  Sparse and Dense Matrix Classes and Methods
methods                 Formal Methods and Classes
mgcv                    Mixed GAM Computation Vehicle with GCV/AIC/REML
                    Smoothness Estimationnlme                    Linear and Nonlinear Mixed Effects Models
nnet                    Feed-Forward Neural Networks and Multinomial
                    Log-Linear Modelsparallel                Support for Parallel computation in R
rpart                   Recursive Partitioning and Regression Trees
spatial                 Functions for Kriging and Point Pattern
                    Analysissplines                 Regression Spline Functions and Classes
stats                   The R Stats Package
stats4                  Statistical Functions using S4 Classes
survival                Survival Analysis
tcltk                   Tcl/Tk Interface
tools                   Tools for Package Development
utils                   The R Utils Package

Get all packages currently loaded in the R environment

search()

When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.

[1] ".GlobalEnv"        "package:stats"     "package:graphics" 
[4] "package:grDevices" "package:utils"     "package:datasets" 
[7] "package:methods"   "Autoloads"         "package:base"

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Install a New Package

There are two ways to add new R packages. One is installing directly from the CRAN directory and another is downloading the package to your local system and installing it manually.

Install directly from CRAN

The following command gets the packages directly from CRAN webpage and installs the package in the R environment. You may be prompted to choose a nearest mirror. Choose the one appropriate to your location.

 install.packages("Package Name")
 
# Install the package named "XML".
 install.packages("XML")

Install package manually

Go to the link R Packages to download the package needed. Save the package as a .zip file in a suitable location in the local system.

Now you can run the following command to install this package in the R environment.

install.packages(file_name_with_path, repos = NULL, type = "source")

# Install the package named "XML"
install.packages("E:/XML_3.98-1.3.zip", repos = NULL, type = "source")

Load Package to Library

Before a package can be used in the code, it must be loaded to the current R environment. You also need to load a package that is already installed previously but not available in the current environment.

A package is loaded using the following command −

library("package Name", lib.loc = "path to library")

# Load the package named "XML"
install.packages("E:/XML_3.98-1.3.zip", repos = NULL, type = "source")

October 29, 2024

Data Frames

A data frame is a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column.

Following are the characteristics of a data frame.

The column names should be non-empty.
The row names should be unique.
The data stored in a data frame can be of numeric, factor or character type.
Each column should contain same number of data items.

Create Data Frame

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
  "2015-03-27")),   stringsAsFactors = FALSE
)
# Print the data frame.			
print(emp.data)

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

 emp_id    emp_name     salary     start_date
1     1     Rick        623.30     2012-01-01
2     2     Dan         515.20     2013-09-23
3     3     Michelle    611.00     2014-11-15
4     4     Ryan        729.00     2014-05-11
5     5     Gary        843.25     2015-03-27

Get the Structure of the Data Frame

The structure of the data frame can be seen by using str() function.

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
  "2015-03-27")),   stringsAsFactors = FALSE
)
# Get the structure of the data frame.
str(emp.data)

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

'data.frame':   5 obs. of  4 variables:
 $ emp_id    : int  1 2 3 4 5
 $ emp_name  : chr  "Rick" "Dan" "Michelle" "Ryan" ...
 $ salary    : num  623 515 611 729 843
 $ start_date: Date, format: "2012-01-01" "2013-09-23" "2014-11-15" "2014-05-11" ...

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Summary of Data in Data Frame

The statistical summary and nature of the data can be obtained by applying summary() function.

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
  "2015-03-27")),   stringsAsFactors = FALSE
)
# Print the summary.
print(summary(emp.data))

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

     emp_id    emp_name             salary        start_date        
 Min.   :1   Length:5           Min.   :515.2   Min.   :2012-01-01  
 1st Qu.:2   Class :character   1st Qu.:611.0   1st Qu.:2013-09-23  
 Median :3   Mode  :character   Median :623.3   Median :2014-05-11  
 Mean   :3                      Mean   :664.4   Mean   :2014-01-14  
 3rd Qu.:4                      3rd Qu.:729.0   3rd Qu.:2014-11-15  
 Max.   :5                      Max.   :843.2   Max.   :2015-03-27

Extract Data from Data Frame

Extract specific column from a data frame using column name.

