bbolker

download.file("https://fx.sauder.ubc.ca/etc/CADpages.pdf", dest = "CADpages.pdf")
library(tabulapdf)
xx <- extract_tables("CADpages.pdf")
txt <- xx[[1]][[1]][-(1:4)] |>
  ## glitches
  gsub(pattern = "[[:alpha:]]", replacement = "") |>
  gsub(pattern = "-0.\\s+", replacement = " 0.") |>
  gsub(pattern = "-",  replacement = "", fixed = TRUE)
txt <- txt[-length(txt)]
hdr <- strsplit(xx[[1]][[1]][4], " ")[[1]]

bbolker

## scraped from Edwards et al 2021 PNAS "Exhaled aerosol increases with COVID-19 infection, age, and obesity" doi: 10.1073/pnas.2021830118
library(ggplot2); theme_set(theme_bw())
dd <- read.table(header = TRUE, text = "
BMI_years exhaled_particles super_spreader
200.035325376  50.0311243148 n 
353.718561745  26.9914433584 n 
362.042435728  75.3702213313 n 
374.750834553  7.02162110504 n 
386.246395482  30.3918564146 n 
393.241199689  0.387986576661 n 

bbolker

## https://help.displayr.com/hc/en-us/articles/360003582875-How-to-Import-a-Wikipedia-Table-Using-R
library(rvest)
page = read_html("https://en.wikipedia.org/wiki/List_of_chemical_elements")
my.table = html_node(page, ".wikitable") |>
  html_table(fill = TRUE)
nm_vec <- my.table[[3]][-1]
vowels <- c("a","e","i","o","u")
consonants <- setdiff(letters, vowels)
cc <- function(x) paste(x, collapse = "")
str <- sprintf("^.*[%s]([%s]+[%s]*)$", cc(consonants), cc(vowels), cc(consonants))

bbolker

library(readxl)
library(dplyr)
library(ggplot2)
library(ragg)
## https://www.sthda.com/english/wiki/ggplot2-themes-and-background-colors-the-3-elements#google_vignette
theme_set(theme_classic(base_size = 16) +
            theme(panel.background = element_rect(fill = "lightgray")))


library(scales) # trans_new() is in the scales library

bbolker

## https://bsky.app/profile/benjiturnbull.bsky.social/post/3lpevi67z4c2w
library(lme4)
dd <- data.frame(f = factor(rep(1:3, each = 10)))
set.seed(105) ## seed-hack to get a singular fit ...
dd$y <- simulate(~1 + (1|f), newdata = dd,
                 newparams = list(beta = 0, sigma = 1, theta = 0.01))[[1]]
m <- lmer(y ~ 1 + (1|f), data = dd)
performance::icc(m) ## NA
multilevelTools::iccMixed("y", "f", data = dd) ## ICC(f) == 0
flexplot::icc(m)$icc ## 0

bbolker

library(glmmTMB)
set.seed(101)
dd <- data.frame(x = rnorm(1000))

dd$y0 <- simulate_new( ~ 1,
                     seed = 101,
                     ziformula = ~ 1,
                     family = ziGamma(link = "log"),
                     newdata = dd,
                     newparams = list(

bbolker

download.file("https://www.census.gov/foreign-trade/balance/country.xlsx", dest = "balance.xlsx")
library(readxl)
library(dplyr)
library(ggplot2); theme_set(theme_bw())
library(ggrepel)

bdat <- read_excel("balance.xlsx") |>
    filter(year == 2024) |>
    select(countrycode=CTY_CODE, country=CTYNAME,imports=IYR,exports=EYR)

bbolker

library(gstat)
library(lattice)

# Create Data Points (Random)
n <- 50
data3D <- data.frame(x = runif(n), y = runif(n), z = runif(n), v = rnorm(n))
coordinates(data3D) = ~x+y+z

# Create empty grid to krige
range1D <- seq(from = 0, to = 1, length = 20)

bbolker

## https://fediscience.org/@[email protected]/113439584737810431

library(MASS)   # for eqscplot
library(RTMB)

set.seed(101)
n <- 1000
X <- cbind(
  x1 = rnorm(n , 10, 2),
  x2 = rnorm(n, 7, 1),

bbolker

## simulate example (NOT satisfying constraints, but large and close enough that we
##  probably won't hit singular fits etc. etc.)

library(glmmTMB)
set.seed(1001)
nx <- 3
N <- 1000
ng <- 20
dd <- replicate(nx, rnorm(N), simplify = FALSE) |> as.data.frame() |> setNames(paste0("x", 1:nx))
dd$g <- factor(sample(1:ng, replace = TRUE, size = N))