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**What is R?****Where is it used?****The R Environment:****R Studio:****RStudio Projects:****Installing Packages:****>install.packages("cluster")****Basic symbols in R:****Data Types in R:**- Numeric Data: It indicates integers and decimals that may be positive or negative or zero. In R numeric and integer both indicate numeric data
- Character Data: Its string data type. character and factor both represent character data in R
- Dates Data: The type of data is the date, where Date and POSICXct are widely used functions in R
- Logical: Logical is representing data as TRUE OR FALSE. TRUE is 1 and FALSE is 0.
**Vectors and Matrices:****Data Frames and Lists:****Lists:****Reading Data into R:****Head and Tail in R:****Graphical Options:****Control Statements and Loops:**__If and Else____For loops____While loops__**cbind and rbind:****Analyzing Data in R:**__Basic Statistics:____Linear Model:____Generalized Linear Model:__**Time Series Analysis:**

R is a very powerful tool for analysing, visualizing, and reporting of statistical data. It is an Open Source software and is very interactive. Unlike traditional language like C++ where we have to write the entire code to see the results, the results in R will be reflected for one command at a time.

The language is used mainly by statisticians and data miners. It is used in the banking sector, food start-ups, hospitals, real estate developers, insurance companies, online advertising, and pharmaceuticals. It is also popular in the advanced machine learning platform.

R offers data computation and graphical representation of data. Big data can be handled through this software.

The following standard R interface is displayed when we open the R software:

Various Integrated Development Environment (IDEs) are built for R. Among all these IDEs, RStudio is the most popular one.

RStudio was created by the team led by JJ Allaire. It is available for Windows, Mac, and Linux. RStudio is an improvement over the standard R and its interface looks like the following figure:

A Project is a primary feature in RStudio. It is a collection of files. To view projects, we need to click on File>>New Project.

Clicking on the above will result in the following project creation:

We need to choose any one of the three options to start a project.

There are many ways to install packages in R. In RStudio, we can access the packages tab by clicking on it. The following figure shows the RStudio Packages pane.

**Another way to install packages is to write a simple command in console:**

In R, we use "<-" symbol to assign a value to a variable. That is if we assign 2 to x we would write the command as x<-2.

The symbol ,"==" denotes equals to. And "!=" denotes not equal to.

A Vector is a collection of the same type of elements. In R, a vector is written as c(1,2,3,4). It plays a very important role in R. The following command shows us how to write a vector in R:

> x=c(1,2,3,4)

>x

[1] 1 2 3 4

From the above, we see if we write the command x=c(1,2,3,4) and then write x, the values 1,2,3,4 would come as the result.

Matrices are a very important structure in Mathematics and are often used in Statistics. We use the function matrix to construct a matrix in R. nrow, ncol, and dim functions in R denotes the number of rows, the number of columns, and the dimension of matrix respectively The following command shows us how to write a matrix in R:

> #create a matrix of order 2*5

> A <-matrix (1:10,nrow=2)

> A

[,1] [,2] [,3] [,4] [,5]

[1,]

1 3 5

7 9

[2,]

2 4 6

8 10

> # create another matrix of order 2*5

> B<-matrix (31:40,nrow=2)

> B

[,1] [,2] [,3] [,4] [,5]

[1,]

31 33 35

37 39

[2,]

32 34 36

38 40

> nrow(A)

[1] 2

> ncol(B)

[1] 5

> dim(B)

[1] 2 5

> # Add the two matrices

> A+B

[,1] [,2] [,3] [,4] [,5]

[1,]

32 36 40

44 48

[2,]

34 38

42 46 50

> # Multiply the two matrices

> A*B

[,1] [,2] [,3] [,4] [,5]

[1,]

31 99 175

259 351

[2,]64 136

216 304 400

In the above, # is used to write a comment. Often when we have many codes, we use comments to make the reader understand what the code means. 1:10 means numbers from 1 to 10 and 31:40 means numbers from 31 to 40. Lastly, we have performed Matrix addition and multiplication.

Data Frames is a vital part in R and is written as data.frames. In data.frames, each column is a vector having the same length. The following command in R shows how to write a Data Frame:

> x<-50:41

> y<--8:1

> z<-c("Oranges","Pineapples","Blueberry","Cherries","Avocados", "Bananas","Apples","Grapes","Litchis","Mangoes")

> DFrame<-data.frame(x,y,z)

> DFrame

x y z

1 50 -8 Oranges

2 49 -7 Pineapples

3 48 -6 Blueberry

4 47 -5 Cherries

5 46 -4 Avocados

6 45 -3 Bananas

7 44 -2 Apples

8 43 -1 Grapes

9 42 0 Litchis

10 41 1 Mangoes

From the above, we see data.frames allow columns to have a different type of data. The first two columns are numeric type, while the last column is of character type.

The List acts a container that holds arbitrary objects of the same or different types of data. That is to say, a list can contain all numerics or all characters or a mix of these two types of data. The function List is used to denote List in R. The following command in R shows how to write a list in R:

> #create a list of a single element, where it is a vector having 2 elements

> list(c(1,2))

[[1]]

[1] 1 2

> #create a list of two element, one is a 3 element vector, and 2nd element is 4 element vector

> list(c(1,2,5),45:48)

[[1]]

[1] 1 2 5

[[2]]

[1] 45 46 47 48

R can read data from CSVs, SQL, SPSS, SAS, Minitab, Stata, etc. The following are the functions that are used for reading data from different software:

Type/Name of Software

Functions in R

Reading CSVs

read.table

Reading SPSS

read.spss

Reading SAS

read.ssd

Reading Minitab

read.mtp

The Head function shows the first few rows of a matrix or data frames, whereas the Tail function shows the last few rows of them. The Head function is written as "head", while the Tail function is written as "tail".

