R Programming for Biologists: Beginners Level
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00
Aim: The aim of the workshop is to empower biologists with essential skills in R programming, fostering a comprehensive understanding of its applications in biological research. This workshop aims to demystify the complexities of R, providing participants, from beginners to intermediate users, with a solid foundation in utilizing R for data analysis in the biological sciences. Through a combination of theoretical knowledge and hands-on practical sessions, attendees will gain proficiency in data manipulation, statistical analysis, and visualization, enabling them to harness the full potential of R as a powerful tool for extracting meaningful insights from biological datasets. By the end of the workshop, participants will be equipped with the confidence and skills necessary to integrate R seamlessly into their research workflows, ultimately enhancing the efficiency and depth of their biological analyses.
Aim
R Programming for Biologists: Beginners Level teaches R basics for biological data handling, simple statistics, and clear plots. Learn core coding, work with lab datasets (CSV/TSV), and create reproducible outputs for faster analysis.
Program Objectives
- R Setup: RStudio, projects, scripts, packages, help.
- Core R: objects, vectors, data frames, indexing, functions.
- Data Handling: import, clean, filter, summarize, export.
- Visualization: ggplot2 plots for experiments.
- Basic Statistics: descriptive stats + simple tests (intro).
- Good Practice: reproducible workflow, clean code, documentation.
- Mini Project: complete a small end-to-end analysis.
Program Structure
Module 1: Getting Started with R
- Install R/RStudio; create projects; organize folders.
- Console vs script; working directory; help system.
- Install/load packages; tidyverse overview.
- First task: load data and compute summaries.
Module 2: Fundamentals (Objects, Vectors, Indexing)
- Data types: numeric, character, logical, factor.
- Vectors + indexing; basic calculations.
- Missing values (NA): detect and handle.
- Key functions + simple custom function.
Module 3: Data Frames for Lab Data
- Data frames/tibbles; inspect and summarize.
- Import/export: CSV/TSV; common format issues.
- Clean: rename columns, fix types, remove duplicates.
- Filter/sort: control vs treatment examples.
Module 4: Wrangling with dplyr
- select, filter, mutate, arrange, summarise, group_by.
- Group summaries: mean/SD by condition, replicates, time points.
- Joins (intro): combine metadata + measurements.
- Reshape (intro): wide vs long for plotting.
Module 5: Visualization with ggplot2
- Bar, line, scatter, boxplot, histogram.
- Biology plots: growth curves, QC plots, distributions.
- Clarity: labels, scales, legends, honest visuals.
- Export plots for reports/slides.
Module 6: Basic Statistics (Intro)
- Mean/median, SD, IQR; distribution checks (basic).
- t-test and chi-square concepts (intro).
- p-values + effect size thinking (intro); common mistakes.
- Simple reporting of results.
Module 7: Reproducible Reporting
- Save clean tables/plots to an outputs folder.
- RMarkdown/Quarto basics (intro).
- Document assumptions with comments + README.
- Reproducibility checklist.
Final Mini Project
- Analyze a provided biology dataset (growth/enzyme/qPCR Ct table/non-diagnostic/survey).
- Workflow: import → clean → summarize → plot → optional simple test.
- Deliverables: script/notebook + results table + 2–3 plots + short report.
Participant Eligibility
- UG/PG students in Biotechnology, Microbiology, Genetics, Life Sciences, Bioinformatics
- Researchers/lab members handling experimental datasets
- Beginners (no coding required)
Program Outcomes
- Write basic R scripts and use RStudio confidently.
- Import, clean, and summarize biology datasets.
- Create clear plots for experiments.
- Run and interpret simple stats (intro level).
- Deliver a reproducible mini project.
Program Deliverables
- e-LMS Access: lessons, datasets, templates.
- Starter Pack: setup guide, import templates, dplyr cheatsheet, ggplot scripts.
- Practice Tasks: exercises with solutions.
- Project Support: guidance for mini project completion.
- Assessment: certification after assignments + mini project.
- e-Certification and e-Marksheet: digital credentials on completion.
Future Career Prospects
- Research Data Assistant (Life Sciences)
- Junior Bioinformatics/Data Analysis Trainee
- Lab Data Analyst (Entry-level)
- Biostatistics Assistant (Entry-level)
Job Opportunities
- Labs & Universities: data cleaning, plotting, reporting support.
- Biotech/Pharma: basic analytics and QC summaries.
- CROs/Core Facilities: documentation and results reporting support.
- Startups: experiment data handling and visualization.
| Category | E-LMS, E-LMS+Video, E-LMS+Video+Live Lectures |
|---|
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What You’ll Gain
- Full access to e-LMS
- Publication opportunity
- Self-assessment & final exam
- e-Certificate
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