mroutley

library(tidyverse)
library(magrittr)
library(cansim)

# For filtering later, here are the locations and products that we'll want
keep_GEO <- c("Canada")
keep_products <- c("Total Industrial product price index (IPPI), excluding energy and petroleum products",
                   "Plastic and rubber products [P32]", 
                   "Lumber and other wood products [P41]", 
                   "Fabricated metal products and construction materials [P63]", 

mroutley

mroutley

library(readxl)
library(tidyverse)
library(magrittr)
library(rmapshaper)

# Shapefiles --------------------------------------------------------------

# Federal Electoral Districts shapefile

shape_file_url <- "http://ftp.maps.canada.ca/pub/elections_elections/Electoral-districts_Circonscription-electorale/Elections_Canada_2019/federal_electoral_districts_boundaries_2019.shp.zip"

mroutley

library(tidyverse)
library(magrittr)
library(cansim)

# For filtering later, here are the locations and products that we'll want
keep_GEO <- c("Canada", "Toronto, Ontario", "Ontario")
keep_products <- c("All-items", "Alcoholic beverages", "Apples", 
                   "Transportation", "Public transportation", "Parking fees")

cansim_table <- "18-10-0004"

mroutley

library(magrittr)
ttc_gtfs <- tidytransit::feedlist %>% 
  dplyr::filter(t=="TTC GTFS") %>%
  dplyr::pull(url_d) %>%
  tidytransit::read_gtfs()

attach(ttc_gtfs)
routes %>% dplyr::inner_join(trips, by="route_id") %>%
  dplyr::inner_join(stop_times) %>%
  dplyr::inner_join(stops, by = "stop_id") %>%

mroutley

library(magrittr)

# Federal Electoral Districts shapefile
shape_file_url <- "http://ftp.maps.canada.ca/pub/elections_elections/Electoral-districts_Circonscription-electorale/federal_electoral_districts_boundaries_2015/federal_electoral_districts_boundaries_2015_shp_en.zip"
download.file(shape_file_url,
              destfile = "data-raw/federal_electoral_districts_boundaries_2015_shp_en.zip", quiet = TRUE)
unzip("data-raw/federal_electoral_districts_boundaries_2015_shp_en.zip", exdir="data-raw/federal_electoral_districts_boundaries_2015_shp_en")
federal_shapefile <- sf::read_sf("data-raw/federal_electoral_districts_boundaries_2015_shp_en",
                                 layer = "FED_CA_2_2_ENG") %>%
  sf::st_transform(crs = "+init=epsg:4326")

mroutley

url <- "https://www.elections.ca/res/rep/off/ovr2015app/41/data_donnees/pollresults_resultatsbureauCanada.zip"
pins::pin(url, name = "2015_fed_results_zip")
unzip(pins::pin_get("2015_fed_results_zip"), exdir=paste("data-raw", "pollresults", sep = "/"))

on_files <- list.files(path = "data-raw/pollresults",
                       pattern = "pollresults_resultatsbureau35[[:digit:]]{3}.csv",
                       full.names = TRUE)
federal_results <- do.call("rbind", lapply(on_files, function(.file){readr::read_csv(.file)}))

mroutley

---
title: "Supply-demand charts in R"
output: html_notebook
---

```{r setup}
library(tidyverse)
library(devtools)
install_github("andrewheiss/reconPlots")
library(reconPlots)

mroutley

pick_numbers <- function() {
  trunc(runif(4, min = 1, max = 100))
}

winning_number <- function(numbers) {
  numbers[2:4][which.min(abs(numbers[2:4] - numbers[1]))]
}

data <- replicate(1000000, winning_number(pick_numbers()))
hist(data,

mroutley

library(tidyverse)

data <- readxl::read_excel("Eco Ben Cleansed Passenger Counts v1.xlsx",
                           sheet = "Data") %>% 
  dplyr::transmute( # Shorten column names
    passengers = `Station Passenger Count FINAL`,
    delay = `System Minutes Delayed`
  )

# Calculate current sd in delays