Programming languages1:1 mentoring

R Programming for Statistics and Data

Use R to clean data, make clear charts, run statistical tests and share reports with the tidyverse and R Markdown.

Level:: Beginner to intermediate
Format:: 1:1 online mentoring

Free, no-obligation chat · Affordable plans that fit your budget · Start this week

1:1 personal attentionOne mentor, one student, at your pace.
Flexible timingsSessions that fit your week and time zone.
Affordable plansPlans that fit your budget.
Practise between sessionsShort problems that build confidence.

About this course

Learn R Programming for Statistics and Data online

R is the language many statisticians, researchers and data analysts use to explore data, test ideas and draw charts. It is powerful, but beginners often struggle with its unusual syntax, data frames, and knowing which statistical test to use and how to read the output.

This course follows the standard R path and the book R for Data Science: R basics, data structures, importing data, tidying and transforming with dplyr and tidyr, visualisation with ggplot2, descriptive statistics, probability, hypothesis tests, linear and logistic regression, and reproducible reports with R Markdown and Quarto.

There are no self-study lessons for R on MentorNxt yet (our free lessons cover Java and Python), so this course is taught through affordable 1:1 online mentoring. Message us on WhatsApp or email, or fill in the short form, for a free, no-obligation chat. We will suggest a plan that fits your budget and flexible timings in your time zone, and you can start this week.

Who this course is for

University students taking statistics or research methods
Researchers who need to analyse and report their data
Analysts moving from Excel to a scriptable tool
Beginners interested in data science
Learners preparing for a data analytics role

Outcomes

What you'll learn

Write R code using vectors, lists, data frames and functions
Import data from CSV and Excel files and tidy it with tidyr
Transform data with dplyr verbs and the pipe
Draw clear, well-labelled charts with ggplot2
Calculate descriptive statistics and explain distributions
Run and interpret t-tests, chi-squared tests and ANOVA
Fit and interpret linear and logistic regression models
Create a reproducible report with R Markdown or Quarto

Skills you'll gain

R
RStudio
tidyverse
dplyr
ggplot2
tidyr
Statistics
Hypothesis testing
Regression
R Markdown
Data cleaning
Reproducible research

Detailed syllabus

R Programming for Statistics and Data syllabus

Every module lists its topics and what you will be able to do. Your mentor plans sessions around them and your own goals.

