---
title: "Analyzing NYC Climate Projections: Extreme Events and Sea Level Rise"
output: rmarkdown::html_vignette
author: "Emma Tupone"
vignette: >
  %\VignetteIndexEntry{Analyzing NYC Climate Projections: Extreme Events and Sea Level Rise}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, include = FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
library(nycOpenData)
library(dplyr)
library(ggplot2)
```

## Introduction

This vignette demonstrates how to use the 38ps-fnsg() function to explore projected extreme climate events and sea level rise for New York City using the [New York City Climate Projections: Extreme Events and Sea Level Rise](https://data.cityofnewyork.us/Environment/New-York-City-Climate-Projections-Extreme-Events-a/38ps-fnsg/about_data) dataset on the NYC Open Data portal.

The dataset provides projections under different climate scenarios, including:

- Number of heatwaves per year
- Cooling and heating degree days
- Projected sea level rise

Researchers, city planners, and policymakers can use this information to understand future climate risks, prepare for extreme weather events, and plan adaptation strategies.

## Retrieve a Sample of Data

```{r retrieve-sample}
sample_data <- nyc_pull_dataset("38ps-fnsg", limit = 10)
nyc_list_datasets()

sample_data
```

This code retrieves 10 rows of data from the NYC Open Data endpoint for extreme events and sea level rise projections.

## Summarize Key Metrics

```{r key-metrics}
summary_table <- sample_data |>
  select(period, number_of_heatwaves_year, cooling_degree_days, heating_degree_days) |> dplyr::slice_head(n = 10)

summary_table
```

-   Period=climate period (e.g., "Baseline", "2030s")
-   number_of_heatwaves_year=projected heatwaves per year
-   cooling_degree_days/heating_degree_days=metrics of temperature extremes

This table gives a quick overview of projected extreme events for different scenarios.

## Visualization

```{r visualization}
plot_data <- sample_data |>
  mutate(number_of_heatwaves_year = as.numeric(number_of_heatwaves_year))

ggplot(plot_data, aes(x = period, y = number_of_heatwaves_year)) +
  geom_col() +
  labs(
    title = "Projected Number of Heatwaves by Climate Period",
    x = "Climate Period",
    y = "Projected Heatwaves per Year"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

This plot shows how the number of heatwaves is projected to change across scenarios. It helps visualize future climate risks at a glance.