malcolmbarrett

# Prepare a question about this code and post it on the RStudio Community thread listed in the assignment. 
# 1. Ask a clear question about the code in English. Tell us what you expect and what is happening.
# 2. Use the reprex package to create an example in R. 
# Your reprex should be something that I can copy and paste on my machine and run right away.
# Do NOT just share a screenshot of your RStudio session. 

# Consider these questions while preparing your reprex: 
# What do we expect this code to do, and what is happening?
# Are the data accesible? Do we need to use the diabetes dataset, or will a built-in dataset do?
# Is every part of this code necessary to show the problem?

malcolmbarrett

library(tidyverse)
library(broom)
library(rsample)
# remotes::install_github("malcolmbarrett/cidata")
library(cidata)

# fit ipw model for a single bootstrap sample
fit_ipw <- function(split, ...) {
  # get bootstrapped data sample
  .df <- analysis(split)

malcolmbarrett

library(gt)
library(htmltools)

euro_table <- tibble::tribble(
  ~Country, ~`Pct Above Normal`, ~`Excess Deaths`, ~`Time Period`,
  "United Kingdom", 67, 53300, "Mar. 14 - May 1",
  "Spain", 60,	31500, "Mar. 16 - May 3",
  "Belgium", 50,	5300, "Mar. 16 - Apr. 19",
  "Netherlands", 50, 8700,	"Mar. 16 - Apr. 26",
  "Italy", 49, 24600, "March",

malcolmbarrett

set.seed(1234)
c <- rnorm(1000)
e <- c * 2 + rnorm(1000)
o <- e * 3 + c *.5 + rnorm(1000)
s <- o * -1.5 + rnorm(1000)

df <- data.frame(c, e, o, s)

broom::tidy(lm(o ~ e + c, data = df))
broom::tidy(lm(o ~ e + c + s, data = df))

malcolmbarrett

library(ggdag)
dag <- dagify(
  x ~ z,
  y ~ z,
  exposure = "x",
  outcome = "y"
) %>% 
  tidy_dagitty()

dag %>% 

malcolmbarrett

transform_to_rr <- function(estimate, type) {
  if (type == "or") estimate <- sqrt(estimate)
  if (type == "hr") {
    estimate <- (1 - 0.5^sqrt(estimate)) / (1 - 0.5^sqrt(1 / estimate))
  }
  if (estimate < 1) estimate <- 1 / estimate
    
  estimate
}

malcolmbarrett

---
title: "Presentation Ninja"
subtitle: "⚔<br/>with xaringan"
author: "Yihui Xie"
date: "2016/12/12 (updated: `r Sys.Date()`)"
output:
  xaringan::moon_reader:
    css: ["default", "hiddencloud"]
    lib_dir: libs
    nature:

malcolmbarrett

library(ggplot2)
df <- data.frame(
  y = c(42, 71, 76, 79),
  ci_lower = c(40, 69, 74, 77),
  ci_upper = c(44, 73, 78, 81),
  year = c("baseline", "year 1", "year 2", "year 3")
)

ggplot(df, aes(x = year, y = y)) + 
  geom_col(fill = "#0072B2", width = .7) + 

malcolmbarrett

library(ggdag)
confounder_triangle() %>% 
  tidy_dagitty() %>% 
  dplyr::mutate(dashed = ifelse(to == "y", "dashed", "solid")) %>% 
  ggplot(aes(x, y, xend = xend, yend = yend)) +
  geom_dag_point() +
  geom_dag_text() +
  geom_dag_edges_link(
    aes(label = to, edge_linetype = dashed), 
    angle_calc = 'along',

malcolmbarrett

# install.packages(c("here", "fs", "stringr", "purrr", "git2r"))

# to add invisibly in your R profile, open with usethis::edit_r_profile()
# then define it in an environment, e.g.
# .env <- new.env()
# .env$move_slides_to_web <- {function definition}

move_slides_to_web <- function(folder = NULL, index = NULL) {
  
  if (is.null(folder)) {