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R is one of the most widely used tools for data processing and analysis. It finds applications for both academic and professional purposes. R programming language is used by companies globally. With the increase in demand for data science, there is a huge demand for R programming. Universities and colleges have introduced R as part of the curriculum and students are required to understand the concepts of R programming thoroughly. However, it is not so easy for students to learn R programming in a short time. If you are one of such students, then avail the best online R assignment help from us.
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R is an opensource programming language that is used to create the best software environment to do statistical computing and preparing 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. It can run on almost all operating systems including Linux, UNIX, Windows, Mac, and others 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.
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R programming assignment help is a popular service that is offered by us. The students who avail of R programming help include:
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 is used to execute the R code and allows 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 is similar to that of R Markdown. This enables you to create appealing reports in R without 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.
Shiny is an R package that makes it easier for you to create web applications using the R programming language. You can also embed applications on the web pages 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.
A few of the libraries in R include:
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There are six different types of machine learning algorithms that are widely used in the R programming language:
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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:
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A few of the reasons to use R programming for statistical computing and graphics include:
There is a myriad of uses of R programming globally. A few of the applications of this programming language include:
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) 



















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
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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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