Josh and Stephanie meeting 1

step one: import data

library(maps)
library(leaflet)
library(dplyr)

## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

data(countyMapEnv) #load maps package data

#load dummy data
dummyData <- read.csv("./DummyData.csv")

#show summary of dummyData
summary(dummyData)

##        ID            RespDate             County      CertStatus   
##  Min.   :  1.0   11/18/16:  8   Alameda      : 21   Min.   :0.000  
##  1st Qu.:100.8   10/27/16:  6   Los Angeles  : 21   1st Qu.:0.000  
##  Median :200.5   10/13/16:  5   Riverside    : 21   Median :0.000  
##  Mean   :200.5   10/20/16:  5   Sacramento   : 21   Mean   :0.455  
##  3rd Qu.:300.2   11/12/16:  5   San Diego    : 21   3rd Qu.:1.000  
##  Max.   :400.0   11/5/16 :  5   San Francisco: 21   Max.   :1.000  
##                  (Other) :366   (Other)      :274

#summarize by County
sumByCounty <- tapply(dummyData$ID, dummyData$County, length)
sumByCounty <- as.data.frame(sumByCounty)
sumByCounty$NAME <- row.names(sumByCounty)

making the map in leaflet

#Here is an example of a leaflet map
  library(rgdal)

## Loading required package: sp
## rgdal: version: 1.0-4, (SVN revision 548)
##  Geospatial Data Abstraction Library extensions to R successfully loaded
##  Loaded GDAL runtime: GDAL 1.11.2, released 2015/02/10
##  Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.2/Resources/library/rgdal/gdal
##  Loaded PROJ.4 runtime: Rel. 4.9.1, 04 March 2015, [PJ_VERSION: 491]
##  Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.2/Resources/library/rgdal/proj
##  Linking to sp version: 1.1-1

  #loading shapefile
  counties <- readOGR("./shapefiles", layer="cb_2014_us_county_20m")

## OGR data source with driver: ESRI Shapefile 
## Source: "./shapefiles", layer: "cb_2014_us_county_20m"
## with 3220 features
## It has 9 fields

## Warning in readOGR("./shapefiles", layer = "cb_2014_us_county_20m"): Z-
## dimension discarded

  #filtering for only california
  counties <- subset(counties, counties@data$STATEFP=="06")
  
  #making a leaflet map of california counties!
  leaflet() %>% addTiles() %>% addPolygons(data=counties)

  #merging in your data into this shapefile
  counties@data = data.frame(counties@data, sumByCounty[match(counties@data[,"NAME"], sumByCounty[,"NAME"]),])
  
  
  #set color palette
  colorRamp <- colorRamp(c("#eb4f3f","#e9db4f","#56e460"), interpolate="spline")
  palette <- colorNumeric(colorRamp, counties@data$sumByCounty)
  
  leaflet() %>% addProviderTiles("CartoDB.Positron") %>%
    addPolygons(
      weight=2,
      opacity = 0.8,
      data=counties,
      color = ~palette(sumByCounty),
       popup = ~paste("<strong>County:</strong>",NAME,
                       "<br>",
                       "<strong>Count:</strong>",sumByCounty)
      ) %>% addLegend(title = "Counts by county", pal = palette, values = counties@data$sumByCounty, opacity = 0.9, position="bottomleft")