RMRWR
1
bibliography: [book.bib, packages.bib]
1.1
Who This Book is For
1.2
Prerequisites
1.3
The (Upward) Spiral of Success Structure
1.4
Motivation for this Book
1.5
The Scientific Reproducibility Crisis
1.6
Features of a Bookdown electronic book
1.7
What this Book is Not
1.7.1
This Book is Not A Statistics Text
1.7.2
This Book Does Not Provide Comprehensive Coverage of the R Universe
1.8
Some Guideposts
1.9
Helpful Tools
1.9.1
Demonstrations in Flipbooks
1.9.2
Learnr Coding Exercises
1.9.3
Coding
2
Getting Started and Installing Your Tools
2.1
Goals for this Chapter
2.2
Website links needed for this Chapter
2.3
Pathway for this Chapter
2.4
Installing R on your Computer
2.5
Windows-Specific Steps for Installing R
2.5.1
Testing R on Windows
2.6
Mac-specific Installation of R
2.6.1
Testing R on the Mac
2.6.2
Successful testing!
2.7
Installing RStudio on your Computer
2.7.1
Windows Install of RStudio
2.7.2
Testing Windows RStudio
2.7.3
Installing RStudio on the Mac
2.7.4
Testing the Mac Installation of RStudio
2.7.5
Critical Setup - Tuning Up Your RStudio Installation
2.8
Installing Git on your Computer
2.8.1
Installing Git on macOS
2.8.2
Installing Git on Windows
2.8.3
Installing Git on Linux
2.9
Getting Acquainted with the RStudio IDE
3
A Tasting Menu of R
3.1
Setting the Table
3.2
Goals for this Chapter
3.3
Packages needed for this Chapter
3.4
Website links needed for this Chapter
3.5
Setting up RPubs
3.6
Open a New Rmarkdown document
3.7
Knitting your Rmarkdown document
3.7.1
Installing Packages
3.7.2
Loading Packages with library()
3.8
Your Turn to Write Text
3.9
Wrangle Your Data
3.10
Summarize Your Data
3.11
Visualize Your Data
3.12
Statistical Testing of Differences
3.13
Publish your work to RPubs
3.14
The Dessert Cart
3.14.1
Interactive Plots
3.14.2
Animated Graphics
3.14.3
A Clinical Trial Dashboard
3.14.4
A Shiny App
3.14.5
An Example of Synergy in the R Community
4
Introduction to Reproducibility
4.1
First Steps to Research Reproducibility
4.1.1
Have a Plan
4.1.2
Treat Your Raw Data Like Gold
4.1.3
Cleaning and Analyzing Your Data
4.1.4
The First Level of Reproducibility
4.1.5
The Second Level of Reproducibility
4.2
The Foundations of Reproducibility
5
Importing Your Data into R
5.1
Reading data with the {readr} package
5.1.1
Test yourself on scurvy
5.1.2
What is a path?
5.1.3
Try it Yourself
5.2
Reading Excel Files with readxl
5.2.1
Test yourself on read_excel()
5.3
Bringing in data from other Statistical Programs (SAS, Stata, SPSS) with the {haven} package
5.4
Other strange file types with rio
5.5
Data exploration with glimpse, str, and head/tail
5.5.1
Taking a glimpse with
glimpse()
5.5.2
Try this out yourself.
5.5.3
Test yourself on strep_tb
5.5.4
Examining Structure with
str()
5.5.5
Test yourself on the scurvy dataset
5.5.6
Examining a bit of data with
head()
and
tail()
5.5.7
Test yourself on the printing tibbles
5.6
More exploration with skimr and DataExplorer
5.6.1
Test yourself on the
skim()
results
5.6.2
Test yourself on the
create_report()
results
5.7
Practice loading data from multiple file types
5.8
Practice saving (writing to disk) data objects in formats including csv, rds, xls, xlsx and statistical program formats
5.9
How do readr and readxl parse columns?
5.10
What are the variable types?
