report.rmd

---
title: "Air Pollution Trends across Canada, 2001-2020"
params: 
  daterange: NA,
  territory: NA,
  city: NA,
  napsid: NA,
  pollutants: NA
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
library(plotly)
library(shiny)
library(fmsb)
library(plyr)
library(dplyr)
library(readr)
library(tidyr)
library(forcats)
library(ggplot2)
library(leaflet)
library(lubridate)
library(rmarkdown)
library(knitr)

pollutants <- c(
  "CO" = "Carbon monoxide",
  "NO" = "Nitrogen oxide",
  "NO2" = "Nitrogen dioxide",
  "NOX" = "Nitrogen oxides",
  "O3" = "Ozone",
  "SO2" = "Sulphur dioxide",
  "PM2.5" = "Particulate matter, max diameter of 2.5μm",
  "PM10" = "Particulate matter, of max diameter of 10μm"
)

pollutant.colors <-
  RColorBrewer::brewer.pal(length(pollutants), pollutant.color.palette)

data <- read_csv(NAPS_dataset_path, show_col_types = FALSE) |>
  mutate(
    Territory_Name = case_when(
      Territory == "AB" ~ "Alberta",
      Territory == "BC" ~ "British Columbia",
      Territory == "MB" ~ "Manitoba",
      Territory == "NB" ~ "New Brunswick",
      Territory == "NL" ~ "Newfoundland",
      Territory == "NS" ~ "Nova Scotia",
      Territory == "NT" ~ "Northwest Territories",
      Territory == "NU" ~ "Nunavut",
      Territory == "ON" ~ "Ontario",
      Territory == "PE" ~ "Prince Edward Island",
      Territory == "QC" ~ "Quebec",
      Territory == "SK" ~ "Saskatchewan",
      Territory == "YU" ~ "Yukon",
      TRUE ~ NA
    )
  ) |>
  mutate_if(is.character, utf8::utf8_encode) |>
  mutate(
    City = paste0(City, ", ", Territory),
    Territory = paste0(Territory_Name, " (", Territory, ")")
  ) |>
  mutate(
    NAPSID = fct_relevel(as.factor(NAPSID)),
    City = fct_relevel(as.factor(City)),
    Territory = fct_relevel(as.factor(Territory)),
    Pollutant = fct_relevel(as.factor(Pollutant), names(pollutants))
  )
```

## Selections
  **Date Range:** `r params$daterange`
  
  **Territory:** `r params$territory`
  
  **City:** `r params$city`
  
  **Monitoring Station ID:** `r params$napsid`
  
  **Pollutants:** `r params$pollutant`
  
## Snapshot of the Data
```{r data}
# Filters the data based on user selections
    data_filtered <- data |>
      filter(between(Date, params$date[1], params$date[2])) |>
      filter(Pollutant %in% params$pollutant)
    
    if (length(params$territory) > 0) {
      data_filtered <- data_filtered |>
        filter(Territory %in% params$territory)
    }
    
    if (length(params$city) > 0) {
      data_filtered <- data_filtered |>
        filter(City %in% params$city)
    }
    
    if (length(params$napsid) > 0) {
      data_filtered <- data_filtered |>
        filter(NAPSID %in% params$napsid)
    }
    
    data_filtered <- data_filtered |>
      mutate(
        City = fct_drop(City),
        Territory = fct_drop(Territory),
        Pollutant = fct_drop(Pollutant)
      )
    
kable(head(data_filtered))
```

## Plots

```{r bar-plot, echo=FALSE}
bar_plot <- data_filtered |>
      arrange(Value) |>
      ggplot(aes(x = Date, y = Value, fill = Pollutant)) +
      geom_col() +
      scale_x_date(date_breaks = "years" , date_labels = default.date.format) +
      labs(x = "Date",
           y = "Pollutant level (ppm)",
           title = "Breakdown by pollutants of the monthly average concentration") +
      scale_fill_brewer(palette = pollutant.color.palette) +
      theme_classic() +
      theme(axis.text.x = element_text(
        angle = 90,
        vjust = 0.5,
        hjust = 1
      ))
ggplotly(bar_plot)
```

```{r radar-plot, echo=FALSE}
data_radar <- data_filtered |>
      group_by(Pollutant) |>
      summarise(Value = mean(Value)) |>
      pivot_wider(names_from = "Pollutant", values_from = "Value")
    max <- plyr::round_any(max(data_radar), 10, f = `ceiling`)
    n_col <- ncol(data_radar)
    data_radar <- rbind(rep(max, n_col),
                        rep(0, n_col),
                        data_radar)
fmsb::radarchart(data_radar, title = "Pollutants")
```

