grantmcdermott

library(estimatr)
library(data.table)

## Function for single simulation
dm_sim = function(...) {
  ## Params
  b0 = 1; b1 = 1; rhoXZ = 0.5; rho1 = 0.25
  rho2_vec = c(0.5, 0.25, 0, -0.25, -0.5)
  nobs1 = 1000; nobs2 = 100
  ## "In" sample draw

grantmcdermott

## Context: https://twitter.com/grant_mcdermott/status/1293673082352099328

library(broom) ## remotes::install_github('tidymodels/broom')
library(ggplot2)

## gen fake data
set.seed(123)
x = sort(runif(100, min = 0, max = 5))
y = x^2 + rnorm(100)
dat = data.frame(x, y)

grantmcdermott

#' Compare expected returns of 15- vs 30-year home refi.
#'
#' This very simple function assumes you split your disposable income between
#' mortgage repayments and stock investments. The goal is to compare ROI at the end
#' of 30 years, assuming you initially have a choice between 15- and 30-year fixed rate 
#' mortgage. 
#' 
#' @details Under the 15-year scenario, we assume that you pay the monthly premium 
#' until the loan is fully repaid (i.e. after 15 years) and then immediately switch 
#' over to investing the exact same amount for another 15 years. Under the 30-year 

grantmcdermott

## Context: https://twitter.com/grant_mcdermott/status/1272747522562244608

## Note: To get the most out of this function (private repos, etc.), I recommend
## adding a GitHub Personal Access Token to your .Renviron file. The easiest way
## to do this is with the {usethis} package. See here: 
## https://happygitwithr.com/github-pat.html#step-by-step

library(gh)
library(purrr)
library(data.table)

grantmcdermott

#!/bin/sh

DDIR='/usr/share/applications'

## There are two basic hidpi cases that we need to distinguish and correct for:
## 1) Electron-based apps and 2) QT-based apps

## 1) Electron-based apps (append '--force-scale-device-factor=2' to executable)
for FILE in atom.desktop brave-browser.desktop code-oss.desktop google-chrome.desktop skypeforlinux.desktop slack.desktop
do

grantmcdermott

## Context: https://twitter.com/grant_mcdermott/status/1249825585175523328

set.seed(123)
m = matrix(1:10000000, ncol = 1000)

x_pos = sample.int(nrow(m), size = nrow(m)/2)
y_pos = sample.int(ncol(m), size = ncol(m)/2)
m_pos = (y_pos-1)*nrow(m) + x_pos

library(data.table)

grantmcdermott

## Context: https://twitter.com/grant_mcdermott/status/1220409051013697553

library(tidycensus)
# census_api_key("YOUR KEY GOES HERE", install = TRUE)
library(tigris)
options(tigris_use_cache = TRUE)
library(sf)
library(tidyverse)

## Get ZIP codes for whole US. First time you run this it will take a while, but 

grantmcdermott

## Context: https://twitter.com/grant_mcdermott/status/1210654424600731648

library(tidyverse)
library(broom)
library(hrbrthemes) ## theme(s) I like

df = 
  mtcars %>%
  mutate(vs = factor(vs), am = factor(am))

grantmcdermott

## Context: https://twitter.com/hsantanna/status/1196184374481506304
## Thanks to Will May: https://twitter.com/williamcmay/status/1196268860418281472?s=20

library(tidyverse)
library(rvest)
library(memoise)

ssp_url = 'http://www.ssp.sp.gov.br/estatistica/pesquisa.aspx'

## get the region/municipality form

grantmcdermott

## Background: https://twitter.com/JamesBland_Econ/status/1192519670009434114

# devtools::install_github("ropensci/RSelenium")
library(RSelenium)

## Start Selenium driver and browser client 
rsd = rsDriver(browser = "firefox")  # "chrome" is the default
rsc = rsd$client

## Navigate to the page