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5),
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25),
   
   start_date = as.Date(c("2012-01-01","2013-09-23","2014-11-15","2014-05-11",
  "2015-03-27")),   stringsAsFactors = FALSE
)
# Extract Specific columns.
result <- data.frame(emp.data$emp_name,emp.data$salary)
print(result)

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

  emp.data.emp_name emp.data.salary
1              Rick          623.30
2               Dan          515.20
3          Michelle          611.00
4              Ryan          729.00
5              Gary          843.25

Extract the first two rows and then all columns

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5),
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25),
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
  "2015-03-27")),   stringsAsFactors = FALSE
)
# Extract first two rows.
result <- emp.data[1:2,]
print(result)

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

  emp_id    emp_name   salary    start_date
1      1     Rick      623.3     2012-01-01
2      2     Dan       515.2     2013-09-23

Extract 3^rd and 5^th row with 2^nd and 4^th column

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
	start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
  "2015-03-27")),   stringsAsFactors = FALSE
)

# Extract 3rd and 5th row with 2nd and 4th column.
result <- emp.data[c(3,5),c(2,4)]
print(result)

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

  emp_name start_date
3 Michelle 2014-11-15
5     Gary 2015-03-27

Expand Data Frame

A data frame can be expanded by adding columns and rows.

Add Column

Just add the column vector using a new column name.

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
  "2015-03-27")),   stringsAsFactors = FALSE
)

# Add the "dept" coulmn.
emp.data$dept <- c("IT","Operations","IT","HR","Finance")
v <- emp.data
print(v)

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

  emp_id   emp_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     5    Gary        843.25    2015-03-27       Finance

Add Row

To add more rows permanently to an existing data frame, we need to bring in the new rows in the same structure as the existing data frame and use the rbind() function.

In the example below we create a data frame with new rows and merge it with the existing data frame to create the final data frame.

# Create the first data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
  "2015-03-27")),   dept = c("IT","Operations","IT","HR","Finance"),
   stringsAsFactors = FALSE
)

# Create the second data frame
emp.newdata <- 	data.frame(
   emp_id = c (6:8), 
   emp_name = c("Rasmi","Pranab","Tusar"),
   salary = c(578.0,722.5,632.8), 
   start_date = as.Date(c("2013-05-21","2013-07-30","2014-06-17")),
   dept = c("IT","Operations","Fianance"),
   stringsAsFactors = FALSE
)

# Bind the two data frames.
emp.finaldata <- rbind(emp.data,emp.newdata)
print(emp.finaldata)

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

  emp_id     emp_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      5     Gary        843.25     2015-03-27       Finance
6      6     Rasmi       578.00     2013-05-21       IT
7      7     Pranab      722.50     2013-07-30       Operations
8      8     Tusar       632.80     2014-06-17       Fianance

October 29, 2024

Factors

Factors are the data objects which are used to categorize the data and store it as levels. They can store both strings and integers. They are useful in the columns which have a limited number of unique values. Like “Male, “Female” and True, False etc. They are useful in data analysis for statistical modeling.

Factors are created using the factor () function by taking a vector as input.

Example

# Create a vector as input.
data <- c("East","West","East","North","North","East","West","West","West","East","North")

print(data)
print(is.factor(data))

# Apply the factor function.
factor_data <- factor(data)

print(factor_data)
print(is.factor(factor_data))

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

[1] "East"  "West"  "East"  "North" "North" "East"  "West"  "West"  "West"  "East" "North"
[1] FALSE
[1] East  West  East  North North East  West  West  West  East  North
Levels: East North West
[1] TRUE

Factors in Data Frame

On creating any data frame with a column of text data, R treats the text column as categorical data and creates factors on it.

