Source - National Center for Chronic Disease Prevention and Health Promotion | Division of Population Health

• Behavioral Risk Factor Surveillance System (BRFSS) http://www.cdc.gov/sleep/data_statistics.html

• Plotting Alaska, Hawaii https://gregorybooma.wordpress.com/2013/01/05/map-of-the-united-states-including-alaska-and-hawaii-with-r/

library(maps)
library(mapdata)
library(Hmisc)

## Warning: package 'Hmisc' was built under R version 3.1.3

## Loading required package: grid
## Loading required package: lattice
## Loading required package: survival
## Loading required package: splines
## Loading required package: Formula

## Warning: package 'Formula' was built under R version 3.1.3

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 3.1.3

## 
## Attaching package: 'Hmisc'
## 
## The following objects are masked from 'package:base':
## 
##     format.pval, round.POSIXt, trunc.POSIXt, units

Steps to generate US Map

1)Import and clean dataset

setwd('~/Documents/IM Vault/IST 719/Poster/Sample Dataset/Sleeping/')
df2014 <- sasxport.get('BRFSS/2014/LLCP2014.XPT', as.is = TRUE)

## Processing SAS dataset LLCP2014   ..

df2014 <- df2014[,c('x.state', 'sleptim1', 'x.age.g', 'x.race', 'x.rfdrhv4', 'sex', 'smokday2',
                    'exerany2', 'genhlth', 'physhlth', 'menthlth')]

stateMapping <- read.csv('BRFSS/StateMapping.csv', stringsAsFactors = F)
df2014 <-  subset(df2014, x.state != 66 & x.state != 72)
df2014 <- merge(df2014, stateMapping, by = 'x.state')
df2014 <- subset(df2014, sleptim1 <=24)

2)Label data and assign state name to vector

df2014$sleptim1_min <- df2014$sleptim1 *60
state_avg <- aggregate(df2014[, 'sleptim1_min'], list(df2014$State), mean)
values <- (state_avg$x - min(state_avg$x))

valueLabel <- c(1:length(values))
for (i in 1:length(values)) {
  if(values[i]<5) valueLabel[i] <- .2
  else if(values[i]<10) valueLabel[i] <- .4
  else if(values[i]<15) valueLabel[i] <- .6
  else if(values[i]<20) valueLabel[i] <- .8
  else valueLabel[i] <- 1
}
names(valueLabel) <- state_avg$Group.1
head(valueLabel)

##    Alabama     Alaska    Arizona   Arkansas California   Colorado 
##        0.6        0.6        0.8        1.0        0.6        1.0

map('state')

3)Map labeled values to particular areas of map

tmp <- map('state',plot=FALSE,namesonly=TRUE )
tmp <- match(gsub('(:.*)','',tmp),c(tolower(state_avg$Group.1)))
map('state', fill=TRUE,
    col=rgb(colorRamp(c('pink','darkblue'))(valueLabel), max=255)[tmp])

4)Add not-connected area - Alaska

tmp <- map('world2Hires', "USA:Alaska",plot=FALSE,namesonly=TRUE )
tmp <- match('alaska',c(tolower(state_avg$Group.1)))
map("world2Hires", "USA:Alaska", fill = TRUE, 
    col=rgb(colorRamp(c('pink', 'darkblue'))(valueLabel), max=255)[tmp])

5)Add not-connected area - Hawaii

tmp <- map('world2Hires', "Hawaii",plot=FALSE,namesonly=TRUE )
tmp <- match('hawaii', c(tolower(state_avg$Group.1)))
map("world2Hires", "Hawaii", fill = TRUE,
    col=rgb(colorRamp(c('pink', 'darkblue'))(valueLabel), max=255)[tmp])