dill

After asking on mastodon, I finally decided to get an 11" MacBook Air from ebay for £89. It arrived and worked!

It's not super fast: quad core i5 at 1.6GHz, 4Gb RAM, 128Gb SSD and a battery that lasted 4ish hours. Unfortunately, a UK keyboard. I decided to install Debian bookworm from a USB stick. With no fiddling at all, I had a new system installed within an hour (actually less but I was on a work call for the whole time and didn't see when it finished). I did review what was on the Debian wiki but this was pretty out-of-date.

During installation I chose to just go with the Xfce desktop environment, as I thought GNOME/KDE would be too much for an older machine like this one. More below on memory issues.

Things that didn't work out of the box:

dill

### which states of the lower 48 have cities named for them?
### David L Miller 2024

# get all the place names and their lat/long
# https://simplemaps.com/data/us-cities
places <- read.csv("uscities.csv")

library(stringr)
library(maps)
library(ggplot2)

dill

# generate a random phylogenetic tree, then apply multidimensional
# scaling to the distances and see what the 2D plot looks like

library(ape)

layout(matrix(1:12, 3, 4, byrow=TRUE))

for (i in 1:3){
  # generate a tree
  rr <- rtree(20)

dill

# example using an MRF for ordered categorical predictors

library(mgcv)


# simulate some data...
set.seed(2)
dat <- gamSim(1,n=400,dist="normal",scale=2)

# fit a simple model

dill

library(ggplot2)
library(emoGG)
library(readr)
library(tidyr)
library(dplyr)

# let's visualise the content of season 1 of Bob Ross' The Joy of Painting
dat <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/bob-ross/elements-by-episode.csv")

# just get what we need and select season 1

dill

dill

# find bad variable/function names in a package
library(stringr)

# retrieve args and functions exported by a package
get_pkgdat <- function(pkgname){

  contents <- objects(paste0("package:", pkgname))

  fns <- Filter(function(x) is.function(eval(parse(text=x))), contents)

dill

  # need to define half-normal point transect detection function pdf
  dhnpt <<- nimbleFunction(
      run = function(x = double(0), b0 = double(0), b1 = double(0),
                     covar = double(0), width = double(0),
                     log = integer(0, default = 0)) {
          returnType(double(0))

        # calculate scale parameter
        sigma <- exp(b0 + b1*covar)

dill

#' USFWS palette generator
#'
#' Palettes taken from https://www.instagram.com/p/CFP2H7JBSxR/
#'
#' @param n Number of colours desired (max 5!). If omitted, uses all colours.
#' @param name one of "sockeye", "coaster", "dolly" or "florida"
#' @param type Either "continuous" or "discrete". Use continuous if you want
#'   to automatically interpolate between colours.
#' @return A vector of colours.
#' @export

dill

# put all yr models in a list
model_list <- list(b_norm, b_term, b_term_sel)
# name them for nice labels
names(model_list) <- c("No selection", "Shrinkage smoother", "Extra penalty")
# all the terms (there's probably a gratia built-in for this)
term_list <- c("s(Depth)", "s(Bottom)", "s(Surface)")

library(gratia)

# pre-storage