This list covers the wider purpose R packages that we have found most useful and those that most commonly form dependencies to the pharma-specific packages.
Another useful reference is this document from the R Foundation titled Regulatory Compliance and Validation Issues - A Guidance Document for the Use of R in Regulated Clinical Trial Environments, which shows a list of base and recommended R packages.
Tables
There is a current industry working group ongoing effort around tables and the advantages of different packages here, which will result in a paper. In general, the problem is best divided into the following categories:
Table Creation
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Creates tables for HTML, PDF, Microsoft Word and PowerPoint documents from R Markdown
Easily generate information-rich, publication-quality tables from R
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An R package to create styled tables in multiple output formats, with a friendly, modern interface.
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Create and combine tables with a ggplot2/patchwork syntax.
Formatting and Rendering
There are different solutions already available, but during R/Pharma there was an announcement of the RStudio package tgen to solve the rendering problems consistently.
Graphs
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An implementation of the Grammar of Graphics in R, and the most popular plotting package for static plots in R.
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Colorblind-Friendly Color Maps for R
Utilities
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Set of packages with a common API for data manipulation and TLGs (includes dplyr, ggplot2, etc)
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Static code analysis for R that checks adherence to a given style, syntax errors and possible semantic issues
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Pretty-prints R code without changing the user's formatting intent
Helps you create reproducible environments for your R projects
Statistical packages
It would be impossible to capture all the relevant statistical packages, as there are potentially thousands of potential methods that could be applied in a study. Below we include a handful that are likely to be used in the majority of trial reporting events, and refer to the Cran Task Views (CTVs) that attempt to maintain a landscape of available packages other several important domains.
stats - Base R package with a lot of functionality useful for design and analysis of clinical trials
survival - Core survival analysis routines
car - R companion to applied regression
emmeans - Estimated marginal means, aka least-squares means
mmrm - Mixed models for repeated measures (link)
lme4 - Fit linear and generalized linear mixed-effects models
lmerTest - Tests in linear mixed effects models
broom - takes the messy output of built-in functions in R and turns them into tidy tibbles
VGAM - Vector generalized linear and additive models
It is also worth attracting attention here to the wider CTVs across several pertinent topics, such as:
Clinical Trials, Survival, Bayes, Missing data, and PK.
As you can appreciate with R such an ever-evolving language we would never be able to include and maintain a complete list of all recommended packages here, but we hope that this page helps to introduce to some of the most common packages used both within and beyond pharma.