5.11
Controlling Parsing
5.12
Chapter Challenges
5.13
Future forms of data ingestion
6
Wrangling Rows in R with Filter
6.1
Goals for this Chapter
6.2
Packages needed for this Chapter
6.3
Pathway for this Chapter
6.4
Logical Statements in R
6.5
Filtering on Numbers - Starting with A Flipbook
6.5.1
Your Turn - learnr exercises
6.6
Filtering on Multiple Criteria with Boolean Logic
6.6.1
Your Turn - learnr exercises
6.7
Filtering Strings
6.7.1
Your Turn - learnr exercises
6.8
Filtering Dates
6.8.1
Your Turn - learnr exercises
6.9
Filtering Out or Identifying Missing Data
6.9.1
Working with Missing data
6.9.2
Your Turn - learnr exercises
6.10
Filtering Out Duplicate observations
6.11
Slicing Data by Row
6.12
Randomly Sampling Your Rows
6.12.1
Your Turn - learnr exercises
6.13
Further Challenges
6.14
Explore More about Filtering
7
The Basics of R
7.1
Why Programming?
7.2
Programming Fears
7.3
Thinking about Wofkflow
7.4
Files in R
7.4.1
Data Files
7.4.2
Script files
7.4.3
Other files
7.5
Paths in R
7.6
Creating variables in R
7.7
The Pipe Operator
7.8
R Dialects
8
Wrangling Columns in R with Select, Rename, and Relocate
8.1
Goals for this Chapter
8.2
Packages needed for this Chapter
8.3
Pathway for this Chapter
8.4
Tidyselect Helpers in R
8.5
Selecting a Column Variables
8.5.1
Try this out
8.6
Selecting Columns that are Not Contiguous
8.7
Selecting Columns With Logical Operators
8.8
Further Challenges
8.9
Explore More about Filtering
9
Using Mutate to Make New Variables (Columns)
9.1
Calculating BMI
9.2
Recoding categorical or ordinal data
9.3
Calculating Glomerular Filtration Rate
10
Mutating Joins to Combine Data Sources
10.1
What are Joins?
10.2
What are Mutating Joins?
10.3
Let’s Start with Left Joins
10.4
Left Join in Action
10.5
Left Join in Practice
10.6
Quick Quiz
10.7
Problem variable names
10.8
Right Join in Action
10.9
Right Join in Practice
10.10
Inner Joins
10.11
Quick Quiz
10.12
Now Let’s take a Look at the result
10.13
Full Joins
10.14
Quick Quiz
10.15
Now Let’s take a Look at the result
11
Interpreting Error Messages
11.1
The Common Errors Table
11.2
Examples of Common Errors and How to fix them
11.2.1
Missing Parenthesis
11.2.2
An Extra Parenthesis
11.2.3
Missing pipe
%>%
in a data wrangling pipeline
11.2.4
Missing + in a ggplot pipeline
11.2.5
Pipe
%>%
in Place of a
+
11.2.6
Missing Comma Within a Function()
11.2.7
A Missing Object
11.2.8
One Equals Sign When you Need Two
11.2.9
Non-numeric argument to a binary operator
11.3
Errors Beyond This List
11.4
When Things Get Weird
11.4.1
Restart your R Session (Shift-Cmd-F10)
11.5
References:
12
The Building Blocks of R: data types, data structures, functions, and packages
12.1
Data Types
12.2
Data Structures
12.3
Examining Data Types and Data Structures
12.4
Functions
12.5
Packages
12.6
The Building Blocks of R
13
Building Your Table One with the {gtsummary} Package
13.1
Using tbl_summary() from the gtsummary package
13.2
Making a Basic table
13.2.1
Challenges:
13.3
Multiple Dimensions
13.4
New Challenges
13.5
Even More Help
13.6
Figuring out Column names
13.7
Even More Challenges
13.8
Adding Some Formatting
13.8.1
Formatting with {gt}
13.9
A Fancier Version for gt
13.9.1
The {flextable} package
13.10
A Fancier Version for Flextable
14
Tips for Hashtag Debugging your Pipes and GGPlots
14.1
Debugging
14.2
The Quick Screen
14.3
Systematic Hunting For Bugs in Pipes
14.4
Systematic Hunting For Bugs in Plots
14.5
Your Turn to Debug a Plot
14.6
Hashtag Debugging
14.7
Pipe 2
14.8
Plot 2
14.9
Plot3
14.10
Pipe 3
15
Finding Help in R
15.1
Programming in R
15.2
Starting with Help!
