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Investigating Inmate Incidents
Isley Jean-Pierre
Introduction
This dataset contains information about the kind of violent incident
that happens among inmates in prison. Each row provides the incident ID,
the date the incident was reported, the incident type, and the facility
where it occurred. A researcher might want to use this dataset to
investigate the kind of crimes inmates commit while they are
incarcerated.
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_slash_stab function, we can investigate
the most recent violent incidents that occurred among incarcerated
individuals to understand their nature and fatality.
Let’s take a look at the dataset.
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_slash_stab(limit = 100)
small_sample
#> # A tibble: 100 × 4
#> incident_id reported_dt incident_type facility
#> <chr> <chr> <chr> <chr>
#> 1 227264 2026-01-24T21:10:00.000 Stabbing GRVC
#> 2 226926 2026-01-17T19:58:00.000 Stabbing OBCC
#> 3 226877 2026-01-16T17:51:00.000 Stabbing NIC
#> 4 226700 2026-01-13T15:10:00.000 Stabbing GRVC
#> 5 226465 2026-01-08T07:09:00.000 Stabbing RNDC
#> 6 226408 2026-01-06T23:25:00.000 Stabbing RMSC
#> 7 226398 2026-01-06T19:34:00.000 Stabbing GRVC
#> 8 226206 2026-01-01T11:14:00.000 Stabbing OBCC
#> 9 226110 2025-12-30T13:21:00.000 Stabbing EMTC
#> 10 225938 2025-12-26T20:33:00.000 Stabbing OBCC
#> # ℹ 90 more rows
# Seeing what columns are in the dataset
colnames(small_sample)
#> [1] "incident_id" "reported_dt" "incident_type" "facility"
unique(small_sample$facility)
#> [1] "GRVC" "OBCC" "NIC" "RNDC" "RMSC" "EMTC" "RESH"
The nyc_slash_stab() function can filter based off any
of the columns in the dataset. To filter, we add
filters = list() and put whatever filters we would like
inside. From our colnames call before, we know that there
is a column called “incident_type” which we can use to accomplish
this.
incident_slash_stab <- nyc_slash_stab(limit = 3, filters = list(incident_type = "Stabbing"))
incident_slash_stab
#> # A tibble: 3 × 3
#> incident_id reported_dt incident_type
#> <chr> <chr> <chr>
#> 1 76348 2016-02-10T02:49:00.000 Stabbing
#> 2 78554 2016-04-23T12:54:00.000 Stabbing
#> 3 79040 2016-05-08T23:18:00.000 Stabbing
# Checking to see the filtering worked
unique(incident_slash_stab$incident_type)
#> [1] "Stabbing"
Note.
The previous sections were riddled with errors in my console! The
next step would be to investigate the “facility” column and potentially
run correlational analysis and plot the results between “incident_type”
and “facility”.
Further investigation.
This section was meant to look into the most recurring incident types
among inmates and take note of their severity. But for future
inquiries:
# Creating the datasets
slash <- nyc_slash_stab(limit = 50, filters = list(facility = "AMKC", incident_type = "Slashing"))
stab <- nyc_slash_stab(limit = 50, filters = list(facility = "AMKC", incident_type = "Stabbing"))
# Calling head of our new dataset
head(slash)
#> # A tibble: 6 × 4
#> incident_id reported_dt incident_type facility
#> <chr> <chr> <chr> <chr>
#> 1 150379 2021-04-30T12:11:00.000 Slashing AMKC
#> 2 96849 2017-10-02T04:18:00.000 Slashing AMKC
#> 3 97197 2017-10-11T08:27:00.000 Slashing AMKC
#> 4 99471 2017-12-08T00:57:00.000 Slashing AMKC
#> 5 102244 2018-02-14T01:30:00.000 Slashing AMKC
#> 6 103827 2018-03-27T11:34:00.000 Slashing AMKC
head(stab)
#> # A tibble: 6 × 4
#> incident_id reported_dt incident_type facility
#> <chr> <chr> <chr> <chr>
#> 1 98323 2017-11-09T22:45:00.000 Stabbing AMKC
#> 2 101638 2018-01-30T09:26:00.000 Stabbing AMKC
#> 3 122579 2019-07-16T21:02:00.000 Stabbing AMKC
#> 4 122747 2019-07-20T14:17:00.000 Stabbing AMKC
#> 5 127101 2019-10-29T20:17:00.000 Stabbing AMKC
#> 6 134023 2020-04-06T19:42:00.000 Stabbing AMKC
# Quick check to make sure our filtering worked
nrow(slash)
#> [1] 50
nrow((stab))
#> [1] 50
unique(slash$facility)
#> [1] "AMKC"
unique(stab$facility)
#> [1] "AMKC"
This code should allow us to see how slashing and stabbing incidents
vary by facilities.
Mini analysis
As an example of how this dataset can be used for exploratory
analysis, the code below groups incidents by facility and incident type,
then visualizes the resulting counts. This approach offers a
straightforward way to compare patterns of violence across
locations.
data <- nyc_slash_stab(limit = 100) %>%
filter(incident_type %in% c("Slashing", "Stabbing")) %>%
count(facility, incident_type, name = "count")
ggplot(data, aes(x = incident_type, y = count, fill = facility)) +
geom_col(position = "dodge") +
theme_minimal() +
labs(
title = "Slashing vs Stabbing Incidents by Facility",
x = "Incident Type",
y = "Number of Incidents",
fill = "Facility"
)
This figure shows incident types by
facility.
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.
If certain codes had worked as intneded in this vignette, one could
see how the package provides a seamless workflow for targeted data
retrieval, automated filtering, and rapid visualization. Importantly,
It’s worth noting that some sections need to be improved as soon as
possible.
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/.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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