# Create the vectors for data frame.
height <- c(132,151,162,139,166,147,122)
weight <- c(48,49,66,53,67,52,40)
gender <- c("male","male","female","female","male","female","male")

# Create the data frame.
input_data <- data.frame(height,weight,gender)
print(input_data)

# Test if the gender column is a factor.
print(is.factor(input_data$gender))

# Print the gender column so see the levels.
print(input_data$gender)

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

  height weight gender
1    132     48   male
2    151     49   male
3    162     66 female
4    139     53 female
5    166     67   male
6    147     52 female
7    122     40   male
[1] TRUE
[1] male   male   female female male   female male  
Levels: female male

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Changing the Order of Levels

The order of the levels in a factor can be changed by applying the factor function again with new order of the levels.

data <- c("East","West","East","North","North","East","West",
   "West","West","East","North")
# Create the factors
factor_data <- factor(data)
print(factor_data)

# Apply the factor function with required order of the level.
new_order_data <- factor(factor_data,levels = c("East","West","North"))
print(new_order_data)

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

 [1] East  West  East  North North East  West  West  West  East  North
Levels: East North West
 [1] East  West  East  North North East  West  West  West  East  North
Levels: East West North

Generating Factor Levels

We can generate factor levels by using the gl() function. It takes two integers as input which indicates how many levels and how many times each level.

Syntax

gl(n, k, labels)

Following is the description of the parameters used −

n is a integer giving the number of levels.
k is a integer giving the number of replications.
labels is a vector of labels for the resulting factor levels.

Example

v <- gl(3, 4, labels = c("Tampa", "Seattle","Boston"))
print(v)

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

Tampa   Tampa   Tampa   Tampa   Seattle Seattle Seattle Seattle Boston 
[10] Boston  Boston  Boston 
Levels: Tampa Seattle Boston

October 29, 2024

Arrays

Arrays are the R data objects which can store data in more than two dimensions. For example − If we create an array of dimension (2, 3, 4) then it creates 4 rectangular matrices each with 2 rows and 3 columns. Arrays can store only data type.

An array is created using the array() function. It takes vectors as input and uses the values in the dim parameter to create an array.

Example

The following example creates an array of two 3×3 matrices each with 3 rows and 3 columns.

# Create two vectors of different lengths.
vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)

# Take these vectors as input to the array.
result <- array(c(vector1,vector2),dim = c(3,3,2))
print(result)

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

, , 1

 &#91;,1] &#91;,2] &#91;,3][1,]    5   10   13
[2,]    9   11   14
[3,]    3   12   15

, , 2

 &#91;,1] &#91;,2] &#91;,3][1,]    5   10   13
[2,]    9   11   14
[3,]    3   12   15

Naming Columns and Rows

We can give names to the rows, columns and matrices in the array by using the dimnames parameter.

# Create two vectors of different lengths.
vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)
column.names <- c("COL1","COL2","COL3")
row.names <- c("ROW1","ROW2","ROW3")
matrix.names <- c("Matrix1","Matrix2")

# Take these vectors as input to the array.
result <- array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,column.names,
   matrix.names))
print(result)

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

, , Matrix1

 COL1 COL2 COL3ROW1    5   10   13
ROW2    9   11   14
ROW3    3   12   15

, , Matrix2

 COL1 COL2 COL3ROW1    5   10   13
ROW2    9   11   14
ROW3    3   12   15

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Accessing Array Elements

# Create two vectors of different lengths.
vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)
column.names <- c("COL1","COL2","COL3")
row.names <- c("ROW1","ROW2","ROW3")
matrix.names <- c("Matrix1","Matrix2")

# Take these vectors as input to the array.
result <- array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,
   column.names, matrix.names))

# Print the third row of the second matrix of the array.
print(result[3,,2])

# Print the element in the 1st row and 3rd column of the 1st matrix.
print(result[1,3,1])

# Print the 2nd Matrix.
print(result[,,2])

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

COL1 COL2 COL3 
   3   12   15 
[1] 13
 COL1 COL2 COL3ROW1    5   10   13
ROW2    9   11   14
ROW3    3   12   15

Manipulating Array Elements

As array is made up matrices in multiple dimensions, the operations on elements of array are carried out by accessing elements of the matrices.

