R Programming Assignment Help
R programming is the programming language that is used by companies globally. With the increase in demand for data science, there is a huge demand for R programming. The universities and colleges have introduced R programming as part of the curriculum and students are required to understand the concepts of R programming thoroughly and use this programming language to write the code. However, it is not so easy for students to learn R programming in a short time. We have experts who hold R programming and work with all the problems related to R. With a pool of dedicated Data Scientists and Programming experts we offer the best online R assignment help. We are the most trusted and reliable R Programming homework help provider for students across the globe.
What Is R Programming?
R programming language is an opensource programming language that is used to create the best software environment to do statistical computing and prepare graphics. This statistical language is used by statisticians, data analysts, and data miners to mine data and do statistical data analysis. The popularity of this language can be seen when the researchers are conducting polls, surveys, and other literary works.
The excellent part of R Programming is that it saves time and helps deliver accurate results. This is also efficient in handling data and storage facilities. R Programming language is compatible to use with C, C++, Java, FORTRAN, and .Net. This interpreted language would not need any kind of compiler to write a program from the code that is already used before. R Programming is available with General Public License. It can run on almost all operating systems including Linux, UNIX, Windows, Mac, and other operating systems. The best thing is that it allows integration with other statistical software like SAS and SPSS. This plays a critical role in carrying out clinical trials, research works, and medicine.
R Programming is one of the most popular software used across various industries and hence many students take this subject as a part of their coursework. If you are one of the students who need R Programming Assignment Help to secure excellent grades in your coursework, then seek help from our statistics assignment help experts. We provide affordable and quality work within deadlines. The solution delivered is accurate.
Which students need R Programming Assignment Help?
R programming assignment help is the popular service that is offered by us. The students who avail of R programming help include:
Math/ Statistics Students: Statistics and mathematics are the students who come from mathematics and programming backgrounds. Though R programming may be easier for them, they would need the help of experts due to a lack of time or knowledge on the topic. Some of the universities have a very rigorous curriculum. In a few places, the students may have to put a lot of effort into learning the concepts as they may get little help from their professors. However, if you are given the task to write an R programming assignment, you can seek our experts' help. They complete the assignment for you.
Medical/Biology students: It is essential for medical students to have knowledge of statistics to evaluate diagnoses and come up with treatment plans. The statistics will work wonders when you apply them to huge datasets. There is a special statistics software that is required to do analysis, which is chosen for evaluation, reinforced learning, and for complicated decisionmaking strategies. It is not the same to interpret the data and write the code for doing statistical tests. Everyone is aware of how busy the life of medical students would be. Learning how to program is a humongous task for students. However, by taking our R programmer's help you can get the required assistance in writing R programming assignments flawlessly. The task completed by our experts will help you secure the best grades in the examination.
Social Science Students: Social science students with subjects such as political science, psychology, business, and economics require to learn advanced math and programming. However, programming is not so easy for them to learn quickly. You have to complete hypothesis testing shortly with the help of RStudio. For the computer savvy, it becomes easier to learn to program. R data analysis is a powerful tool to test hypotheses. Population datasets will generate insights, and theories and provide plans for real action.
Business Analytics/Intelligence Students: Business intelligence and intelligence use R programming, which is designed exclusively for statisticians. This is used for statistical computations and generating rich graphics. It is a widely used language by mathematicians, data miners, data scientists, and statisticians for doing data analysis.
Data Science Students: Data science students are used to handling, storing, and thoroughly analyzing data. It is widely used for data modeling and analyzing data. By learning R for data science, students can perform statistical analysis and develop data visualizations. Data science students may find it difficult to work on the assignments. However, by seeking our help, students can get the required help on time.
Multiple R Programming Assignment Help offered by us
R Studio Assignment Help: IDE is required to learn R programming. IDE is the platform that allows you to install the programming language. For instance, for the python programming language, the Juyter notebook will be used as an IDE. Eclipse IDE will be used for Java development. R studio is an IDE that is used for R programming. It helps you to manage all your programming efforts easily.
R Markdown Assignment Help: R Markdown is used to execute the R code and allow you to save the results in various formats. There are different functionalities that are offered by R Markdown such as it helps you to automatically create HTML files to let you open the solution in the browser, and create a pdf file. There is no need for you to copy and paste the code and get the answers from the console. You can easily create a word document.
R Notebook Assignment Help: R Notebook is similar to that of R Markdown. This enables you to create appealing reports in R without you having to copy the R code and move the results to Word and various other programs. Using R Notebook, you can create an HTML file, meaning that the code is executed with the help of a knitting process. R Notebook previews the output that you get by executing the code in RStudio.
