RMRWR
1
Preface
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
Hashtag Debugging
14.6
Pipe 2
14.7
Plot 2
14.8
Plot3
14.9
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
22
Simple example of a t-test
22.1
Common Problem
22.1.1
How Skewed is Too Skewed?
22.1.2
Visualize the Distribution of data variables in ggplot
22.1.3
Visualize the Distribution of data$len in ggplot
22.1.4
Results of Shapiro-Wilk
22.1.5
Try it yourself
22.1.6
Mammal sleep hours
22.2
One Sample T test
22.2.1
How to do One Sample T test
22.2.2
Interpreting the One Sample T test
22.2.3
What are the arguments of the t.test function?
22.3
Insert flipbook for ttest here
22.3.1
Flipbook Time!
22.4
Fine, but what about 2 groups?
22.4.1
Setting up 2 group t test
22.4.2
Results of the 2 group t test
22.4.3
Interpreting the 2 group t test
22.4.4
2 group t test with wide data
22.4.5
Results of 2 group t test with wide data
22.5
3 Assumptions of Student’s t test
22.5.1
Testing Assumptions of Student’s t test
22.6
Getting results out of t.test
22.6.1
Getting results out of t.test
22.7
Reporting the results from t.test using inline code
22.7.1
For Next Time
23
Sample Size Calculations with
{pwr}
23.1
Sample Size for a Continuous Endpoint (t-test)
23.2
One Sample t-test for Lowering Creatinine
23.3
Paired t-tests (before vs after, or truly paired)
23.4
2 Sample t tests with Unequal Study Arm Sizes
23.5
Testing Multiple Options and Plotting Results
23.6
Your Turn
23.6.1
Scenario 1: FEV1 in COPD
23.6.2
Scenario 2: BNP in CHF
23.6.3
Scenario 3: Barthel Index in Stroke
23.7
Sample Sizes for Proportions
23.8
Sample size for two proportions, equal n
23.9
Sample size for two proportions, unequal arms
23.10
Your Turn
23.10.1
Scenario 1: Mortality on Renal Dialysis
23.10.2
Scenario 2: Intestinal anastomosis in Crohn’s disease
23.10.3
Scenario 3: Metformin in Donuts
23.11
add chi square
23.12
add correlation test
23.13
add anova
23.14
add linear model
23.15
add note on guessing effect sizes - cohen small, medium, large
23.16
Explore More
24
More dplyr
24.0.1
Rename
24.0.2
Re-arrange your variables/columns
24.0.3
Find distinct rows
24.0.4
Select a group of rows with slice()
24.0.5
Randomly sample some rows with sample_n() or sample_frac()
25
Randomization for Clinical Trials with R
25.1
Printing these on Cards
25.2
Now, try this yourself
25.3
Now Freestyle
26
Univariate ggplots to Visualize Distributions
26.1
Histograms
26.1.1
Comparisons of Distributions with Histograms
26.1.2
Histograms and Categories
26.2
Density Plots
26.2.1
Comparisons with Density plots
26.3
Comparing Distributions Across Categories
26.4
Boxplots
26.5
Violin Plots
26.6
Ridgeline Plots
26.6.1
Including Plots
26.6.2
Including Points
26.6.3
Including Points
26.6.4
Including Points
26.6.5
Including Points
27
Bivariate ggplot2 Scatterplots to Visualize Relationships Between Variables
27.1
Packages used in this Chapter
27.2
Data Exploration and Validation (DEV)
27.3
Scatterplots
27.3.1
Micro-quiz!
27.4
Mapping More Variables
27.5
Inheritance and Layering in ggplot2
27.6
Aesthetic mapping Micro-Quiz!
