revodavid

# Run Simon Urbanek's benchmark v2.5

cat("R version\n")
cat("=========\n")
print(R.version)

if(exists("Revo.version")) {
  cat("Revo version")
  cat("============")
  print(Revo.version)

revodavid

library(rgl)
open3d()
comet <- readOBJ(url("http://sci.esa.int/science-e/www/object/doc.cfm?fobjectid=54726"))
shade3d(comet, col="gray")

revodavid

## Adapted from https://gist.github.com/abresler/46c36c1a88c849b94b07
## Blog post Jan 21
## http://blog.revolutionanalytics.com/2015/01/a-beautiful-story-about-nyc-weather.html
library(checkpoint)
checkpoint("2015-01-28")
library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)

revodavid

library(checkpoint)
checkpoint("2015-03-04")
require(devtools)

## no way to install EBImage reproducibly
source("http://bioconductor.org/biocLite.R")
biocLite("EBImage")

# latest commits as of 2015-03-04
install_github("ramnathv/rblocks", ref="a85e748390c17c752cc0ba961120d1e784fb1956")

revodavid

## source: https://github.com/toddwschneider/nyc-taxi-data/blob/master/analysis/analysis.R
dropoffs = query("SELECT * FROM dropoff_by_lat_long_cab_type ORDER BY count")
dropoffs = mutate(dropoffs, cab_type_id = factor(cab_type_id))

p = ggplot() +
  geom_polygon(data = ex_staten_island_map,
               aes(x = long, y = lat, group = group),
               fill = "#080808", color = "#080808") +
  geom_point(data = dropoffs,
             aes(x = dropoff_long, y = dropoff_lat, alpha = count, size = count, color = cab_type_id)) +

revodavid

## R script and data by Kieran Healey
## https://twitter.com/kjhealy/status/669567682178654208
datafile = "blog.revolutionanalytics.com/downloads/tdata.csv"
library(ggplot2)
x = read.csv(paste0("http://",datafile))
ggplot(x) + geom_tile(aes(x=H,y=T,fill=tc))+scale_fill_identity()

revodavid

library(emoGG)
library(ggplot2)

# set the am variable to be different emoji
mtcars$am[mtcars$am==1] <- "1f697"
mtcars$am[mtcars$am==0] <- "1f68c"

# use am as the emoji aesthetic
ggplot(mtcars, aes(wt, mpg, emoji=am))+ geom_emoji()

revodavid

rf_ga3
1010 samples
58 predictors
2 classes: 'PS', 'WS' 

Maximum generations: 100 
Population per generation: 20 
Crossover probability: 0.8 
Mutation probability: 0.1 
Elitism: 0 

revodavid

library(checkpoint)
checkpoint("2016-04-22")

library(weatherData)

city <- "SJC"
cityLongName <- "San Jose"
yearStart <- 1991
yearEnd <- 2015

revodavid

inPowerBI <- exists("dataset")
if (inPowerBI) {
    weatherHistory <- dataset
    weatherHistory$Date <- as.Date(paste(dataset$Year, dataset$Month, dataset$Day, sep = "/"), format = "%Y/%b/%d")
    #warning(paste(dataset$Year, dataset$Month, dataset$Day, sep = "/")[1])
    #warning(weatherHistory$Date[1])
}

library(checkpoint)
checkpoint("2016-04-22")