Module 1
Getting started with R
Tools and the basics of the language.
- Installing R and RStudio
- The console, scripts and projects
- Objects and assignment
- Vectors and types
- Functions and help pages
- Installing and loading packages
- Reading error messages
By the end of this module you can
- Set up R and run scripts in an RStudio project
- Use vectors and functions to do simple calculations
Covered in 1:1 mentoring sessions
Module 2
Data structures
The building blocks for data in R.
- Vectors, factors and indexing
- Lists
- Matrices
- Data frames and tibbles
- Missing values (NA)
- Dates and times with lubridate
By the end of this module you can
- Subset and modify vectors and data frames
- Handle missing values on purpose
Covered in 1:1 mentoring sessions
Module 3
Importing and tidying data
Getting data into a shape you can use.
- Reading CSV and Excel files with readr and readxl
- Tidy data principles
- pivot_longer and pivot_wider
- Separating and uniting columns
- Cleaning text with stringr
- Joining tables
By the end of this module you can
- Import a messy file and tidy it
- Join two tables on a key
Covered in 1:1 mentoring sessions
Module 4
Transforming data with dplyr
Asking questions of your data.
- The pipe operator
- filter, select and arrange
- mutate and case_when
- group_by and summarise
- count and distinct
- Window functions overview
- Working with factors using forcats
By the end of this module you can
- Answer a question about a dataset with a dplyr pipeline
- Group and summarise data by category
Covered in 1:1 mentoring sessions
Module 5
Visualisation with ggplot2
The grammar of graphics.
- Aesthetics, geoms and layers
- Scatter, line, bar, histogram and box plots
- Facets
- Scales, colours and themes
- Labels and annotations
- Saving plots
- Choosing the right chart
By the end of this module you can
- Create a clear, labelled chart for a given question
- Customise a theme for a report
Covered in 1:1 mentoring sessions
Module 6
Programming in R
Writing reusable code.
- Writing functions
- Conditions and loops
- Iteration with purrr
- Debugging
- Writing readable code
- Packages and project structure
By the end of this module you can
- Write a function to automate a repeated task
- Replace a loop with map functions
Covered in 1:1 mentoring sessions
Module 7
Descriptive statistics and probability
Summarising and understanding data.
- Mean, median, variance and standard deviation
- Quartiles and outliers
- Distributions: normal, binomial, Poisson
- Sampling and the central limit theorem
- Correlation
- Simulation in R
By the end of this module you can
- Describe a dataset with the right summary measures
- Simulate a sampling distribution
Covered in 1:1 mentoring sessions
Module 8
Hypothesis testing
Deciding whether an effect is real.
- Null and alternative hypotheses
- p-values and confidence intervals
- One-sample and two-sample t-tests
- Paired tests
- Chi-squared tests
- ANOVA and post-hoc tests
- Non-parametric alternatives
By the end of this module you can
- Choose and run the right test for a question
- Report a result with an effect size and confidence interval
Covered in 1:1 mentoring sessions
Module 9
Regression models
Modelling relationships.
- Simple and multiple linear regression
- Reading lm output
- Checking assumptions and residuals
- Categorical predictors and interactions
- Logistic regression
- Model comparison
- Predictions with the broom package
By the end of this module you can
- Fit a regression model and explain its coefficients
- Check whether model assumptions hold
Covered in 1:1 mentoring sessions
Module 10
Reproducible reports
Sharing your results.
- R Markdown and Quarto basics
- Code chunks and options
- Tables and figures
- Parameters and inline code
- Exporting to HTML, PDF and Word
- Introduction to Shiny
By the end of this module you can
- Produce a complete report that rebuilds from code and data
- Present results to a non-technical reader
Covered in 1:1 mentoring sessions

Ways to learn

How you can learn R Programming for Statistics and Data

R Programming for Statistics and Data mentor, 1:1 online

A mentor who knows this course plans sessions around your goal, explains each idea with fresh examples and checks your understanding. Personal attention, flexible timings in your time zone and affordable plans that fit your budget.

Talk to our team

How it works

Your learning journey

Four simple steps to start R Programming for Statistics and Data with a mentor. A real person replies to you; there is no booking system.

Step 1: Talk to us
Message us on WhatsApp or email, or fill in the short form. The first chat is free, with no obligation.
Step 2: Get your plan
We listen to your goal, level and deadlines, then suggest a plan that fits your budget and timings.
Step 3: Learn with your mentor
Online 1:1 sessions with a mentor who knows your course, explains clearly and checks your understanding.
Step 4: Practise & track progress
Keep going between sessions with practice, and watch your confidence grow week by week.

Learn by doing

Projects and practice

Exploring a public dataset
Import, tidy and explore a public dataset with dplyr and ggplot2, and write up three findings.
A/B test analysis
Run a t-test or chi-squared test on experiment data and report the result with a confidence interval.
Regression on housing or health data
Fit and check a regression model, explain the coefficients and predict for new cases.
Capstone: reproducible report
Choose a question, analyse the data and publish a clear R Markdown or Quarto report.

Your progress

What you'll be able to do

Milestones from the start, middle and end of the syllabus.

Set up R and run scripts in an RStudio project
Early on
Write a function to automate a repeated task
Halfway
Produce a complete report that rebuilds from code and data
By the end

FAQs

R Programming for Statistics and Data: frequently asked questions

Is there a self-study option for R?

