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**Why You Choose R Programming For Data Analysis and Modeling?**

If you want to choose one from Python & R to handle data science and machine learning task. Both language are has own strength and importance, and both are great choice for data science and machine learning, R has some unique strength and features so it important for data scientist:

R is built for statistics

R is a popular language for data science at top tech firms

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**Top Features of R Programming**
**Open-source**
R is an open-source software environment. It is free of cost and can be adjusted and adapted according to the userâ€™s and the projectâ€™s requirements.
You can make improvements and add packages for additional functionalities.
R is freely available. You can learn how to install R, Download and start practicing it.
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**Strong Graphical Capabilities**
R can produce static graphics with production quality visualizations and has extended libraries providing interactive graphic capabilities.
This makes data visualization and data representation very easy.
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**Highly Active Community**
R has an open-source library which is supported by its growing number of users. The R environment is continuously growing. This growth is due to its large user-base.
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**A Wide Selection of Packages**
CRAN or Comprehensive R Archive Network houses more than 10,000 different packages and extensions that help solve all sorts of problems in data science.
High-quality interactive graphics, web application development, quantitative analysis or machine learning procedures, there is a package for every scenario available.
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**Comprehensive Environment**
R has a very comprehensive development environment meaning it helps in statistical computing as well as software development.
R is an object-oriented programming language. It also has a robust package called R shiny which can be used to produce full-fledged web apps
**Can Perform Complex Statistical Calculations**
R can be used to perform simple and complex mathematical and statistical calculations on data objects of a wide variety. It can also perform such operations on large data sets.
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**Running Code Without a Compiler**
R is an interpreted language which means that it does not need a compiler to make a program from the code. R directly interprets provided code into lower-level calls and pre-compiled code.
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**Interfacing with Databases**
R contains several packages that enable it to interact with databases like R oracle, Open Database Connectivity Protocol, R MySQL, etc
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**Machine Learning**
R can be used for machine learning as well. The best use of R when it comes to machine learning is in case of exploration or when building one-off models.
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**Data Wrangling**
Data wrangling is the process of cleaning complex and inconsistent data sets to enable convenient computation and further analysis. This is a very time taking process.
R with its extensive library of tools can be used for database manipulation and wrangling

**R Programming Visualization Help**
At Realcode4you you can get all R programming Visualization Related Help, you can help in R programming Visualization Assignment help, R Programming Visualization Homework Help, R Programming Visualization Coursework Help, etc.
Visualization techniques used to deliver insights in data using visual cues such as graphs, charts, maps, and many others. The popular data visualization tools that are available are Tableau, Plotly, R, Google Charts, Infogram, and Kibana.
R is a language that is designed for statistical computing, graphical data analysis, and scientific research. It support many visualization libraries. Top R Libraries for Data Visualization that are commonly used these days. There many R programming visualization libraries; **ggplot2**, **Plotly**, **Lattice**, **Dygraphs**, **RGLLeaflet**
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There are many types of R Programming Graphs or Plot in which you can get Assignment Help, Project Help, Homework Help and coursework Help: **Bar Plot**, **Histogram**, **Scatter Plot**, **Line Plot**, **Pie Chart**, **Time Series Analysis**, **Box Plot**, etc.

**Hire Expert To Get Help In Time Series Analysis**
Time Series in R is used to see how an object behaves over a period of time. In R, it can be easily done by ts() function with some parameters. Time series takes the data vector and each data is connected with timestamp value as given by the user.
Syntax:
objectName <- ts(data, start, end, frequency)
At Realcode4you you can get all types of time series analysis help like:
**Univariate Analysis: **Univariate analysis is the technique of comparing and analyzing the dependency of a single predictor and a response variable. The prefix "uni" means one, emphasizing the fact that the analysis only accounts for one variable's effect on a dependent variable. In simple way we can say analysis using single variable or visualization using single variable called univariate analysis.
**Multivariate Analysis: **Multivariate analysis deals with the statistical analysis of data collected on more than one dependent variable. Multivariate techniques are popular because they help organizations to turn data into knowledge and thereby improve their decision making.
**Forecasting: **Time series forecasting occurs when you make scientific predictions based on historical time stamped data. It involves building models through historical analysis and using them to make observations and drive future strategic decision-making.

**Sample Paper Related To R Programming**

**Exercise 1.**

Load the ncaa2018.csv data set and create histograms, QQ-norm and box-whisker plots for ELO. Add a title

to each plot, identifying the data.

**Exercise 2.**

Review Exercise 1, Homework 6, where you calculated skewness and kurtosis. The reference for this exercise,

https://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm,

The following example shows histograms for 10,000 random numbers generated from a normal, a double exponential, a Cauchy, and a Weibull distribution.

We will reproduce the histograms for these samples, and add qqnorm and box-whisker plots.

**Part a**

Use the code below from lecture to draw 10000 samples from the normal distribution.

