brshallo

Using pwiser

library(tidyverse)
if (!require(pwiser) ) remotes::install_github("brshallo/pwiser")

penguins <- palmerpenguins::penguins %>% na.omit()

t_test_statistic <- function(x, y){
 t.test(x, y) %&gt;% 

brshallo

library(tidyverse)
library(logspline)
library(spatstat.core)

set.seed(123)
x <- rnorm(100)

# comparing speeds of building different CDF's
microbenchmark::microbenchmark(

brshallo

library(tidyverse)

penguins <- palmerpenguins::penguins %>% na.omit()

### ACTUALLY THE BEST WAY TO DO THIS WOULD BE LIKE:
# penguins %>% 
#   group_nest(species, year) %>% 
#   pivot_wider(names_from = year,
#               values_from = data)

brshallo

library(tidyverse)

defaultW <- getOption("warn")
options(warn = -1)

data(lending_club, package = "modeldata")
df_ca <- filter(lending_club, addr_state == "CA")
df_tx <- filter(lending_club, addr_state == "TX")

brshallo

# copied from spatstat.utils::paren()
paren <- function (x, type = "(") {
  if (length(x) == 0) 
    return(x)
  if (identical(type, "")) 
    type <- "blank"
  switch(type, `(` = {
    out <- paste("(", x, ")", sep = "")
  }, `[` = {
    out <- paste("[", x, "]", sep = "")

brshallo

library(tidyverse)

set.seed(1234)
data <- map(1:3, ~runif(10)) %>% 
  set_names(paste0("x", 1:3)) %>% 
  as_tibble() %>% 
  mutate(letters = letters[1:10])

data

brshallo

library(tidyverse)
library(Hmisc)

data(lending_club, package = "modeldata")
lending_club <- mutate(lending_club, funded_amnt = as.double(funded_amnt))

bootstap_grouped_weighted <- function(df, target, groups, wt){
  options(dplyr.summarise.inform = FALSE)
  

brshallo

library(tidyverse)

# Setting-up example hiearchy data
rep_unlist <- function(vec, n){
  map(vec, rep, n) %>% unlist()
}

truth <- tibble(
  country = rep_unlist(c("USA", "Canada"), 4),

brshallo

library(tidyverse)

mods_stats <- iris %>% 
  as_tibble() %>% 
  group_by(Species) %>% 
  nest() %>% 
  mutate(lm_mods = map(data, ~lm(Sepal.Length ~ Sepal.Width, data = .x))) %>% 
  mutate(summary_stats = map(lm_mods, broom::glance))

brshallo

library(ggplot2)
library(dplyr)
library(purrr)

# Binomial distribution method
probs_survive <- 
  tibble(survivors = 1:16) %>% 
  mutate(prob = dbinom(16 - survivors, size = 18, prob = 0.5))