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# 103.1.4 R Data types

##### The basic Data Types in R

In previous section, we studied about R Packages, now we will be studying about R Data Types.

This is the most important topic of our current session. R has vectors, data frames, lists.

R Vectors

Everything in R is stored as a Vector by default, most of the times.

If I say my name:

` >name<-“karthik”`

The structure of name, Str(name) is Char

The data-type of name, is.vector(name)-TRUE. So, by default everything we create is a vector.

```>Age<-29
>Is.vector(age):TRUE```

Thus everything is a vector; basically, a vector is a combination/group of all basic elements that are put together. There are many advantages of using Vector like we need not write loops for making operations on vectors. c() is a concatenation operator. We can also say that vectors are a generalized version for arrays.

```>Age <- c(15, 17, 16, 15, 16)
>English<- c(40, 56, 30, 68, 35)
>Science<- c(85, 80, 74, 39, 65)
>Name<- c("John", "Bob", "Kevin", "Smith", "Rick")
>is.vector(Age)
>True
>is.vector(English)
>True
>is.vector(Name)
>True```

Many mathematical functions can be applied without loops. If we want to add 3 to vector-age, we need not use loop and run, simply we can write :

```>Age+3
 18 20 19 18 19
>English1<- English+10
>Total<- English1 + Science
>Total
 135 146 114 117 110
>Age/Total
 0.1111111 0.1164384 0.1403509 0.1282051 0.1454545

```

Another example is as below

```>x <- rnorm(100,mean=20,sd=5)
>mean(x)
 20.16493
>x-mean(x)```
``` ##     -0.56962602   1.20292587  -7.99204832   2.82564771   1.18117481
##     -5.24518066  -2.68735732  -3.27495626  -0.28558601   0.74857039
##    -0.47749603 -10.15470947  -1.06874992   2.31996526   2.84350465
##     8.16148504   0.25051846   6.19313835   2.59168828   0.29868066
##    -3.82597413   5.39263180   4.67593290  -0.43454849  -8.25625452
##     7.84636863  -1.06371741   0.50440888  -3.09572178   3.99628700
##     9.38662504  -2.09353644  -1.95195873   8.14326027  -6.18426536
##     3.46082316   2.48187522 -11.74572064   0.35223491   1.67869236
##    -6.35170720  -1.45205184   2.91846245  -1.49338586   1.11208055
##    -8.72848627  -7.52563928   6.15454046  -1.22060564  10.02838035
##     1.70506238   1.41094805 -10.72651241  -0.66319353  -7.20567753
##     2.73986617   2.68289335  -2.83861944  -8.99196764   3.97441791
##    -0.74130073   8.79819626 -14.87133600   3.95593514   1.10624785
##    -2.89865994  -3.68172179   0.41355051   9.62255710  -9.98099032
##    12.66459589   4.30046314   9.53252294  -3.40578675  -1.04795097
##    -3.09459639  -0.24209619   4.98138610  -0.70323448  -4.26956279
##     3.35833387   5.09742073 -14.16394754   4.97869260   1.15177611
##     0.60028795  -4.74663476  -1.96627009   5.71759434   0.02098097
##    -0.49594059  -3.92591152  -6.29043650  -5.41646894  -2.31449988
##     1.87594179   8.00307231   3.92296652  10.13654537   2.36044150```

Accessing of vector elements

We need to use the [] operator to access the elements

```>Age
 15 17 16 15 16```

For accessing the third element of Age vector

```>Age
 16```

The 2nd, 3rd, 4th and 5th elements of the Age vector

```Age[2:5]
 17 16 15 16```

The 1st, 3rd and 5th elements of Age vector

```>Age[c(1,3,5)]
 15 16 16```

To eliminate or ignore the second value and get rest of values.

```>Age[-2]
 15 16 15 16```

Replace 3rd element with 19

```>Age<-19
>Age
 15 17 19 15 16```

Adding a new element to the vector

```>Age<-22
>Age
 15 17 19 15 16 22```

To introduce value to an arbitrary position ,

```>Age<-42
>Age
 15 17 19 15 16 22 NA NA NA 42```

Every other value will be NA and 10th element will be 42.

We can also try to give all these numbers in cluster

```>Age[6:10]<-c(23,25,26,29,33,35)
>Age
 15 17 19 15 16 23 25 26 29 33 35```

To find the vector type, We use the Class() of the vector to find the vector type

```>class(Age)
"numeric"

>class(Name)
"character"```

The succeeding posts will discuss on Data Frames.

In next section, we will be studying about R Data Frames.

20th June 2017
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