The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
New York City Population by Borough 1950 -
2040
Robert Hutto
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
library(nycOpenData)
library(ggplot2)
library(dplyr)
library(tidyr)
Introduction
New York City is made up of five boroughs: the Bronx, Brooklyn,
Manhattan, Queens, and Staten Island. The population of each of these
boroughs has varied throughout the past century.
The nyc_borough_population() function provides access to
historical census data and population projections from 1950 to 2040,
allowing one to analyze demographic trends across Brooklyn, Bronx,
Manhattan, Queens, and Staten Island.
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.
Pulling a Small Sample
Let’s start by pulling a small sample to see the structure:
small_sample <- nyc_borough_population(limit = 5)
small_sample
#> # A tibble: 5 × 22
#> age_group borough `_1950` _1950_boro_share_of_nyc_tota…¹ `_1960`
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Total Population "NYC Total" 7891957 100.00 7781984
#> 2 Total Population " Bronx" 1451277 18.39 1424815
#> 3 Total Population " Brooklyn" 2738175 34.70 2627319
#> 4 Total Population " Manhattan" 1960101 24.84 1698281
#> 5 Total Population " Queens" 1550849 19.65 1809578
#> # ℹ abbreviated name: ¹`_1950_boro_share_of_nyc_total`
#> # ℹ 17 more variables: `_1960_boro_share_of_nyc_total` <chr>, `_1970` <chr>,
#> # `_1970_boro_share_of_nyc_total` <chr>, `_1980` <chr>,
#> # `_1980_boro_share_of_nyc_total` <chr>, `_1990` <chr>,
#> # `_1990_boro_share_of_nyc_total` <chr>, `_2000` <chr>,
#> # `_2000_boro_share_of_nyc_total` <chr>, `_2010` <chr>,
#> # `_2010_boro_share_of_nyc_total` <chr>, `_2020` <chr>, …
# Seeing what columns are in the dataset
colnames(small_sample)
#> [1] "age_group" "borough"
#> [3] "_1950" "_1950_boro_share_of_nyc_total"
#> [5] "_1960" "_1960_boro_share_of_nyc_total"
#> [7] "_1970" "_1970_boro_share_of_nyc_total"
#> [9] "_1980" "_1980_boro_share_of_nyc_total"
#> [11] "_1990" "_1990_boro_share_of_nyc_total"
#> [13] "_2000" "_2000_boro_share_of_nyc_total"
#> [15] "_2010" "_2010_boro_share_of_nyc_total"
#> [17] "_2020" "_2020_boro_share_of_nyc_total"
#> [19] "_2030" "_2030_boro_share_of_nyc_total"
#> [21] "_2040" "_2040_boro_share_of_nyc_total"
Pulling Full Dataset
Now let’s pull the complete dataset to work with:
population_data <- nyc_borough_population()
head(population_data)
#> # A tibble: 6 × 22
#> age_group borough `_1950` _1950_boro_share_of_nyc_…¹ `_1960`
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Total Population "NYC Total" 7891957 100.00 7781984
#> 2 Total Population " Bronx" 1451277 18.39 1424815
#> 3 Total Population " Brooklyn" 2738175 34.70 2627319
#> 4 Total Population " Manhattan" 1960101 24.84 1698281
#> 5 Total Population " Queens" 1550849 19.65 1809578
#> 6 Total Population " Staten Island" 191555 2.43 221991
#> # ℹ abbreviated name: ¹`_1950_boro_share_of_nyc_total`
#> # ℹ 17 more variables: `_1960_boro_share_of_nyc_total` <chr>, `_1970` <chr>,
#> # `_1970_boro_share_of_nyc_total` <chr>, `_1980` <chr>,
#> # `_1980_boro_share_of_nyc_total` <chr>, `_1990` <chr>,
#> # `_1990_boro_share_of_nyc_total` <chr>, `_2000` <chr>,
#> # `_2000_boro_share_of_nyc_total` <chr>, `_2010` <chr>,
#> # `_2010_boro_share_of_nyc_total` <chr>, `_2020` <chr>, …
Filtering by Borough
We can filter for a specific borough. Let’s look at Brooklyn’s
population over time:
brooklyn_pop <- nyc_borough_population(filters = list(borough = " Brooklyn"))
brooklyn_pop
#> # A tibble: 1 × 22
#> age_group borough `_1950` `_1950_boro_share_of_nyc_total` `_1960`
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Total Population " Brooklyn" 2738175 34.70 2627319
#> # ℹ 17 more variables: `_1960_boro_share_of_nyc_total` <chr>, `_1970` <chr>,
#> # `_1970_boro_share_of_nyc_total` <chr>, `_1980` <chr>,
#> # `_1980_boro_share_of_nyc_total` <chr>, `_1990` <chr>,
#> # `_1990_boro_share_of_nyc_total` <chr>, `_2000` <chr>,
#> # `_2000_boro_share_of_nyc_total` <chr>, `_2010` <chr>,
#> # `_2010_boro_share_of_nyc_total` <chr>, `_2020` <chr>,
#> # `_2020_boro_share_of_nyc_total` <chr>, `_2030` <chr>, …
Mini analysis
Let’s visualize population trends across boroughs. First, we need to
reshape the data from wide to long format: ## Mini analysis
Let’s visualize population trends across boroughs. First, we need to
reshape the data from wide to long format:
# Get full dataset and filter for Total Population rows only
population_data <- nyc_borough_population()
# Clean borough names and filter to get individual boroughs (exclude NYC Total)
borough_data <- population_data %>%
mutate(borough = trimws(borough)) %>% # Remove leading/trailing spaces
filter(age_group == "Total Population", borough != "NYC Total")
# Reshape from wide to long format
pop_long <- borough_data %>%
select(borough, `_1950`, `_1960`, `_1970`, `_1980`, `_1990`, `_2000`, `_2010`, `_2020`, `_2030`, `_2040`) %>%
pivot_longer(cols = starts_with("_"), names_to = "year", values_to = "population") %>%
mutate(
year = as.numeric(gsub("_", "", year)),
population = as.numeric(population)
)
# Create line chart
ggplot(pop_long, aes(x = year, y = population, color = borough)) +
geom_line(linewidth = 1) +
geom_point(size = 2) +
scale_y_continuous(labels = scales::comma) +
theme_minimal() +
labs(
title = "NYC Population by Borough: 1950-2040",
subtitle = "Historical data and projections",
x = "Year",
y = "Population",
color = "Borough"
) +
theme(legend.position = "bottom")
We can also look at which borough is projected to have the largest
population in 2040:
pop_long %>%
filter(year == 2040) %>%
arrange(desc(population))
#> # A tibble: 5 × 3
#> borough year population
#> <chr> <dbl> <dbl>
#> 1 Brooklyn 2040 2840525
#> 2 Queens 2040 2412649
#> 3 Manhattan 2040 1691617
#> 4 Bronx 2040 1579245
#> 5 Staten Island 2040 501109
Summary
The nyc_borough_population() function provides easy
access to demographic data for New York City spanning from 1950-2040.
This enables analysis of long-term population trends, comparisons across
boroughs, and exploration of projected future changes.
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/.
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.
Health stats visible at Monitor.