15.3
The Magic of Vignettes
15.4
Googling the Error Message
15.5
You Know What You Want to Do, but Don’t Know What Package or Function to Use
15.5.1
CRAN Task Views
15.5.2
Google is Your Friend
15.6
Seeking Advanced Help with a Minimal REPREX
16
The Basics of Base R
16.1
Dimensions of Data Rectangles
16.2
Naming columns
16.3
Concatenation
16.4
Sequences
16.5
Constants
16.6
Fancier Sequences
16.7
Mathematical functions
16.8
Handling missing data (NAs)
16.9
Cutting Continuous data into Levels
17
Updating R, RStudio, and Your Packages
17.1
Installing Packages
17.1.1
Installing Packages from Github
17.1.2
Problems with Installing Packages
17.2
Loading Packages with Library
17.3
Updating R
17.4
Updating RStudio
17.5
Updating Your Packages
18
Major R Updates (Where Are My Packages?)
18.1
When to Upgrade R
18.2
Preparing for a Minor or Major R Upgrade
18.2.1
Before you upgrade R
18.2.2
STEP 1: Clean up old, unused packages
18.2.3
STEP 2: Build a dataframe of your currently installed packages.
18.2.4
STEP 3: Removing unwanted Packages from the dataframe
18.2.5
STEP 4: Upgrading R to the new Version
18.2.6
STEP 5: Upgrading the RStudio Version
18.3
STEP 6: Rebuilding All of your Packages in One (Automated) Step
18.4
Checking the new library path
18.5
Now Check your list of Packages
18.6
Updating Packages
19
Intermediate Steps Toward Reproducibility
19.1
Level 3 Reproducibility
19.1.1
Creating a New Project in RStudio
19.1.2
File paths and the {here} package
19.2
Code Review with a Coding Partner
19.2.1
Checklist for Code Review
19.3
Sharing code on GitHub
20
Building Table One for a Clinical Study
20.1
Packages Needed for this Chapter:
20.2
Pathway for this Chapter
20.3
Baseline Characteristics
20.4
Building Your Table 1
20.4.1
Updating Variable Labels
20.4.2
Updating Variable Values
20.4.3
Table 1 separated by Treatment Arm
20.4.4
Styling our Table 1
20.4.5
Adding A Column Spanner
20.4.6
Further Styling our Table 1
20.4.7
Your Turn
20.5
Try this with a new dataset
20.6
Making Modifications to the trial table
20.7
More Modifications to the trial table
20.8
Taking Control of the Stats
20.8.1
Your Turn
21
Comparing Two Measures of Centrality
21.0.1
Applying the t test
21.1
Common Problem
21.1.1
How Skewed is Too Skewed?
21.1.2
Visualize the Distribution of data variables in ggplot
21.1.3
Visualize the Distribution of data$len in ggplot
21.1.4
Results of Shapiro-Wilk
21.1.5
Try it yourself
21.1.6
Mammal sleep hours
21.2
One Sample T test
21.2.1
How to do One Sample T test
21.2.2
Interpreting the One Sample T test
21.2.3
What are the arguments of the t.test function?
21.3
Insert flipbook for ttest here
21.3.1
Flipbook Time!
21.4
Fine, but what about 2 groups?
