BOOKDOWN: authoring books and technical publications with r markdown

Preface
About the Authors
I Data wrangling and visualisation
	Why we love R
		Help, what\'s a script?
		What is RStudio?
		Getting started
		Getting help
		Work in a Project
		Restart R regularly
		Notation throughout this book
	R basics
		Reading data into R
			Import Dataset interface
			Reading in the Global Burden of Disease example dataset
		Variable types and why we care
			Numeric variables (continuous)
			Character variables
			Factor variables (categorical)
			Date/time variables
		Objects and functions
			data frame/tibble
			Naming objects
			Function and its arguments
			Working with objects
			<- and =
			Recap: object, function, input, argument
		Pipe - %>%
			Using . to direct the pipe
		Operators for filtering data
			Worked examples
		The combine function: c()
		Missing values (NAs) and filters
		Creating new columns - mutate()
			Worked example/exercise
		Conditional calculations - if_else()
		Create labels - paste()
		Joining multiple datasets
			Further notes about joins
	Summarising data
		Get the data
		Plot the data
		Aggregating: group_by(), summarise()
		Add new columns: mutate()
			Percentages formatting: percent()
		summarise() vs mutate()
		Common arithmetic functions - sum(), mean(), median(), etc.
		select() columns
		Reshaping data - long vs wide format
			Pivot values from rows into columns (wider)
			Pivot values from columns to rows (longer)
			separate() a column into multiple columns
		arrange() rows
			Factor levels
		Exercises
			Exercise - pivot_wider()
			Exercise - group_by(), summarise()
			Exercise - full_join(), percent()
			Exercise - mutate(), summarise()
			Exercise - filter(), summarise(), pivot_wider()
	Different types of plots
		Get the data
		Anatomy of ggplot explained
		Set your theme - grey vs white
		Scatter plots/bubble plots
		Line plots/time series plots
			Exercise
		Bar plots
			Summarised data
			Countable data
			colour vs fill
			Proportions
			Exercise
		Histograms
		Box plots
		Multiple geoms, multiple aes()
			Worked example - three geoms together
		All other types of plots
		Solutions
		Extra: Advanced examples
	Fine tuning plots
		Get the data
		Scales
			Logarithmic
			Expand limits
			Zoom in
			Exercise
			Axis ticks
		Colours
			Using the Brewer palettes:
			Legend title
			Choosing colours manually
		Titles and labels
			Annotation
			Annotation with a superscript and a variable
		Overall look - theme()
			Text size
			Legend position
		Saving your plot
II Data analysis
	Working with continuous outcome variables
		Continuous data
		The Question
		Get and check the data
		Plot the data
			Histogram
			Quantile-quantile (Q-Q) plot
			Boxplot
		Compare the means of two groups
			t-test
			Two-sample t-tests
			Paired t-tests
			What if I run the wrong test?
		Compare the mean of one group: one sample t-tests
			Interchangeability of t-tests
		Compare the means of more than two groups
			Plot the data
			ANOVA
			Assumptions
		Multiple testing
			Pairwise testing and multiple comparisons
		Non-parametric tests
			Transforming data
			Non-parametric test for comparing two groups
			Non-parametric test for comparing more than two groups
		Finalfit approach
		Conclusions
		Exercises
			Exercise
			Exercise
			Exercise
			Exercise
		Solutions
	Linear regression
		Regression
			The Question (1)
			Fitting a regression line
			When the line fits well
			The fitted line and the linear equation
			Effect modification
			R-squared and model fit
			Confounding
			Summary
		Fitting simple models
			The Question (2)
			Get the data
			Check the data
			Plot the data
			Simple linear regression
			Multivariable linear regression
			Check assumptions
		Fitting more complex models
			The Question (3)
			Model fitting principles
			AIC
			Get the data
			Check the data
			Plot the data
			Linear regression with finalfit
			Summary
		Exercises
			Exercise
			Exercise
			Exercise
			Exercise
		Solutions
	Working with categorical outcome variables
		Factors
		The Question
		Get the data
		Check the data
		Recode the data
		Should I convert a continuous variable to a categorical variable?
			Equal intervals vs quantiles
		Plot the data
		Group factor levels together - fct_collapse()
		Change the order of values within a factor - fct_relevel()
		Summarising factors with finalfit
		Pearson\'s chi-squared and Fisher\'s exact tests
			Base R
		Fisher\'s exact test
		Chi-squared / Fisher\'s exact test using finalfit
		Exercises
			Exercise
			Exercise
			Exercise
	Logistic regression
		Generalised linear modelling
		Binary logistic regression
			The Question (1)
			Odds and probabilities
			Odds ratios
			Fitting a regression line
			The fitted line and the logistic regression equation
			Effect modification and confounding
		Data preparation and exploratory analysis
			The Question (2)
			Get the data
			Check the data
			Recode the data
			Plot the data
			Tabulate data
		Model assumptions
			Linearity of continuous variables to the response
			Multicollinearity
		Fitting logistic regression models in base R
		Modelling strategy for binary outcomes
		Fitting logistic regression models with finalfit
			Criterion-based model fitting
		Model fitting
			Odds ratio plot
		Correlated groups of observations
			Simulate data
			Plot the data
			Mixed effects models in base R
		Exercises
			Exercise
			Exercise
			Exercise
			Exercise
		Solutions
	Time-to-event data and survival
		The Question
		Get and check the data
		Death status
		Time and censoring
		Recode the data
		Kaplan Meier survival estimator
			KM analysis for whole cohort
			Model
			Life table
		Kaplan Meier plot
		Cox proportional hazards regression
			coxph()
			finalfit()
			Reduced model
			Testing for proportional hazards
			Stratified models
			Correlated groups of observations
			Hazard ratio plot
		Competing risks regression
		Summary
		Dates in R
			Converting dates to survival time
		Exercises
			Exercise
			Exercise
		Solutions
III Workflow
	The problem of missing data
		Identification of missing data
			Missing completely at random (MCAR)
			Missing at random (MAR)
			Missing not at random (MNAR)
		Ensure your data are coded correctly: ff_glimpse()
			The Question
		Identify missing values in each variable: missing_plot()
		Look for patterns of missingness: missing_pattern()
		Including missing data in demographics tables
		Check for associations between missing and observed data
			For those who like an omnibus test
		Handling missing data: MCAR
			Common solution: row-wise deletion
			Other considerations
		Handling missing data: MAR
			Common solution: Multivariate Imputation by Chained Equations (mice)
		Handling missing data: MNAR
		Summary
	Notebooks and Markdown
		What is a Notebook?
		What is Markdown?
		What is the difference between a Notebook and an R Markdown file?
		Notebook vs HTML vs PDF vs Word
		The anatomy of a Notebook / R Markdown file
			YAML header
			R code chunks
			Setting default chunk options
			Setting default figure options
			Markdown elements
		Interface and outputting
			Running code and chunks, knitting
		File structure and workflow
			Why go to all this bother?
	Exporting and reporting
		Which format should I use?
		Working in a .R file
		Demographics table
		Logistic regression table
		Odds ratio plot
		MS Word via knitr/R Markdown
			Figure quality in Word output
		Create Word template file
		PDF via knitr/R Markdown
		Working in a .Rmd file
		Moving between formats
		Summary
	Version control
		Setup Git on RStudio and associate with GitHub
		Create an SSH RSA key and add to your GitHub account
		Create a project in RStudio and commit a file
		Create a new repository on GitHub and link to RStudio project
		Clone an existing GitHub project to new RStudio project
		Summary
	Encryption
		Safe practice
		encryptr package
		Get the package
		Get the data
		Generate private/public keys
		Encrypt columns of data
		Decrypt specific information only
		Using a lookup table
		Encrypting a file
		Decrypting a file
		Ciphertexts are not matchable
		Providing a public key
		Use cases
			Blinding in trials
			Re-contacting participants
			Long-term follow-up of participants
		Summary
	Appendix
	Bibliography
	Index