27.7
Controlling Point Shape, Size, and Color Manually
27.7.1
Manual Shapes
27.7.2
Manual Sizes
27.7.3
Manual Color
28
Extensions to ggplot
28.1
Goals for this Chapter
28.2
Packages Needed for this chapter
28.3
A Flipbook of Where We Are Going With ggplot Extensions
28.3.1
MAKE FLIPBOOK
28.4
A Waffle Plot
28.5
An Alluvial Plot
28.6
Lollipop Plots
28.7
Dumbbell Plots
28.8
Spaghetti Plots with Summary Smoothed Lines for Change Over Time
28.9
Swimmer Plots
28.10
Adding Significance Comparisons with {ggsignif}
29
Customizing Plot Scales
29.1
Goals for this Chapter
29.2
Packages Needed for this chapter
29.3
A Flipbook of Where We Are Going With Scales
29.4
A Basic Scatterplot
29.5
But what if you want the scale for risk to start at 0?
29.6
But this axis does not really start at Exactly 0
29.7
Control the Limits and the Breaks
29.8
Test what you have learned
29.9
Continuous vs. Discrete Plots and Scales
29.10
Using Scales to Customize a Legend
29.11
Test what you have learned
29.11.1
More Examples with Flipbooks
30
Helping out with ggplot
30.1
ggx::gghelp()
30.2
Getting more help with theming with ggThemeAssist
30.3
Website helpers for ggplot
30.4
Getting Even more help with esquisse
31
Functions
31.1
Don’t repeat yourself
31.2
Your Turn
31.3
Freestyle
31.3.1
Acknowledgement
31.4
Read More
32
Using Found (Web) Data
32.1
Found Poetry
32.2
Found Data
32.3
Download Example
32.4
Datapasta (small table) Example
32.5
Your Turn
32.6
{rvest} Example
32.7
Your Turn
32.8
API example with {tidycensus}
32.9
Challenges
32.10
Advanced Challenge - Dynamic Websites
33
Linear Regression and Broom for Tidying Models
33.1
Packages needed
33.2
Building a simple base model with {lm}
33.2.1
Producing manuscript-quality tables with {gtsummary}
33.3
Is Your Model Valid?
33.4
Making Predictions with Your Model
33.4.1
Predictions from new data
33.5
Choosing predictors for multivariate modeling – testing, dealing with collinearity
33.5.1
Challenges
33.6
presenting model results with RMarkdown
33.6.1
Challenges
33.7
presenting model results with a Shiny App
33.7.1
Challenges
34
Logistic Regression and Broom for Tidying Models
34.1
The Model Summary
34.2
Evaluating your Model Assumptions
34.3
Converting between logit, odds ratios, and probability
35
Fast and Frugal Trees with the {FFTrees} Package
35.1
Setup
35.2
The Breast Cancer Dataset
35.2.1
Data Inspection
35.2.2
Check Your Progress
35.3
Building a FFTrees Model for Breast Cancer
35.4
Your Turn with Heart Disease Data
35.4.1
Test what you have learned
35.5
Your Turn to Build and Interpret a Model
35.6
Now build your FFTrees model to predict improved status (vs. death)
36
A Gentle Introduction to Shiny
36.1
What is Shiny?
36.2
The Basic Structure of a Shiny App
36.2.1
The weirdness of a Shiny app
36.3
The User Interface Section Structure
36.4
The Server Section Structure
36.5
How to Run an App
36.5.1
How to Stop an App
36.6
Building a Very Simple App (Version 1)
36.6.1
The ui section
36.6.2
The server section
36.7
Edit this App (Version 2)
36.8
Building a User Interface for Inputs and Outputs
36.8.1
Inputs
36.8.2
Outputs
36.9
Building a Functioning Server Section
36.9.1
Using the input values & Data
36.9.2
Wrangling and Calculating
36.9.3
Rendering to HTML Outputs
36.10
Building a Simple Shiny App (Version 3)
36.11
Publishing Your Shiny App on the Web
36.12
More to Explore
37
Sharing Models with Shiny
37.0.1
Packages Needed for this Chapter
37.1
Setting up and Saving Models
37.1.1
Linear Model
37.1.2
Logistic Model
37.1.3
Random Forest Model
37.2
Building a Shiny App for the Linear Model
37.2.1
The Default Shiny App
37.2.2
Editing the
ui
sidebarPanel
for the Input Predictor Variables
37.2.3
Editing the
server
section to make Predictions
37.2.4
Editing the mainPanel in the ui section to display your Prediction
37.3
Building a Shiny App for the Logistic Model
37.3.1
The Default Shiny App
37.3.2
Editing the
ui
sidebarPanel
for the Input Predictor Variables
37.3.3
Editing the
server
section to make Predictions
37.3.4
Editing the mainPanel in the ui section to display your Prediction
37.4
Building a Shiny App for the Random Forest Model
37.5
Challenge Yourself
38
Introduction to R Markdown
38.1
What Makes an Rmarkdown document?
