TJ Mahr 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.

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)

library(ggplot2)

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

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)

# 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

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

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()
#> ************

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")

How I made the ordinal plot for this paper https://onlinelibrary.wiley.com/doi/10.1111/dmcn.15198

Local .Rprofile detected at C:\Users\Tristan\Documents\WiscRepos\2021-10-hl-vss\.Rprofile

library(tidyverse)

if (FALSE) {
  # Read in the models and make the tribbles for the demo
  draws_epred_means <- targets::tar_read("draws_epred_means")

	``` 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)