# Working With NYC Wetlands Data

#### Shannon Joyce

    knitr::opts_chunk$set(warning = FALSE, message = FALSE)
    library(nycOpenData)
    library(ggplot2)
    library(dplyr)
    library(knitr)

## Introduction

New York City is home to many wetland features. In an effort to grow awareness of their existence and multitude, this dataset containing the geographic locations and descriptions of wetland features was created. In R, the `nycOpenData` package can be used to pull this data directly.

The `nycOpenData` package provides a streamlined interface for accessing New York City’s vast open data resources. It connects directly to the NYC Open Data Portal. It is currently utilized as a primary tool for teaching data acquisition in Reproducible Research Using R, helping students bridge the gap between raw city APIs and tidy data analysis.

By using the `nyc_wetlands()` function, we can gather the most recently listed wetland features in New York City, and filter based upon any of the columns inside the dataset.

> Note: `nyc_wetlands()` automatically sorts in descending order based on the verificationstatusyear column. Due to this order, the first group of rows are `Unverified`, so the verificationstatus year is omitted for those rows.

## Pulling a Small Sample

To start, let’s pull a small sample to see what the data looks like. By default, the function pulls in the 10,000 most recent additions, however, let’s change that to only see the latest 3 additions. To do this, we can set `limit = 3`.

    small_sample <- nyc_wetlands(limit = 3)
    small_sample
    #> # A tibble: 3 × 6
    #>   classname objectid verificationstatus verificationstatusyear multipolygon.type
    #>   <chr>     <chr>    <chr>              <chr>                  <chr>
    #> 1 Emergent  786      verified-Rapid fi… 2024                   MultiPolygon
    #> 2 Emergent  754      Verified - Rapid … 2024                   MultiPolygon
    #> 3 Emergent  745      Verified - Rapid … 2024                   MultiPolygon
    #> # ℹ 1 more variable: multipolygon.coordinates <list>

    # Seeing what columns are in the dataset
    colnames(small_sample)
    #> [1] "classname"                "objectid"
    #> [3] "verificationstatus"       "verificationstatusyear"
    #> [5] "multipolygon.type"        "multipolygon.coordinates"

Fantastic! We successfully pulled wetlands data from the NYC Open Data Portal.

Let’s now pull the complete dataset to work with:

## Pulling Full Dataset

    wetlands_data <- nyc_wetlands(limit = 100)

    # Let's take a look at what our full dataset looks like
    head(wetlands_data)
    #> # A tibble: 6 × 6
    #>   classname objectid verificationstatus verificationstatusyear multipolygon.type
    #>   <chr>     <chr>    <chr>              <chr>                  <chr>
    #> 1 Emergent  6521     Verified - Rapid … 2024                   MultiPolygon
    #> 2 Forested  6506     Verified - Rapid … 2024                   MultiPolygon
    #> 3 Forested  6440     Verified - Rapid … 2024                   MultiPolygon
    #> 4 Forested  6522     Verified - Rapid … 2024                   MultiPolygon
    #> 5 Emergent  6515     Verified - Rapid … 2024                   MultiPolygon
    #> 6 Scrub/Sh… 6507     Verified - Rapid … 2024                   MultiPolygon
    #> # ℹ 1 more variable: multipolygon.coordinates <list>

In our small sample data, the first few rows’ verification status were `Unverified`. Let’s see what the other values in that column are:

    unique(wetlands_data$verificationstatus)
    #> [1] "Verified - Rapid Field Protocol" "Verified - Desktop"
    #> [3] "Verified - Wetland Delineation"  "verified-Rapid field protocol"
    #> [5] "Verified Rapid Field Protocol"

Now that we see the different values in the `verificationstatus` column, let’s filter out all of the unverified wetland features:

    # Creating the dataset
    verified_wetlands <- wetlands_data %>% filter(verificationstatus != "Unverified")

    # Quick check to make sure our filtering worked
    nrow(verified_wetlands)
    #> [1] 100
    unique(verified_wetlands$verificationstatus)
    #> [1] "Verified - Rapid Field Protocol" "Verified - Desktop"
    #> [3] "Verified - Wetland Delineation"  "verified-Rapid field protocol"
    #> [5] "Verified Rapid Field Protocol"

Success! Now that we have our full list of verified wetland features in NYC, let’s take a look at some of its descriptive stats.

## Mini Analysis

Let’s create a summary table showing how many wetland features were verified each year:

    verified_per_year <- verified_wetlands %>%
      group_by(verificationstatusyear) %>%
      count(verificationstatusyear)

    verified_per_year %>% kable(caption = "Verified Wetland Features Per Year")

Verified Wetland Features Per Year
verificationstatusyear | n
--- | ---
2024 | 100

Let’s create a bar graph to see how many wetlands of each classification are verified!

    ggplot(data = verified_wetlands, aes(x = classname)) +
      geom_bar(fill = "forestgreen") +
      labs(title = "Total Number of Wetland Features By Classification", x = "Classification Name", y = "Total Count") +
      theme_minimal()

Though this vignette only demonstrates a simple use of this function, the inclusion of geospatial data allows users to map these wetland features using the provided multipolygon coordinates.

## Summary

The `nycOpenData` package serves as a robust interface for the NYC Open Data portal, streamlining the path from raw city APIs to actionable insights. By abstracting the complexities of data acquisition—such as pagination, type-casting, and complex filtering—it allows users to focus on analysis rather than data engineering.

As demonstrated in this vignette, the package provides a seamless workflow for targeted data retrieval, automated filtering, and rapid visualization.

## How to Cite

If you use this package for research or educational purposes, please cite it as follows:

Martinez C (2026). nycOpenData: Convenient Access to NYC Open Data API Endpoints. R package version 0.1.6, https://martinezc1.github.io/nycOpenData/.