Classwork 9

geom_sf() and ggmap()

Loading R packages

library(tidyverse)
library(skimr)
library(ggthemes)
library(ggmap)
library(sf)

Question 1. NYC dog bites with geom_sf()

The following dataset is for Question 1:

# GeoPackage file (.gkpg), a modern format for map data
nyc_zips_sf <- st_read("https://bcdanl.github.io/data/nyc_zips_sf.gkpg")  # sf format

Reading layer `nyc_zips_sf' from data source 
  `https://bcdanl.github.io/data/nyc_zips_sf.gkpg' using driver `GPKG'
Simple feature collection with 262 features and 11 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -74.25576 ymin: 40.49584 xmax: -73.6996 ymax: 40.91517
Geodetic CRS:  WGS 84

nyc_dog_bites <- read_csv("https://bcdanl.github.io/data/nyc_dog_bites_all.csv") 

nyc_dog_license <- read_csv(
  'https://bcdanl.github.io/data/nyc_dog_license.csv')

• Plot a ZIP code level map of dog bite rate. \[ \text{Dog Bite Rate} = \frac{\text{Number of dog bites}}{\text{Number of dogs}} \]

Question 2. Google Map Routes

• Plot a Google Map of Manhattan showing the following three routes:

route1 <- route(
  from = "Central Park, NYC",
  to   = "Empire State Building, NYC",
  mode = "driving",
  structure = "route"
)

route2 <- route(
  from = "Empire State Building, NYC",
  to   = "Times Square, NYC",
  mode = "driving",
  structure = "route"
)

route3 <- route(
  from = "Times Square, NYC",
  to   = "Central Park, NYC",
  mode = "driving",
  structure = "route"
)

• In your map:
  • Include a legend identifying each route;
  • Mark both origins and destinations with distinct points;
  • Add labels to clearly indicate each origin and destination.

Question 3. 🚕 NYC Yellow Taxi Trip Analysis to the MET

You are provided a sample dataset of NYC yellow taxi trips from March 2016:

nyc_taxi <- read_csv("https://bcdanl.github.io/data/yellow_tripdata_2016-03-sample.csv") 

Q3a

Create a data.frame whose taxi trip drop off is at the Metropolitan Museum of Art (MET), and taxi trip pick up is within Manhattan.

• Coordinates for the MET drop-off are defined by the range_lon_MET and range_lat_MET vectors:

range_lon_MET <- c(-73.963, -73.9615)
range_lat_MET <- c(40.7785, 40.780)

• Coordinates for the Manhattan pick-up are defined by the range_lon and range_lat vectors:

range_lon_Manhattan <- c(-74.03, -73.92)
range_lat_Manhattan <- c(40.70, 40.88)

Q3b

From the resulting data.frame in Q3a, retrieve the Google Maps’ routes for the top 2 most expensive taxi trips to the MET.

Q3c

• Plot a Google Map of Manhattan showing the top 2 most expensive tax trip path to the MET.
• Visualize pick-up locations using a marker.
• Display the label of “Metropolitan Museum of Art” on the map.

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