The nycdogs package contains three datasets, nyc_license, nyc_bites, and nyc_zips. They contain, respectively, data on all licensed dogs in New York city, data on reported dog bites in New York city, and geographical data for New York city at the zip code level.
Installation
nycdogs is a data package, bundling several datasets into a convenient format. The relatively large size of the data in the package means it is not suitable for hosting on CRAN, the core R package repository. There are two ways to install it.
Install direct from GitHub
You can install the beta version of nycdogs from GitHub with:
devtools::install_github("kjhealy/nycdogs")Installation using drat
While using install_github() works just fine, it would be nicer to be able to just type install.packages("nycdogs") or update.packages("nycdogs") in the ordinary way. We can do this using Dirk Eddelbuettel’s drat package. Drat provides a convenient way to make R aware of package repositories other than CRAN.
First, install drat:
if (!require("drat")) {
install.packages("drat")
library("drat")
}Then use drat to tell R about the repository where nycdogs is hosted:
drat::addRepo("kjhealy")You can now install nycdogs:
install.packages("nycdogs")To ensure that the nycdogs repository is always available, you can add the following line to your .Rprofile or .Rprofile.site file:
drat::addRepo("kjhealy")With that in place you’ll be able to do install.packages("nycdogs") or update.packages("nycdogs") and have everything work as you’d expect.
Note that the drat repository only contains data packages that are not on CRAN, so you will never be in danger of grabbing the wrong version of any other package.
Loading the data
The package works best with the tidyverse libraries and the simple features package for mapping.
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
#> ✓ tibble 3.1.6 ✓ dplyr 1.0.7
#> ✓ tidyr 1.1.4 ✓ stringr 1.4.0
#> ✓ readr 2.1.1 ✓ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x readr::edition_get() masks testthat::edition_get()
#> x dplyr::filter() masks stats::filter()
#> x purrr::is_null() masks testthat::is_null()
#> x dplyr::lag() masks stats::lag()
#> x readr::local_edition() masks testthat::local_edition()
#> x dplyr::matches() masks tidyr::matches(), testthat::matches()
library(sf)
#> Linking to GEOS 3.9.1, GDAL 3.2.3, PROJ 7.2.1; sf_use_s2() is TRUELoad the data:
To look at the tibble that contains the licensing data, do this:
nyc_license
#> # A tibble: 493,072 × 9
#> animal_name animal_gender animal_birth_year breed_rc borough zip_code
#> <chr> <chr> <dbl> <chr> <chr> <int>
#> 1 Paige F 2014 Pit Bull (or Mi… Manhat… 10035
#> 2 Yogi M 2010 Boxer Bronx 10465
#> 3 Ali M 2014 Basenji Manhat… 10013
#> 4 Queen F 2013 Akita Crossbreed Manhat… 10013
#> 5 Lola F 2009 Maltese Manhat… 10028
#> 6 Ian M 2006 Unknown Manhat… 10013
#> 7 Buddy M 2008 Unknown Manhat… 10025
#> 8 Chewbacca F 2012 Labrador (or Cr… Manhat… 10013
#> 9 Heidi-Bo F 2007 Dachshund Smoot… Brookl… 11215
#> 10 Massimo M 2009 Bull Dog, French Brookl… 11201
#> # … with 493,062 more rows, and 3 more variables: license_issued_date <date>,
#> # license_expired_date <date>, extract_year <dbl>Example
You can use the nyc_zips object to create a map of, for example, where dogs with a particular name live:
boro_names <- c("Manhattan", "Queens", "Brooklyn",
"Bronx", "Staten Island")
nyc_coco <- nyc_license %>%
filter(extract_year == 2016,
borough %in% boro_names) %>%
group_by(zip_code, animal_name) %>%
tally() %>%
ungroup() %>%
complete(zip_code, animal_name,
fill = list(n = 0)) %>%
filter(animal_name == "Coco") %>%
mutate(freq = n / sum(n),
pct = round(freq*100, 2))
nyc_coco
#> # A tibble: 191 × 5
#> zip_code animal_name n freq pct
#> <int> <chr> <dbl> <dbl> <dbl>
#> 1 10001 Coco 8 0.00932 0.93
#> 2 10002 Coco 7 0.00816 0.82
#> 3 10003 Coco 5 0.00583 0.58
#> 4 10004 Coco 1 0.00117 0.12
#> 5 10005 Coco 1 0.00117 0.12
#> 6 10006 Coco 0 0 0
#> 7 10007 Coco 3 0.00350 0.35
#> 8 10009 Coco 8 0.00932 0.93
#> 9 10010 Coco 9 0.0105 1.05
#> 10 10011 Coco 10 0.0117 1.17
#> # … with 181 more rows
coco_map <- left_join(nyc_zips, nyc_coco)
#> Joining, by = "zip_code"
## Map theme
theme_nymap <- function(base_size=9, base_family="") {
require(grid)
theme_bw(base_size=base_size, base_family=base_family) %+replace%
theme(axis.line=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank(),
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid=element_blank(),
panel.spacing=unit(0, "lines"),
plot.background=element_blank(),
legend.justification = c(0,0),
legend.position = c(0.1, 0.6),
legend.direction = "horizontal"
)
}
coco_map %>% ggplot(mapping = aes(fill = pct)) +
geom_sf(color = "gray80", size = 0.1) +
scale_fill_binned(guide = "bins", type = "viridis", option = "A") +
labs(fill = "Percent of all NYC\ndogs named Coco") +
annotate(geom = "text", x = -74.145, y = 40.85,
label = "Where's Coco?", size = 6) +
theme_nymap() +
guides(fill = guide_bins(title.position = "top",
label.position = "bottom")) 
