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library(pROC)
plot(roc(test_set$bad_widget, glm_response_scores, direction="<"),
     col="yellow", lwd=3, main="The turtle finds its way")
## 
## Call:
## roc.default(response = test_set$bad_widget, predictor = glm_response_scores,     direction = "<")
## 
## Data: glm_response_scores in 59 controls (test_set$bad_widget FALSE) < 66 cases (test_set$bad_widget TRUE).
## Area under the curve: 0.9037
glm_simple_roc <- simple_roc(test_set$bad_widget=="TRUE", glm_link_scores)

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set.seed(1)
N <- 2000
P <- 0.01
rare_success <- sample(c(TRUE, FALSE), N, replace=TRUE, prob=c(P, 1-P))
guess_not <- rep(0, N)
plot(roc(rare_success, guess_not), print.auc=TRUE)
## 
## Call:
## roc.default(response = rare_success, predictor = guess_not)
## 

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## Plot all Starbucks locations using OpenStreetMap
## Credit: http://www.computerworld.com/article/2893271/business-intelligence/5-data-visualizations-in-5-minutes-each-in-5-lines-or-less-of-r.html
library(checkpoint)
checkpoint("2016-08-22")

file <- "https://opendata.socrata.com/api/views/ddym-zvjk/rows.csv"
starbucks <- read.csv(file)
library(leaflet); library(magrittr)
leaflet() %>% addTiles() %>% setView(-84.3847, 33.7613, zoom = 16) %>% 
  addMarkers(data = starbucks, lat = ~ Latitude, lng = ~ Longitude, popup = starbucks$Name)

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## Plot last 6 months of ANTM share price
## Credit: http://www.computerworld.com/article/2893271/business-intelligence/5-data-visualizations-in-5-minutes-each-in-5-lines-or-less-of-r.html
library(checkpoint)
checkpoint("2016-08-22")

library(quantmod)
getSymbols("ANTM", auto.assign=TRUE)
barChart(ANTM, subset = 'last 6 months')

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## Plot Atlanta area unemployment
## Credit: http://www.computerworld.com/article/2893271/business-intelligence/5-data-visualizations-in-5-minutes-each-in-5-lines-or-less-of-r.html
library(checkpoint)
checkpoint("2016-08-22")

library(quantmod)
getSymbols("ATLA013URN", src = "FRED")
names(ATLA013URN) = "rate"
library(dygraphs)
dygraph(ATLA013URN, main = "Atlanta area unemployment")

revodavid

## Credit: http://www.computerworld.com/article/2893271/business-intelligence/5-data-visualizations-in-5-minutes-each-in-5-lines-or-less-of-r.html
library(checkpoint)
checkpoint("2016-08-22")

## Correlation plot
file <- "https://github.com/smach/NICAR15data/raw/master/testscores.csv"
testdata <- read.csv(file, stringsAsFactors = FALSE)
library(ggvis)
ggvis(testdata, ~ pctpoor, ~ score) %>%
  layer_points(size := input_slider(10, 310, label = "Point size"), opacity := input_slider(0, 1, label = "Point opacity")) %>%

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findMyId <- function() {
  # Requires https://github.com/ROpenSciLabs/miner
  # dig a hole one block deep and stand in it to be found
  # returns the IDs of players standing in a hole
  ids <- getPlayerIds()
  holehiders <- NULL
  for (id in ids) {
    pos <- getPlayerPos(id, tile=TRUE)
    surround <- getBlocks(pos[1]-1,pos[2],pos[3]-1, pos[1]+1, pos[2], pos[3]+1)[,1,]
    inhole <- all(c(surround[1,2],surround[2,1],surround[2,3], surround[3,2])!=0)

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library(doAzureParallel)

# generate a credentials json file "cluster", then...

registerDoAzureParallel(cluster)

# Run 5 million option pricing simulations 
closingPrices <- foreach(i = 1:50, .combine='c') %dopar% {
  replicate(100000, getClosingPrice())
}

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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()

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fit_glm <- glm(bad_widget ~ x, training_set, family=binomial(link="logit"))

glm_link_scores <- predict(fit_glm, test_set, type="link")

glm_response_scores <- predict(fit_glm, test_set, type="response")

score_data <- data.frame(link=glm_link_scores, 
                         response=glm_response_scores,
                         bad_widget=test_set$bad_widget,
                         stringsAsFactors=FALSE)