R Workflow

R Workflow for Reproducible Data Analysis and Reporting


Department of Biostatistics
School of Medicine
Vanderbilt University


May 26, 2024

flowchart LR
R[R Workflow] --> Rformat[Report formatting]
Rformat --> Quarto[Quarto setup<br><br>Using metadata in<br>report output<br><br>Table and graph formatting]
R --> DI[Data import] --> Annot[Annotate data<br><br>View data dictionary<br>to assist coding]
R --> Do[Data overview] --> F[Observation filtration<br>Missing data patterns<br>Data about data]
R --> P[Data processing] --> DP[Recode<br>Transform<br>Reshape<br>Merge<br>Aggregate<br>Manipulate]
R --> Des[Descriptive statistics<br>Univariate or simple<br>stratification]
R --> An[Analysis<br>Stay close to data] --> DA[Descriptive<br><br>Avoid tables by using<br>nonparametric smoothers] & FA[Formal]
R --> CP[Caching<br>Parallel computing<br>Simulation]


This work is intended to foster best practices in reproducible data documentation and manipulation, statistical analysis, graphics, and reporting. It will enable the reader to efficiently produce attractive, readable, and reproducible research reports while keeping code concise and clear. Readers are also guided in choosing statistically efficient descriptive analyses that are consonant with the type of data being analyzed. The Statistical Thinking article R Workflow provides an overview of this book and includes some more motivation from the standpoint of doing good scientific research.Comments

Anyone who claims to be able to do good data science without coding is misleading you. Coding is one of the most valuable skills for data preparation and analysis, and it leads to personal efficiency, reproducibility, and maintainability. Learning how to write concise, elegant, debug-able code that generalizes to handle more complex tasks is not an insurmountable goal for anyone dealing with data, and R Workflow is intended to assist you in this regard.

The methods in R Workflow will be helpful to anyone who analyzes data, whether they work in business, marketing, manufacturing, journalism, finance, science, observational research, experimental research, and virtually any field needing to understand data. The book is best suited for those having at least rudimentary experience in running R commands, but 3  R Basics points readers to excellent resources for learning R from scratch. R can also be learned by starting with some standard analysis templates such as this in this Github repository.

The work also showcases RStudio’s Quarto which is a new standard for making beautiful and reproducible reports with R and other languages. This book also captures what I’ve learned in using R (and its precursor S) heavily in biomedical research and clinical trials since 1991. See my Statistical Thinking blog fharrell.com and resources at hbiostat.org for more.

The term “workflow” connotes a rigid step-by-step process of data processing and reporting. In one’s day-to-day usage of R, myriad needs arise, and much creativity is needed to get the most insights from data while writing reliable code that generates reproducible results. R Workflow will equip R users/analysts with a variety of powerful and flexible tools that will assist them in attacking a huge variety of problems and producing elegant reports while reducing the amount of coding required.

A video covering many parts of the first 13 chapters may be found here.

The general statistical analysis/inference companion to this book is Biostatistics for Biomedical Research which is a reproducible book with numerous examples of R code. For and in-depth text and course notes on reproducible regression modeling with R, including extensive case studies, see RMS.

Resources for Learning Quarto

The author wishes to thank the R Core team and R package developers along with RStudio for the free software they have developed that has revolutionized statistical computing, reporting, and reproducible research. Thanks to Titus von der Malsburg for careful reading of the text and for reporting numerous typographical and grammatical errors and a few programming errors. Thanks to Norm Matloff, University of California Davis, who provided big ideas to improve the preface and motivation for the book.