R Shiny Assignment Help: Shiny is an R package that is easier for you to create web applications using the R programming language. You can also embed applications on the web pages or embed them in R Markdown docs or use them to develop dashboards. The Shiny apps can be used for JavaScript actions, HTML widgets, and CSS themes.
Master the Libraries in R from our R Programming Experts
Few of the libraries in R include:
tidyverse (general R library including some of the libraries below): Tidyverse is the most critical R package of data science. There are various other packages that are available under Tidyverse that help you to interact with the data with ease. There are many things that you do with the data like visualizing, transforming, and subsetting. It offers all utilities that are required to clean the data.
Ggplot2: Ggplot2 is the widely used opensource visualization package that is a part of the statistical programming language R. The package is a plotting package that lets you create complicated plots using the data that is in the data frame. There is a programmatic interface to specify the type of variables in the plot, how to display them, and various visual properties.
Dplyr: It is the critical package of Tidyverse in R programming language. It contains a lot of functions that are used to manipulate data frames in an interactive way. The majority of data analysts make use of this library to transform the datasets that are available currently into the format that is suitable for you to carry out data analysis and visualization.
Tidyr: It is an R package that has R functions, compiled code, and sample data. It is stored in a directory called the library in the R environment. When installing the R package, by default this package is installed. The purpose of this package is to make the process of creating Tidy data simple.
Readr: Readr is a package that is best to read rectangular data. It is mainly designed to parse various data types. The main purpose of the package is to read huge flat files briskly.
Tibble: Tibble is the critical part of the Tidyverse data structure that allows you to analyze data in a tidy format. Tibbles is a new kind of data frame where data frames are the kind of data structures that allow you to store huge data sets in R.
Stringr: The Stringr package would offer a lot of functions that work with strings. The package will offer you wrappers and simplify the manipulation of using character strings in R language.
Plotly (interactive graphs): It is the R graphing library that would allow you to make highly interactive graphs. These allow you to make scatter plots, area charts, bar charts, histograms, subplots, 3D charts, and so on.
stargazer (beautiful regression tables): It is an R package that allows you to create Latex code, HTML code, and ASCII text to create a properly formatted regression table.
R Markdown: It is a file format that allows you to make dynamic documents using R. The R Markdown is written in the Markdown language and has huge chunks of embedded R code.
Shiny app: This might be extremely challenging for R newbies! The shiny app allows you to develop highly interactive web apps. The best thing about this app is that it lets you extend R code to the web.
Learn such Libraries in R and get the required R Data Science assignment help from our trusted experts. We ensure that all your data science projects are delivered on time with high quality.
Key Topics in R
Some of the topics in R programming language include:
Function: Function has a series of statements that are put together to perform a specific task. R has many inbuilt functions and produces its own functions. Our experts are wellversed on this topic to complete the assignment flawlessly. The assignment helps you score flying grades.
Matrices: Matrices are the R objects in which the elements are arranged in a twodimensional rectangular layout. It has many data types. We will produce matrices with characters or logical values that are not used widely.
Vectors and lists: Vectors are homogenous data structures with elements of similar data types like integer, character, logical, and numeric. There are heterogeneous data structures that have mixed types of data.
Data Frames: Data frame is a kind of table or twodimensional array structure that has columns with 1 variable value and rows that have values from each of the columns.
Factors: Factors are the data structures in R that would express the significance of each level and are relevant for arranging variables categorically.
R packages: The package that is R will have various library functions such as select (), filter (), arrange (), and summarize (). Some of the packages of R include Dplyr, superml, tree, MASS, ggplot2, and so on.
ML Algorithms in R Programming
There are six different types of machine learning algorithms that are widely used in the R programming language:
Linear Regression
Im () package that in linear regression model would be widely used for training the data in R programming. It also offers a linear relationship between x, y, and various features.
Logistic regression
Glm () is a kind of stats package that is used for training the logistic regression in R. It has a linear decision boundary that would help you to classify various data points.
Naive Bayes
Naive_bayes () is a package that is available under naive Bayes that is widely used for training the Naive Bayes model available in R. It is a kind of algorithm that is based completely on the Prior as well as Posterior probabilities.
SVM
SVM () is a package that is available under the e1071 package which allows you to train the support vector machine model in R. Regression as well as classification will be used for estimating the density.
Decision tree
Tree () is available in trees package that is used to train the decision tree available in R. Similar to SVM, both regression and classification would be done through binary recursive partitioning.
KNN
kNN () is a DMwR package used to train the knearest neighbor model in R. Data normalization can also be done prior to using the training model to train the data.
Clustering
Kmeans () is the stats package that would let you do kmeans clustering on the data matrix that is available in R. This is the unsupervised algorithm that lets you segment and group the data that is labeled.
Learn all such Machine Learning algorithms through applications of R from our proficient experts. Submit your requirements with us and avail instant Machine Learning Assignment Help.
Features or Advantages of R Programming Language
R programming language would be used for statistical analysis. Students who find it difficult to complete R programming can seek the help of our experts. Language has its set of advantages or features. These include:

