R for Researchers: Statistics, Visualization and Data Analys

Name: R for Researchers: Statistics, Visualization and Data Analys
Rating: 3.8 (293 reviews)

Master R Programming for Scientific Research

Created bySenior Assist Prof Azad Rasul

Last updated 8/2026

English

English [Auto],

What you'll learn

Develop practical skills in data manipulation, importing, and exporting using R.
Master descriptive statistics, correlations, ANOVA, and t-tests for research analysis.
Create basic, advanced, and animated graphs with R for insightful data visualization.
Enhance your creativity in data processing and statistical analysis in scientific research.
Gain proficiency in using R for comprehensive research projects and reporting.

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.
Basics of 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
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.
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.
Basics of AI and Deep Learning in R

Creating Basic Plots in R4:26
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 Windrose 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.
Effective 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.
Bonus0:03
The next course: Python for Scientific Research
https://www.udemy.com/course/python-for-researchers/?referralCode=886CCF5C552567F1C4E7

Requirements

No prior programming experience is required

Description

Are you ready to elevate your research with the power of R programming? This course is designed to take you from the fundamentals to advanced techniques, all while applying real-world examples from diverse research fields.

With over 12 years of experience in programming and scientific research, I will guide you through essential skills that will transform the way you approach data analysis, statistics, and visualization. You’ll learn how to manipulate, import, and export data, conduct robust statistical analyses including correlations, ANOVA, and t-tests, and create both basic and advanced graphs using R programming.

Throughout the course, you’ll gain hands-on experience with RStudio, mastering everything from data handling to crafting dynamic visualizations. Unlike traditional software like SPSS and Excel, R offers endless possibilities for customization and creativity in your research.

By the end of this course, you’ll be able to:

Process and analyze data creatively using R for research.
Manipulate, import, and export data effectively.
Perform comprehensive statistical analyses, including correlations, ANOVA, and t-tests.
Create basic, advanced, and animated graphs with R.
Utilize RStudio as your go-to tool for scientific research.

Join me, Assist. Prof. Azad Rasul, and unlock the full potential of R in your research journey. Let’s get started!

Who this course is for:

Researchers and students who are attempting to efficiently use R programming for their scientific research.

R for Researchers: Statistics, Visualization and Data Analys

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

Advanced R Programming for AI2 lectures • 16min

Data Visualization and Reporting in R5 lectures • 25min

Course Wrap-Up and Bonus3 lectures • 5min

Requirements

Description

Who this course is for: