دانلود کتاب R for Health Data Science

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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