R language is highly flexible and is widely used for statistics and data science. It is also used in genetics and biology for doing future predictions and data analysis.

R language is a vector programming language that allows you to add a series of functions into a single vector without having to maintain any loop

It is a costeffective programming language that allows you to analyze data and graphics.

It allows you to carry out statistical computation shortly and offer you with flawless content

It allows you to handle data and offer a storage facility

It supports multiple operating systems such as Windows, Linux, MAC, and so on.

It supports various data types, arrays, and objects and you can also use this with the other programming languages without any roadblocks.
Master all such features of R Programming through a unique stepbystep from our statistics experts. We offer very affordable yet reliable R Programming assignment help.
Disadvantages of R Programming Language
Every programming language has its own drawback and the same is for R programming language. These are the disadvantages that are with this language.
Weak origin: R is the language that is developed on the basis of the S programming language, which is the oldest of all programming languages. It is a package that won’t allow 3D graphics or dynamic graphics. It produces 3D visuals and animated visuals with the help of tools like Plotly and Ggplot2.
Basic security: Programming languages like Python will offer high security and R has few limitations. It is not capable of being incorporated into web applications.
Language is difficult to learn: R is complicated for people to master. The learning curve is also short and it makes it difficult for people to master. People who have never learned R programming may find it difficult to learn this language.
Why use R for statistical computing and graphics?
A few of the reasons to use R programming for statistical computing and graphics include:
Open source and free: You can easily download this programming language under the license GNU. The source is available to check what is happening. R package can also be accessed with the same license and therefore can be used to execute commercial applications.
Popularity: It is the generalpurpose language compared to C#. It is not just used for developing apps, but also used in the field of data science and machine learning.
Run on various platforms: You can run this on various platforms like Windows, Linux, and Mac. You can write the code on one platform and can import the code that is written on this platform to another platform.
Increase the job opportunities: People who have knowledge of R programming can become data scientists and the job opportunities for them are huge in the market.
Applications of R programming in the real world
There is a myriad of uses of R programming globally. A few of the applications of this programming language include:
IT sector: IT companies will use R programming to gain business insights and it is widely used in small, medium, and large companies. They use this to develop AI products. It is used with devices that deal with information.
Banking: Banks will make use of R to display credit chances and to carry out studies on various hazards. The banks will make use of SAS instruments. Using R, researchers will find out the money losses.
Social media: Social media has a lot of information. The sentimental analysis and various other social media mining data are carried out using statistical tools and one amongst them is R. Data that is found on social media is not organized. This data will be used for segmenting customers and targeting them to market the products.
Run R Programming on Your Computer
It is easy to run R programming on various systems such as Windows, MAC, Linux, and other operating systems.
To run R programming on the system, you must follow these steps:

