Author: Christian Martinez

# Getting Started with nycOpenData

#### Christian Martinez

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

## Introduction

Welcome to the `nycOpenData` package, a R package dedicated to helping R users connect to the NYC Open Data Portal!

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, helping users bridge the gap between raw city APIs and tidy data analysis. It does this in two ways:

### The `nyc_pull_dataset()` function

The primary way to pull data in this package is the `nyc_pull_dataset()` function, which works in tandem with `nyc_list_datasets()`. You do not need to know anything about API keys or authentication.

The first step would be to call the `nyc_list_datasets()` to see what datasets are in the list and available to use in the `nyc_pull_dataset()` function. The provides information for thousands of datasets found on the portal.

    nyc_list_datasets() |> head()
    #> # A tibble: 6 × 26
    #>   key              uid   name  datasetinformation_a…¹ description type  category
    #>   <chr>            <chr> <chr> <chr>                  <chr>       <chr> <chr>
    #> 1 nyc_independent… 6ggx… NYC … NYC Independent Budge… "New York … data… City Go…
    #> 2 x2016_2017_guid… xr5s… 2016… Department of Educati… "New York … data… Educati…
    #> 3 competitive_sea… d6di… Comp… Mayor's Office of Con… "Citywide … data… Business
    #> 4 x2015_2016_stud… hti8… 2015… Department of Educati… "Student D… data… Educati…
    #> 5 x2019_20_demogr… ycfm… 2019… Department of Educati… "Student a… data… Educati…
    #> 6 x2012_2015_hist… pffu… 2012… Department of Educati… "Daily lis… data… Educati…
    #> # ℹ abbreviated name: ¹​datasetinformation_agency
    #> # ℹ 19 more variables: legislativecompliance_datasetfromtheopendataplan <chr>,
    #> #   url <chr>, update_datemadepublic <chr>, update_updatefrequency <chr>,
    #> #   last_data_updated_date <chr>,
    #> #   legislativecompliance_candatasetfeasiblybeautomated <chr>,
    #> #   update_automation <chr>, legislativecompliance_hasdatadictionary <chr>,
    #> #   legislativecompliance_containsaddress <chr>, …

The output includes columns such as the dataset title, description, and link to the source. The most important pieces are the key and uid. You need either in order to use the `nyc_pull_dataset()` function. You can put either the key value or uid value into the `dataset =` filter inside of `nyc_pull_dataset()`.

For instance, if we want to pull the the dataset `Motor Vehicle Collisions - Crashes`, we can use either of the methods below:

    nyc_motor_vehicle_collisions_data <- nyc_pull_dataset(
      dataset = "h9gi-nx95", limit = 2)

    nyc_motor_vehicle_collisions_data <- nyc_pull_dataset(
      dataset = "motor_vehicle_collisions_crashes", limit = 2)

No matter if we put the `uid` or the `key` as the value for `dataset =`, we successfully get the data!

### The `nyc_any_dataset()` function

The easiest workflow is to use `nyc_list_datasets()` together with `nyc_pull_dataset()`. However, there are ample datasets on the portal, with new ones being added all the time, and so the list does not have all of the datasets.

In the event that you have a particular dataset you want to use in R that is not in the list, you can use the `nyc_any_dataset()`. The only requirement is the dataset’s API endpoint (a URL provided by the NYC Open Data portal). Here are the steps to get it:

1. On the NYC Open Data Portal, go to the dataset you want to work with.
2. Click on “Export” (next to the actions button on the right hand side).
3. Click on “API Endpoint”.
4. Click on “SODA2” for “Version”.
5. Copy the API Endpoint.

Below is an example of how to use the `nyc_any_dataset()` once the API endpoint has been discovered, that will pull the same data as the `nyc_pull_dataset()` example:

    nyc_motor_vehicle_collisions_data <- nyc_any_dataset(json_link = "https://data.cityofnewyork.us/resource/h9gi-nx95.json", limit = 2)

### Rule of Thumb

While both functions provide access to NYC Open Data, they serve slightly different purposes.

In general:

- Use `nyc_pull_dataset()` when the dataset is available in `nyc_list_datasets()`
- Use `nyc_any_dataset()` when working with datasets outside the catalog

Together, these functions allow users to either quickly access the datasets or flexibly query any dataset available on the NYC Open Data portal.

## Real World Example

NYC has a population of almost 8.5 million people, and while there are a lot of people taking public transportation, there are still many drivers. Unfortunately, there are sometimes crashes that take place, and all collision data are contained in the dataset, found here. In R, the `nycOpenData` package can be used to pull this data directly.

By using the `nyc_pull_dataset()` function, we can gather the most recent 311 calls in New York City, and filter based upon any of the columns inside the dataset.

