Objectives

Using the spatial visualization techniques, explore this data set on Pennsylvania hospitals (http://www.arcgis.com/home/item.html?id=eccee5dfe01e4c4283c9be0cfc596882). Create a series of 5 maps that highlight spatial differences in hospital service coverage for the state of PA.

To help you in getting the data imported into R, I have included the code below:

To import the data I use the foreign package, if you do not have it than be sure to install it prior to testing the code.

library(foreign)
library(ggplot2)
## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang
library(ggmap)
## Warning: package 'ggmap' was built under R version 3.6.1
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
setwd("C:/Users/xingc/Documents/Harrisburg/Fall 2019/512 Data Visilization - Thursday/HW/HW 5")
dat <- read.dbf("pennsylv.dbf")

The dataset contains a number of variables about each hospital, many of them are clear and straight forward.

names(dat)
##   [1] "acc_trauma" "air_amb"    "als"        "arc_street" "arc_zone"  
##   [6] "bas_ls"     "bassinets"  "bb_id"      "bc_beds"    "bc_sus_bed"
##  [11] "beds_sus"   "birthing_r" "bone_marro" "burn_car"   "burn_care" 
##  [16] "card_beds"  "card_surge" "card_sus_b" "cardiac"    "cardiac_ca"
##  [21] "cardio_reh" "chemo"      "city"       "clin_lab"   "clin_psych"
##  [26] "county"     "countyname" "ct_scan"    "cty_key"    "cystoscopi"
##  [31] "deliv_rms"  "dental"     "detox_alc_" "diag_radio" "diag_xray" 
##  [36] "doh_hosp"   "doh_phone"  "emer_dept"  "endoscopie" "fac_id"    
##  [41] "facility"   "flu_old"    "fred_con_1" "fred_conta" "fred_email"
##  [46] "fred_fax"   "fred_hosp"  "fred_pager" "fred_phone" "gamma_knif"
##  [51] "gen_outpat" "gene_counc" "heart_tran" "helipad"    "hemodial_c"
##  [56] "hemodial_m" "hosp_id"    "hospice"    "hyper_cham" "icu"       
##  [61] "icu_beds"   "icu_sus_be" "inpat_flu_" "inpat_pneu" "kidney_tra"
##  [66] "labor_rms"  "lic_beds"   "lic_dent"   "lic_dos"    "lic_mds"   
##  [71] "lic_pod"    "linear_acc" "lithotrips" "liver_tran" "loc_method"
##  [76] "ltc"        "mcd"        "mcd_key"    "mcd_name"   "medical"   
##  [81] "mob_ccu"    "mob_icu"    "mri"        "ms1"        "neo2_beds" 
##  [86] "neo2_sus_b" "neo3_beds"  "neo3_sus_b" "neuro_surg" "neurology" 
##  [91] "obs_gyn"    "occ_ther"   "optometry"  "organ_bank" "ped_trauma"
##  [96] "pediatric"  "pet"        "pharmacy"   "phys_med"   "phys_ther" 
## [101] "podiatry"   "providerid" "psych"      "psych_inpa" "reg_trauma"
## [106] "resp_ther"  "so_flu_65u" "social_wor" "speech_pat" "street"    
## [111] "surgical"   "surgical_s" "thera_radi" "typ_org"    "typ_serv"  
## [116] "ultrasound" "x"          "y"          "zip"

Now create 5 maps, including descriptions, that highlight the spatial distribution of hospital services in the state of PA. Upload these maps as a document to rpubs.com and submit that link the Moodle assignment.

register_google(key = "AIzaSyDTsnB3PQ5p6KU41yKXvgJUgUdtk6rrDqU")
pa = geocode("pennsylvania", source = "google")
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=pennsylvania&key=xxx
qmplot(x, y, data = dat, size = I(3),alpha = I(0.8),extent = "device", geom = "point",main = "Locations of all the hospitals in the dataset", xlab = "Longitude", ylab = "Latitude",source = "google",maptype = "roadmap",zoom = 7)
## Source : https://maps.googleapis.com/maps/api/staticmap?center=40.942826,-77.681596&zoom=7&size=640x640&scale=2&maptype=roadmap&language=en-EN&key=xxx

qmplot(x, y, data = dat, size = beds_sus,alpha = I(0.8),extent = "device", geom = "point",main = "Hospitals and the number of beds", xlab = "Longitude", ylab = "Latitude",source = "google",maptype = "roadmap",zoom = 7)
## Source : https://maps.googleapis.com/maps/api/staticmap?center=40.942826,-77.681596&zoom=7&size=640x640&scale=2&maptype=roadmap&language=en-EN&key=xxx
## Warning: Removed 31 rows containing missing values (geom_point).

amb<-subset(dat, air_amb == "Y")
qmplot(x, y, data = amb, size = I(3),alpha = I(0.8),,extent = "device", geom = "point",main = "Hospitals that offers air ambulance service", xlab = "Longitude", ylab = "Latitude",source = "google",maptype = "roadmap",zoom = 7)
## Source : https://maps.googleapis.com/maps/api/staticmap?center=41.040851,-77.606184&zoom=7&size=640x640&scale=2&maptype=roadmap&language=en-EN&key=xxx
## Warning: Ignoring unknown parameters: NA

ED<-subset(dat, emer_dept == "Y")
qmplot(x, y, data = ED, size = I(3),alpha = I(0.8),colour=air_amb,extent = "device", geom = "point",main = "Hospitals with have emergency department and air ambulance service", xlab = "Longitude", ylab = "Latitude",source = "google",maptype = "roadmap",zoom = 7)
## Source : https://maps.googleapis.com/maps/api/staticmap?center=40.942648,-77.681596&zoom=7&size=640x640&scale=2&maptype=roadmap&language=en-EN&key=xxx

pet_hos<-subset(dat, pet == "Y")
qmplot(x, y, data = pet_hos, size = I(3),alpha = I(0.8),extent = "device", geom = "point",main = "Hospitals that take care of pets", xlab = "Longitude", ylab = "Latitude",source = "google",maptype = "roadmap",zoom = 7)
## Source : https://maps.googleapis.com/maps/api/staticmap?center=40.960082,-77.71003&zoom=7&size=640x640&scale=2&maptype=roadmap&language=en-EN&key=xxx