Getting Started with nycOpenData

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: ¹datasetinformation_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:

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

Getting Started with nycOpenData

Christian Martinez

Introduction

The `nyc_pull_dataset()` function

The `nyc_any_dataset()` function

Rule of Thumb

Real World Example

Mini analysis

Summary

How to Cite

Getting Started with nycOpenData

Christian Martinez

Introduction

The nyc_pull_dataset() function

The nyc_any_dataset() function

Rule of Thumb

Real World Example

Mini analysis

Summary

How to Cite

The `nyc_pull_dataset()` function

The `nyc_any_dataset()` function