Go to the official website of R programming

Click the CRAN link that is on the left side of the page

Select an appropriate mirror

Click download to download the R programming for the operating system that you have been using

Download the pkg binary that is available for you

Run the file and follow the instructions carefully to install R
These are the steps that you must follow to run R programming. You can run R on both 32bit and 64bit on Windows.
So, do not wait any further. Submit your assignment now and get the best R assignment help.
Bayesian Statistics  Mayor Continuous & Discrete Probability Distribution Functions  
Normal (Gaussian), Uniform, Exponential, Pascal, Binomial, etc. Cumulative Distribution Function (CDF)  Linear Univariate, Multivariate Regression and Modelling  
Parametric Tests  Frequentist & Bayesian inference: PValues & Confidence Intervals  
NonParametric Statistics  Statistical Tests  ANOVA, Student's Ttest, Ftest, Chisquare test  
Analysis of Variance  Exploratory Data Analysis (EDA)  
Singular Value Decomposition (SVD) 
 

 

 

 

 

 


Example of A Simple R Programming Code Written By Our Expert
Code for: The Default Tree Generated
Solution:
# uploading the packages and data
library(readxl)
library(rpart)
library(rpart.plot)
library(caret)
da < read_excel("C:/Users/Home/Desktop/FacebookData.xlsx")
# Factorize variables
da$Category < as.factor(da$Category)
da$Type < as.factor(da$Type)
da$`Post Weekday` < as.factor(da$`Post Weekday`)
da$Paid < as.factor(da$Paid)
da$`Page total likes` < as.numeric(da$`Page total likes`)
# Binning the dependent variable
summary(da$`Lifetime Engaged Users`)
da$`Lifetime Engaged Users` < cut(da$`Lifetime Engaged Users`, c(9,1000,11452), include.lowest = TRUE, labels=c("<1000>1000"))
da$`Lifetime Engaged Users` < as.factor(da$`Lifetime Engaged Users`)
# creating train and test sets
set.seed(123)
index_train = sample(1:nrow(da), 2 / 3 * nrow(da))
training_set = da[index_train, ]
test_set = da[index_train, ]
# Task 1
set.seed(123)
tree1 = rpart(`Lifetime Engaged Users` ~ Category + Type + `Post Weekday` + Paid + `Page total likes`, da = training_set)
prp(tree1, type=1, extra=1)
# Task 2
var_imp < varImp(tree1)
var_imp
tree1$variable.importance
# Task 3
predictions_1 < predict(tree1, test_set, type = 'class')
cm_1 = confusionMatrix(test_set$`Lifetime Engaged Users`, predictions_1)
cm_1
# Task 4
set.seed(123)
tree2 = rpart(`Lifetime Engaged Users` ~ Type + Paid + `Post Weekday`, da = training_set)
prp(tree2, type=1, extra=1)
predictions_2 < predict(tree2, test_set, type = 'class')
cm_2 = confusionMatrix(test_set$`Lifetime Engaged Users`, predictions_2)
cm_2
Frequenly Asked Questions (FAQs) Related To R Programming Assignment Help
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And some related topics like STATA, SAS and SPSS Assignment Help
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R is an environment and programming language for statistical computing. The R programming language has many features related to statistics and graphics (linear and nonlinear models, traditional statistical tests, timeseries analysis, clustering, classification, etc.). One of R's advantages is how easy it is to construct welldesigned publicationquality graphs using mathematical symbols and calculations when needed. Small design choices in graphics have been carefully picked as defaults, but the user retains complete discretion.
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