# Analyzing NYC Climate Projections: Extreme Events and Sea Level Rise

#### Emma Tupone

## Introduction

This vignette demonstrates how to use the `nyc_events_sealevel` function to explore projected extreme climate events and sea level rise for New York City.

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.

## Load the Package

    library(nycOpenData)
    library(dplyr)
    library(ggplot2)
    library(knitr)

## Retrive a Sample of Data

    sample_data <- nyc_events_sealevel(limit = 10)
    sample_data

    ## # A tibble: 10 × 16
    ##    period           sea_lelel_rise number_of_days_year_…¹ number_of_days_year_…²
    ##    <chr>            <chr>          <chr>                  <chr>
    ##  1 Baseline (1981-… n/a            69                     17
    ##  2 2030s (10th Per… 6 in           85                     27
    ##  3 2030s (25th Per… 7 in           85                     27
    ##  4 2030s (75th Per… 11 in          99                     46
    ##  5 2030s (90th Per… 13 in          104                    54
    ##  6 2050s (10th Per… 12 in          91                     32
    ##  7 2050s (25th Per… 14 in          99                     38
    ##  8 2050s (75th Per… 19 in          100                    62
    ##  9 2050s (90th Per… 23 in          121                    69
    ## 10 2080s (10th Per… 21 in          104                    46
    ## # ℹ abbreviated names: ¹​number_of_days_year_with, ²​number_of_days_year_with_1
    ## # ℹ 12 more variables: number_of_days_year_with_2 <chr>,
    ## #   number_of_days_year_with_3 <chr>, number_of_heatwaves_year <chr>,
    ## #   average_lenth_of_heat_waves <chr>, number_of_days_year_with_4 <chr>,
    ## #   number_of_days_year_with_5 <chr>, cooling_degree_days <chr>,
    ## #   number_of_days_year_with_6 <chr>, heating_degree_days <chr>,
    ## #   number_of_days_year_with_7 <chr>, number_of_days_year_with_8 <chr>, …

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

## Summarize Key Metrics

    summary_table <- sample_data %>%
      select(period, number_of_heatwaves_year, cooling_degree_days, heating_degree_days) %>%
      head(10)
    summary_table

    ## # A tibble: 10 × 4
    ##    period         number_of_heatwaves_…¹ cooling_degree_days heating_degree_days
    ##    <chr>          <chr>                  <chr>               <chr>
    ##  1 Baseline (198… 2                      1156                4659
    ##  2 2030s (10th P… 3                      1397                3589
    ##  3 2030s (25th P… 3                      1471                3766
    ##  4 2030s (75th P… 6                      1757                4049
    ##  5 2030s (90th P… 7                      1903                4240
    ##  6 2050s (10th P… 4                      1568                3102
    ##  7 2050s (25th P… 5                      1713                3384
    ##  8 2050s (75th P… 8                      2124                3754
    ##  9 2050s (90th P… 9                      2335                3996
    ## 10 2080s (10th P… 6                      1817                2298
    ## # ℹ abbreviated name: ¹​number_of_heatwaves_year

- 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

    ggplot(sample_data, aes(x = period, y = as.numeric(number_of_heatwaves_year))) +
      geom_col() +
      labs(
        title = "Projected Number of Heatwaves by Scenario",
        x = "Climate Scenario",
        y = "Number of Heatwaves"
      ) +
      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 glance.