Introduction
NYC has a population of almost 8.5 million people. By calling 311,
residents are able to make comments, inquiries, complaints, and requests
to the city agencies. All 311 service requests are contained in the
dataset, found
here. 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_311() function, we can gather the most
recent 311 calls in New York City, and filter based upon any of the
columns inside the dataset.
Note: nyc_311() automatically sorts in descending order
based on the created_date column.
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
requests, however, let’s change that to only see the latest 3 requests.
To do this, we can set limit = 3.
small_sample <- nyc_311(limit = 3)
small_sample
#> # A tibble: 3 × 32
#> unique_key created_date agency agency_name complaint_type descriptor
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 67591844 2026-01-24T02:05:45.0… NYPD New York C… Noise - Resid… Loud Talk…
#> 2 67590426 2026-01-24T02:05:27.0… NYPD New York C… Noise - Resid… Loud Musi…
#> 3 67583315 2026-01-24T02:04:34.0… NYPD New York C… Noise - Resid… Banging/P…
#> # ℹ 26 more variables: location_type <chr>, incident_zip <chr>,
#> # incident_address <chr>, street_name <chr>, cross_street_1 <chr>,
#> # cross_street_2 <chr>, intersection_street_1 <chr>,
#> # intersection_street_2 <chr>, address_type <chr>, city <chr>,
#> # landmark <chr>, status <chr>, community_board <chr>,
#> # council_district <chr>, police_precinct <chr>, bbl <chr>, borough <chr>,
#> # x_coordinate_state_plane <chr>, y_coordinate_state_plane <chr>, …
# Seeing what columns are in the dataset
colnames(small_sample)
#> [1] "unique_key" "created_date"
#> [3] "agency" "agency_name"
#> [5] "complaint_type" "descriptor"
#> [7] "location_type" "incident_zip"
#> [9] "incident_address" "street_name"
#> [11] "cross_street_1" "cross_street_2"
#> [13] "intersection_street_1" "intersection_street_2"
#> [15] "address_type" "city"
#> [17] "landmark" "status"
#> [19] "community_board" "council_district"
#> [21] "police_precinct" "bbl"
#> [23] "borough" "x_coordinate_state_plane"
#> [25] "y_coordinate_state_plane" "open_data_channel_type"
#> [27] "park_facility_name" "park_borough"
#> [29] "latitude" "longitude"
#> [31] "location.type" "location.coordinates"
Fantastic! We successfully pulled 311 data from the NYC Open Data
Portal.
Let’s now 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_311 <- nyc_311(limit = 3, filters = list(borough = "BROOKLYN"))
brooklyn_311
#> # A tibble: 3 × 32
#> unique_key created_date agency agency_name complaint_type descriptor
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 67591844 2026-01-24T02:05:45.0… NYPD New York C… Noise - Resid… Loud Talk…
#> 2 67583315 2026-01-24T02:04:34.0… NYPD New York C… Noise - Resid… Banging/P…
#> 3 67586147 2026-01-24T02:03:40.0… NYPD New York C… Noise - Resid… Banging/P…
#> # ℹ 26 more variables: location_type <chr>, incident_zip <chr>,
#> # incident_address <chr>, street_name <chr>, cross_street_1 <chr>,
#> # cross_street_2 <chr>, intersection_street_1 <chr>,
#> # intersection_street_2 <chr>, address_type <chr>, city <chr>,
#> # landmark <chr>, status <chr>, community_board <chr>,
#> # council_district <chr>, police_precinct <chr>, bbl <chr>, borough <chr>,
#> # x_coordinate_state_plane <chr>, y_coordinate_state_plane <chr>, …
# Checking to see the filtering worked
unique(brooklyn_311$borough)
#> [1] "BROOKLYN"
Success! From calling the brooklyn_311 dataset we see
there are only 3 rows of data, and from the unique() call
we see the only borough featured in our dataset is BROOKLYN.
One of the strongest qualities this function has is its ability to
filter based off of multiple columns. Let’s put everything together and
get a dataset of the last 50 311 requests from the New York
Police Department in Brooklyn.
# Creating the dataset
brooklyn_nypd <- nyc_311(limit = 50, filters = list(agency = "NYPD", borough = "BROOKLYN"))
# Calling head of our new dataset
head(brooklyn_nypd)
#> # A tibble: 6 × 36
#> unique_key created_date agency agency_name complaint_type descriptor
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 67591844 2026-01-24T02:05:45.0… NYPD New York C… Noise - Resid… Loud Talk…
#> 2 67583315 2026-01-24T02:04:34.0… NYPD New York C… Noise - Resid… Banging/P…
#> 3 67586147 2026-01-24T02:03:40.0… NYPD New York C… Noise - Resid… Banging/P…
#> 4 67591820 2026-01-24T02:03:14.0… NYPD New York C… Illegal Parki… Posted Pa…
#> 5 67583301 2026-01-24T02:02:49.0… NYPD New York C… Noise - Comme… Loud Musi…
#> 6 67587630 2026-01-24T02:01:57.0… NYPD New York C… Noise - Resid… Banging/P…
#> # ℹ 30 more variables: location_type <chr>, incident_zip <chr>,
#> # incident_address <chr>, street_name <chr>, cross_street_1 <chr>,
#> # cross_street_2 <chr>, intersection_street_1 <chr>,
#> # intersection_street_2 <chr>, address_type <chr>, city <chr>,
#> # landmark <chr>, status <chr>, community_board <chr>,
#> # council_district <chr>, police_precinct <chr>, bbl <chr>, borough <chr>,
#> # x_coordinate_state_plane <chr>, y_coordinate_state_plane <chr>, …
# Quick check to make sure our filtering worked
nrow(brooklyn_nypd)
#> [1] 50
unique(brooklyn_nypd$agency)
#> [1] "NYPD"
unique(brooklyn_nypd$borough)
#> [1] "BROOKLYN"
We successfully created our dataset that contains the 50 most recent
requests regarding the NYPD 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 complaint_type column, to
figure out what NYC residents in Brooklyn are complaining about to the
NYPD.
To do this, we will create a bar graph of the complaint types.
# Visualizing the distribution, ordered by frequency
ggplot(brooklyn_nypd, aes(y = reorder(complaint_type, complaint_type, length))) +
geom_bar(fill = "steelblue") +
theme_minimal() +
labs(
title = "Most Recent NYPD 311 Complaints (Brooklyn)",
subtitle = "Top 50 service requests",
x = "Number of Complaints",
y = "Type of Complaint"
)
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.
