Day 1 – Basics

• Getting and installing R
• The RStudio Environment
• The basics of R
  • Variables
  • Data Types
  • Reading data
  • Calling functions
  • Missing Data
• Basic Math
• Advanced Data Structures
  • data.frames
  • lists
  • matrices
  • arrays
• Reading Data into R
  • read.table
  • RODBC
  • Binary data
• Matrix Calculations
• Data Munging
  • Base R
  • plyr
  • reshape2
• Writing functions
• Conditionals
• Loops
• String manipulation and regular expressions
• Visualization
  • Base R
  • ggplot2

Day 2 – Modeling

• Basic Statistics
  • Probability Distributions
  • Averages, standard deviations and correlations
  • t-test
• Linear Models
  • Simple linear regression
  • Multiple Regression
• Generalized Linear Models
  • Logistic Regression
  • Poisson Regression
• Survival Analysis
• Assessing Model Quality
  • MSE
  • AIC
  • BIC
  • Residual Analysis
• Time Series
  • ARIMA
  • Vector Autoregression
  • GARCH
• Variable Selection

Day 3 – Machine Learning

• Variable selection for high dimensional data with glmnet
• Reduce uncertainty with weakly informative priors and Bayesian regression
• K-Means clustering
• Hierarchical clustering
• Multidimensional scaling
• Decision Trees for classification
• Random Forests for ensembling decision trees
• Bootstrap for measuring uncertainty
• Cross validation for model assessment
• Support Vector Machines
• Neural Networks

Day 4 – Data Presentation and Portability

• Reproducible reports using knitr
• Basic Introduction to Markdown
• Using knitr to automatically generate reports with embedded analytics
• Using Markdown and knitr to automatically generate websites with embedded analytics
• Using Markdown and knitr to make HTML5 slideshows with embedded analytics
• Advanced plotting
  • ggplot2 recap
  • rCharts
  • ggvis
• Building R Packages
• Shiny Overview

Day 5 – High Performance Computing with R

• Benchmarking code using microbenchmark
• The different speeds of various aggregation functions
  • aggregate
  • tapply
  • plyr
  • data.table
• Fast manipulation using dplyr
• Running dplyr commands in a database
• Parallel Code
  • foreach
  • doParallel
  • plyr
• Integrating C++
  • Rcpp

Given my extensive time abroad I thought it would be good to look at it all on a map using the Leaflet package in R.

Using the Google Maps API we can look up the latitude and longitude of the visited cities.

library(XML)
library(plyr)

cities <- c('Hong Kong', 'Haripal, India', 'Kolkata, India', 'Jaipur, India', 'Agra, India', 'Delhi, India', 
            'Singapore', 'Kuala Lumpur, Malaysia', 'Geroge Town, Malaysia')
lat.long <- function(place)
{
    theURL <- sprintf('http://maps.google.com/maps/api/geocode/xml?sensor=false&address=%s', place)
    doc <- xmlToList(theURL)
    data.frame(Place=place, Latitude=as.numeric(doc$result$geometry$location$lat), Longitude=as.numeric(doc$result$geometry$location$lng), stringsAsFactors=FALSE)
}

places <- adply(cities, 1, lat.long)

knitr::kable(places[, -1], digits=3, row.names=FALSE)

Now that we have the coordinates we use Leaflet to plot them.

library(leaflet)
leaflet(data=places) %>% addTiles() %>% setView(90, 15, zoom=4) %>% addPopups(lng=~Longitude, lat=~Latitude, popup=~Place) %>% addPolylines(~Longitude, ~Latitude, data=places[c(1, 3, 2:9, 1), ]) %>% addMarkers(lng=~Longitude, lat=~Latitude, popup=~Place, icon=JS("L.icon({iconUrl: 'https://www.jaredlander.com/images/jaredlanderfavicon.png', iconSize: [20, 20]})"))

Calculating all the miles traveled could be as simple as looking it up on TripIt, or we could do some quick Haversine distance calculations with the geosphere package.

First, we get the coordinates for New York, Minneapolis and Boston to have a complete picture of the distance.

newCities <- adply(c('New York, NY', 'Minneapolis, MN', 'Boston, MA'), 1, lat.long)
allPlaces <- rbind(newCities[c(1, 2, 1), ], places[c(1, 3, 2:9, 1), ], newCities[c(1, 3, 1), ])

Then in order to use distHaversine we need to set up a to and from relationship between the places. The easiest way will be to just shift the columns.

library(useful)

## Loading required package: ggplot2

shiftedPlaces <- shift.column(data=allPlaces, columns=names(places)[-1], newNames=c('To', 'Lat2', 'Long2'))

Now we can calculate the distance. This assumes that all trips followed a great circle, which might not be the case, especially for the car and rail portions of the trip.

library(geosphere)

## Loading required package: sp

shiftedPlaces$Distance <- distHaversine(shiftedPlaces[, c("Longitude", "Latitude")], shiftedPlaces[, c("Long2", "Lat2")], r=3959)

In total this led to 25,727 miles traveled.

knitr::kable(shiftedPlaces[, -1], digits=c(1, 3, 3, 1, 3, 3, 0), row.names=FALSE)

leaflet(data=allPlaces) %>% addTiles() %>% setView(80, 20, zoom = 3) %>% addPolylines(~Longitude, ~Latitude) %>% addMarkers(lng=~Longitude, lat=~Latitude, popup=~Place, icon=JS("L.icon({
    iconUrl: 'https://www.jaredlander.com/images/jaredlanderfavicon.png', iconSize: [20, 20]})"))