R for Researchers: Statistics, Graphs and Data Analysis

Name: R for Researchers: Statistics, Graphs and Data Analysis
Rating: 3.8 (293 reviews)

Learn R from scratch - data handling, statistics, publication-quality graphs, reports and deep learning

Created bySenior Assist Prof Azad Rasul

Last updated 9/2026

English

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What you'll learn

Install R and RStudio and find your way around the interface, with no prior programming experience
Import, prepare, manipulate and export research data in R
Run descriptive statistics, correlations, t-tests, ANOVA and multiple linear regression
Create basic plots, advanced and animated graphs, and wind rose diagrams
Generate research reports directly from R
Get started with deep learning in R
Recognise and debug the R errors that most often trip up beginners

Course content

6 sections • 24 lectures • 1h 49m total length

Introduction to the Course and Installing R3:39
Learn to use R for data analysis, statistical computations, and visualizations, install R and RStudio, handle data import and manipulation, and explore AI and deep learning basics in R.
Setting Up Your Working Directory4:18
Set and change your working directory in R for researchers, learn to list, delete, and remove contents, locate data sets, and verify file existence.
Overview of R Functions3:39
Explore types of functions in R—built-in, package-based, and user-defined—learn their basic syntax with name, arguments, and body, and apply examples like max, min, mean, and a mile to kilometer converter.
Managing R Packages2:50
Learn how R packages extend the library with functions and data sets, and manage them by installing, loading with library, and updating or removing via install.packages, remove.packages, and update.packages.
Quiz: Functions and Packages in R

Understanding Data Types in R3:57
Explore essential data types in R, including vectors with c(), factors, and data frames; learn creating and editing columns and computing basic statistics like min, median, mean, and max.
Importing Data into R4:30
Learn how to import data into R using read.csv and other readers, assign a data object, and view it as a table from local or online sources with proper delimiters.
Preparing and Downloading Annual Weather Data4:09
Learn to import annual weather data for station 46210, convert dates, compute annual means or sums for key variables, visualize results, and export year-by-year CSV files from 2014 to 2023.
Exporting Data from R3:52
Learn how to export data from R by saving data frames to CSV or tab-delimited files, save multiple objects together, and reload them in new session with read or load.
Data Manipulation Techniques8:56
Master data manipulation techniques in R for researchers, including rename, select, filter, mutate to create new columns, group to summarize means, and reshape or join data frames.
Quiz: Creating and Manipulating Data Frames

Performing Descriptive Statistics5:24
Learn to perform descriptive statistics in R by loading data, cleaning column names with clean_names, and computing range, mean, standard deviation, quintiles, and confidence intervals, with apply and describe functions.
Conducting Correlation Analysis5:42
Explore correlation analysis in research, distinguish association from causation, and compare Pearson, Spearman, and Kendall methods, including p-values and significance, with examples like IQ versus TV time.
Performing ANOVA in R3:52
Explore performing anova in R to compare group means, interpret f and p-values from anova summaries, and extend to analysis of covariance.
Applying Student's t-test3:46
Apply Student's t-test to compare means between two groups, using degrees of freedom and p-values to assess significance, with examples from treatment vs control and first versus second exam data.
Multiple Linear Regression Analysis5:14
Apply multiple linear regression in R to model an outcome using predictors such as LSD and temperature. Interpret p-values, R-squared, and model diagnostics to assess fit.
Quiz: Applying Statistical Methods in R

Introduction to Deep Learning with R (Part 1)10:10
Build a neural network in R with the iris data, covering data prep, one-hot encoding, dense layers, and training with categorical cross-entropy in a 67/33 train-test split.
Introduction to Deep Learning with R (Part 2)5:48
Fine-tune a deep neural network in R by stacking dense layers on a sequential model, using ReLU and softmax for iris classification, then train and evaluate performance.
Quiz: Deep Learning Basics in R

Creating Basic Plots in R4:26
Learn to create basic plots in R for research, including histograms, scatterplots, pie charts, and key statistics like minimum, maximum, mean, and quartiles.
Advanced and Animated Graphs7:37
Explore advanced and animated graphs in R using ggplot, transitions, and time-based storytelling with monthly air quality data through animated line and scatter plots, saved as figures.
Wind Rose Plots for Different Time Periods7:20
Create windrose plots in r using open air and roadmate with noaa data for yearly, monthly, and seasonal aggregations. Learn to import data, loop over years, and save pngs.
Homework: Create a Wind Rose Plot for Heathrow Station0:08
Generating Research Reports in R5:02
Generate research reports in R using the Data Explorer package to describe data, explore correlations, customize score reports, and create plots (histograms, scatterplots, principal component analysis) saved as pdfs.
Quiz: Commenting in R Code

Common R Errors and How to Debug Them0:15
Course Summary and Final Thoughts5:02
Master fundamentals of R, including functions and packages, then apply data handling, descriptive statistics, anova, t-tests, artificial intelligence in R, and windrose plots for professional reporting.
Bonus Resources for Continuing with R0:03
The next course: Python for Scientific Research
https://www.udemy.com/course/python-for-researchers/?referralCode=886CCF5C552567F1C4E7

Requirements

No prior programming experience required - R is taught from installation onwards
R and RStudio are free and run on Windows, macOS and Linux
A dataset of your own is useful but not needed; the course works with real weather data
Basic familiarity with statistical concepts helps, but each test is explained as it is used

Description

A short, practical start in R - built around what research actually asks of you.

Most researchers do not need to become R developers. They need to import a dataset, run the right test, produce a figure a journal will accept, and be able to do it again next month when the data changes. That is what this course covers, in under two focused hours.

You will install R and RStudio, set a working directory, and get to grips with functions and packages. Then into data handling - types, importing, preparing and downloading annual weather data, exporting and manipulating.

The statistics

Descriptive statistics
Correlation analysis
ANOVA
Student's t-test
Multiple linear regression

The figures and reports

Basic plots in R
Advanced and animated graphs
Wind rose plots across different time periods, with a homework exercise using Heathrow station data
Generating research reports directly from R

There is also an introduction to deep learning in R across two lectures, and a lecture on the errors that most often stop beginners - what they mean and how to fix them.

Before you enrol

No programming experience is needed; R is taught from installation onwards. R and RStudio are free and run on Windows, macOS and Linux. Basic familiarity with statistical concepts helps, but each test is explained as it is used. This is a compact course - it will get you working in R quickly rather than cover every corner of the language.

Taught by Dr. Azad Rasul, Assistant Professor of Remote Sensing, with a PhD in Geography, over 12 years of programming in research, and more than 150,000 students enrolled across his Udemy courses.

Enrol now and run your next analysis in code you can re-run.

Who this course is for:

Researchers and postgraduate students who need statistics but have never written code
Scientists using SPSS or Excel who want analysis they can re-run and share
PhD candidates preparing figures and statistics for a thesis or paper
Environmental and climate researchers working with weather and time-series data
Anyone who needs R for a course or a job and wants a short, practical start

R for Researchers: Statistics, Graphs and Data Analysis

What you'll learn

Explore related topics

Course content

Getting Started with R Programming4 lectures • 14min

Data Handling in R5 lectures • 25min

Statistical Analysis in R5 lectures • 24min

Introduction to Deep Learning in R2 lectures • 16min

Data Visualization and Reporting in R5 lectures • 25min

Course Wrap-Up and Bonus3 lectures • 5min

Requirements

Description

Who this course is for: