Using Local Law 18 Pay Report Data

Jonah Dratfield

Introduction

Local Law 18 of 2019 (LL18) requires the Department of Citywide Administrative Services (DCAS) to collect data about the city’s municipal workforce each year. This information is then made available to the Office of Data Analytics (ODA) 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_pull_dataset function, we can gather the most recent statistics from Local Law 18 and filter based upon any of the columns inside the dataset.

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_pull_dataset("nyc_locallaw18_payreport", limit = 3)
small_sample
#> # A tibble: 3 × 11
#>   data_year agency_name         eeo_4_job_category pay_band lower_pay_band_bound
#>       <dbl> <chr>               <chr>              <chr>                   <dbl>
#> 1      2022 TECHNOLOGY & INNOV… TECHNICIANS        95000-9…                95000
#> 2      2022 TECHNOLOGY & INNOV… TECHNICIANS        85000-8…                85000
#> 3      2022 TECHNOLOGY & INNOV… TECHNICIANS        85000-8…                85000
#> # ℹ 6 more variables: upper_pay_band_bound <dbl>, employee_status <chr>,
#> #   race <chr>, ethnicity <chr>, gender <chr>, number_of_employees <chr>

# Seeing what columns are in the dataset
names(small_sample)
#>  [1] "data_year"            "agency_name"          "eeo_4_job_category"  
#>  [4] "pay_band"             "lower_pay_band_bound" "upper_pay_band_bound"
#>  [7] "employee_status"      "race"                 "ethnicity"           
#> [10] "gender"               "number_of_employees"

Fantastic! We successfully pulled Local Law 18 Pay Report data from the NYC Open Data Portal.

Let’s now take an example of the first 3 returned rows with less than 5 employees. The nyc_pull_dataset function can filter based on any of the columns in the dataset. To filter, we add filters = list() and put whatever filters we would like inside. From our names call before, we know that there is a column called “number_of_employees” which we can use to accomplish this.

lessthan5_locallaw18payreport <- nyc_pull_dataset("nyc_locallaw18_payreport", limit = 3, filters = list(number_of_employees = "<5"))
lessthan5_locallaw18payreport 
#> # A tibble: 3 × 11
#>   data_year agency_name         eeo_4_job_category pay_band lower_pay_band_bound
#>       <dbl> <chr>               <chr>              <chr>                   <dbl>
#> 1      2022 TECHNOLOGY & INNOV… TECHNICIANS        95000-9…                95000
#> 2      2022 TECHNOLOGY & INNOV… TECHNICIANS        85000-8…                85000
#> 3      2022 TECHNOLOGY & INNOV… TECHNICIANS        85000-8…                85000
#> # ℹ 6 more variables: upper_pay_band_bound <dbl>, employee_status <chr>,
#> #   race <chr>, ethnicity <chr>, gender <chr>, number_of_employees <chr>


# Checking to see the filtering worked
lessthan5_locallaw18payreport |>
  distinct(number_of_employees)
#> # A tibble: 1 × 1
#>   number_of_employees
#>   <chr>              
#> 1 <5

Success! From calling the nyc_locallaw18_payreport from nyc_pull_dataset we see there are only 3 rows of data, and from the distinct() call we see the only featured “row groups” are those with less than 5 employees.

One of the strongest qualities this function has is its ability to filter based on of multiple columns. Let’s put everything together and get a dataset of the last 15 pieces of data from the Local law 18 Pay Report bracket for “Technology & Innovation” with “less than 5 employees” and “female employees.”

# Creating the dataset
lessthan5TI_payreport <- nyc_pull_dataset(
  "nyc_locallaw18_payreport",
  limit = 15,
  filters = list(
    number_of_employees = "<5",
    agency_name = "TECHNOLOGY & INNOVATION",
    gender = "Female"
  )
)

# Calling head of our new dataset
lessthan5TI_payreport |>
  slice_head(n = 6)
#> # A tibble: 6 × 11
#>   data_year agency_name         eeo_4_job_category pay_band lower_pay_band_bound
#>       <dbl> <chr>               <chr>              <chr>                   <dbl>
#> 1      2022 TECHNOLOGY & INNOV… ADMINISTRATIVE SU… 35000-3…                35000
#> 2      2022 TECHNOLOGY & INNOV… ADMINISTRATIVE SU… 35000-3…                35000
#> 3      2022 TECHNOLOGY & INNOV… ADMINISTRATIVE SU… 35000-3…                35000
#> 4      2022 TECHNOLOGY & INNOV… ADMINISTRATIVE SU… 35000-3…                35000
#> 5      2022 TECHNOLOGY & INNOV… ADMINISTRATIVE SU… 35000-3…                35000
#> 6      2022 TECHNOLOGY & INNOV… ADMINISTRATIVE SU… 40000-4…                40000
#> # ℹ 6 more variables: upper_pay_band_bound <dbl>, employee_status <chr>,
#> #   race <chr>, ethnicity <chr>, gender <chr>, number_of_employees <chr>

# Quick check to make sure our filtering worked
lessthan5TI_payreport |>
  summarize(rows = n())
#> # A tibble: 1 × 1
#>    rows
#>   <int>
#> 1    15

lessthan5TI_payreport |>
  distinct(agency_name)
#> # A tibble: 1 × 1
#>   agency_name            
#>   <chr>                  
#> 1 TECHNOLOGY & INNOVATION

lessthan5TI_payreport |>
  distinct(gender)
#> # A tibble: 1 × 1
#>   gender
#>   <chr> 
#> 1 Female

We successfully created our dataset that contains the 15 most recent rows for the bracket of “Technology & Innovation” with “less than 5 employees” and “female employees.”

Mini analysis

Now that we have successfully pulled the data and have it in R, let’s do a mini analysis, using the ethnicity column, to figure out the ethnicity of those in the aforementioned groups.

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

# Visualizing the distribution, ordered by frequency

lessthan5TI_payreport |>
  count(ethnicity) |>          # count how many rows fall in each ethnicity
  ggplot(aes(
    x = n,                       # n = number of rows per ethnicity
    y = reorder(ethnicity, n)    # reorder ethnicities by their counts
  )) +
  geom_col(fill = "steelblue") + # geom_col uses the counts we already computed
  theme_minimal() +
  labs(
    title = "Ethnicity of Female Employees in Bracket of TI Agencies with Fewer Than 5 Employees",
    subtitle = "Most Recent 15 Records",
    x = "Number of Records",
    y = "Ethnicity"
  )

Bar chart showing the ethnicity of female workers in municipal departments with less than 5 people in Technology & Innovation (15 most recent).

This graph shows us not only how many rows there are in the bracket of Technology & Innovation agencies with fewer than 5 employees (female employees only), but what the ethnicities of the workers there are.

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/.