38.2
Trying out RMarkdown with a Mock Manuscript
38.3
Inserting Code Chunks
38.3.1
Code Chunk Icons
38.4
Including Plots
38.5
Including Tables
38.6
Including Links and Images
38.6.1
Links
38.6.2
Images
38.7
Other languages in code chunks
38.8
Code Chunk Options
38.9
How It All (Rmarkdown + {knitr} + Pandoc) Works
38.10
Knitting and Editing (and re-Knitting() Your Rmd document
38.11
Try Out Other Chunk Options
38.12
The
setup
chunk
38.13
Markdown syntax
38.14
2nd Header
38.14.1
3rd Header
38.15
Line Breaks and Page Breaks
38.16
Making Lists
38.16.1
Ordered Lists
38.16.2
Un-ordered lists
38.16.3
Nested Lists
38.17
The Easy Button - Visual Markdown Editing
38.17.1
Try inserting a list, a table and a block-quote
38.18
Inline Code
38.18.1
Try inserting some in-line R code
38.19
A Quick Quiz
39
Rmarkdown Output Options
39.1
Microsoft Word Output from Rmarkdown
39.1.1
Making a Styles Reference File for Microsoft Word
39.1.2
Let’s Practice This.
39.1.3
Re-formatting Your Template
39.1.4
Using Your New Styles Template
39.1.5
Now you are ready!
39.2
PDF Output from RMarkdown
39.2.1
LaTeX and tinytex
39.2.2
Knitting to PDF
39.3
Microsoft Powerpoint Output from Rmarkdown
39.3.1
Tables in Powerpoint
39.3.2
Images in Powerpoint
39.3.3
Plots in Powerpoint
40
Adding Citations to your RMarkdown
41
Quarto is a Next-Generation RMarkdown
41.1
Goals for this Chapter
41.2
Packages Needed for this chapter
41.3
Introducing Quarto
41.4
A Tour of Quarto
41.5
Opening a New Quarto Document
41.6
Annotating code in Quarto
41.7
The Visual Editor vs. Source Editor in Quarto
41.8
Adding Code Chunks
41.9
Organized Options in Code Chunks with the Hash-Pipe #|
41.10
Stating Global Options in Your YAML Header
41.10.1
Code Options and Code Folding
41.10.2
Parameters
41.11
Figures
41.12
Tables
41.13
Inline Code and Caching
41.14
Quarto at the Command Line
41.15
Citations in Quarto
41.16
Challenge Yourself
41.17
Exploring further
42
Running R from the UNIX Command Line
42.1
What is the UNIX Command line?
42.2
Why run R from the command line?
42.3
How do you get started?
42.3.1
On a Mac
42.3.2
On a Windows PC
42.4
The Yawning Blackness of the Terminal Window
42.5
Where Are We?
42.6
Cleaning Up
42.7
Other helpful file commands
42.8
What about R?
42.9
What about just a few lines of R?
