Aim
R Programming for Biologists: Beginners Level teaches R from scratch for biological data analysis. Learn R basics, data handling, tidy data workflows, simple statistics, and plots for lab and research datasets.
Program Objectives
- R Basics: RStudio, scripts, packages, help system.
- Data Types: vectors, matrices, lists, data frames.
- Data Handling: import/export, cleaning, reshaping.
- Tidy Workflow: dplyr and tidyr basics.
- Plots: ggplot2 basics for biological data.
- Stats: descriptive stats and basic hypothesis tests.
- Reproducibility: project structure and reporting basics.
- Capstone: analyze a small biological dataset.
Program Structure
Module 1: Getting Started with R
- Installing R and RStudio.
- RStudio tour: console, scripts, environment, plots.
- Packages: install, load, update.
- Reading help pages and using examples.
Module 2: R Data Basics
- Variables and data types.
- Vectors and indexing.
- Matrices and basic operations.
- Factors and handling categorical data.
Module 3: Working with Data Frames
- Creating and inspecting data frames.
- Importing CSV/TSV files.
- Filtering and selecting columns.
- Handling missing values.
Module 4: Data Cleaning and Tidy Data
- Wide vs long format.
- dplyr: filter, select, mutate, summarize.
- tidyr: pivoting and separating columns.
- Joining datasets (intro).
Module 5: Visualization with ggplot2
- Scatter plots, bar plots, histograms.
- Boxplots and distribution plots.
- Grouping and faceting.
- Exporting figures.
Module 6: Basic Statistics for Biology
- Mean, median, SD, IQR.
- t-test and ANOVA concepts (intro).
- Correlation basics.
- Interpreting p-values and limitations.
Module 7: Biological Data Mini-Workflows
- Simple gene expression table handling (intro).
- Sequence statistics table handling (overview).
- Metadata and experimental design tables.
- Creating summary tables for reports.
Module 8: Reproducible Analysis and Reporting
- R projects and folder structure.
- Saving outputs and writing clean scripts.
- R Markdown basics (intro).
- Sharing results and plots.
Final Project
- Analyze a small biological dataset (lab/experimental table).
- Deliverables: cleaned data + plots + summary stats + short report.
- Submit: R script or R Markdown report.
Participant Eligibility
- Biology, Biotechnology, Microbiology, Bioinformatics students and professionals
- No coding experience required
- Basic stats helpful
Program Outcomes
- Write R code for biological data analysis.
- Clean and reshape datasets using tidy tools.
- Create clear plots using ggplot2.
- Build a small project for your portfolio.
Program Deliverables
- e-LMS Access: lessons, exercises, datasets.
- Toolkit: scripts, templates, cheat sheets.
- Assessment: certification after project submission.
- e-Certification and e-Marksheet: digital credentials.
Future Career Prospects
- Bioinformatics Trainee
- Research Assistant (Data)
- Biostatistics Trainee
- Biological Data Analyst (Entry-level)
Job Opportunities
- Research Labs: data cleaning and analysis support.
- Universities: genomics and ecology research groups.
- Biotech/CROs: reporting and analytics teams.
- Healthcare/Diagnostics: data support roles.
Reviews
There are no reviews yet.