Choropleth maps of sum of PM2.5 by county for each of the 4 years of data given in the Exploratory Data Analysis Project 2.

Author: BillSeliger

ChorMap.md

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+---
+title: "Choropleth Map for PM2.5 Emissions Data"
+author: "BillSeliger"
+date: "Saturday, January 31, 2015"
+output: html_document
+---
+I have a high interest in mapping geospatial data and wanted to work with the data from the Exploratory Data Analysis Project 2 further so I created this additonal code which creates a Choropleth, a type of heat map, based on the total PM2.5 Emissions by each FIPS county.  For more information on FIPS county code see this page <http://en.wikipedia.org/wiki/FIPS_county_code>
+
+The original Course Project is here - <https://class.coursera.org/exdata-010/human_grading>
+
+This is almost entirely drawn from the code of others found on the internet - many different sources contributed.  
+
+First, I check to see if the data is loaded to R and if not, I set the working directory and read in the data.  Because this data is in RDS format we use the readRDS function:
+
+
+```r
+  if(!exists("NEI")) {
+    print("Reading NEI file")
+    setwd("C:/Users/rr046302/Documents/Bill's Stuff/Coursera/Exploratory Data Analysis/Project 2")
+    NEI <- readRDS("summarySCC_PM25.rds")
+    setwd("C:/Users/rr046302/Documents/Bill's Stuff/Coursera/Reproducible Research/DataScienceSpecialization.github.io")
+  }
+```
+
+Next I require several R packages - require checkes to see if the packages are loaded and if not loads them:
+
+
+```r
+  require(maps)
+  require(ggmap)
+  require(doBy)
+```
+
+Next I set the color for the color bands - here I have chosen a set of magenta colors to indicate that the Emissions is a measure of a deleterious substance in the environment:
+
+
+```r
+colors = c("#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", 
+             "#980043")
+```
+
+Using the doBy package I summarize Emissions by fips and year, using the FUNction sum:
+
+```r
+  fipssum <- summaryBy(Emissions ~fips + year, data = NEI, keep.names = TRUE, FUN = sum) 
+```
+
+Usign the cut and breaks function I cut the Emissions sum into 6 equal parts.  Note that I do this prior to filtering on the year as I want all 4 years to use the same color buckets so that the comparisons across all 4 years are appropriate:
+
+```r
+  fipssum$colorBuckets <- cut(fipssum$Emissions, breaks=c(quantile(fipssum$Emissions, probs = seq(0, 1, by = 0.2))),
+                              labels=c(1,2,3,4,5), include.lowest=TRUE)
+```
+
+We'll start with the data for 1999 (you can fork the repo on Github that has the following code set up as a function):
+
+```r
+NEIyear <- 1999
+```
+
+And filter on year:
+
+```r
+  year <- subset (fipssum, year == NEIyear) 
+```
+
+I create an object of the colorBuckets that will be used for setting colors when I make the plot
+
+```r
+  colorsmatched <- year$colorBuckets 
+```
+
+A title is created using the NEIyear that was called by the user in the function call:
+
+```r
+  title <- paste("NEI PM2.5 Emissions by county ", NEIyear)  
+```
+
+The penultimate step is to create the plot:
+
+```r
+  windows()
+  map("county")
+```
+
+![plot of chunk unnamed-chunk-10](figure/unnamed-chunk-10-1.png) 
+
+```r
+  map("county", col = colors[colorsmatched], fill = TRUE, resolution = 0, 
+      lty = 0, projection = "polyconic")
+  title(title)
+  leg.txt <- c("bottom", "2nd", "3rd", "4th", "5th quintile")
+  legend("topright", leg.txt, horiz = TRUE, fill = colors)
+```
+
+![plot of chunk unnamed-chunk-10](figure/unnamed-chunk-10-2.png) 
+
+We'll run it again for 2002 data:
+
+```r
+NEIyear <- 2002
+  year <- subset (fipssum, year == NEIyear) 
+  colorsmatched <- year$colorBuckets
+  title <- paste("NEI PM2.5 Emissions by county ", NEIyear)  
+  map("county", col = colors[colorsmatched], fill = TRUE, resolution = 0, 
+      lty = 0, projection = "polyconic")
+  title(title)
+  leg.txt <- c("bottom", "2nd", "3rd", "4th", "5th quintile")
+  legend("topright", leg.txt, horiz = TRUE, fill = colors)
+```
+
+![plot of chunk unnamed-chunk-11](figure/unnamed-chunk-11-1.png) 
+
+And again for 2005 data:
+
+```r
+NEIyear <- 2005
+  year <- subset (fipssum, year == NEIyear)
+  colorsmatched <- year$colorBuckets
+  title <- paste("NEI PM2.5 Emissions by county ", NEIyear)  
+  map("county", col = colors[colorsmatched], fill = TRUE, resolution = 0, 
+      lty = 0, projection = "polyconic")
+  title(title)
+  leg.txt <- c("bottom", "2nd", "3rd", "4th", "5th quintile")
+  legend("topright", leg.txt, horiz = TRUE, fill = colors)
+```
+
+![plot of chunk unnamed-chunk-12](figure/unnamed-chunk-12-1.png) 
+
+And finally for 2008 data:
+
+```r
+NEIyear <- 2008
+  year <- subset (fipssum, year == NEIyear) 
+  colorsmatched <- year$colorBuckets
+  title <- paste("NEI PM2.5 Emissions by county ", NEIyear)  
+  map("county", col = colors[colorsmatched], fill = TRUE, resolution = 0, 
+      lty = 0, projection = "polyconic")
+  title(title)
+  leg.txt <- c("bottom", "2nd", "3rd", "4th", "5th quintile")
+  legend("topright", leg.txt, horiz = TRUE, fill = colors)
+```
+
+![plot of chunk unnamed-chunk-13](figure/unnamed-chunk-13-1.png) 
+
+Lastly, because this was a function and the dataset exists within the function environment I check to see if the dataset is in the global environment; if it is not I assign the NEI object to the global environment:
+
+```r
+if(!exists("NEI")) {
+  print("Assigning NEI to Global Environment")
+  assign("NEI", NEI, envir=globalenv())
+}
+```
+
+This code is avaialble in a Github repo as a function.  Find the repo here <https://github.com/BillSeliger/ExData_Plotting2>