Let’s take an example of the last 3 requests from the borough Brooklyn. The `nyc_311()` function can filter based off any of the columns in the dataset. To filter, we add `filters = list()` and put whatever filters we would like inside. From our `colnames` call before, we know that there is a column called “borough” which we can use to accomplish this.

    brooklyn_collisions <- nyc_pull_dataset(dataset = "h9gi-nx95",limit = 2, timeout_sec = 90, filters = list(borough = "BROOKLYN"))
    brooklyn_collisions
    #> # A tibble: 2 × 27
    #>   crash_date          crash_time borough  zip_code latitude longitude
    #>   <dttm>              <chr>      <chr>       <dbl>    <dbl>     <dbl>
    #> 1 2023-11-01 00:00:00 1:29       BROOKLYN    11230     40.6     -74.0
    #> 2 2021-09-11 00:00:00 9:35       BROOKLYN    11208     40.7     -73.9
    #> # ℹ 21 more variables: on_street_name <chr>, off_street_name <chr>,
    #> #   number_of_persons_injured <dbl>, number_of_persons_killed <dbl>,
    #> #   number_of_pedestrians_injured <dbl>, number_of_pedestrians_killed <dbl>,
    #> #   number_of_cyclist_injured <dbl>, number_of_cyclist_killed <dbl>,
    #> #   number_of_motorist_injured <dbl>, number_of_motorist_killed <dbl>,
    #> #   contributing_factor_vehicle_1 <chr>, contributing_factor_vehicle_2 <chr>,
    #> #   contributing_factor_vehicle_3 <chr>, collision_id <dbl>, …

    # Checking to see the filtering worked
    brooklyn_collisions |>
      distinct(borough)
    #> # A tibble: 1 × 1
    #>   borough
    #>   <chr>
    #> 1 BROOKLYN

Success! From calling the `brooklyn_collisions` dataset we see there are only 2 rows of data, and from the `distinct()` call we see the only borough featured in our dataset is BROOKLYN.

One of the strongest qualities this function has is its ability to filter based off of multiple columns. Let’s put everything together and get a dataset of the last 50 collisions in Brooklyn involving a Sedan.

    # Creating the dataset
    brooklyn_sedan <- nyc_pull_dataset("h9gi-nx95", limit = 50, timeout_sec = 90, filters = list(vehicle_type_code1 = "Sedan", borough = "BROOKLYN"))

    # Calling head of our new dataset
    brooklyn_sedan |>
      slice_head(n = 6)
    #> # A tibble: 6 × 29
    #>   crash_date          crash_time borough  zip_code latitude longitude
    #>   <dttm>              <chr>      <chr>       <dbl>    <dbl>     <dbl>
    #> 1 2021-09-11 00:00:00 9:35       BROOKLYN    11208     40.7     -73.9
    #> 2 2021-12-14 00:00:00 21:10      BROOKLYN    11207     40.7     -73.9
    #> 3 2021-12-14 00:00:00 20:03      BROOKLYN    11226     40.7     -74.0
    #> 4 2021-12-14 00:00:00 17:31      BROOKLYN    11230     40.6     -74.0
    #> 5 2021-12-14 00:00:00 20:13      BROOKLYN    11215     40.7     -74.0
    #> 6 2021-12-14 00:00:00 12:54      BROOKLYN    11217     40.7     -74.0
    #> # ℹ 23 more variables: cross_street_name <chr>,
    #> #   number_of_persons_injured <dbl>, number_of_persons_killed <dbl>,
    #> #   number_of_pedestrians_injured <dbl>, number_of_pedestrians_killed <dbl>,
    #> #   number_of_cyclist_injured <dbl>, number_of_cyclist_killed <dbl>,
    #> #   number_of_motorist_injured <dbl>, number_of_motorist_killed <dbl>,
    #> #   contributing_factor_vehicle_1 <chr>, collision_id <dbl>,
    #> #   vehicle_type_code1 <chr>, contributing_factor_vehicle_2 <chr>, …

    # Quick check to make sure our filtering worked
    brooklyn_sedan |>
      summarize(rows = n())
    #> # A tibble: 1 × 1
    #>    rows
    #>   <int>
    #> 1    50

    brooklyn_sedan |>
      distinct(vehicle_type_code1)
    #> # A tibble: 1 × 1
    #>   vehicle_type_code1
    #>   <chr>
    #> 1 Sedan

    brooklyn_sedan |>
      distinct(borough)
    #> # A tibble: 1 × 1
    #>   borough
    #>   <chr>
    #> 1 BROOKLYN

We successfully created our dataset that contains the 50 requests regarding a Sedan in the borough Brooklyn.

### Mini analysis

Now that we have successfully pulled the data and have it in R, let’s do a mini analysis on using the `contributing_factor_vehicle_1` column, to figure out what are the main reasons for the collisions.

To do this, we will create a bar graph of the complaint types.

    # Visualizing the distribution, ordered by frequency
    brooklyn_sedan |>
      count(contributing_factor_vehicle_1) |>
      ggplot(aes(
        x = n,
        y = reorder(contributing_factor_vehicle_1, n)
      )) +
      geom_col(fill = "steelblue") +
      theme_minimal() +
      labs(
        title = "Top 50 Collisions in Brooklyn Involving a Sedan",
        x = "Number of Collisions",
        y = "Contributing Factor"
      )

ar chart showing the frequency of collision contributing factors in Brooklyn involving a Sedan.

This graph shows us not only which complaints were made, but how many of each complaint were made.

## 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 0.2.0, https://martinezc1.github.io/nycOpenData/.