knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
knitr::opts_chunk$set(warning = FALSE, message = FALSE) library(nycOpenData) library(ggplot2) library(dplyr) library(tidyr)
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.
Let's start by pulling a small sample to see the structure:
small_sample <- nyc_borough_population(limit = 5) small_sample # Seeing what columns are in the dataset colnames(small_sample)
Now let's pull the complete dataset to work with:
population_data <- nyc_borough_population() head(population_data)
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
Let's visualize population trends across boroughs. First, we need to reshape the data from wide to long format:
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))
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.
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/.
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