Zev Ross zross

An Introduction to R for Data Science

During the workshop itself you only need a laptop and a browser. I will give you a URL and login information and you will run the software in the cloud. This way we're all working with the same software, same files and we don't have to use workshop time with software installation issues.

But, if you are adventurous and want to try installing the software locally on your machine before the workshop you can follow these instructions. You can e-mail me with questions beforehand ([email protected]) and I can answer questions about your installation at the workshop as time permits.

Quick setup instructions

You will need to download and install the most recent version of R and the most recent version of RStudio. You will also need to install some R packages. Once you have R and RStudio installed you can install the packages by running this code in the console window:

R date formatting

#strptime() to convert from character to date/time
#format() to reformat the date

format(strptime("2001-05-05", "%Y-%m-%d"), "%A %B%e, %Y")
#"Saturday May 5, 2001"

format(strptime("2001-05-05 01:05 PM", "%Y-%m-%d %I:%M %p"), "%d/%m/%Y %H:%M")

	library(dplyr)
	nc = st_read(system.file("shape/nc.shp", package="sf"))
	nc$blah <- sample(c(1,2,3), size = nrow(nc), replace = TRUE)
	nc %>% group_by(blah) %>% summarize(avg_bir74 = mean(BIR74))

	library(sf)
	library(RPostgreSQL)
	dat <- data.frame(ID = 1:2,
	long = c(-74.003234, -73.983317),
	lat = c(40.731863, 40.722684))
	dat_sf <- st_as_sf(dat, coords = c("long", "lat"),
	crs = 4326,
	agr = "identity")
	con <- dbConnect(PostgreSQL(), dbname = "mydb", host = "localhost")
	st_write_db(con, dat_sf, "mydata")

	# Joe Cheng provided this code

	library(shiny)
	#' Call `input <- createInputWrapper(input)` at the top of
	#' your Shiny Server function.
	createInputWrapper <- function(input) {
	env <- new.env(parent = emptyenv())
	env$impl <- input
	env$blacklist <- new.env(parent = emptyenv())

	# **********************************************************************************
	# Data Science with R, Part 2 - Predictive modeling and machine learning: Exercise 1
	# **********************************************************************************

	# This exercise is designed to remind you of key R
	# concepts and code that you will need to be familiar
	# with before participating in Part 2 of the R workshop.
	# Please feel free to use Google, stackoverflow etc to help
	# you answer questions, treat them as if they were real-world
	# challenges

	library(shiny)

	source("junk_inputwrapper.R")


	server <- function(input, output, session) {

	input <- createInputWrapper(input)
	output$plot <- renderUI({

	<!DOCTYPE html>
	<html>
	<head>
	<title>Leaflet GeoJSON Example</title>
	<meta charset="utf-8">
	<meta content="width=device-width, initial-scale=1.0" name="viewport">
	<link href="http://cdn.leafletjs.com/leaflet-0.7/leaflet.css" rel="stylesheet">
	<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.9.0/jquery.min.js"></script>
	<script src="http://cdn.leafletjs.com/leaflet-0.7/leaflet.js"></script>
	<style>