aaronschiff

# The objective is to make a faceted ggplot 'heatmap' with geom_tile where the
# rendered tiles in the output PNG file have a fixed width and height,
# regardless of whatever else is in the chart.

# When ggplot charts are built, the height and width of all components are
# predetermined *except* for the facet panels. Thus the approach here is to
# render an initial version of the chart to determine the height and width
# of the predetermined elements. Then the overall height and width of the
# output PNG are calculated based on what is needed to give the rendered
# tiles the desired size.

aaronschiff

# Setup
library(conflicted)
library(tidyverse)
library(lubridate)
library(readxl)

conflict_prefer("filter", "dplyr")
conflict_prefer("lag", "dplyr")
conflict_prefer("lead", "dplyr")

aaronschiff

library(tidyverse)

example_dat <- tribble(
  ~year, ~group,  ~revenues, ~costs, 
  2015,  "Apple", 1000,      500, 
  2016,  "Apple", 1500,      600, 
  2017,  "Apple", 1200,      550, 
  2015,  "Pear",  2000,      1100, 
  2016,  "Pear",  2500,      1300, 
  2017,  "Pear",  2900,      1500, 

aaronschiff

# Make a Japanese-style timetable using AT public transport data (better version)
# You'll need to download AT's GTFS data file first from https://at.govt.nz/about-us/at-data-sources/google-transit-feed/

library(magrittr)
library(tidyverse)
library(hms)
library(lubridate)
library(stringr)
library(ggthemes)

aaronschiff

# Make a Japanese-style timetable using AT public transport data
# You'll need to download AT's GTFS data file first from https://at.govt.nz/about-us/at-data-sources/google-transit-feed/

library(magrittr)
library(tidyverse)
library(hms)
library(lubridate)
library(stringr)
library(ggthemes)

aaronschiff

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

aaronschiff

# Map Christchurch property data

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

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

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

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, ]