---
title: "Demo"
author: "Jens von Bergmann"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Demo}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  eval = nzchar(Sys.getenv("COMPILE_VIG"))
)
```

```{r}
library(VancouvR)
library(dplyr)
library(tidyr)
library(ggplot2)
```


### Get a list of property tax related datasets
```{r}
search_cov_datasets("property-tax") |>
  select(dataset_id,title)
```

### Get metadata for tax report
```{r}
get_cov_metadata("property-tax-report") |>
  tail(10)
```

### Get an overview of land and building values in RS zones
```{r}
search_cov_datasets("property-tax") |>
  pull(dataset_id) |>
  lapply(function(ds)
    aggregate_cov_data(ds,
                       group_by="tax_assessment_year as Year",
                       where="zoning_district like 'RS-' or zoning_district like 'R1-1'",
                       select="sum(current_land_value) as Land, sum(current_improvement_value) as Building")) |> 
  bind_rows() |>
  mutate(Date=as.Date(paste0(as.integer(Year)-1,"-07-01"))) |>
  pivot_longer(c("Land","Building")) |>
  ggplot(aes(x=Date,y=value,color=name,group=name)) +
  geom_point(shape=21) +
  geom_line() +
  scale_x_date(breaks=as.Date(paste0(seq(2005,2050,5),"-07-01")),date_labels="%b %Y") +
  scale_y_continuous(labels=function(x)scales::dollar(x, scale=10^-9,suffix="Bn"),
                     breaks=2^seq(0,10)*5*10^9,trans="log") +
  labs(title="City of Vancouver RS/R1-1 zoned land and building values",
       x=NULL, color=NULL, y="Aggregate value (nominal)")
```


### Get data for property tax report and property polygons
When metadata indicates that the data has a spatial componenet the package will automatically return the data in `sf` format. 

```{r}
tax_data <- get_cov_data(dataset_id = "property-tax-report",
                         where="tax_assessment_year='2026'",
                         select = "current_land_value, land_coordinate as tax_coord")
property_polygons <- get_cov_data(dataset_id="property-parcel-polygons") |>
  sf::st_transform(26910)
```

### Compute and plot relative land values
```{r}
plot_data <- property_polygons |> 
  left_join(tax_data |> group_by(tax_coord) |> summarize(current_land_value=sum(current_land_value)),by="tax_coord") |>
  mutate(rlv=current_land_value/as.numeric(sf::st_area(geometry))) |>
  mutate(rlvd=cut(rlv,breaks=c(-Inf,1000,2000,3000,4000,5000,7500,10000,25000,50000,Inf),
                  labels=c("<$1k","$1k-$2k","$2k-$3k","$3k-$4k","$4k-$5k","$5k-$7.5k","$7.5k-$10k",
                           "$10k-$25k","$25k-$50k",">$50k"),
                  ordered_result = TRUE))
ggplot(plot_data) +
  geom_sf(aes(fill=rlvd),color=NA) +
  scale_fill_viridis_d(option="magma",na.value="darkgrey") +
  labs(title="2026 assessment relative land values",fill="Value per m^2",caption="CoV Open Data") +
  coord_sf(datum=NA)
```

