R Programming Homework Help (24x7 R help online)

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R programming existed as one of the oldest programming languages in the world. With the sudden boom in data science, it has gained immense popularity and students want to understand the subject thoroughly. But R is not an easy language and students need a lot of time to get command over this language. That is the reason students are always on the lookout for expert R help online.

If you are worried about completing your R assignment or homework, you can connect with us at FavTutor. We have a team of experts who are professionals in R programming and have years of experience in working on any problem related to R. Since our inception, we have been helping hundreds of students so you could be the next one to get our R programming homework help.

What is R?

R is a programming language and software package setting for applied mathematics analysis, graphics illustration, and coverage. The core of R is an interpreted computer language that permits branching and iteration like programming exploitation functions. R permits integration with the procedures written within the C, C++, .Net, Python, or algebraic language. R is a well-developed, easy and effective programming language with good data handling and storage facility It also provides a collection of operators for calculations on arrays, lists, vectors, and matrices.

R is not only used in academics, but many large companies like Google, Uber, Airbnb, Facebook use an R programming language in their products. The most frequent use of R programming is data analysis. Data analysis with R is done in a step-by-step manner like programming, transforming, discovering, modeling, and communicating with the result. Apart from data analysis, R programming is also used for statistical inference and machine learning algorithms.

Being a complex programming language, students always face challenges while working with the R language. It is very tough to learn and understand complicated statistical tools and techniques used by R as a coding language. Let us go through some of the key topics in R where students find a hard time working with.

Key Topics in R

Let us understand some of the key topics of R programming language below:

• Function: A function could be a set of statements organized along to perform a particular task. R contains a sizable amount of in-built functions and also the user will produce their own functions.
• Matrices: Matrices are the R objects during which the element is organized during a two-dimensional rectangular layout. They contain parts of equivalent data types. Although we will produce a matrix containing solely characters or solely logical values, they're not of a lot of use.
• Vectors and Lists: Vectors in R are homogeneous data structures that contain elements of the same data type mostly integer, character, numeric or logical. Lists are heterogeneous data structures containing elements of mixed data types.
• Data Frames: A Data frame could be a table or a two-dimensional array-like structure during which every column contains values of 1 variable row contains one set of values from each column.
• Factors: Factors in R are data structures that signify levels and are the most appropriate for categorical variables
• R packages: Packages in R are basically libraries containing a set of library functions. For example: dplyr is an R package that contains library functions, mutate(), select(), filter(), summarise() and arrange(). In a nutshell, dplyr is the data manipulation package of R. Some other common packages of R include stats, superml, tree, MASS, ggplot2, etc.

ML Algorithms in R Programming

Below are the 6 common machine learning algorithms which are applied using R programming language-

1. Linear Regression: lm() under the stats package is used for training a Linear Regression Model on Training Data in R. It models a linear relationship between features, X, and continuous target y.
2. Logistic Regression: glm() under the stats package is used for training a Logistic Regression Model in R. It models a linear decision boundary for the classification of data points.
3. Naive Bayes: naive_bayes() under the naive Bayes package is used for training a Naive Bayes Model in R. It is a classification algorithm that is based on Prior and Posterior Probabilities.
4. SVM: SVM() under the e1071 package is for training a Support Vector Machine Model in R. Both regression and classification can be performed along with density estimation.
5. Decision Tree: tree() under the tree package is for training a Decision Tree in R. Like SVM, both regression and classification can be performed by binary recursive partitioning.
6. KNN: kNN() under the DMwR package is for training a k-nearest neighbor model in R. It also performs Data Normalization before training the model with Training Data.
7. Clustering: kmeans() under the stats package performs k-Means Clustering on the given data matrix in R. It is an Unsupervised ML Algorithm that is used for segmentation and data-grouping on unlabelled data.

Which R Programming Concepts are Most Difficult for Students?

The following are some subtopics with which students usually face problems, and our R programming experts can help you with them:

1. Complex Coding Structures: R programming involves intricate coding structures that can be intimidating for newcomers. Understanding the unique syntax and structure of R can be a significant challenge, especially for those new to programming.
2. Statistical Complexity: R is widely used for statistical analysis, which means students must grapple with complex statistical concepts. Learning to apply these concepts effectively within the R environment can be demanding.
3. Data Manipulation Hurdles: While R is known for its data manipulation capabilities, students may struggle to harness this power. Handling and transforming data, especially with large datasets, can be perplexing for beginners.
4. Package Puzzles: R relies on packages and libraries to extend its functionality. Selecting the right packages and understanding how to install and utilize them can be a source of confusion for students.
5. Debugging Dilemmas: Just like any programming language, R can produce errors and bugs in code. Troubleshooting and deciphering error messages can pose challenges for students.
6. Time-Consuming Tasks: R assignments, particularly those involving extensive data analysis or intricate statistical models, can be time-consuming. Balancing these assignments with other coursework can be demanding.
7. Limited Practical Experience: While students might grasp the theoretical aspects of R, applying it to real-world problems can be challenging. Practical experience is crucial for bridging this gap.
8. Scarcity of Helpful Resources: Finding accessible and informative documentation and support resources for R can be a struggle. Students often need reliable sources to clarify doubts and enhance their learning.
9. Visualization Vexation: R excels in data visualization, but crafting meaningful and visually appealing plots and graphs can be a daunting task for students.
10. Keeping Up with Updates: R is a dynamic language with frequent updates and new packages. Staying current with the latest features and best practices can be a demanding task for students.

Advantages and Features of R programming language

R is one of the most used programming languages for statistical and analysis purposes. But just like every other programming language, R has its own set of advantages. Check out some of this advantages in detail below:

• R language is very flexible and hence it is prominently used in the field of data science and statistics. Apart from this, it is also used in the field of biology and genetics to draw out predictions and analyses.
• As R language is a vector programming language, it is easy to add functions to a single vector without putting it in a loop.
• It is the best option for business as it is one of the affordable programming languages. It provides amazing visualizations and graphics.
• It enables us to perform virtual statistical computation in a short amount of time and provide error-free content.
• R programming language provides the best and most effective data handling and storage facilities.
• It allows the user to perform multiple calculations at the same time using a single command.
• R programming language can work on multiple operating systems like Mac, Windows, Linux, etc.
• It supports the data frames, arrays, and various other data types at the same time and it is also easily compatible with other programming languages.

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At FavTutor, our R experts help you in teaching any complex R concept and completing your homework or assignments on time. With many years of experience, they are experts in providing best R homework help to college or school students. We value your time and hence help you in completing your assignments on time.You can also connect with our R experts for any query related to the assignment or homework. Moreover, our services can be availed at affordable prices, and students do not feel the pinch of paying through their pocket money. If you are stuck at homework, get Instant R help online and secure better grades.

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