21.4.1
Setting up 2 group t test
21.4.2
Results of the 2 group t test
21.4.3
Interpreting the 2 group t test
21.4.4
2 group t test with wide data
21.4.5
Results of 2 group t test with wide data
21.5
3 Assumptions of Student’s t test
21.5.1
Testing Assumptions of Student’s t test
21.6
Getting results out of t.test
21.6.1
Getting results out of t.test
21.7
Reporting the results from t.test using inline code
21.7.1
For Next Time
22
Sample Size Calculations with
{pwr}
22.1
Sample Size for a Continuous Endpoint (t-test)
22.2
One Sample t-test for Lowering Creatinine
22.3
Paired t-tests (before vs after, or truly paired)
22.4
2 Sample t tests with Unequal Study Arm Sizes
22.5
Testing Multiple Options and Plotting Results
22.6
Your Turn
22.6.1
Scenario 1: FEV1 in COPD
22.6.2
Scenario 2: BNP in CHF
22.6.3
Scenario 3: Barthel Index in Stroke
22.7
Sample Sizes for Proportions
22.8
Sample size for two proportions, equal n
22.9
Sample size for two proportions, unequal arms
22.10
Your Turn
22.10.1
Scenario 1: Mortality on Renal Dialysis
22.10.2
Scenario 2: Intestinal anastomosis in Crohn’s disease
22.10.3
Scenario 3: Metformin in Donuts
22.11
add chi square
22.12
add correlation test
22.13
add anova
22.14
add linear model
22.15
add note on guessing effect sizes - cohen small, medium, large
22.16
Explore More
23
Randomization for Clinical Trials with R
23.1
Printing these on Cards
23.2
Now, try this yourself
23.3
Now Freestyle
24
Univariate ggplots to Visualize Distributions
24.1
Histograms
24.1.1
Comparisons of Distributions with Histograms
24.1.2
Histograms and Categories
24.2
Density Plots
24.2.1
Comparisons with Density plots
24.3
Comparing Distributions Across Categories
24.4
Boxplots
24.5
Violin Plots
24.6
Ridgeline Plots
24.6.1
Including Plots
24.6.2
Including Points
24.6.3
Including Points
24.6.4
Including Points
24.6.5
Including Points
25
Bivariate ggplot2 Scatterplots to Visualize Relationships Between Variables
25.1
Packages used in this Chapter
25.2
Data Exploration and Validation (DEV)
25.3
Scatterplots
25.3.1
Micro-quiz!
25.4
Mapping More Variables
25.5
Inheritance and Layering in ggplot2
25.6
Aesthetic mapping Micro-Quiz!
25.7
Controlling Point Shape, Size, and Color Manually
25.7.1
Manual Shapes
25.7.2
Manual Sizes
25.7.3
Manual Color
26
Extensions to ggplot
26.1
Goals for this Chapter
26.2
Packages Needed for this chapter
26.3
A Flipbook of Where We Are Going With ggplot Extensions
26.3.1
MAKE FLIPBOOK
26.4
A Waffle Plot
26.5
An Alluvial Plot
26.6
Lollipop Plots
26.7
Dumbbell Plots
26.8
Spaghetti Plots with Summary Smoothed Lines for Change Over Time
26.9
Swimmer Plots
26.10
Adding Significance Comparisons with {ggsignif}
27
Customizing Plot Scales
27.1
Goals for this Chapter
27.2
Packages Needed for this chapter
27.3
A Flipbook of Where We Are Going With Scales
27.4
A Basic Scatterplot
27.5
But what if you want the scale for risk to start at 0?
27.6
But this axis does not really start at Exactly 0
27.7
Control the Limits and the Breaks
27.8
Test what you have learned
27.9
Continuous vs. Discrete Plots and Scales
27.10
Using Scales to Customize a Legend
27.11
Test what you have learned
27.11.1
More Examples with Flipbooks
28
Helping out with ggplot
28.1
ggx::gghelp()
28.2
Getting more help with theming with ggThemeAssist
28.3
Website helpers for ggplot
28.4
Getting Even more help with esquisse
29
Mapping Health Data in R
29.1
The {tigris} package and Basic Mapping in R
29.2
Mapping with OpenStreetMap Data
29.3
Another Example
30
Mapping Population By County
30.1
Finding a Census Field
30.1.1
Mapping Commute Times
30.1.2
Mapping Work From Home
30.2
Mapping and Merging Health Data
30.2.1
Solution
30.3
Now to merge
30.4
Texas Health Data
30.4.1
Solution to ACS
30.4.2
Solution to Merge
30.4.3
Solution to Mapping
30.5
Mapping California
30.6
A bit of US geographic unit history
31
problems with extraction - 223 divisions not named county
31.1
Merge problems
32
try matching by GEOID (in us counties) = fips (health data)
33
use county names from health data
33.1
Mapping Distances
33.2
We can narrow this down to a smaller Area, such as Philadelphia, and driving time to the Children’s Hospital of Philadelphia (CHOP).
