bschneidr

import polars as pl
import pandas as pd

# Read input data as a data frame with two columns
input = pl.from_pandas(
  pd.read_table("day-1/input", sep = "   ", header = None, 
                names = ["list_1", "list_2"])
)

# Sort each column separately, 

bschneidr

read.table("day-1/input", col.names = c("list_1", "list_2")) |>
  apply(MARGIN = 2, sort) |>
  apply(MARGIN = 1, FUN = \(x) abs(x[2] - x[1])) |>
  sum()

bschneidr

sudo apt-get install libopenblas-dev
sudo apt-get install liblapacke-dev
sudo apt-get install liblapacke
sudo apt-get install libopenblas-serial-dev
sudo apt-get install libopenblas0-serial
sudo apt-get install libopenblas0

bschneidr

library(dplyr)
library(tidyr)
library(survey)

# Load example data
# 'apipop' has one row per school

  data('api', package = 'survey')

# Draw a sample of school districts

bschneidr

library(dplyr)
library(broom)
library(tidyr)

# Make example data
  my_data <- data.frame(x = sample(1:5, size = 10, replace = TRUE),
                        y = sample(1:5, size = 10, replace = TRUE))

# Generate every pair of variables
  var_list <- colnames(my_data)

bschneidr

describe_design(
  srs_stage(id = "PSU_ID", method = "SRS",
            stratum = "PSU_STRATUM_ID"),
  nonresponse_stage(response_indicator = "PSU_RESPONDENT"),
  pps_stage(id = "SSU_ID", method = "PPS",
            stratum = "SSU_STRATUM_ID"),
  nonresponse_stage(response_indicator = "SSU_RESPONDENT")
)

bschneidr

n = 5

# Establish quadratic form

quad_form_matrix <- matrix(nrow = n, ncol = n)
for (i in seq(n)) {
  for (j in seq(n)) {
    if (i == j) {
      quad_form_matrix[i,j] <- (1/n)
    } else {

bschneidr

#' @title Wilson's confidence interval for complex survey designs
#' @description Calculate Wilson's confidence interval for a proportion,
#' with the effective sample size determined using a design-unbiased
#' estimate of the complex survey design effect.
#'
#' @param x A formula, vector, or matrix.
#' @param design A survey.design or svyrep.design object
#' @param na.rm Should cases with missing values be dropped?
#' @param level The confidence level required
#' @param ... Additional arguments to pass on to \code{svymean()}

bschneidr

#' @title Wilson's confidence interval for complex survey designs
#' @description Calculate Wilson's confidence interval for a proportion,
#' with the effective sample size determined using a design-unbiased
#' estimate of the complex survey design effect.
#'
#' @param x A formula, vector, or matrix.
#' @param design A survey.design or svyrep.design object
#' @param na.rm Should cases with missing values be dropped?
#' @param level The confidence level required
#'

bschneidr

suppressPackageStartupMessages({
  library(survey)
  library(svrep)
  library(polyapost)
})

set.seed(1999)

# Load example survey data ----
  data("lou_vax_survey", package = 'svrep')