kmiddleton

Modern R Development Guide

This document captures current best practices for R development, emphasizing modern tidyverse patterns, performance, and style. Last updated: August 2025

Core Principles

1. Use modern tidyverse patterns - Prioritize dplyr 1.1+ features, native pipe, and current APIs
2. Profile before optimizing - Use profvis and bench to identify real bottlenecks
3. Write readable code first - Optimize only when necessary and after profiling
4. Follow tidyverse style guide - Consistent naming, spacing, and structure

kmiddleton

#' Convert geomorph 3D array to edma_data class
#'
#' @param A 2D or 3D array of landmarks
#' @param name Character: Name for data set
#' @param IDs Vector of IDs
#'
#' @return Object of class "edma_data"
#' @export
#'
#' @examples 

kmiddleton

ssPlot <- function(X, Y, b, do.plot = TRUE, do.labels = TRUE){
  n <- length(X)
  pred <- (X * b + (mean(Y) - b * mean(X)))
  SSy <- sum((Y - pred) ^ 2)

  M <- tibble(X, Y, pred)

  if (do.plot) {
    p <- ggplot() +
      geom_point(data = tibble(X = mean(X), Y = mean(Y)),

kmiddleton

shade_normal <- function(q, tail = "both", mean = 0, sd = 1) {
  require(tidyverse, quietly = TRUE)
  require(cowplot)
  crit <- qnorm(q, mean, sd)
  M <- tibble(x = seq(-4, 4, length = 200),
                  y = dnorm(x))
  p <- ggplot(M, aes(x, y)) +
    geom_line() +
    labs(x = "Value", y = "Relative Likelihood")
  lower <- geom_ribbon(data = subset(M, x < crit),

kmiddleton

library(tidyverse)
library(cowplot)

if (Sys.info()['sysname'] == "Darwin") {
  suff <- ".pdf"
} else {
  suff <- ".wmf"
}

base_size <- 36

kmiddleton

library(ape)
library(nlme)
library(geiger)

set.seed(3879742)

tree <- sim.bdtree(b=1, d=0.2, stop=c("taxa", "time"), n=10, t=4, seed=0, extinct=TRUE)
plot(tree)

y <- sim.char(tree, 0.02, 1)[, , 1]