33.3
Driving to UCLA Reagan Hospital
34
Functions
34.1
Don’t repeat yourself
34.2
Your Turn
34.3
Freestyle
34.3.1
Acknowledgement
34.4
Read More
35
Using Found (Web) Data
35.1
Found Poetry
35.2
Found Data
35.3
Download Example
35.4
Datapasta (small table) Example
35.5
Your Turn
35.6
{rvest} Example
35.7
Your Turn
35.8
API example with {tidycensus}
35.9
Challenges
35.10
Advanced Challenge - Dynamic Websites
36
Linear Regression and Broom for Tidying Models
36.1
Packages needed
36.2
Building a simple base model with {lm}
36.2.1
Producing manuscript-quality tables with {gtsummary}
36.3
Is Your Model Valid?
36.4
Making Predictions with Your Model
36.4.1
Predictions from new data
36.5
Choosing predictors for multivariate modeling – testing, dealing with collinearity
36.5.1
Challenges
36.6
presenting model results with RMarkdown
36.6.1
Challenges
36.7
presenting model results with a Shiny App
36.7.1
Challenges
37
Logistic Regression and Broom for Tidying Models
37.1
The Model Summary
37.2
Evaluating your Model Assumptions
37.3
Converting between logit, odds ratios, and probability
38
Fast and Frugal Trees with the {FFTrees} Package
38.1
Setup
38.2
The Breast Cancer Dataset
38.2.1
Data Inspection
38.2.2
Check Your Progress
38.3
Building a FFTrees Model for Breast Cancer
38.4
Your Turn with Heart Disease Data
38.4.1
Test what you have learned
38.5
Your Turn to Build and Interpret a Model
38.6
Now build your FFTrees model to predict improved status (vs. death)
39
A Gentle Introduction to Shiny
39.1
What is Shiny?
39.2
The Basic Structure of a Shiny App
39.2.1
The weirdness of a Shiny app
39.3
The User Interface Section Structure
39.4
The Server Section Structure
39.5
How to Run an App
39.5.1
How to Stop an App
39.6
Building a Very Simple App (Version 1)
39.6.1
The ui section
39.6.2
The server section
39.7
Edit this App (Version 2)
39.8
Building a User Interface for Inputs and Outputs
39.8.1
Inputs
39.8.2
Outputs
39.9
Building a Functioning Server Section
39.9.1
Using the input values & Data
39.9.2
Wrangling and Calculating
39.9.3
Rendering to HTML Outputs
39.10
Building a Simple Shiny App (Version 3)
39.11
Publishing Your Shiny App on the Web
39.12
More to Explore
40
Sharing Models with Shiny
40.0.1
Packages Needed for this Chapter
40.1
Setting up and Saving Models
40.1.1
Linear Model
40.1.2
Logistic Model
40.1.3
Random Forest Model
40.2
Building a Shiny App for the Linear Model
40.2.1
The Default Shiny App
40.2.2
Editing the
ui
sidebarPanel
for the Input Predictor Variables
40.2.3
Editing the
server
section to make Predictions
40.2.4
Editing the mainPanel in the ui section to display your Prediction
40.3
Building a Shiny App for the Logistic Model
40.3.1
The Default Shiny App
40.3.2
Editing the
ui
sidebarPanel
for the Input Predictor Variables
40.3.3
Editing the
server
section to make Predictions
40.3.4
Editing the mainPanel in the ui section to display your Prediction
40.4
Building a Shiny App for the Random Forest Model
40.5
Challenge Yourself
41
Introduction to R Markdown
41.1
What Makes an Rmarkdown document?