# Create two vectors of different lengths.
vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)

# Take these vectors as input to the array.
array1 <- array(c(vector1,vector2),dim = c(3,3,2))

# Create two vectors of different lengths.
vector3 <- c(9,1,0)
vector4 <- c(6,0,11,3,14,1,2,6,9)
array2 <- array(c(vector1,vector2),dim = c(3,3,2))

# create matrices from these arrays.
matrix1 <- array1[,,2]
matrix2 <- array2[,,2]

# Add the matrices.
result <- matrix1+matrix2
print(result)

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

     [,1] [,2] [,3]
[1,]   10   20   26
[2,]   18   22   28
[3,]    6   24   30

Calculations Across Array Elements

We can do calculations across the elements in an array using the apply() function.

Syntax

apply(x, margin, fun)

Following is the description of the parameters used −

x is an array.
margin is the name of the data set used.
fun is the function to be applied across the elements of the array.

Example

We use the apply() function below to calculate the sum of the elements in the rows of an array across all the matrices.

# Create two vectors of different lengths.
vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)

# Take these vectors as input to the array.
new.array <- array(c(vector1,vector2),dim = c(3,3,2))
print(new.array)

# Use apply to calculate the sum of the rows across all the matrices.
result <- apply(new.array, c(1), sum)
print(result)

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

, , 1

 &#91;,1] &#91;,2] &#91;,3][1,]    5   10   13
[2,]    9   11   14
[3,]    3   12   15

, , 2

 &#91;,1] &#91;,2] &#91;,3][1,]    5   10   13
[2,]    9   11   14
[3,]    3   12   15

[1] 56 68 60

October 29, 2024

Matrices

Matrices are the R objects in which the elements are arranged in a two-dimensional rectangular layout. They contain elements of the same atomic types. Though we can create a matrix containing only characters or only logical values, they are not of much use. We use matrices containing numeric elements to be used in mathematical calculations.

A Matrix is created using the matrix() function.

Syntax

The basic syntax for creating a matrix in R is −

matrix(data, nrow, ncol, byrow, dimnames)

Following is the description of the parameters used −

data is the input vector which becomes the data elements of the matrix.
nrow is the number of rows to be created.
ncol is the number of columns to be created.
byrow is a logical clue. If TRUE then the input vector elements are arranged by row.
dimname is the names assigned to the rows and columns.

Example

Create a matrix taking a vector of numbers as input.

# Elements are arranged sequentially by row.
M <- matrix(c(3:14), nrow = 4, byrow = TRUE)
print(M)

# Elements are arranged sequentially by column.
N <- matrix(c(3:14), nrow = 4, byrow = FALSE)
print(N)

# Define the column and row names.
rownames = c("row1", "row2", "row3", "row4")
colnames = c("col1", "col2", "col3")

P <- matrix(c(3:14), nrow = 4, byrow = TRUE, dimnames = list(rownames, colnames))
print(P)

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

     [,1] [,2] [,3]
[1,]    3    4    5
[2,]    6    7    8
[3,]    9   10   11
[4,]   12   13   14
 &#91;,1] &#91;,2] &#91;,3][1,]    3    7   11
[2,]    4    8   12
[3,]    5    9   13
[4,]    6   10   14
 col1 col2 col3row1    3    4    5
row2    6    7    8
row3    9   10   11
row4   12   13   14

Accessing Elements of a Matrix

Elements of a matrix can be accessed by using the column and row index of the element. We consider the matrix P above to find the specific elements below.

