###################################
# OTAGO SUMMER SCHOOL TURTORIAL 1 #
###################################

#install and load needed packages
install.packages("sf")
install.packages("dplyr")
install.packages("ggplot2")
install.packages("tidyverse")
install.packages("tmap", repos = c("https://r-tmap.r-universe.dev",
                                   "https://cloud.r-project.org"))
library(sf)
library(dplyr)
library(ggplot2)
library(tmap)
library(mapview)
library(tidyverse)

#### Tasks and goals ####
#                       #
# load in spatial data  #
# explore attributes    #
# print map             #
#                       #
#########################

## It is nice practice to set up a working directory where your data is stored/will output
## note: use "/" instead of "\", or "\\"
wd <- "CHANGE TO YOUR DIRECTORY [FILE WITH TUTORIAL DATA]"    # working directory 
setwd(wd)   # set the working directory
getwd()     # to get your working directory details


## Now bring in your data
auckland_cau <- st_read("CHANGE HERE TO NAME OF SHP") #since your working directory was set you just need your shp file
View(auckland_cau) # will open dataset

## identify what cau is the largest
summary(auckland_cau) #first review the summary statistics for the whole dataset
largest_cau <- auckland_cau[which.max(auckland_cau$AreaKM), ]
print(largest_cau)

## Now make a printable map
map_auckland <- st_transform(auckland_cau, 2193)
map_auckland <- tm_shape(map_auckland) + tm_polygons()
class(map_auckland)
map_auckland_cau <-  map_auckland + 
  tm_compass(type = "arrow", position = c("bottom", "right")) + # North arrow
  tm_scalebar(breaks = c(0, 2.5, 5), position = c("bottom", "left"))+ # scale bar
  tm_polygons (col = "AreaKM", palette = "Greens", style = "quantile") + #makes chloropleth map where darker green indicates larger CAU [can try out different colors or styles]
  tm_title("Auckland Census Area Units")
print(map_auckland_cau)
tmap_save(tm = map_auckland_cau, filename = "AucklandCAU.tiff") ##export map as tiff


###################################
# OTAGO SUMMER SCHOOL TURTORIAL 2 #
###################################

######## Tasks and goals  ########
#                                #
#   geocode stroke address file  #
#                                #
##################################
# Install needed library for geocoding
if("ggmap" %in% installed.packages()==FALSE){install.packages(pkgs=c("ggmap"),
                                                              repos = "http://cran.us.r-project.org")} 

library(ggmap)     # see help file on ggmap 

mykey <- "GOOGLE API GOES HERE" ##add your api, free up to 10,000 
register_google(key = mykey)

# Load stroke data
strokes <- read.csv("CHANGE NAME HERE TO CSV FILE") #if set working directory, just put the name of the file 
# Create list of text addresses
stroke_address_list <- with(strokes, paste(Street, Dependent_Locality, Locality, Country, 
                                           sep = ", ")) 

# Check the  address_list to make sure they look correct
print(stroke_address_list[1:5])
print(length(stroke_address_list))   # Does this match the number of rows in strokes?
n <- length(stroke_address_list)
print(stroke_address_list[(n-5):n])


# Geocode! This can take time to run on larger lists. It also costs money (if doing more than 10,000 addresses)!
# Be positive that stroke_address_list is correct and what you want to geocode before starting
long_lat <- geocode(stroke_address_list, output='more') 

print(long_lat[0:5,])   # How does it look? Did addresses geocode to rooftop? 

# Now plot them to see the distribution across the city
strokes_geocoded <- st_as_sf(long_lat, coords = c("lon", "lat"), crs = 2193)


###################################
#  OTAGO SUMMER SCHOOL BONUS #1   #
###################################

######## Tasks and goals  ########
#                                #
#  download spatial data         #
#  append tabular data           # 
#                                #
##################################

## Now bring in the data you just downloaded from NZ Stats webpage [https://datafinder.stats.govt.nz/]
nz_stat_areas <- st_read("CHANGE HERE TO NAME OF SHP") #since your working directory was set you just need your shp file
View(nz_stat_areas) # will open dataset
names(nz_stat_areas)
str(nz_stat_areas) #check data type

# Load text data that was in folder
asm_rate_bySA2 <- read.csv("CHANGE NAME HERE TO CSV FILE") #if set working directory, just put the name of the file 
names(asm_rate_bySA2)
str(asm_rate_bySA2) #check data type
asm_rate_bySA2$SA22018_V1 <- as.character(asm_rate_bySA2$SA22018_V1) #need field data type to match to be able to join

##join the 2 files together
asm_and_sa2 <- nz_stat_areas %>% 
  left_join (asm_rate_bySA2, by = "SA22018_V1")

## Now make a map
asm_and_sa2_map <- st_transform(asm_and_sa2, 2193)
asm_and_sa2_map <- tm_shape(asm_and_sa2_map) + tm_polygons()
class(asm_and_sa2_map)
asm_and_sa2_map <-  asm_and_sa2_map + 
  tm_compass(type = "arrow", position = c("bottom", "right")) + # North arrow
  tm_scalebar(breaks = c(0, 250, 500), position = c("bottom", "right"))+ # scale bar
  tm_polygons (fill = "ASM_Rate", col = "grey", fill.scale = tm_scale_intervals(style = "quantile", values = "brewer.blues")) + #makes chloropleth map
  tm_title("New Zealand ASM Rate")
print(asm_and_sa2_map)

tmap_save(tm = asm_and_sa2_map, filename = "asm_and_sa2_map.tiff") ##export map as tiff