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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 open-source 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.
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 ensuring 100% accuracy.
R programming assignment help is a popular service that is offered by us. The students who avail of R programming help include:
Math/ Statistics Students: Though R may be easier for such students, 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. If you are given the task to complete an R assignment and you are struggling with it, you can seek our experts' help. We offer quality R assignment help.
Medical/Biology students: We are all 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 solving R programming assignments flawlessly. The task completed by our experts will help you secure the best grades in the examination.
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.
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.
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 open-source 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.
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.
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. Submit your assignment today to seek R Logistic regression assignment help.
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 the 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 k-nearest 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 k-means 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. Reach out to us for R clustering assignment help.
Learn all such Machine Learning algorithms through applications of R from our proficient experts. Submit your requirements with us and avail instant R Machine Learning Assignment Help.
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:
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 general-purpose 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.
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.
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Bayesian Statistics |
Mayor Continuous & Discrete Probability Distribution Functions | ||
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Normal (Gaussian), Uniform, Exponential, Pascal, Binomial, etc. Cumulative Distribution Function (CDF) |
Linear Univariate, Multivariate Regression and Modelling | ||
| Parametric Tests | Frequentist & Bayesian inference: P-Values & Confidence Intervals | ||
| Non-Parametric Statistics | Statistical Tests - ANOVA, Student's T-test, F-test, Chi-square test | ||
| Analysis of Variance | Exploratory Data Analysis (EDA) | ||
| Singular Value Decomposition (SVD) |
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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