There are many options to create statistical graphs in R. For advance graphical features, we need to install and load ggplot2.

if and else statements are used when we need to check if something is TRUE, then we need to perform an action, otherwise we do not perform it. The following shows how to write such functions in R:

> #create the function

> check.bool<-function(x)

+ {

+ if(x==2){

# if input is equal to 2, print How are you?

+ print("How are you?")

+ }

+ else{

+

+ # otherwise print who are you?

+ print("Who are you?")

+ }

+ }

For loop is used as an index that gets repeated or iterated. The following shows how a for loop works in R:

> for (i in 25:30)

+ {

+ print(i)

+ }

[1] 25

[1] 26

[1] 27

[1] 28

[1] 29

[1] 30

While loops are used to run the code as long as the conditions are proved to be true. The following shows an illustration for a while loop:

> x<-2

> while(x<=10)

+ {

+ print(x)

+ x=x+1

+ }

[1] 2

[1] 3

[1] 4

[1] 5

[1] 6

[1] 7

[1] 8

[1] 9

[1] 10

The cbind function in R is used to combine data frames, matrices, and vectors by columns. The rbind function does the same operation as above by using the rows instead of the columns.

The following are some of the Statistical analyses that can be performed in R:

In order to find the descriptive statistics of the data in R, we use mean for finding mean, median for finding median, sd for finding standard deviations, cor for correlation and so on. Following is an example of Basic Statistics analysis in R:

> x=c(52,100,43,86,121,85,46,12,91,100,45,47,136,161,25,112)

> summary(x)

Min. 1st Qu. Median Mean 3rd Qu. Max.

12.00 45.75 85.50 78.88 103.00 161.00

> quantile(x)

0% 25% 50% 75% 100%

12.00 45.75 85.50 103.00 161.00

> mean(x)

[1] 78.875

> mode(x)

[1] "numeric"

> sd(x)

[1] 42.13925

Linear regression models can be analysed in R using function "lm". For ANOVA we use the function "aov". Following is an example showing a simple linear analysis in R:

> ageLM<-lm(age~circumference,data=Orange)

> ageLM

Call:

lm(formula = age ~ circumference, data = Orange)

Coefficients:

(Intercept) circumference

16.604 7.816

> summary(ageLM)

Call:

lm(formula = age ~ circumference, data = Orange)

Residuals:

Min 1Q Median 3Q Max

-317.88 -140.90 -17.20 96.54 471.16

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 16.6036 78.1406 0.212 0.833

circumference 7.8160 0.6059 12.900 1.93e-14 ***

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 203.1 on 33 degrees of freedom

Multiple R-squared: 0.8345, Adjusted R-squared: 0.8295

F-statistic: 166.4 on 1 and 33 DF, p-value: 1.931e-14

Sometimes when linear regression can't be fitted, we need to use the generalized model. We use glm function for such cases. Logistic regression, Poisson regression, etc. are some of the examples of the generalized linear model. Following is an illustration of running a generalized linear model in R:

> counts <- c(18,17,15,20,10,20,25,13,12)

> outcome <- gl(3,1,9)

> treatment <- gl(3,3)

> print(d.AD <- data.frame(treatment, outcome, counts))

treatment outcome counts

1 1

1 18

2 1

2 17

3 1

3 15

4 2

1 20

5 2

2 10

6 2

3 20

7 3

1 25

8 3

2 13

9 3

3 12

> glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())

> anova(glm.D93)

Analysis of Deviance Table

Model: poisson, link: log

Response: counts

Terms added sequentially (first to last)

Df Deviance Resid. Df Resid. Dev

NULL 8 10.5814

outcome 2

5.4523 6 5.1291

treatment 2

0.0000 4 5.1291

> summary(glm.D93)

Call:

glm(formula = counts ~ outcome + treatment, family = poisson())

Deviance Residuals:

1

2 3 4 5 6 7 8 9

-0.67125 0.96272

-0.16965 -0.21999 -0.95552

1.04939 0.84715 -0.09167

-0.96656

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) 3.045e+00

1.709e-01 17.815 <2e-16 ***

outcome2 -4.543e-01

2.022e-01 -2.247 0.0246 *

outcome3 -2.930e-01

1.927e-01 -1.520 0.1285

treatment2 1.338e-15

2.000e-01 0.000 1.0000

treatment3 1.421e-15

2.000e-01 0.000 1.0000

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for poisson family taken to be 1)

Null deviance: 10.5814 on 8

degrees of freedom

Residual deviance: 5.1291 on 4

degrees of freedom

AIC: 56.761

Number of Fisher Scoring iterations: 4

Basic and advanced time series analysis can be carried out in R. For instance if we want to create time-series objects we use the function ts. The following is one such example:

> ts(1:10, frequency = 4, start = c(1959, 2)) # 2nd Quarter of 1959

Qtr1 Qtr2 Qtr3 Qtr4

1959 1

2 3

1960 4

5 6 7

1961 8

9 10

> print( ts(1:10, frequency = 7, start = c(12, 2)), calendar = TRUE)

p1 p2 p3 p4 p5 p6 p7

12 1

2 3 4

5 6

13 7

8 9 10

> # print.ts(.)

> ## Using July 1954 as start date:

> gnp <- ts(cumsum(1 + round(rnorm(100), 2)),

+ start = c(1954, 7), frequency = 12)

> plot(gnp) # using 'plot.ts' for time-series plot

We get the following plot as the output in Plot area of RStudio:

R can thus handle complicated data and thus helping the statisticians or the researchers with their analysis. Our R homework help will help you to write complex R codes.

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