R Language

INTRODUCTION:

R is an open source programming language and software environment for statistical computing

and graphics. The R language is widely used among statisticians and data miners for developing

statistical software and data analysis. But, we are going to integrate R with Hadoop in order to

manage BigData efficiently. Here, we have tried to figure out some basic commands of R

followed by logical implementation using R as well.

BASIC COMMANDS:

GetDirectory:

getwd() [ R language is case sensitive ]

Assignment:

Single value:

s < - 3 or s = 3 [The value of s is 3]

Multiple values:

s < - c (1, 2, 3) [The value of s is 1, 2, 3]

Or

s < - c (1:3) [The value of s is 1, 2, 3]

Mean:

mean(x) [The mean of x i.e. 1, 2, 3 is 2]

Variance:

var(x) [The variance of x i.e.1, 2, 3 is 1]

Linear Model:

lm_1 < - lm(y~x) [The linear model between two variables will be shown]

Graphical Representation:

plot (lm_1) [The linear model will be graphically represented]

Summary:

summary (lm_1) [The summary of the linear model will be shown]

List of variables:

ls () [ The list of all variables used will be shown]

Reading .csv files:

read.table ( file=”sample.csv”)

[Files can be read in this way, mostly csv files]

Reading .xls files:

install.packages("gdata") [ It can be done manually too. Go to Packages at the

top. Select any location, preferably USA (CA 1).

Then select gdata from the list. ]

library(gdata) [ Use of Library for reading an Excel file. ]

setwd("D:/R Statistics") [ Set working directory ]

y=read.xls("iris.xls") [Read the .xls/.xlsx file that is present in the working

directory. ]

y [ Print the contents of the excel file. ]

These were some basic knowledge on R. Now, some logical implementations are being laid

down below:

LOGICAL IMPLEMENTATION:

Conditional:

Example:

x=5 # Creates sample data

if(x!=5)

{

print(1)

} else

{

print(2)

}

*else should be printed after “}”, not in a new line.

Output:

[1] 2

Ifelse:

Ifelse statements operate on vectors of variable length.

Syntax:

ifelse(test, true_value, false_value)

Example:

x = 1:10 # Creates sample data

ifelse(x<5 | x>8, x, 0)

Output:

[1] 1 2 3 4 0 0 0 0 9 10

For loop:

Example:

x=5

for(i in seq(along=x))

{

if(x==5)

print(1)

else

print(2)

}

Output:

[1] 1

*The use of “along” prints the value for once only. At the same time, the absence of along

will print the value x times likewise;

x=5

for(i in seq(x))

{

if(x==5)

print(1)

else

print(2)

}

Output:

[1] 1 [1] 1 [1] 1 [1] 1 [1] 1

While Loop:

Example:

z = 0

while(z < 5)

{

z = z + 2

print(z)

}

Output:

[1] 2

[1] 4

[1] 6

Apply loop:

Example 1(Coloumwise operation):

x= matrix(c(1:9),3,3)

apply(x, 1, sum)

Output:

[1] 12 15 18

Example 2(Rowwise operation):

x= matrix(c(1:9),3,3)

apply(x, 2, sum)

Output:

[1] 6 15 24

lapply:

Applies a function to elements in a list or a vector and returns the results in a list.

Exmple:

# create a list with 2 elements

l = list(a = 1:10, b = 11:20)

# the mean of the values in each element

lapply(l, mean)

Output:

$a

[1] 5.5

$b

[1] 15.5

# the sum of the values in each element

lapply(l, sum)

Output:

$a

[1] 55

$b

[1] 155

sapply:

Exmple1:

li = list("klaus","martin","georg")

sapply(li,toupper)

Output:

[1] "KLAUS" "MARTIN" "GEORG"

Exmple2:

# create a list with 2 elements

l <- list(a = 1:10, b = 11:20)

# mean of values using sapply

l.mean <- sapply(l, mean)

# what type of object was returned?

class(l.mean)

[1] "numeric"

# it's a numeric vector, so we can get element "a" like this

l.mean[['a']]

Output:

[1] 5.5

vapply:

vapply is similar to sapply, but has a pre-specified type of return value, so it can be safer (and

sometimes faster) to use.

Exmple:

l <- list(a = 1:10, b = 11:20)

# fivenum of values using vapply

l.fivenum <- vapply(l, fivenum, c(Min.=0, "1st Qu."=0, Median=0, "3rd Qu."=0, Max.=0))

class(l.fivenum)

[1] "matrix"

# let's see it

l.fivenum

Output:

a b

Min. 1.0 11.0

1st Qu. 3.0 13.0

Median 5.5 15.5

3rd Qu. 8.0 18.0

Max. 10.0 20.0

REFERENCE:

[1] http://www.r-project.org

[2] http://www.cyclismo.org/tutorial/R/types.html

[3] http://manuals.bioinformatics.ucr.edu/home/programming-in-r

[4] http://nsaunders.wordpress.com/2010/08/20/a-brief-introduction-to-apply-in-r