R for bioinformatics
Most biology papers are analysed in R. Learn the language properly and then make figures that a reviewer cannot pick apart.
Two courses. 109 lessons. From your first vector to a figure that goes straight into a manuscript.
Who this is for
You are new to R
You have read R code in a methods section and understood none of it. Start here and write your own instead of asking a collaborator every time.
You still work in Excel
The spreadsheet copes until the file gets big or a reviewer asks for one change. A script reruns the whole analysis in seconds.
Your figures let you down
You can analyse the data but your plots still look like defaults. Good figures in biology are made in ggplot2 and you can learn it here.
What you will learn
The language itself
Variables and functions and vectors and data types and packages and control flow. Every example uses biological data rather than made-up numbers.
Real data handling
You will read messy files and reshape them and deal with missing values before any analysis begins. This is where most projects go wrong.
Analysis you can defend
You will run the standard analyses on your own data and understand what each step does. That matters when somebody questions your results.
ggplot2 layer by layer
Aesthetics and geoms and scales and facets and themes. A figure stops being a mystery and becomes four lines of code you can edit.
Figures a journal will accept
You will control colour and labels and size and export settings. The file comes out right the first time.
Work you can reproduce
Your analysis runs end to end from raw file to final plot. The thesis chapter still works a year after you wrote it.
What you'll be able to do
You will load and clean a biological dataset in R without help. You will run the analyses your field expects. You will build figures in ggplot2 layer by layer. The whole analysis becomes a script you can run again next year.
The path · 2 courses in order
-
1
$49.00 Add to cart -
2
$69.00 Add to cart
Tools you will be using
- R
- RStudio
- ggplot2
- Bioconductor
- data frames
- CRAN packages
What you will have built by the end
- A cleaned biological dataset loaded and summarised entirely in R
- A multi-panel ggplot2 figure with your own colours and labels
- Figures exported at journal resolution straight from the script
- One script that reproduces a whole analysis from raw file to final plot
Every lesson in order
1 R for Bioinformatics: Fundamentals
Introduction 3 lessons
- Introduction to R in Bioinformatics & R Installation
- The R Studio Interface Explanation
- Comments
Variables & Functions 5 lessons
- Sample & Replacement
- Variable Declaration and Objects
- Built-in Functions & ARGS
- Write Your Own Functions And Arguments
- Scripts
Vectors & Data Types 13 lessons
- Attributes and Names
- Characters
- Doubles
- Logicals
- Factors
- Atomic Vectors
- Integers
- Dim & Dimensions
- + 5 more lessons in this section
Packages 3 lessons
- Packages
- Getting Help with Help Packages
- Install Bioinformatics Packages
Biological Data Analysis 13 lessons
- Zero Notation for Subsetting Biological Datasets
- Loading Biological Data
- Saving Biological Data
- R Notation & Selecting Values from Biological Dataset
- Data Frames
- Positive Integers for Subsetting Biological Dataset
- Negative Integers for Subsetting Biological Dataset
- Dollar Signs for Biological Dataset Subsetting
- + 5 more lessons in this section
Control Flow 3 lessons
- If Else Statement
- For Loops & Biological Data Binding
- While Loops & Reading Multiple Biological Datasets
Introductory R and Data Visualization for Bioinformatics 13 lessons
- Library & Initialization of Packages
- ggplot2: Key components
- ggplot2: Human Mitochondrial Proteome & Aesthetics (Size, Shape, Color)
- ggplot2: Facetting of Human Genome
- ggplot2: Smooth Out the Biological Data
- ggplot2: Boxplots for Human Mitochondrial Proteome
- ggplot2: Histograms for Human Mitochondrial Pattern Finding
- ggplot2: Frequency Plots for Human Mitochondrial Information Frequency Mining
- + 5 more lessons in this section
2 Data Visualisation with ggplot2 in R
Segment 1: Introduction to R Language & Installation 3 lessons
- Introduction to R in Bioinformatics & R Installation
- The R Studio Interface
- Comments
Segment 2: Introduction to Functions & Variables in R 5 lessons
- Variable Declaration & Objects
- Built-In Functions And Args
- Sample & Replacement
- Write Your Own Functions & Arguments
- Scripts
Segment 3: Introduction to Packages & Data Types in R 17 lessons
- Packages
- Install Packages
- Library & Initialize Packages
- Getting Help with Help Pages
- Atomic Vectors
- Doubles
- Integers
- Characters
- + 9 more lessons in this section
Segment 4: Control Flow & Biological Data Pre-processing in R 16 lessons
- If Else Statements
- For Loops & Biological Data Binding
- While Loops & Reading Multiple Biological Datasets
- Data Frames
- Loading Biological Data
- Saving Biological Data
- R Notation & Selecting Values from Biological Dataset
- Positive Integers for Subsetting Biological Dataset (DataFrame)
- + 8 more lessons in this section
Segment 5: Biological Data Visualization via ggplot2 in R 15 lessons
- Introduction to ggplot2 for Biological Datasets
- ggplot2: Key components
- ggplot2: Human Mitochondrial Proteome & Aesthetics (Size, Shape, Color)
- ggplot2: Facetting of Human Genome
- ggplot2: Smooth Out the Biological Data
- ggplot2: Boxplots for Human Mitochondrial Proteome
- ggplot2: Histograms for Human Mitochondrial Pattern Finding
- ggplot2: Frequency Plots for Human Mitochondrial Information Frequency Mining
- + 7 more lessons in this section
Questions people ask before they start
Do I need Python first?
No. R and Python are separate paths and plenty of biologists only ever use R. Pick whichever language the papers in your field use.
Does this cover Bioconductor?
You will install and use Bioconductor packages. The language skills taught here are the ones every Bioconductor workflow assumes you already have.
Do I need both courses?
Take Fundamentals first if R is new to you. The visualisation course assumes you can already write basic R. Buying both together applies the bundle price and a membership includes them anyway.
How long will it take me?
About fifteen hours of video. Most people finish in a month at a few hours a week and your access never expires.
Ready to start?
Buy the courses on their own or open the whole library with a membership. This path is included in both plans.