42.10
Running an R Script from the Terminal
42.11
Rendering an Rmarkdown file from the Terminal
43
Secure Passwords in R
43.1
Setting New Keys
44
Dates and Times in R
44.1
Data Types for Dates and Times
44.2
Using POSIXlt
44.3
Formatting dates
44.3.1
Code Chunk Icons
44.4
Including Plots
44.5
Including Tables
44.6
Other languages in code chunks
44.7
Code Chunk Options
44.8
Try Out Other Chunk Options
44.9
The
setup
chunk
44.10
The Easy Button - Visual Markdown Editing
44.11
A Quick Quiz
45
Protecting PHI (Protected Health Information)
45.1
Protecting (Not Inadvertently Sharing) PHI
45.2
Identifying PHI
45.3
Selectively Deleting PHI
45.4
Problems with PHI-free data
45.5
Encrypting PHI
45.5.1
Generating Public and Private keys
45.6
Sharing synthetic data with {synthpop}
46
Building Data Pipelines with {targets}
46.1
What Does {targets} Do?
46.2
Air Quality Analysis
46.2.1
Prepping The Functions.R file
46.2.2
Checking Your Functions
46.2.3
Set Up the Pipeline
46.2.4
Pre-Build Checks
46.2.5
Changing the Pipeline
46.3
Your Turn - A Tuberculosis Analysis Pipeline
46.3.1
Making new Functions
46.3.2
Testing functions
46.4
Resetting functions before the Pipeline is Built
46.4.1
Setting Up {targets}
46.5
Editing the
_targets.R
File
46.5.1
Running the Pipeline
46.5.2
Modificatons to the Pipeline
46.5.3
Modify the Plot
46.6
Next Steps
47
Colors and Scales in {ggplot2}
47.1
Goals for this Chapter
47.2
Colors in R and {ggplot2}
47.2.1
Using pre-defined color names
47.2.2
Using color hex codes
47.2.3
Screen vs. Print Colors
47.2.4
Transparency and hex colors
47.2.5
More obscure ways to select colors
47.2.6
Using color palettes
47.2.7
Color-blind friendly palettes
47.3
Sequential, Diverging, and Qualitative Palettes
47.4
Choosing Colors with Meaning
48
Using the {tabulapdf} package tp extract tables from PDFs
48.1
Why Tables from PDFs?
48.2
Extracting Tables from PDFs
48.3
Extracting Tables from PDFs
48.4
Extracting Tables from the PDF
48.5
Extracting a Specific Table
48.6
Extracting a Specific Table
48.7
Viewing and Targeting the PDF
48.8
Your Turn
48.8.1
Explore More Features
49
Using the {heemod} package to Evaluate Markov Models of Health Economic Strategies
49.1
Why Health Economic Evaluation and Markov Models?
49.2
Preparation for Modeling
49.3
Health States
49.4
Utilities
49.5
Transition Probabilities
49.6
Costs
49.6.1
Maintenance Health Care Costs
49.7
Cycle Time
49.8
Building Your Model and Calculating Outcomes
49.9
A First Model
49.10
The Transition State Matrix
49.11
Attach Values to States
49.12
Combining Transitions and Values into A Model
49.13
Run the Model
49.14
Your Turn
49.14.1
Explore More Features
49.14.2
Explore Health Economics Beyond {heemod}
50
Using your .Rprofile and .Renviron file and RStudio Code Snippets
50.1
Setting up your .Rprofile file
50.2
Setting up your .Renviron file
50.2.1
Explore REDCapTidieR and Quarto Dashboards
51
Using your .Rprofile and .Renviron file and RStudio Code Snippets
51.1
Setting up your .Rprofile file
51.2
Setting up your .Renviron file
51.2.1
Using RStudio Code Snippets
51.3
Code Folding and Sections in RStudio
51.3.1
Explore More
52
Data Exploration and Validation with the {pointblank} Package
52.1
Goals for this Chapter
52.2
Packages needed for this Chapter
52.3
Pathway for this Chapter
52.4
Getting Started with the {pointblank} package
52.5
Starting with a Quick Scan of Your Table.
52.6
Creating an Agent
52.7
Examples of Validation Rules
52.8
Adding Validation Rules
52.9
Interrogating the Data with Your Agent
52.10
Documenting and Fixing Data Issues
52.11
Setting action Levels
52.12
Try it Yourself
52.13
Further Challenges
52.14
Additional Resources
Title holder
References
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Reproducible Medical Research with R
References