Not yet. MentorNxt's free self-study lessons cover only Java and Python today, so R is taught through 1:1 mentoring. Your mentor plans sessions around your goals, explains each idea with short examples and reviews your work with you at your pace.

Do I need to know programming or statistics first?

No. The course starts from the basics of R and builds the statistics as you go. Your mentor adjusts the pace to your background.

R or Python for data?

R is especially strong for statistics, research and charts. Python is broader for software and machine learning. Many people learn both, and we have a Python data course too. Your mentor can help you choose for your goal.

Do you teach tidyverse or base R?

Mostly the tidyverse, because it is readable and widely used, with enough base R to understand other people's code. If your course uses base R only, your mentor will follow it.

Can a mentor help with my college project or assignment?

Yes. Mentors explain concepts, review your design and help you debug your own code and interpret output with you. They never complete graded work or projects for you.

How much does mentoring cost?

1:1 mentoring is personal online tutoring and is affordable, with plans that fit your budget. Tell us your goal and how often you would like sessions, and our team will suggest an option that works for you in a free, no-obligation chat.

How do I get started?

Message us on WhatsApp or email, or fill in the short form on this page. Our team replies, talks through your goal and level, and arranges a mentor at flexible timings in your time zone. You can start this week.

Free, no-obligation chat

Talk to our team about R Programming for Statistics and Data

Tell us what you need. Our support team replies within one working day, by email or WhatsApp, whichever you prefer, and arranges your mentor.

Prefer to talk first?

Message the support team directly. We usually reply the same day.

Chat on WhatsApp Email us

Our mentors teach and explain. They never complete graded work, tests or assessments for students.

Keep going

Syllabus references

Last reviewed9 November 2026

Ready to start R Programming for Statistics and Data?

Message us on WhatsApp or email, or fill in the short form. Our team replies with a plan that fits your goal and budget, and you can start this week.

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Learn R Programming for Statistics and Data online

Who this course is for

What you'll learn

Skills you'll gain

R Programming for Statistics and Data syllabus

Getting started with R

Data structures

Importing and tidying data

Transforming data with dplyr

Visualisation with ggplot2

Programming in R

Descriptive statistics and probability

Hypothesis testing

Regression models

Reproducible reports

How you can learn R Programming for Statistics and Data

R Programming for Statistics and Data mentor, 1:1 online

Your learning journey

Step 1: Talk to us

Step 2: Get your plan

Step 3: Learn with your mentor

Step 4: Practise & track progress

Projects and practice

Exploring a public dataset

A/B test analysis

Regression on housing or health data

Capstone: reproducible report

What you'll be able to do

R Programming for Statistics and Data: frequently asked questions

Talk to our team about R Programming for Statistics and Data

Related courses

Data Analysis with Python

Data Science with Python

SQL and Databases

Machine Learning with Python

Excel & VBA for Programming

Syllabus references

Ready to start R Programming for Statistics and Data?

Learn R Programming for Statistics and Data online

Who this course is for

What you'll learn

Skills you'll gain

R Programming for Statistics and Data syllabus

Getting started with R

Data structures

Importing and tidying data

Transforming data with dplyr

Visualisation with ggplot2

Programming in R

Descriptive statistics and probability

Hypothesis testing

Regression models

Reproducible reports

How you can learn R Programming for Statistics and Data

R Programming for Statistics and Data mentor, 1:1 online

Your learning journey

Step 1: Talk to us

Step 2: Get your plan

Step 3: Learn with your mentor

Step 4: Practise & track progress

Projects and practice

Exploring a public dataset

A/B test analysis

Regression on housing or health data

Capstone: reproducible report

What you'll be able to do

R Programming for Statistics and Data: frequently asked questions

Talk to our team about R Programming for Statistics and Data

Related courses

Data Analysis with Python

Data Science with Python

SQL and Databases

Machine Learning with Python

Excel & VBA for Programming

Syllabus references

Ready to start R Programming for Statistics and Data?