41.2
Trying out RMarkdown with a Mock Manuscript
41.3
Inserting Code Chunks
41.3.1
Code Chunk Icons
41.4
Including Plots
41.5
Including Tables
41.6
Including Links and Images
41.6.1
Links
41.6.2
Images
41.7
Other languages in code chunks
41.8
Code Chunk Options
41.9
How It All (Rmarkdown + {knitr} + Pandoc) Works
41.10
Knitting and Editing (and re-Knitting() Your Rmd document
41.11
Try Out Other Chunk Options
41.12
The
setup
chunk
41.13
Markdown syntax
41.14
2nd Header
41.14.1
3rd Header
41.15
Line Breaks and Page Breaks
41.16
Making Lists
41.16.1
Ordered Lists
41.16.2
Un-ordered lists
41.16.3
Nested Lists
41.17
The Easy Button - Visual Markdown Editing
41.17.1
Try inserting a list, a table and a block-quote
41.18
Inline Code
41.18.1
Try inserting some in-line R code
41.19
A Quick Quiz
42
Rmarkdown Output Options
42.1
Microsoft Word Output from Rmarkdown
42.1.1
Making a Styles Reference File for Microsoft Word
42.1.2
Let’s Practice This.
42.1.3
Re-formatting Your Template
42.1.4
Using Your New Styles Template
42.1.5
Now you are ready!
42.2
PDF Output from RMarkdown
42.2.1
LaTeX and tinytex
42.2.2
Knitting to PDF
42.3
Microsoft Powerpoint Output from Rmarkdown
42.3.1
Tables in Powerpoint
42.3.2
Images in Powerpoint
42.3.3
Plots in Powerpoint
43
Adding Citations to your RMarkdown
44
Quarto is a Next-Generation RMarkdown
44.1
Goals for this Chapter
44.2
Packages Needed for this chapter
44.3
Introducing Quarto
44.4
A Tour of Quarto
44.5
Opening a New Quarto Document
44.6
Annotating code in Quarto
44.7
The Visual Editor vs. Source Editor in Quarto
44.8
Adding Code Chunks
44.9
Organized Options in Code Chunks with the Hash-Pipe #|
44.10
Stating Global Options in Your YAML Header
44.10.1
Code Options and Code Folding
44.10.2
Parameters
44.11
Figures
44.12
Tables
44.13
Inline Code and Caching
44.14
Quarto at the Command Line
44.15
Citations in Quarto
44.16
Challenge Yourself
44.17
Exploring further
45
Running R from the UNIX Command Line
45.1
What is the UNIX Command line?
45.2
Why run R from the command line?
45.3
How do you get started?
45.3.1
On a Mac
45.3.2
On a Windows PC
45.4
The Yawning Blackness of the Terminal Window
45.5
Where Are We?
45.6
Cleaning Up
45.7
Other helpful file commands
45.8
What about R?
45.9
What about just a few lines of R?
45.10
Running an R Script from the Terminal
45.11
Rendering an Rmarkdown file from the Terminal
46
Secure Passwords in R
46.1
Setting New Keys
47
Dates and Times in R
47.1
Data Types for Dates and Times
47.2
Using POSIXlt
47.3
Formatting dates
47.3.1
Code Chunk Icons
47.4
Including Plots
47.5
Including Tables
47.6
Other languages in code chunks
47.7
Code Chunk Options
47.8
Try Out Other Chunk Options
47.9
The
setup
chunk
47.10
The Easy Button - Visual Markdown Editing
47.11
A Quick Quiz
48
Protecting PHI (Protected Health Information)
48.1
Protecting (Not Inadvertently Sharing) PHI
48.2
Identifying PHI
48.3
Selectively Deleting PHI
48.4
Problems with PHI-free data
48.5
Encrypting PHI
48.5.1
Generating Public and Private keys
48.6
Sharing synthetic data with {synthpop}
49
Building Data Pipelines with {targets}
49.1
What Does {targets} Do?
