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Divya R

Student’s Rating 4.97/5

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Knowledge 94%
Speed 100%
Accuracy 99%
  • BioStatistics, Statistics & Probability (Matlab, MINITAB, EXCEL, R, JMP etc)

  • Computer Science ( C, C++, Python etc)

Neeraj K

Student’s Rating 4.93/5

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Knowledge 97%
Speed 95%
Accuracy 97%
  • BioStatistics, Statistics & Probability (Matlab, MINITAB, EXCEL, R, JMP etc)

Shubhendu M

Student’s Rating 4.96/5

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Knowledge 99%
Speed 96%
Accuracy 94%
  • BioStatistics, Statistics & Probability (Matlab, MINITAB, EXCEL, R, JMP etc)

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Best R Tutor

Top tutors. Top grades in R & Statistics!

You have come to the best place to get a tutor for R. At My Engineering Buddy, we connect students 24X7 with experienced R studio tutors. They can help you learn R programming concepts, solve problems, and submit projects and homework assignments on time.

Our online R statistics tutors have helped students from all around the world. Our online tutoring service is available 24X7. You can contact our student helpline representative using WhatsApp and easily book sessions. They will suggest the best R statistics tutor for your requirements.

Our hourly rates are affordable and provide value for money. The expert R tutors have experience teaching students worldwide, including in the USA, European, and Middle Eastern countries. You can learn the entire syllabus or selected topics requiring more expertise. Their expert guidance will surely help you score top grades and succeed academically.

What is R?

R is an open-source programming language for analytics and graphics hugely popular for data and statistical analysis. It runs with the support of the R Foundation for Statistical Computing, and the R core development team maintains it. Data scientists at major companies and organizations use it for business intelligence and decision-making.

It is available for free and can run on almost all the major operating systems. Its syntax is intuitive and user-friendly. Anyone irrespective of any background in programming can learn R though the experience of programming may help a little bit.

R as a statistical tool

R analytics is helpful in graphics and statistical computations. It is helpful in analytics for building practical models and identifying patterns. R programming language is the coding language for many statistical software packages.

If you want to learn R programming for Statistics check out our online statistics tutoring page and contact top statistics tutors.

Where is R used?

Corporations like Google, Facebook, and Accenture heavily use R statistical analysis techniques and R programming language.

R programming is one of the languages of choice among data analysts and statisticians worldwide as a statistics research tool. A lot of machine learning research and deep learning projects use R.

Various fields like medicine, business, and sports deploy R computational techniques. Some of the leading industries and specializations using R are:

  • Finance – The finance sector is the most extensive R and data science user. For financial tasks and computations, R has an advanced statistical suite.
  • Banking – Many leading banks use a combination of statistical software and analytical tools like R and SAS. It is helpful in many types of risk analytics, including credit risk modeling.
  • Healthcare – R is the statistical tool of choice for computing and predicting the potential communicability of various diseases, genetic sequence analysis, testing the efficacy and safety of combinations of pharmaceutical drugs. R has a Bioconductor package for genomic data analysis. R is also heavily used in critical pre-clinical trials for new medical techniques and drugs.
  • Manufacturing – Many large manufacturing companies use R for their marketing decision-making and business strategies. They adjust their manufacturing volumes using R and data analytics for predicting demand and market trends.
  • Social media
  • Information technology
  • Research and academics
  • E-commerce

Key things to learn from our expert R tutor:

  • Use and navigate the R environment
  • Importing, exploring, modeling, and visualization of data
  • How to effectively use R for statistical analysis
  • Split, merge, and combine data sets
  • Using data mining techniques to construct linear and nonlinear models
  • Methods of reshaping and manipulating data
  • Using the rgl package for interactive 3D visualizations
  • Contributed packages like ggvis and ggplot2
  • Recursion, closures, and anonymous functions
  • Data types and data structures
  • Loading, assembling and disassembling data
  • Bundle reusable R functions
  • Automating common development tasks
  • Learning and improving package writing
  • Text-mining methods using tidytext
  • Rearrangement of complex data in simpler practical formats
  • Finding key insights to make new predictions
  • Input and output, linear regression, and graphics
  • Build effective workflows by integrating NLP (natural language processing)
  • Exploring and presenting data visually with R language’s graphical capabilities
  • Cluster analysis, time series analysis, and classification methodologies
  • Object-oriented and functional programming
  • Performing data analysis
  • Running mathematical simulations
  • Applying machine learning to solve real-world problems

R programming books

Given below is a list of some of the best R programming textbooks.

  1. R for Data Science – Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund. Publisher – O’Reilly Media
  2. Discovering Statistics Using R by Andy Field, Jeremy Miles, Zoe Field. Publisher – SAGE Publications Ltd
  3. The Book of R – A First Course in Programming and Statistics by Tilman M. Davies. Publisher – No Starch Press
  4. The Art of R Programming – A Tour of Statistical Software Design by Norman Matloff. Publisher – No Starch Press
  5. R Cookbook – Proven Recipes for Data Analysis, Statistics, and Graphics by JD Long, Paul Teetor. Publisher – O’Reilly Media
  6. R For Dummies by Andrie de Vries, Joris Meys. Publisher – For Dummies
  7. Learning R: A Step-by-Step Function Guide to Data Analysis by Richard Cotton. Publisher – O’Reilly Media
  8. Machine Learning with R – Expert techniques for predictive modeling by Brett Lantz. Publisher – Packt Publishing
  9. Text Mining with R – A Tidy Approach by Julia Silge, David Robinson. Publisher – O’Reilly Media
  10. R in Action – Data analysis and graphics with R and Tidyverseby Robert I Kabacoff. Publisher -‎ Manning

Related: Statistical analysis software list | SAS Tutor | Regression Analysis Tutors | Data Science Tutors

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R topics

  • Introduction and basics of R
  • Basic principles for application in data science
  • R data types
  • Logical and arithmetic operators
  • Factor in R – continuous and categorical variables
  • R matrix – Create, slice, add column, print
  • Data Preparation for R
  • Make R lists and select their elements
  • Create, select, append, and subset in R data frames
  • Data manipulation (join) and cleaning (spread) in R Dplyr
  • Using Order() to sort a data frame
  • Merging partial and full match data frames
  • R programming functions
  • While and for loops in R
  • IF, ELSE, and ELSE IF statements
  • Pearson and Spearman correlations with matrix in R
  • Group_by() and summarise aggregate functions in R
  • apply(), sapply(), lapply(), tapply() R functions
  • Importing data into R from Excel, CSV, Stata, SPSS, and SAS Files
  • Replacing missing values(NA) in R using na.rm and na.omit
  • Exporting Data from R to Excel and CSV
  • R Select(), Arrange(), Filter(), Pipeline
  • Data analysis using R
  • Drawing scatter plot with R using ggplot2
  • Making boxplots in R
  • One way and two way ANOVA with R
  • One sample and paired T-tests in R
  • Machine Learning with R
  • Regression in R — simple, stepwise, and multiple linear regression
  • R Decision Tree – classification tree and Code in R with Example
  • R Random Forest with examples
  • K-means clustering and examples in R
  • Generalized linear model (GLM) in R
  • Differences between bar graph and histogram
  • The differences among Python, R, and SAS