`norm.sample <- rnorm(10000, mean=0, sd=1)`

Look up the corresponding r* functions in R for the Cauchy distribution (use location=0, scale=1), and the Weibull distribution (use shape = 1.5). For the double exponential, use you can use the *laplace functions from the rmutil library, or you can use rexp(10000) - rexp(10000)

Draw 10000 samples from each of these distributions. Calculate skewness and kurtosis for each sample. You may use your own function, or use the moments library.

**Part b**

Plot the histograms for each distribution. Use par(mfrow=c(2,2)) in your code chunk to combine the four histogram in a single plot. Add titles to the histograms indicating the distribution. Set the x-axis label to show the calculated skewness and kurtosis, i.e. skewness = ####, kurtosis = ####

`par(mfrow=c(2,2))`

**Part c**

Repeat Part b, but with QQ-norm plots.

`par(mfrow=c(2,2))`

**Part d**

Repeat Part b, but with box-whisker plots.

par(mfrow=c(2,2))

Hints for SAS. If you create the samples in IML, use

Normal = j(1, 10000, .);

call randgen(Normal, "NORMAL", 0, `);

You can generate samples in the data step using

do i = 1 to 10000;

Normal = rand('NORMAL',0,1);

output;

end;

RAND doesnâ€™t provide a Laplace option, but you can create samples from this distribution by

rand('EXPONENTIAL')-rand('EXPONENTIAL');

To group multiple plots, use

ods graphics / width=8cm height=8cm;

ods layout gridded columns=2;

ods region;

... first plot

ods region;

... second plot

ods layout end;

You might need to include

ods graphics off;

ods graphics on;

ODS GRAPHICS / reset=All;

to return the SAS graphics output to normal.

**Exercise 3.**

We will create a series of graphs illustrating how the Poisson distribution approaches the normal distribution with large Î». We will iterate over a sequence of lambda, from 2 to 64, doubling lambda each time. For each lambda draw 1000 samples from the Poisson distribution.

Calculate the skewness of each set of samples, and produce histograms, QQ-norm and box-whisker plots. You can use par(mfrow=c(1,3)) to display all three for one lambda in one line. Add lambda=## to the title of the histogram, and skewness=## to the title of the box-whisker plot.

**Part b.**

Remember that lambda represents the mean of a discrete (counting) variable. At what size mean is Poisson data no longer skewed, relative to normally distributed data? You might run this 2 or 3 times, with different seeds; this number varies in my experience.

`par(mfrow=c(1,3))`

If you do this in SAS, create a data table with data columns each representing a different Î¼. You can see combined histogram, box-whisker and QQ-norm, for all columns, by calling

```
proc univariate data=Distributions plot;
run;
```

At what Î¼ is skewness of the Poisson distribution small enough to be considered normal?

**Exercise 4**

**Part a**

Write a function that accepts a vector vec, a vector of integers, a main axis label and an x axis label. This function should 1. iterate over each element i in the vector of integers 2. produce a histogram for vec setting the number of bins in the histogram to i 3. label main and x-axis with the specified parameters. 4. label the y-axis to read Frequency, bins = and the number of bins.

Hint: You can simplify this function by using the parameter ... - see ?plot or ?hist

**Part b**

Test your function with the hidalgo data set (see below), using bin numbers 12, 36, and 60. You should be able to call your function with something like

`plot.histograms(hidalgo.dat[,1],c(12,36,60), main="1872 Hidalgo issue",xlab= "Thickness (mm)")`

to plot three different histograms of the hidalgo data set.

If you do this in SAS, write a macro that accepts a table name, a column name, a list of integers, a main axis label and an x axis label. This macro should scan over each element in the list of integers and produce a histogram for each integer value, setting the bin count to the element in the input list, and labeling main and x-axis with the specified parameters. You should label the y-axis to read Frequency, bins = and the number of bins.

Test your macro with the hidalgo data set (see below), using bin numbers 12, 36, and 60. You should be able to call your macro with something like

`%plot_histograms(hidalgo, y, 12 36 60, main="1872 Hidalgo issue", xlabel="Thickness (mm)");`

to plot three different histograms of the hidalgo data set.

Hint: Assume 12 36 60 resolve to a single macro parameter and use %scan. Your macro definition can look something like

`%macro plot_histograms(table_name, column_name, number_of_bins, main="Main", xlabel="X Label")`

**Data**

The hidalgo data set is in the file hidalgo.dat These data consist of paper thickness measurements of stamps from the 1872 Hidalgo issue of Mexico. This data set is commonly used to illustrate methods of determining the number of components in a mixture (in this case, different batches of paper). See

https://www.jstor.org/stable/2290118,

https://books.google.com/books?id=1CuznRORa3EC&lpg=PA95&pg=PA94#v=onepage&q&f=false and

https://books.google.com/books?id=c2_fAox0DQoC&pg=PA180&lpg=PA180&f=false .

Some analysis suggest there are three different mixtures of paper used to produce the 1872 Hidalgo issue; other analysis suggest seven. Why do you think there might be disagreement about the number of mixtures?