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Introduction
1
Introduction to Excel and R
1.1
Estimating the Volume of a Snail
1.1.1
Download and Import the CSV File
1.1.2
Calculate Volume
\(V\)
in Excel
1.1.3
Add a Linear Trendline and Equation
1.1.4
Estimation of Volume for a Snail with Mass 10g
1.2
Getting Ready for R
1.2.1
Download and Install (Windows and Mac only)
1.2.2
Creating Your First R Project in RStudio
1.2.3
Something More Complicated
2
Summarising Data and ANOVA in Excel
2.1
Summarising Data
2.1.1
Download and Import the CSV File
2.1.2
Generating a Summary Table
2.1.3
Presenting Your Summary Table
2.2
Analysis of Variance (ANOVA)
2.2.1
Grouped Boxplots in Excel
2.2.2
Constructing Testable Hypotheses
2.2.3
Performing a One-Way Anova in Excel
2.2.4
Performing Post-hoc Tests in Excel
3
Calibration Curves and Linear Regression in Excel
3.1
Calibration Curves
3.2
Linear Regression
3.2.1
The Linear Regression Equation
3.2.2
Performing a Linear Regression in Excel
3.2.3
Interpreting Your Linear Regression
3.2.4
Making Predictions with Our Model
3.2.5
Extending to Multiple Variables
4
Introduction to R: Part I
4.1
Reading and Inspecting Data
4.2
Subsetting
4.2.1
Slicing
4.2.2
Filtering
4.2.3
So what?
4.3
Summarising Data in R
5
Introduction to R part II: Visualisation
5.1
Mushroom Compost Scenario
5.1.1
The dataset
5.1.2
Further information
5.2
Inspecting and Summarising
5.2.1
Summary Table
5.3
Histogram
5.4
Grouped Boxplot
5.5
Scatterplot
5.6
Just for Fun
6
Statistics in R: Part I
6.1
One-Way ANOVA in R
6.1.1
Importing Data from an Online Source
6.1.2
Summarising (Descriptive Statistics)
6.1.3
Grouped Boxplots in R
6.1.4
Conducting a One-Way ANOVA
6.1.5
Post-Hoc Tests
6.2
Two-way ANOVA in R
6.2.1
Importing and Cleaning the Data
6.2.2
Visualising the data
6.2.3
Conducting a Two-Way ANOVA
6.2.4
Post-Hoc tests
7
Statistics in R: Part II
7.1
Linear Regression in R
7.1.1
Reading the data and calculating a survival column
7.1.2
Visualising the data
7.1.3
Linearising the data
7.1.4
Performing a Linear Regression in R
7.1.5
Calculating LD50
7.2
Multiple Linear Regression (MLR) in R.
7.2.1
Performing an MLR in R
7.2.2
Predicting values for Viable Counts
8
Loops, Logic and Functions: Diving Deeper into R
8.1
What is FASTA?
8.2
Extracting Sequences from a FASTA File
8.3
Writing Functional Code
8.4
Counting Motifs
8.5
The Fun Bit (AKA - The Last Horrible Bit)
8.6
fasta-tools.R
9
Image Analysis in R
9.1
The Doomscroller Virus Pandemic: Scenario
9.1.1
Performing a Manual Count
9.1.2
Installing Packages and Setting Up
9.1.3
Automating The Count
9.1.4
Looping Over All Files in a .zip
9.2
Scenario Extension
9.3
hemo-tools.R
10
Boss Fight
Appendix I: Teach yourself R.
LinkedIn Learning
YouTube
Books
Quantiative Skills in Biosciences I
Quantiative Skills in Biosciences I
R. E. Treharne
2024-10-07
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