Date Sections Changes
2024-05-26 4  Report Formatting Added link to updated recommended general report template
2024-05-04 4.1.1 Annotating Simple Output New subsection for printing calculations in context when code is folded
2024-04-30 10.6.3 Turning a Frequency Table Into Raw Data New subsection on expanding a frequency table into raw data rows
2024-04-21 4.5 Multi-Output Format Reports New collapsed tab with considerations for collaborating with a Word user
2024-04-07 4.8 Advanced Tables That Render to Both HTML and Word New section of advanced tables that work with Word
2024-04-04 4.7 CSS More examples of colorizing text
2024-04-03 10.6.1 Directly Creating a Melted Data Table New data.table example: creating melted aggregate statistics
2024-02-24 9.1.1 Descriptive Graphics for Continuous Variables New examples using ggplot2 for spike histograms and ECDFs
2024-02-18 18.1 Data Table Approach New way to use data.table for simulations
2024-02-11 10.3.1 Adding Variables Depending on Other Variables Being Added New subsection showing how to add multiple new variables to a data table when the new variables depend on each other
2024-02-10 9.4 Multiple Longitudinal Continuous Variables New subsection on exploratory analysis of multiple longitudinal variables
2024-01-07 5.7.1 Depositing Files on REDCap New subsection with R code for automatic file deposit into REDCap file repository
2024-01-07 5.2 Importing and Creating Annotated Analysis Files Re-wrote REDCap API section for latest REDCap R API
2024-01-02 5.7 Secure File Storage and Transmission Showed how to mix interacting and batch processing so passwords will work
2023-10-28 10.2 Adding Aggregate Statistics to Raw Data New subsection showing how to add aggregate summaries to raw data
2023-10-23 10  Data Manipulation and Aggregation Added examples of data table containing lists
2023-10-20 13.2.3 Linear Interpolation to a Vector of Times New longitudinal example on linear interpolation/extrapolation on regularized measurement times
2023-09-16 18.2 Array Approach More array-style simulation examples
2023-07-30 4.3 Quarto Built-in Syntax for Enhancing R Output Mention tabsets, collapsible text, and tricks
2023-07-10 10.3 Adding, Changing, and Removing Variables Added data.table::setcolorder
2023-07-19 3.9 Functions Listed data.table set functions
2023-07-12 5.7 Secure File Storage and Transmission New section on protecting sensitive files
2023-05-11 3.12 Resources for Learning R Added info about learning by running scripts from Github
2023-05-06 11.3 Customizing Tables of Summary Statistics New section on customizing summary statistic tables using gt
2023-04-28 14.2 R Graphics Devices, 14.3 ggplot2 New section on graphical devices, added ggplot2 themes and fonts
2023-04-20 13.2.1 Summarizing Multiple Baseline Measurements New subsection on adding summary statistics to a longitudinal dataset
2023-04-09 4.6.1 gt Package New subsection on gt package
2023-04-08 2.10 Univariate and Bivariate Descriptions, 9  Descriptive Statistics Switched to new describe function output
2023-04-02 4.10 Mixing Graphics and Tables New section on mixing graphics and tables
2023-04-02 1.2 Installing R and RStudio New small subsection with links to installing R and RStudio
2023-03-29 3  R Basics Several new language features covered
2023-03-29 13.2.2 Interpolation/Extrapolation to a Specific Time New subsection on interpolating longitudinal data to a target time
2023-03-26 18.1 Data Table Approach New subsection showing simulation using lapply and rbindlist
2023-03-26 14.3 ggplot2 Example of plotting in a for-loop, and math expressions in caption
2023-03-24 3.11 Interactively Writing and Debugging R Code New section on interactive code writing
2023-03-16 5.2 Importing and Creating Annotated Analysis Files Added description of new features of cleanupREDCap
2023-03-13 10  Data Manipulation and Aggregation New example of data.table by-reference using a list of data tables
2023-03-05 14.3 ggplot2 Added ggplot2 ECDF example, with math rendering; added plotting of ECDFs with different transformations, and labeling with math notation
2023-02-28 10.4 Recoding Variables Examples added for combine.levels
2023-02-26 5.2 Importing and Creating Annotated Analysis Files New csv.get example, expanded Excel, added General tab which discusses the rio package
2023-02-25 10.7 Computing Total Scale Scores in Presence of NAs New section on computing total scores with simple imputation
2023-02-24 10.6.2 Restructuring Multiple Independent Variables New reshaping example
2023-02-23 5.2 Importing and Creating Annotated Analysis Files Added description of new features in importREDCap