```{r line-plot, echo=FALSE}
line_plot <- data_filtered  |>
      group_by(Date, Pollutant) |>
      summarize(Value = mean(Value)) |>
      ggplot(aes(x = Date, y = Value, color = Pollutant)) +
      geom_line() +
      geom_point() +
      scale_x_date(date_breaks = "years" , date_labels = default.date.format) +
      labs(x = "Date",
           y = "Pollutant level (ppm)",
           title = "Monthly pollutant levels") +
      scale_colour_brewer(palette = pollutant.color.palette) +
      theme_classic() +
      theme(axis.text.x = element_text(
        angle = 90,
        vjust = 0.5,
        hjust = 1
      ))
ggplotly(line_plot)
```
## Seasonal Plot

```{r seasonal}
months = c('Jan',
               'Feb',
               'Mar',
               'Apr',
               'May',
               'Jun',
               'Jul',
               'Aug',
               'Sep',
               'Oct',
               'Nov',
               'Dec')
    data_mod <- data_filtered |> mutate(
      month_name = lubridate::month(Date, label = TRUE),
      month_num = lubridate::month(Date, label = FALSE)
    )
    
    options(dplyr.summarise.inform = FALSE)
    
    seasonal_plot <- data_mod |> 
      group_by(Pollutant, month_num) |> 
      summarise(meanValue= mean(Value)) |>
      ggplot(aes(x = month_num,
            y = meanValue,
            color = Pollutant)) +
      scale_colour_brewer(palette = pollutant.color.palette) +
      geom_line(size = 0.6) +
      geom_point(size = 1) +
      labs(x = "Month",
           y = "Pollutant level (ppm)",
           title = "Monthly average Pollutant levels") +
      scale_x_continuous(labels = months,
                         breaks = 1:12,
                         limits = c(1,12)) + 
      theme_classic() + 
      theme(axis.text.x = element_text(angle = 90)) +
      theme(legend.position = c(1.05,0.85))
    
  ggplotly(seasonal_plot)
```

## Map
```{r map, echo=FALSE}
pollutant.color.factor <-
      colorFactor(pollutant.colors, domain = names(pollutants))
    
data_filtered |>
      group_by(NAPSID, Pollutant, Latitude, Longitude, City) |>
      summarize(
        Date.Start = format(min(Date), default.date.format),
        Date.End = format(max(Date), default.date.format),
        Value.Min = min(Value),
        Value.Mean = mean(Value),
        Value.Max = max(Value),
        Value.Count = n()
      ) |>
      mutate(
        label = paste(
          "Monitoring Station ID: <strong>",
          NAPSID,
          "</strong><br>",
          "Location: <strong>",
          City,
          "</strong><br>",
          "Pollutant: <strong>",
          Pollutant,
          "</strong>",
          sep = ""
        ) |>
          lapply(htmltools::HTML),
        popup = paste(
          "Monitoring Station ID: <strong>",
          NAPSID,
          "</strong><br>",
          "Location: <strong>",
          City,
          "</strong><br>",
          "Pollutant: <strong>",
          Pollutant,
          "</strong><br><br>",
          "Record Date Range: <strong>",
          Date.Start,
          "</strong> - <strong>",
          Date.End,
          "</strong><br>",
          "Measurement Values: <strong>",
          round(Value.Min, 2),
          "</strong> - <strong>",
          round(Value.Max, 2),
          "</strong> (Mean: <strong>",
          round(Value.Mean, 2),
          "</strong>)",
          sep = ""
        ) |>
          lapply(htmltools::HTML),
      ) |>
      leaflet() |>
      addProviderTiles(providers$CartoDB.Voyager) |>
      addCircleMarkers(
        lng = ~ Longitude,
        lat = ~ Latitude,
        label = ~ label,
        popup = ~ popup,
        color = ~ pollutant.color.factor(Pollutant),
        radius = 4,
        fillOpacity = 0.8,
        stroke = FALSE,
        clusterOptions = markerClusterOptions(
          iconCreateFunction = JS(
            "function (cluster) {
              return new L.DivIcon({
                html: '<div><span>' + cluster.getChildCount() + '</span></div>',
                className: 'marker-cluster marker-cluster-generic',
                iconSize: new L.Point(40, 40)
              });
            }"
          )
        )
      )
```

Copyright © 2023 Elena Ganacheva, Ritisha Sharma, Ranjit Sundaramurthi, Kelvin Wong.

This software is free and open source, licensed under the MIT license.

Uses the National Air Pollution Surveillance (NAPS) data published by the Government of Canada, governed by the terms of the Open Government License - Canada.