Applied Statistics with R: A Practical Guide for the Life Sciences

Cover
Applied Statistics with R: A Practical Guide for the Life Sciences
Copyright
Dedication
Preface
	Welcome!
	Goals for the book
	Basic layout of the book
	A little background about R
	A little about how R works
	Why learn R?
Acknowledgments
Contents
1: Introduction to R
	1 Introduction to R
		1.1 Overview
		1.2 Getting Started
			1.2.1 Obtaining R
			1.2.2 Installing and loading packages
		1.3 Working From The Script Window
		1.4 Creating Well-documented And Annotated Code
			1. Writing for yourself
			2. Writing for collaborators
			3. Writing for folks in the future who might want to see your code
			1. Always start with your raw data
			2. Annotate, annotate, annotate
			3. Organize your script file into logical sections
			4. Give your objects meaningful and unique names
		1.5 Before We Get Started
		1.6 Creating Objects
		1.7 Functions
		1.8 What Your Data Should Look Like Before Loading Into R
		1.9 Understanding Various Types Of Objects In R
			1.9.1 Vector
			1.9.2 Matrix
			1.9.3 Data frame
			1.9.4 Lists
		1.10 A Litany Of Useful Functions
			1.10.1 Functions for examining data frames or other objects
			1.10.2 General functions for creating or manipulating objects
			1.10.3 Mathematic functions
		1.11 Assignment!
2: Before You Begin (aka Thoughts on Proper Data Analysis)
	2 Before You Begin (aka Thoughts on Proper Data Analysis)
		2.1 Overview
		2.2 Basic Principles Of Experimental Design
		2.3 Blocked Experimental Designs
		2.4 You Can (and Should) Plan Your Analyses Before You Have The Data!
		2.5 Best Practices For Data Analysis
		2.6 How To Decide Between Competing Analyses
		2.7 Data Are Data Are Data
3: Exploratory Data Analysis and Data Summarization
	3 Exploratory Data Analysis and Data Summarization
		3.1 The Resource-by-predation Dataset
		3.2 Reading In The Data File
			3.2.1 Reading the file using read.csv()
		3.3 Data Exploration And Error Checking
			3.3.1 Data structure
			3.3.2 Data exploration and visualization
			3.3.3 Further data exploration and identifying mistakes
			3.3.4 Fixing mistakes in the data
			3.3.5 A note about indexing
		3.4 Summarizing And Manipulating Data
			3.4.1 Reordering your data
			3.4.2 Calculating Means and Standard Errors
		3.5 Assignment!
4: Introduction to Plotting
	4 Introduction to Plotting
		4.1 Principles Of Effective Figure Making
			4.1.1 Defining colors
				4.1.1.1 Numerical colors
				4.1.1.2 Named colors
				4.1.1.3 RGB colors
				4.1.1.4 Hex colors
		4.2 Data Exploration Using Ggplot2
			4.2.1 Boxplots
			4.2.2 Faceting
			4.2.3 Violin plots
			4.2.4 Histograms and density plots
			4.2.5 Scatterplots
		4.3 Plotting Your Data
			4.3.1 Making a barplot in base graphics
			4.3.2 Making a barplot in ggplot2
		4.4 Assignment!
5: Basic Statistical Analyses
	5 Basic Statistical Analyses
		5.1 Determining What Type Of Analysis To Do
		5.2 Avoiding Pseudoreplication
		5.3 Testing For Normality In Your Data
			5.3.1 Looking at the data
			5.3.2 Normality tests
			5.3.3 Understanding error distributions
		5.4 Non-parametric Tests
			5.4.1 The Mann-Whitney U test and the Kruskal-Wallis test
			5.4.2 Student’s t-test
		5.5 Introducing Linear Models
		5.6 One-way Analysis Of Variance—anova
			5.6.1 Diagnostic plots
			5.6.2 A note of caution
		5.7 Multiple Comparisons
		5.8 Assignment!
6: More Linear Models!
	6 More Linear Models!
		6.1 getting started
		6.2 multi-way analysis of variance—anova
			6.2.1 Interpreting an output with interactions
			6.2.2 Posthoc comparisons when you have interactions?
			6.2.3 Posthoc comparisons of all six treatments at once
		6.3 linear regression
			6.3.1 Interpreting the regression
			6.3.2 Plotting the regression
		6.4 analysis of covariance (ancova)
			6.4.1 Interpreting an ANCOVA
			6.4.2 Post-hoc anlyses of an ANCOVA
			6.4.3 Interpreting the summary output for an ANCOVA
		6.5 the predict() function
		6.6 plotting with ggplot() instead of qplot()
		6.7 assignment!
7: Generalized Linear Models (GLM)
	7 Generalized Linear Models (GLM)
		7.1 Understanding Non-normal Data
		7.2 Glms
		7.3 Understanding And Interpreting The Glm
			7.3.1 Comparing the diagnostic plots and making a decision
		7.4 Calculating Statistical Significance With Glms
		7.5 Coding The Data As A Binomial Glm
		7.6 Mixing Glms And Ancovas Together
		7.7 Using The Predict() Function With A Glm
		7.8 Making A Much Easier Glm/ancova Plot Using Ggplot2
		7.9 Assignment!
8: Mixed Effects Models
	8 Mixed Effects Models
		8.1 Understanding Mixed Effects Models
			8.1.1 Coding a mixed effects model
			8.1.2 Interpreting the output
			8.1.3 Diagnostic plots
			8.1.4 GLMM’s (and troubleshooting when they don’t run)
			8.1.5 More about model selection
			8.1.6 Estimating marginal means with mixed models
		8.2 Assignment!
9: Advanced Data Wrangling and Plotting
	9 Advanced Data Wrangling and Plotting
		9.1 The “tidyverse”
		9.2 Basic Data Wrangling
			9.2.1 Group and summarize data
			9.2.2 Use mutate() to calculate new variables
			9.2.3 Use select() to include or exclude certain variables
			9.2.4 Use filter() to remove certain values
			9.2.5 Join two datasets together
		9.3 Advanced Data Wrangling: Spreading And Gathering Your Data
		9.4 Even More Advanced Data Wrangling! Using The Do() Function
		9.5 Making Better Figures With Ggplot2
			9.5.1 Defining colors
		9.6 Basics Of Ggplot2
			9.6.1 Getting started with qplot()
		9.7 Customizing Your Figure
			9.7.1 Changing colors
			9.7.2 Changing the axes limits
			9.7.3 A little more about themes
			9.7.4 Advanced faceting
			9.7.5 Changing your facet labels
			9.7.6 Using ggplot() vs qplot()
		9.8 Combining Data Wrangling With Plotting With Ggplot2
			9.8.1 Let’s make a professional looking bargraph!
			9.8.2 Now let’s really do something cool: combining gathered data and faceting
		9.9 Assignment!
10: Writing Loops and Functions in R
	10 Writing Loops and Functions in R
		10.1 For Loops
		10.2 Understanding Functions
		10.3 Writing Functions
		10.4 How A Function Works
			10.4.1 A realistic and useful function: Let’s calculate the standard error
		10.5 Writing More Complex Functions: An Example Using Simulations
			10.5.1 Starting simple
			10.5.2 Making a very basic function
			10.5.3 Building in arguments
		10.6 Assignment!
11: Final Thoughts
	11 Final Thoughts
		11.1 Understanding Your Data Is The Most Important Precursor To Analyzing It
		11.2 Knowing How To Get Help Is Essential
		11.3 Your Data Analysis Should Be Clear From The Outset And You Should Avoid Questionable Techniques
		11.4 Presenting Your Data In Well-constructed Figures Is Key
Index