49.2
Air Quality Analysis
49.2.1
Prepping The Functions.R file
49.2.2
Checking Your Functions
49.2.3
Set Up the Pipeline
49.2.4
Pre-Build Checks
49.2.5
Building the Pipeline
49.2.6
Changing the Pipeline
49.3
Your Turn - A Tuberculosis Analysis Pipeline
49.3.1
Making new Functions
49.3.2
Testing functions
49.4
Resetting functions before the Pipeline is Built
49.4.1
Setting Up {targets}
49.5
Editing the
_targets.R
File
49.5.1
Running the Pipeline
49.5.2
Modificatons to the Pipeline
49.5.3
Modify the Plot
49.6
Next Steps
50
Colors and Scales in {ggplot2}
50.1
Goals for this Chapter
50.2
Colors in R and {ggplot2}
50.2.1
Using pre-defined color names
50.2.2
Using color hex codes
50.2.3
Screen vs. Print Colors
50.2.4
Transparency and hex colors
50.2.5
More obscure ways to select colors
50.2.6
Using color palettes
50.2.7
Color-blind friendly palettes
50.3
Sequential, Diverging, and Qualitative Palettes
50.4
Choosing Colors with Meaning
51
Using the {flowchart} package for CONSORT diagrams in R
51.1
Why Flowcharts?
51.2
Loading Libraries
51.3
A CONSORT Flowchart for Statins to Prevent HCC
51.4
Data
51.5
Branching
51.6
Splitting into Groups (Treatment Arms)
51.6.1
A Short Tangent on {ifelse}
51.7
Filtering for Completers
51.8
Modify exclusions for more detailed labels
51.8.1
Time for a Quiz
51.9
A More Complicated Study
51.10
Example
51.11
Branching
51.12
Splitting into Groups (Treatment Arms)
51.13
Filtering for Completers
51.14
Modify exclusions for more detailed labels
51.15
Next step
51.16
Now Add These Withdrawal Reasons
51.17
Your Turn
51.17.1
Fatigue Starting Box
51.17.2
Fatigue Branching fr=or Exclusions with fc_filter
51.17.3
Branching Fatigue
51.17.4
Fatigue Completers
51.17.5
Fatigue Text for Exclusions
51.17.6
Fatigue Add text for Exclusions
51.17.7
Withdrawal Reasons
51.17.8
Now add these to Your Flowchart with fc_modify
51.17.9
Final Fatigue Flowchart Drawing fatigue_fc6
51.17.10
Explore More Features
52
Using the {tabulapdf} package tp extract tables from PDFs
52.1
Why Tables from PDFs?
52.2
Extracting Tables from PDFs
52.3
Extracting Tables from PDFs
52.4
Extracting Tables from the PDF
52.5
Extracting a Specific Table
52.6
Extracting a Specific Table
52.7
Viewing and Targeting the PDF
52.8
Your Turn
52.8.1
Explore More Features
53
Using the {heemod} package to Evaluate Markov Models of Health Economic Strategies
53.1
Why Health Economic Evaluation and Markov Models?
53.2
Preparation for Modeling
53.3
Health States
53.4
Utilities
53.5
Transition Probabilities
53.6
Costs
53.6.1
Maintenance Health Care Costs
53.7
Cycle Time
53.8
Building Your Model and Calculating Outcomes
53.9
A First Model
53.10
The Transition State Matrix
53.11
Attach Values to States
53.12
Combining Transitions and Values into A Model
53.13
Run the Model
53.14
Your Turn
53.14.1
Explore More Features
53.14.2
Explore Health Economics Beyond {heemod}
54
Using your .Rprofile and .Renviron file and RStudio Code Snippets
54.1
Setting up your .Rprofile file
54.2
Setting up your .Renviron file
54.2.1
Explore REDCapTidieR and Quarto Dashboards
55
Using your .Rprofile and .Renviron file and RStudio Code Snippets
55.1
Setting up your .Rprofile file
55.2
Setting up your .Renviron file
55.2.1
Using RStudio Code Snippets
55.3
Code Folding and Sections in RStudio
55.3.1
Explore More
Title holder
References
Published with bookdown
Reproducible Medical Research with R
Chapter 32
try matching by GEOID (in us counties) = fips (health data)