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Statistics help in R Programming; Live Tutors are helping students to solve R-programming problems - Get assistance homework and Assignment

**Importance of R Programming:- **

- R is very flexible tool for doing mathematical and statistical analysis and it is a package like mat lab and also programming language like python or JavaScript.
- R is open source and can download and start doing analysis and visualization.
- It can be used in machine learning techniques and algorithms as well and in neural networks. Good tool for exploratory analytics
- All this packages are tested regularly and are comprehensively well documented
- Almost every major organizations uses R and many universities too for projects and most of the startups are using for analytics statistical modeling
- R is often used to do this well, and absolutely R programming language
- The only drawback of R language is it cannot handle huge datasets.

**Difficulties faced by a student while solving R with Programming problems****:**

- R language is case sensitive and very difficult for students to start up with
- R requires a internet facility with high bandwidth or the strong connection that course require and many cases students fail to catch up this
- Need to know correct package to install for the right operations to get through the programming
- Sometimes we may have to create new variable in the data depends on the analysis.
- Sometimes we may have to merge the data from multiple sources and create the consolidated data and very good tool to handle the data analysis and results which are having huge impacts
- R is radically different from other analysis software and is an unavoidable byproduct due to its flexibility.
- Other data science software analysis packages have relatively few commands but each of them have many options to control their output but R feature is different and quite opposite which takes time to get used to it.

**R Programming problems:- **

- R consists of a core and packages. Packages contain functions that are not available in the core software
- Collections of R functions, data, and compiled code
- When you download R, already a number (around 30) of packages are downloaded as well.
- Select the Packages menu and selecting the Install package... then a list of available packages on your system will be displayed.
- Select one and click 'OK', the package is now attached to your current R session. Via the library function
- Before using a function, we need to install the package that contains
- Most mathematical functions and operators can be applied to vectors without writing any loops
- Age+3
- English1<-English+10
- English1<-80
- Total<-English1 + Science
- Total
- Age/Total
- x <-rnorm(1000,mean=20,sd=5)
- x-mean(x)
- A factor is a categorical variable. Useful data type which is better than strings for a specific class of machine learning problems
- Very handy while performing analysis related to categorical data
- Factor may not always strings
- factor() function creates factor variables
- Matrix
- A multidimensional array
- Like a vector makes looping operations very easy, matrix makes some multidimensional calculations very easy
- Works perfectly for a lot of optimization problems which involve intense calculations

**R Functions:**

Ø We are already using some functions like c(), is.vector(), str()

Ø Numeric Functions

- abs(x), sqrt(x), ceiling(x), floor(x), trunc(x), round(x, digits=n) ,signif(x, digits=n), cos(x), sin(x), tan(x) ,log(x), log10(x), exp(x)

Ø String Functions

- substr(x, start=n1, stop=n2), toupper(x), grep(pattern, x , ignore.case=FALSE, fixed=FALSE)

Ø y<-abs(-20)

Ø x<-sum(y+5)

Ø Z<-log(x)

Ø round(Z,1)

Ø cust_id<-"Cust1233416"

Ø id<-substr(cust_id, 5,10)

Ø Up=toupper(cust_id)

Ø grep(4, cust_id)

**Reading large csv file with R**

library(data.table)

yyy = fread("C:\\Users\\abc\\Documents\\Testing.csv", header = TRUE)

Ø **Using bigmemory library**

library(bigmemory)

y <- read.big.matrix("C:\\Users\\abc\\Documents\\Testing.csv", type = "integer", header=TRUE)

dim(y)

#coerce a big.matrix to a matrix

yy= as.matrix(y)

**Support vector machine**

Explains the theory and practical application of support vector machine with R code and which popularized learning algorithm. This works for both classification and regression problems. This is one of the famous techniques which are used widely in data science applications.

- Firstly loading R packages and making sure
- Reading data
- Data Exploration
- Splitting data in to training and validation
- Building SVM model in R

**If Else and nested if Else in R:**

If else part is most important for R programming and there are various to apply conditional statements (If Else if Else) in R. In R there is lot of powerful packages for data manipulation. In later part of this tutorial we will see how IF ELSE statements are used in popular packages.

**Renaming variables**

To rename variables, you have to first install the dplyr package.

# Install the plyr package

install.packages("dplyr")

# Load the plyr package

library(dplyr)

# Rename Q1 variable to var1

Ø mydata <- **rename**(mydata, var1 = Q1)

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