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

**flextable**

Creates tables for HTML, PDF, Microsoft Word and PowerPoint documents from R Markdown

**gt**

Easily generate information-rich, publication-quality tables from R

**huxtable**

An R package to create styled tables in multiple output formats, with a friendly, modern interface.

**mmtable2**

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

**ggplot2**

An implementation of the Grammar of Graphics in R, and the most popular plotting package for static plots in R.

**viridisLite**

Colorblind-Friendly Color Maps for R

### Utilities

**tidyverse**

Set of packages with a common API for data manipulation and TLGs (includes dplyr, ggplot2, etc)

**diffdf**

The diffdf package is designed to enable detailed comparison of two data.frames.

**lintr**

Static code analysis for R that checks adherence to a given style, syntax errors and possible semantic issues

**styler**

Pretty-prints R code without changing the user's formatting intent

**renv**

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.