NYC_Bike_Accidents
In this project I use Anjali Doney’s great tutorial on mapping and apply it to NYC bike data. https://medium.com/fastah-project/a-quick-start-to-maps-in-r-b9f221f44ff3
We will map NYC bike accidents from 2012 through 2018. There were 27,000 accidents involving bikes. More than 100 resulted in the biker dying. Those 2 numbers , 27,000 and 100, forced us into a second project: layering maps. The plotted points on the map are a scatter plot and how to show 100 of one kind in sea of 27,000 of another kind became an issue. I searched for some combination of the correct point size , color, transparency that would not obscure the smaller number. Layering was the solution.
This project requires a google API key and that entails setting up aGoogle Cloud account and giving them a credit card. But I have never gotten a bill for more than 80 cents so don’t hesitate on that account.
This next part is critical. While there are many R Mapping with ggmap tutorials almost none mention how to insert your google key. Here is how: register_google(key = " your number here“)
register_google(key = “xxxxxxxxxxxxxxxxxxxxxxx”)
The Packages
library(tidyverse)## -- Attaching packages ------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.0 v purrr 0.2.5
## v tibble 1.4.2 v dplyr 0.7.8
## v tidyr 0.8.1 v stringr 1.3.1
## v readr 1.1.1 v forcats 0.3.0## -- Conflicts ---------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggmap)## Google Maps API Terms of Service: http://developers.google.com/maps/terms.## Please cite ggmap if you use it: see citation("ggmap") for details.library(ggplot2)#register_google(key = "your key here")
register_google(key = "AIzaSyDNJ-0-SrVT2FyV504iPE1urOEo3n2C0R4")How easy it is to get maps
map.NY<- get_map("New York" , zoom=10)## Source : https://maps.googleapis.com/maps/api/staticmap?center=New+York&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyDNJ-0-SrVT2FyV504iPE1urOEo3n2C0R4## Source : https://maps.googleapis.com/maps/api/geocode/json?address=New%20York&key=AIzaSyDNJ-0-SrVT2FyV504iPE1urOEo3n2C0R4ggmap(map.NY)
nyc<-get_map("New York City" , zoom = 10 , maptype = "toner-lite", source = "google")## maptype = "toner-lite" is only available with source = "stamen".## resetting to source = "stamen"...## Source : https://maps.googleapis.com/maps/api/staticmap?center=New+York+City&zoom=10&size=640x640&scale=2&maptype=terrain&key=AIzaSyDNJ-0-SrVT2FyV504iPE1urOEo3n2C0R4## Source : https://maps.googleapis.com/maps/api/geocode/json?address=New%20York%20City&key=AIzaSyDNJ-0-SrVT2FyV504iPE1urOEo3n2C0R4## Source : http://tile.stamen.com/toner-lite/10/300/383.png## Source : http://tile.stamen.com/toner-lite/10/301/383.png## Source : http://tile.stamen.com/toner-lite/10/302/383.png## Source : http://tile.stamen.com/toner-lite/10/300/384.png## Source : http://tile.stamen.com/toner-lite/10/301/384.png## Source : http://tile.stamen.com/toner-lite/10/302/384.png## Source : http://tile.stamen.com/toner-lite/10/300/385.png## Source : http://tile.stamen.com/toner-lite/10/301/385.png## Source : http://tile.stamen.com/toner-lite/10/302/385.png## Source : http://tile.stamen.com/toner-lite/10/300/386.png## Source : http://tile.stamen.com/toner-lite/10/301/386.png## Source : http://tile.stamen.com/toner-lite/10/302/386.pngggmap(nyc)
Good To Know One’s Boundaries.
‘’’ NYC boundaries West -74.257159 East -73.699215 North 40.915568 South 40.495992 Lattitude distance from equateor ‘’’
Making Boundry Specific Map
One can also just call up a map but in our case the center point of that map is too far West and so to include the entire city we would have had both zoom ouot to far and bring in non NYC areas.
lat<- c(40.915568 , 40.495992)
long<- c(-74.257159 ,-73.699215)
bbox<- make_bbox(long, lat,f=0.05)
c<- get_map(bbox, maptype = "toner-lite" , source = "stamen")## Source : http://tile.stamen.com/toner-lite/11/601/768.png## Source : http://tile.stamen.com/toner-lite/11/602/768.png## Source : http://tile.stamen.com/toner-lite/11/603/768.png## Source : http://tile.stamen.com/toner-lite/11/604/768.png## Source : http://tile.stamen.com/toner-lite/11/601/769.png## Source : http://tile.stamen.com/toner-lite/11/602/769.png## Source : http://tile.stamen.com/toner-lite/11/603/769.png## Source : http://tile.stamen.com/toner-lite/11/604/769.png## Source : http://tile.stamen.com/toner-lite/11/601/770.png## Source : http://tile.stamen.com/toner-lite/11/602/770.png## Source : http://tile.stamen.com/toner-lite/11/603/770.png## Source : http://tile.stamen.com/toner-lite/11/604/770.png## Source : http://tile.stamen.com/toner-lite/11/601/771.png## Source : http://tile.stamen.com/toner-lite/11/602/771.png## Source : http://tile.stamen.com/toner-lite/11/603/771.png## Source : http://tile.stamen.com/toner-lite/11/604/771.pngggmap(c)
Here is the link to the data. Download and asave it as “Bike_Accidents.csv”.
