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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.


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 files:

read.table ( file=”sample.csv”)


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


CONCLUSION:

Some basic commands are being noted down. These may help to gain some primary knowledge

on R. R with Hadoop is still in progress.

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R Language for Statistics and Big Data Analysis

  • 1. 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. 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]
  • 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 files: read.table ( file=”sample.csv”) [Files can be read in this way, mostly csv files] CONCLUSION: Some basic commands are being noted down. These may help to gain some primary knowledge on R. R with Hadoop is still in progress.