dalejbarr

# you will need to install the package RR53 to run this script
# to download and install, use the following two commands:
#  library(devtools)
#  install_github("RR53", "dalejbarr")

library(RR53)

# download Brown et al.'s version of the data from PNAS
# you need only do this once to store a local copy
# comment out from the script after first use

dalejbarr

## Inspired by http://bconnelly.net/2015/06/connecting-r-to-everything-with-ifttt/
## NB: set up IFTTT Maker channel to watch for R_event and then notify.
## Get your own personal maker key from https://ifttt.com/maker
## Put the code snippet below in ~/.Rprofile
buzz_me <- function(msg = "DONE!", event = "R_event") {
    maker_key <- "yOuR_maKER_keY_GoES_HerE"
    url <- paste0("https://maker.ifttt.com/trigger/", event, "/with/key/", maker_key)
    if (requireNamespace("httr", quietly = TRUE)) {
        invisible(httr::POST(url, body = list(value1 = msg), encode = "json"))
    } else {

dalejbarr

## join x and y, replacing any columns in x with matching columns from y
## x    : the table with values to be replaced
## y    : the table containing replacement values
## cols : the key columns (i.e., cols to join on)
replace_join <- function(x, y, cols) {
  keep <- setdiff(names(x), names(y))
  dplyr::inner_join(dplyr::select_(x, .dots = c(cols, keep)), y, cols)
}

## like above, but:

dalejbarr

library(dplyr)
library(readr)
library(ggplot2)

dat <- read_csv("babies-first-names-all-names-all-years2.csv")

# pull out babies born in 1998
dat98 <- filter(dat, yr == 1998) %>%
  group_by(sex) %>%
  filter(rank <= 5)

dalejbarr

---
title: "Scottish Babynames"
author: "Dale Barr"
date: "7 September 2016"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

dalejbarr

## youtube video: https://youtu.be/AJDo9gpkEcg
library("readr")
library("ggplot2")

## read.csv() <- from base R.  DON'T USE!
hw5 <- read_csv("homework_5.csv") # from readr

hw5$A <- factor(hw5$A)
hw5$B <- factor(hw5$B)
hw5$C <- factor(hw5$C)

dalejbarr

library("simgen") # devtools::install_github("dalejbarr/simgen")
library("tibble")
library("ggplot2")
library("parallel")

get_typeI <- function(ns, ni, pops, clust) {
  res <- mcRun(fitanova, mcr.fnArgs = list(wsbi = FALSE),
	       mcr.cluster = clust,
	       mcr.datFn = mkDf,
	       mcr.datArgs = list(wsbi = FALSE, nsubj = ns, nitem = ni),

dalejbarr

library("shiny")
library("ggplot2")

n_obs <- 500L

## Define UI for application that draws a histogram
ui <- fluidPage(

  ## Application title
  titlePanel("Visualizing Raw Data vs Residuals"),