johnschrom

ascvd_estimator = reactive({
    female.risk <- 1.0 / (1.0 + exp( - (
      -12.823110 +
        0.106501 * as.numeric(input$age) +
        0.432440 * as.numeric(input$black) +
        0.000056 * (as.numeric(input$sysbp) ^ 2) +
        0.017666 * as.numeric(input$sysbp) +
        0.731678 * as.numeric(input$rxbp) +
        0.943970 * as.numeric(input$dm) +
        1.009790 * as.numeric(input$cursmoke) +

johnschrom

# Data frame for which genes to include by disease
excl <- data.frame('gene' = c(NA), 'round' = c(NA))

# Remove 30% of genes each round
while(nrow(excl) < nrow(d.qn)) {
  cat('Running with', excl.n, 'genes excluded...\n')
  
  # Run SVM
  svm.model <- svm(t(as.matrix(d.qn[-excl$gene,])), # features, minus excluded genes
                  tc, # class of each sample

johnschrom

# Create a refernce distribution using the median of all samples
ref <- apply(apply(data.normalized[,3:ncol(data.normalized)],
                   2,
                   FUN=sort),
             1,
             FUN=median)
# Create a emperical cdf function for the reference distribution
ref.cdf <- ecdf(ref)

# Quintile normalize!

johnschrom

library(ggplot2)
library(maps)
library(mapproj)

# Thanks to Ernesto at Quantified Self
# http://quantifiedself.com/2014/03/map-moves-data/

# This is set up take data from the "Tracks" export, using:
# https://labs.traqs.me/moves/

johnschrom

library(ggplot2)
library(grid)

ggplot(data, aes(x=x, y=y)) +
  geom_point() +
  # geom_line() +
  ylab('y label') + 
  xlab('x label') + 
  theme_bw() +
  scale_color_manual(values = c('#023F8A', '#BF4D28')) +

johnschrom

# Load Airports
# This comes from: http://openflights.org/data.html
airports <- read.csv('airports.dat', header=F);
colnames(airports) <- c('id', 'name', 'city', 'county', 'faa', 'icao', 'lat', 'lng', 'alt', 'timezone', 'dst')
airports$faa <- as.character(airports$faa)
 
# Load trips
# a file with: "source-airport,destination-airport,date"
# e.g., "MSP,SFO,2014-01-01"
trips <- read.csv('trips.csv', header=F);

johnschrom

# Load Airports
# This comes from: http://openflights.org/data.html
airports <- read.csv('airports.dat', header=F);
colnames(airports) <- c('id', 'name', 'city', 'county', 'faa', 'icao', 'lat', 'lng', 'alt', 'timezone', 'dst')
airports$faa <- as.character(airports$faa)

# Load trips
# a file with: "source-airport,destination-airport,date"
# e.g., "MSP,SFO,2014-01-01"
trips <- read.csv('trips.csv', header=F);

johnschrom

library(ggplot2)
library(maps)
library(mapproj)

###############################################################################
# Step 1: Get data from Foursquare
# If you already have it, then great :) Otherwise, you can use RPI. The source
# is listed below, and there are instructions for getting keys in the readme.
# RPI: https://github.com/johnschrom/RPI

johnschrom

install.packages(file_name_and_path, repos = NULL, type="source")

johnschrom

RewriteCond %{REMOTE_HOST} IP-ADDRESS
RewriteRule .* "http://www.google.com" [R=301,L]