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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)

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set.seed(1)
sim_widget_data <- function(N, noise=100){
  x <- runif(N, min=0, max=100)
  y <- 122 - x/2 + rnorm(N, sd=noise)
  bad_widget <- factor(y > 100)
  data.frame(x, y, bad_widget)
}
widget_data <- sim_widget_data(500, 10)

test_set_idx <- sample(1:nrow(widget_data), size=floor(nrow(widget_data)/4))

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simple_roc <- function(labels, scores){
  labels <- labels[order(scores, decreasing=TRUE)]
  data.frame(TPR=cumsum(labels)/sum(labels), FPR=cumsum(!labels)/sum(!labels), labels)
}

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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")

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

library(weatherData)

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

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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 

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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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## 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()

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## 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)) +

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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")