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library(rgdal)
library(rgeos)
library(maptools)
library(mapproj)

us <- readOGR("http://bl.ocks.org/mbostock/raw/4090846/us.json", "states")
proj4string(us) <- CRS("+proj=merc")

map <- fortify(us)

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library(rvest)

# example of using phantomjs for scraping sites that use a twisty maze
# of javascript to render HTML tables or other tags

# grab phantomjs binaries from here: http://phantomjs.org/
# and stick it somehere PATH will find it

# this example scrapes the user table from:

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library(dplyr)
library(scales)
library(ggplot2)
library(RColorBrewer)

dat_sum <- dat %>% 
  mutate(A=factor(A), 
         B=factor(B)) %>%
  group_by(B, A, Outcome) %>%
  summarise(outcome_total=n()) %>%

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library(rgdal)
library(magrittr)

# save this locally for realz

world <- readOGR("https://raw.githubusercontent.com/AshKyd/geojson-regions/master/data/source/ne_50m_admin_0_countries.geo.json", "OGRGeoJSON")

# assumes 'places' is a data.frame with at least lat/lon columns

places %>%

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Gender  Grade Age Race  Urban/Rural School  Goals Grades  Sports  Looks Money
boy 5 11  White Rural Elm Sports  1 2 4 3
boy 5 10  White Rural Elm Popular 2 1 4 3
girl  5 11  White Rural Elm Popular 4 3 1 2
girl  5 11  White Rural Elm Popular 2 3 4 1
girl  5 10  White Rural Elm Popular 4 2 1 3
girl  5 11  White Rural Elm Popular 4 2 1 3
girl  5 10  White Rural Elm Popular 3 4 1 2
girl  5 10  White Rural Elm Grades  3 4 2 1
girl  5 10  White Rural Elm Sports  3 2 1 4

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library(rgeos)
library(rgdal)
library(httr)
library(dplyr)

geo_init <- function() {

  try({
    GET("http://www.mapcruzin.com/fcc-wireless-shapefiles/cities-towns.zip",
        write_disk("cities.zip"))

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Data sources:
CDC data on vaccine coverage: http://www.cdc.gov/flu/professionals/vaccination/reporti1112/reporti/index.htm
CDC data on current influenza situation:  http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html 
Historical flu data: http://www.cdc.gov/flu/weekly/pastreports.htm
Locations of hospitals in Boston: https://data.cityofboston.gov/Public-Health/Hospital-Locations/46f7-2snz
NetLogo, a simple agent based model: http://ccl.northwestern.edu/netlogo/ 
Nemsis, emergency medical services API (haven't used this one myself): http://www.nemsis.org/v3/downloads/v3Archive.html 
Written for R, but useful for epi basics in any language: cran.r-project.org/doc/contrib/Epicalc_Book.pdf
"Provides access to health statistics and information on hospital inpatient and emergency department utilization": http://hcupnet.ahrq.gov/
Healthcare Research and Quality data directory: http://www.ahrq.gov/data/dataresources.htm

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library(httr)
library(magrittr)
library(dplyr)
library(ggplot2)

# data retrieval ----------------------------------------------------------

pg <- GET("http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt",
          user_agent("Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.75.14 (KHTML, like Gecko) Version/7.0.3 Safari/7046A194A"))

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