bschneidr

# Set the parameters which are beyond the player's control
  prob_win <- 0.6
  alpha <- 1000000L
  beta <- 10000L

# List the possible choices available to the player
  minimum_games <- 1L:50L

# List the series lengths which result from each available choice
# and, for each choice, list the corresponding probability of winning the series

bschneidr

example_strings <- c("Heres the sentince", "Here's anothr")

highlight_bad_text <- function(strings, match = NA) {
  
  bad_text <- hunspell::hunspell(text = as.character(strings),
                                 format = "text",
                                 dict = hunspell::dictionary("en_us"),
                                 ignore = hunspell::en_stats)
  names(bad_text) <- names(strings)

bschneidr

g_statistic_lr_test <- function(x) {
  
  chisq_results <- chisq.test(x)
  
  expected_counts <- chisq_results[['expected']]
  observed_counts <- chisq_results[['observed']]
  
  G_Statistic <- 2 * sum(observed_counts * log(observed_counts/expected_counts),
                         na.rm = TRUE)
  

bschneidr

library(magrittr)
library(dplyr)
library(tidyr)
library(stringr)

library(readr)

# Import the race/hispanic-origin ACS data downloaded through American Fact-Finder ----
b03002_data <- read_csv(file = "Data/ACS/ZCTA/2013-2017/Raw/B03002/ACS_17_5YR_B03002_with_ann.csv",
                        skip = 1L,

bschneidr

#' @title Extract ggplot2 plots from a list
#' @description Takes a list (potentially containing sublists) and extracts all of the ggplot2 'plot-type' objects from that list into a simple list of 'plot-type' objects.
#' @param x A list object, potentially containing sublists.
#' @return Returns a 'flat', single-level list of all the ggplot2 'plot-type' objects from within `x`, reaching recursively into sub-lists as needed. If there are no 'plot-type' objects, returns an empty list.
#' @note Whether an object is a ggplot2 'plot-type' object is defined here as an object with classes 'gg', 'gTree', or 'gtable'.
#' @export
#' @examples
#' 
#' library(ggplot2)
#' 

bschneidr

library(ggplot2)

# This function will udpate the ggplot2 print method used in the session
  wrap_labels_in_print_method <- function (dev_scaler = 12) {
    if (!"ggplot2" %in% .packages()) {
      stop("The ggplot2 package must be installed and loaded.")
    }
    
    # This is a custom printing function that will update wrapping in plot labels
    # to match the width of the current device. (in RStudio, this is the 'Plots' pane)

bschneidr

library(ggplot2)

# This is a custom printing function that will update wrapping in plot labels
# to match the width of the current device. (in RStudio, this is the 'Plots' pane)
  wrap_title_to_device <- function(ggplot_obj, dev_scaler = 12) {
    
    # Retrieve width of current device in inches
      dev_width <- dev.size("in")[1]
    
      

bschneidr

# Define the function

get_hessian <- function(f, as_matrix = FALSE, eval_at = NULL) {
  
  fn_inputs <- all.vars(f); names(fn_inputs) <- fn_inputs
  n_inputs <- length(fn_inputs)
  
  # Obtain the symbolic Hessian as a nested list
  
  result <- lapply(fn_inputs, function(x) lapply(fn_inputs, function(x) NULL))

bschneidr

library(magrittr)
library(tidyverse)
library(readr)
library(readxl)
library(schneidr)

theme_set(theme_schneidr(base_font_family = 'sans',
                         titles_font_family = 'sans'))

# Generate population

bschneidr

library(survey)

# Get example data
data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)

# Estimate means by subpopulation
# Use `covmat = TRUE` to also estimate covariances
mns <- svyby(~api99, ~stype,
             dclus1,