rm(list=ls())

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)
dat <- read.dbf("C:\\Users\\Administrator\\Desktop\\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 to the Canvas assignment.

library(ggmap)
## Loading required package: ggplot2
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
bone_marro <- subset(dat, bone_marro=="Y")
qmplot(x,y, data = bone_marro, colour = "red",
  size = I(3.5), alpha = I(.6), legend = "none",
  mapcolor = "color", extent = "panel", darken = 0.2,
  maptype = "terrain-background", main = "Hospitals with Bone-Marrow related Services")
## Using zoom = 8...
## Source : http://tile.stamen.com/terrain/8/70/94.png
## Source : http://tile.stamen.com/terrain/8/71/94.png
## Source : http://tile.stamen.com/terrain/8/72/94.png
## Source : http://tile.stamen.com/terrain/8/73/94.png
## Source : http://tile.stamen.com/terrain/8/74/94.png
## Source : http://tile.stamen.com/terrain/8/70/95.png
## Source : http://tile.stamen.com/terrain/8/71/95.png
## Source : http://tile.stamen.com/terrain/8/72/95.png
## Source : http://tile.stamen.com/terrain/8/73/95.png
## Source : http://tile.stamen.com/terrain/8/74/95.png
## Source : http://tile.stamen.com/terrain/8/70/96.png
## Source : http://tile.stamen.com/terrain/8/71/96.png
## Source : http://tile.stamen.com/terrain/8/72/96.png
## Source : http://tile.stamen.com/terrain/8/73/96.png
## Source : http://tile.stamen.com/terrain/8/74/96.png
## Source : http://tile.stamen.com/terrain/8/70/97.png
## Source : http://tile.stamen.com/terrain/8/71/97.png
## Source : http://tile.stamen.com/terrain/8/72/97.png
## Source : http://tile.stamen.com/terrain/8/73/97.png
## Source : http://tile.stamen.com/terrain/8/74/97.png

clin_lab <- subset(dat, clin_lab=="N")
qmplot(x,y, data = clin_lab, colour = "red",
  size = I(3.5), alpha = I(.6), legend = "none",
  mapcolor = "color", extent = "panel", darken = 0.2,
  maptype = "toner", main = "Hospitals lacking clinical labs")
## Using zoom = 8...

ct_scan <- subset(dat, ct_scan=="Y")
qmplot(x,y, data = ct_scan, colour = "red",
  size = I(3.5), alpha = I(.6), legend = "none",
  mapcolor = "color", extent = "panel", darken = 0.2,
  maptype = "toner", main = "Hospitals with CT SCAN Services")
## Using zoom = 8...

chemo <- subset(dat, chemo=="N")
qmplot(x,y, data = chemo, colour = "red",
  size = I(3.5), alpha = I(.6), legend = "none",
  mapcolor = "color", extent = "panel", darken = 0.2,
  maptype = "watercolor", main = "Hospitals lacking Chemo Services")
## Using zoom = 8...

emer_dept <- subset(dat, emer_dept=="Y")
qmplot(x,y, data = emer_dept, colour = "red",
  size = I(3.5), alpha = I(.6), legend = "none",
  mapcolor = "color", extent = "panel", darken = 0.2,
  maptype = "watercolor", main = "Hospitals with Emergency Department")
## Using zoom = 8...