tjmahr

withr::local_temp_libpaths()
pak::pkg_install("ggplot2@3.5.2")
#> ℹ Loading metadata database✔ Loading metadata database ... done
#>  
#> → Will install 20 packages.
#> → Will download 18 CRAN packages (11.47 MB), cached: 2 (1.51 MB).
#> + cli            3.6.5  [dl] (1.40 MB)
#> + farver         2.1.2  [dl] (1.52 MB)
#> + ggplot2        3.5.2  [bld]

tjmahr

library(tidyverse)

TLDR: probability functions like pt() can provide log values so that you can avoid numerical problems. (This is why we sum log likelihoods instead of multiplying likelihoods.) So, we can identify the FLOATING-POINT BREAKERS of p-values if we stick to the log scale throughout the pipeline.

tjmahr

library(tidyverse)
# d <- readr::read_tsv("test.tsv") |>
#   janitor::clean_names()
#
# d$total <- d$biden + d$trump + d$other
# d$prop_votes_in <- d$ballots_accepted / d$total
# d$prop_biden <- d$biden / d$total
# datapasta::tribble_paste(d)

tjmahr

library(ggplot2)

geom_crossrange <- function(
    mapping = NULL,
    data = NULL,
    stat = "identity",
    position = "identity",
    ...,
    na.rm = FALSE,

tjmahr

print.file_check_log <- function(x, ...) str(x, ...)
as_file_check_log <- function(x) UseMethod("as_file_check_log")
as_file_check_log.default <- function(x) {
  structure(list(x = x, notes = list()), class = "file_check_log")
}

apply_to_file_check_log <- function(x, fn, ...) {
  results <- fn(x$x, ...)
  l <- list(results)

tjmahr

# Make a csv to read in
db1 <- DBI::dbConnect(duckdb::duckdb())
f <- tempfile("mtcars", fileext = ".csv")
write.csv(mtcars, f)

library(tidyverse)

# Read in the csv.
# Here two column types are hard-coded using a struct

tjmahr

Some of the rows have more columns than the first row of column names.

writeLines(
"a\tb\tc\td\te\tf
1\t2\t3\t4\t5\t6\t
1\t2\t3\t4\t5\t6\t7\t
1\t2\t3\t4\t5\t6\t7\t8
1\t2\t3\t4\t5\t6\t7\t8\t9\t10\t11
1\t2\t3\t4\t5\t6

tjmahr

``` r
A <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), nrow = 3, ncol = 4)
B <- matrix(c(13, 14, 15, 16, 17, 18, 19, 20), nrow = 4, ncol = 2)

dot_product <- function(x, y) {
  mapply(\(x, y) sum(x * y), x, y)
}

rows_of <- function(m) purrr::array_tree(m, 1)
cols_of <- function(m) purrr::array_tree(m, 2)

tjmahr

Local .Rprofile detected at C:\Users\mahr\Documents\WiscRepos\2021-04-kh-td-intel-and-rate\.Rprofile

library(faux)
#> 
#> ************
#> Welcome to faux. For support and examples visit:
#> https://debruine.github.io/faux/
#> - Get and set global package options with: faux_options()
#> ************

tjmahr

library(splines)
model <- lm(mpg ~ ns(wt, df = 3), mtcars)

# make and plot a basis
basis <- ns(sort(mtcars$wt), df = 3)
matplot(basis, type = "l")