# Define the column and row names.
rownames = c("row1", "row2", "row3", "row4")
colnames = c("col1", "col2", "col3")

# Create the matrix.
P <- matrix(c(3:14), nrow = 4, byrow = TRUE, dimnames = list(rownames, colnames))

# Access the element at 3rd column and 1st row.
print(P[1,3])

# Access the element at 2nd column and 4th row.
print(P[4,2])

# Access only the  2nd row.
print(P[2,])

# Access only the 3rd column.
print(P[,3])

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

[1] 5
[1] 13
col1 col2 col3 
   6    7    8 
row1 row2 row3 row4 
   5    8   11   14

Matrix Computations

Various mathematical operations are performed on the matrices using the R operators. The result of the operation is also a matrix.

The dimensions (number of rows and columns) should be same for the matrices involved in the operation.

Matrix Addition & Subtraction

# Create two 2x3 matrices.
matrix1 <- matrix(c(3, 9, -1, 4, 2, 6), nrow = 2)
print(matrix1)

matrix2 <- matrix(c(5, 2, 0, 9, 3, 4), nrow = 2)
print(matrix2)

# Add the matrices.
result <- matrix1 + matrix2
cat("Result of addition","\n")
print(result)

# Subtract the matrices
result <- matrix1 - matrix2
cat("Result of subtraction","\n")
print(result)

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

     [,1] [,2] [,3]
[1,]    3   -1    2
[2,]    9    4    6
 &#91;,1] &#91;,2] &#91;,3][1,]    5    0    3
[2,]    2    9    4
Result of addition 
 &#91;,1] &#91;,2] &#91;,3][1,]    8   -1    5
[2,]   11   13   10
Result of subtraction 
 &#91;,1] &#91;,2] &#91;,3][1,]   -2   -1   -1
[2,]    7   -5    2

Matrix Multiplication & Division

# Create two 2x3 matrices.
matrix1 <- matrix(c(3, 9, -1, 4, 2, 6), nrow = 2)
print(matrix1)

matrix2 <- matrix(c(5, 2, 0, 9, 3, 4), nrow = 2)
print(matrix2)

# Multiply the matrices.
result <- matrix1 * matrix2
cat("Result of multiplication","\n")
print(result)

# Divide the matrices
result <- matrix1 / matrix2
cat("Result of division","\n")
print(result)

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

     [,1] [,2] [,3]
[1,]    3   -1    2
[2,]    9    4    6
 &#91;,1] &#91;,2] &#91;,3][1,]    5    0    3
[2,]    2    9    4
Result of multiplication 
 &#91;,1] &#91;,2] &#91;,3][1,]   15    0    6
[2,]   18   36   24
Result of division 
 &#91;,1]      &#91;,2]      &#91;,3][1,]  0.6      -Inf 0.6666667
[2,]  4.5 0.4444444 1.5000000

October 29, 2024

Author: saqibkhan

Input Data

Reading XML File

Get Number of Nodes Present in XML File

Details of the First Node

Get Different Elements of a Node

XML to Data Frame

Syntax

Example

Writing the Binary File

Reading the Binary File

Install xlsx Package

Verify and Load the “xlsx” Package

Input as xlsx File

Reading the Excel File

Getting and Setting the Working Directory

Input as CSV File

Reading a CSV File

Analyzing the CSV File

Get the maximum salary

Get the details of the person with max salary

Get all the people working in IT department

Get the persons in IT department whose salary is greater than 600

Get the people who joined on or after 2014

Writing into a CSV File

Joining Columns and Rows in a Data Frame

Merging Data Frames

Melting and Casting

Melt the Data

Cast the Molten Data

Check Available R Packages

Get the list of all the packages installed

Install a New Package

Install directly from CRAN

Install package manually

Load Package to Library

Create Data Frame

Get the Structure of the Data Frame

Summary of Data in Data Frame

Extract Data from Data Frame

Expand Data Frame

Add Column

Add Row

Example

Factors in Data Frame

Changing the Order of Levels

Generating Factor Levels

Syntax

Example

Example

Naming Columns and Rows

Accessing Array Elements

Manipulating Array Elements

Calculations Across Array Elements

Syntax

Example

Syntax

Example

Accessing Elements of a Matrix

Matrix Computations

Matrix Addition & Subtraction

Matrix Multiplication & Division