2023-02-18 Many Updated chapter to use Hmisc 5.0 and the pre-release of the new qreport package and dropping use of reptools and movStats from Github. Made use of new Hmisc easy labeling functions hlab, hlabs, vlab.
2023-02-08 2  Case Study: The Titanic Changed rendering of html for contents and describe in anticipation of Hmisc 4.8-0
2023-01-19 14.3 ggplot2 Added how to plot transformed axes
2023-01-16 14.3 ggplot2 Added simpler way to pull labels and units for plotting
2022-12-17 3.9.1 Character Manipulation Functions New subsection on character manipulation functions
2022-12-15 10.8 Text Analysis New subsection on text analysis
2022-12-11 4.9 Diagrams New section on graphviz for diagrams
2022-12-04 3.3 Dates and Time New section on dates and date/times
2022-12-03 14  Graphics, 14.1 Recommended Graphics by Data Types Linked to hex binning example and added new section
2022-12-03 Replaced length(unique(x)) with uniqueN(x) everywhere
2022-11-29 12.2 Non-equi Joins: Closest Matches New rolling join (closest match) example
2022-11-22 10.3 Adding, Changing, and Removing Variables Added let alias for := in data.table
2022-11-09 5.2 Importing and Creating Annotated Analysis Files Discussed multDataOverview function to summarize a list of datasets
2022-11-07 10.4.1 Recoding From an Expression File New section showing how to specify derived variable formulas in a separate file
2022-11-05 5.6 Efficient Storage and Retrieval With qs New section for qs package for object storage
2022-11-05 5.2 Importing and Creating Annotated Analysis Files Much new material on REDCap
2022-11-05 10.3 Adding, Changing, and Removing Variables Examples of in-place data.table changes of variables named in a separate vector
2022-11-01 12.1 Lookup Participant Disposition New subsection with example on looking up participant disposition for multiple clinical trials
2022-10-24 12.2 Non-equi Joins: Closest Matches New subsection on merging with closest matches
2022-10-22 3.9.3 Conditional Function Definition Trick New subsection on conditional function definitions
2022-10-22 3.4 Logical Operators New section on logical operators
2022-10-22 3.6 Subscripting Added more subscripting examples
2022-10-17 10.9 Fast Lookup from Disk Added direct retrieval fst example where row numbers are looked up
2022-10-16 5.5 Efficient Storage and Retrieval With fst Added fst package as alternative to saveRDS
2022-09-24 Preface Link to YouTube video
2022-09-21 14  Graphics, 3.12 Resources for Learning R New links to Irizarry book
2022-09-09 3.12 Resources for Learning R New resources for learning R
2022-08-28 4.7 CSS New section on styling html with css
2022-08-24 3.10 R Formula Language New section on stat model formula language
2022-08-19 14  Graphics Added how to use group= in ggplot2
2022-08-15 3.2 Object Types Added material about R object naming
2022-08-15 Preface Added links to resources for learning Quarto
2022-08-14 2  Case Study: The Titanic Introduce the packages used (thanks to Tom Philips)
2022-07-17 6  Missing Data, 8  Data Overview Moved overall missing data summary to missChk
2022-07-11 14  Graphics New introductory text and references copied from BBR Chapter 4
2022-07-10 2  Case Study: The Titanic New chapter with a case study of methods used in the book (thanks to Norm Matloff)
2022-07-08 11.1 Summary Statistics Using Functions Returning Two-Dimensional Results New section on using data.table with summarization functions that return two-dimensional results
2022-07-07 15  Analysis, 15.7 Third-Order Descriptive Analysis New chart about 1st, 2nd, 3rd order analysis; new section with example of 3rd order
2022-07-06 13  Manipulation of Longitudinal Data, 10  Data Manipulation and Aggregation Re-wrote intro to chapter, added LOCF example, added data table examples using %like%
2022-07-05 10.3 Adding, Changing, and Removing Variables, 10  Data Manipulation and Aggregation Renamed section and added more about removing columns; added link to data.table vignettes
2022-07-04 10.5 Operations on Multiple Data Tables New section on operations on multiple data tables
2022-07-03 10  Data Manipulation and Aggregation New diagram to explain data tables
2022-06-30 Preface Better wording (thanks to Norm Matloff)
2022-06-28 Added Flow diagrams at the start of chapters (thanks to Norm Matloff)
2022-06-27 4.6 HTML Tables New section on making html tables
2022-06-27 3.9 Functions, 3.2 Object Types, 3.5 Missing Values, 4  Report Formatting Added more basic R functions, arrays NAs, how to make knitr use plain text printing of objects such as data frame/tables
2022-06-26 Preface Clarified goals and audience (thanks to Norm Matloff)
2022-06-26 Fixed various typographical errors (thanks to Titus von der Malsburg)
2022-06-15 Published