Joe Thorley joethorley

I think the two most important messages that people can get from a short course are:

a) the material is important and worthwhile to learn (even if it's challenging), and b) it's possible to learn it!

For those reasons, I usually start by diving as quickly as possible into visualisation. I think it's a bad idea to start by explicitly teaching programming concepts (like data structures), because the pay off isn't obvious. If you start with visualisation, the pay off is really obvious and people are more motivated to push past any initial teething problems. In stat405, I used to start with some very basic templates that got people up and running with scatterplots and histograms - they wouldn't necessary understand the code, but they'd know which bits could be varied for different effects.

Apart from visualisation, I think the two most important topics to cover are tidy data (i.e. http://www.jstatsoft.org/v59/i10/ + tidyr) and data manipulation (dplyr). These are both important for when people go off and apply

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🇫🇷 Cette liste en français

	dprint <- function(x, note = NULL, do = getOption("dprint.do", TRUE)) {
	if (!do) return(invisible())
	if (!is.null(note))
	cat("\n", note, "\n")
	cat("\n", deparse(substitute(x)), ": \n", sep = "")
	print(x)
	}

	# "impute" missing binary predictor
	# really just marginalizes over missingness
	# imputed values produced in generated quantities

	N <- 1000 # number of cases
	N_miss <- 100 # number missing values
	x_baserate <- 0.25 # prob x==1 in total sample
	a <- 0 # intercept in y ~ N( a+b*x , 1 )
	b <- 1 # slope in y ~ N( a+b*x , 1 )

	# Code behind this tweet: https://twitter.com/drob/status/1100182329350336513
	library(tidyverse)
	library(gganimate)

	# Setup
	options(gganimate.nframes = 200)
	set.seed(2019)

	simulation <- tibble(roll = 1:10000) %>%
	mutate(result = sample(6, n(), replace = TRUE)) %>%