aaronschiff

DATA_DIR = /Users/aaron/Google\ Drive/Data/Geographic/NZ/Topographic

nzlakes: nzlakes50.shp nzlakes250.shp nzlakes500.shp

nzlakes50.shp: $(DATA_DIR)/50k/nz-lake-polygons-topo-150k/nz-lake-polygons-topo-150k.shp
	rm -f temp.*
	ogr2ogr temp.shp $(DATA_DIR)/50k/nz-lake-polygons-topo-150k/nz-lake-polygons-topo-150k.shp -nln temp
	ogr2ogr nzlakes50.shp temp.shp -sql "SELECT name, OGR_GEOM_AREA/1000 as area FROM temp" -t_srs EPSG:4326
	rm temp.*

aaronschiff

# Maps of overlays in the IHP recommended Auckland Unitary Plan

# Setup
rm(list = ls())
library(maptools)
crs_nztm = CRS("+proj=tmerc +lat_0=0 +lon_0=173 +k=0.9996 +x_0=1600000 +y_0=10000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs")

# Map config
line_width = 1
line_colour = rgb(100/255, 100/255, 100/255)

aaronschiff

# Fun with Unitary Plan data -- all the things!

# Setup
rm(list = ls())
library(maptools)
crs_nztm = CRS("+proj=tmerc +lat_0=0 +lon_0=173 +k=0.9996 +x_0=1600000 +y_0=10000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs")

# Map config
line_width = 1
point_size = 0.25

aaronschiff

# Perform simple regressions on random walks and analyse the results

# Setup
rm(list = ls())
library(broom)
reps = 1000
walk_length = 100

# Generate a random walk of length n
generateRandomWalk = function(n) {

aaronschiff

# Regress a random walk on a bunch of others and choose the model with the highest R-squared
# Analyse out-of-sample forecasting performance of that model

# Setup
rm(list = ls())
walk_length = 100
reps = 100

# Generate a random walk of length n
generateRandomWalk = function(n) {

aaronschiff

# Helper function to read csv files exported from Infoshare
# Categories should be a list of names of the categorical variables to be created
read_infoshare <- function(filename, categories) {
  num_categories <- length(categories)
  dat <- read_csv(filename)
  names(dat)[1] <- "date"
  
  # Drop rows where all columns except the first column are NA, to remove junk at the bottom of the file
  junk_rows <- apply(dat[, -1], 1, function(x) prod(is.na(x))) == 1
  dat <- dat[!junk_rows, ]

aaronschiff

# -----------------------------------------------------------------------------
# Setup
library(magrittr)
library(lubridate)
library(tidyverse)
library(ggplot2)
library(scales)
source("clean-ggplot-theme.R")
# -----------------------------------------------------------------------------

aaronschiff

library(ggplot2)

dat <- data.frame(x = 1:10, y = runif(10))

chart <- ggplot(dat) + 
  geom_bar(aes(x = x, y = y), stat = "identity") + 
  geom_text(aes(x = 2, y = 1.5, label = "This text is jaggy"), size = 6) 

print(chart)

aaronschiff

# Map Christchurch property data

# -----------------------------------------------------------------------------
# Setup
rm(list = ls())
library(magrittr)
library(lubridate)
library(tidyverse)
library(scales)
library(sf)

aaronschiff

# -----------------------------------------------------------------------------
# Setup
rm(list = ls())
library(magrittr)
library(jsonlite)
library(httr)
library(lubridate)
library(tidyverse)
library(scales)
source("clean-ggplot-theme.R")