https://docs.google.com/spreadsheets/d/e/2PACX-1vRAMnk_n7a7VUVFrlXWJgW4wOn1p-jwlqS0mOhpvHJakeXi2nAc1r6K_ScVMufh37flpCjMdpZSxwKC/pubhtml
bike_accidents<-read_csv("Bike_Accidents.csv")## Warning: Missing column names filled in: 'X1' [1]## Parsed with column specification:
## cols(
## .default = col_character(),
## X1 = col_integer(),
## timestamp = col_double(),
## latitude = col_double(),
## longitude = col_double(),
## number_of_cyclist_injured = col_integer(),
## number_of_cyclist_killed = col_integer(),
## survived = col_integer(),
## number_of_motorist_injured = col_integer(),
## number_of_motorist_killed = col_integer(),
## number_of_pedestrians_injured = col_integer(),
## number_of_pedestrians_killed = col_integer(),
## number_of_persons_injured = col_integer(),
## number_of_persons_killed = col_integer(),
## unique_key = col_integer(),
## zip_code = col_integer()
## )## See spec(...) for full column specifications.dim(bike_accidents)## [1] 27527 31Lets drop any rows without complete latitude/longitude.
bike_accidents<-bike_accidents%>%drop_na(latitude)dim(bike_accidents)## [1] 23455 31bike_accidents<- bike_accidents%>%drop_na(longitude)dim(bike_accidents)## [1] 23455 31write.csv(bike_accidents,"Bike_Accidents_Cleaned.csv")Bike_Accidents<- read_csv("Bike_Accidents_Cleaned.csv")## Warning: Missing column names filled in: 'X1' [1]## Warning: Duplicated column names deduplicated: 'X1' => 'X1_1' [2]## Parsed with column specification:
## cols(
## .default = col_character(),
## X1 = col_integer(),
## X1_1 = col_integer(),
## timestamp = col_double(),
## latitude = col_double(),
## longitude = col_double(),
## number_of_cyclist_injured = col_integer(),
## number_of_cyclist_killed = col_integer(),
## survived = col_integer(),
## number_of_motorist_injured = col_integer(),
## number_of_motorist_killed = col_integer(),
## number_of_pedestrians_injured = col_integer(),
## number_of_pedestrians_killed = col_integer(),
## number_of_persons_injured = col_integer(),
## number_of_persons_killed = col_integer(),
## unique_key = col_integer(),
## zip_code = col_integer()
## )## See spec(...) for full column specifications.#Bike_Accidents<- read_csv("Bike_Accidents.csv")head(Bike_Accidents)## # A tibble: 6 x 32
## X1 X1_1 borough contributing_fa~ contributing_fa~ contributing_fa~
## <int> <int> <chr> <chr> <chr> <chr>
## 1 1 95 MANHAT~ Other Vehicular Unspecified <NA>
## 2 2 27426 MANHAT~ Other Vehicular Unspecified <NA>
## 3 3 27427 BRONX Driver Inattent~ Unspecified Unspecified
## 4 4 27428 <NA> Unsafe Speed Unspecified <NA>
## 5 5 27429 <NA> Pedestrian/Bicy~ Unspecified <NA>
## 6 6 27430 <NA> Driver Inattent~ Unspecified <NA>
## # ... with 26 more variables: contributing_factor_vehicle_4 <chr>,
## # contributing_factor_vehicle_5 <chr>, cross_street_name <chr>,
## # timestamp <dbl>, latitude <dbl>, longitude <dbl>, location <chr>,
## # number_of_cyclist_injured <int>, number_of_cyclist_killed <int>,
## # survived <int>, lived <chr>, number_of_motorist_injured <int>,
## # number_of_motorist_killed <int>, number_of_pedestrians_injured <int>,
## # number_of_pedestrians_killed <int>, number_of_persons_injured <int>,
## # number_of_persons_killed <int>, off_street_name <chr>,
## # on_street_name <chr>, unique_key <int>, vehicle_type_code1 <chr>,
## # vehicle_type_code2 <chr>, vehicle_type_code_3 <chr>,
## # vehicle_type_code_4 <chr>, vehicle_type_code_5 <chr>, zip_code <int>First plot. The problem. Those killed are obsucured.
ggmap(c)+
geom_point(data=Bike_Accidents,
aes(longitude,latitude,color=lived), size=5,alpha =0.9)+
labs(x= "Long", y= "Lat" ,
title="P2",
color= "Survived")## Warning: Removed 7 rows containing missing values (geom_point).
No matter what size we make the points nor what we set alpha too the accidents obscure the deaths.
ggmap(c)+
geom_point(data=Bike_Accidents,
aes(longitude,latitude,color=lived), size=1,alpha =0.1)+
labs(x= "Long", y= "Lat" ,
title="P2",
color= "Survived")## Warning: Removed 7 rows containing missing values (geom_point).
ggmap(c)+
geom_point(data=Bike_Accidents,
aes(longitude,latitude,color=lived), size=0.01,alpha =0.5)+
labs(x= "Long", y= "Lat" ,
title="P2",
color= "Survived")## Warning: Removed 7 rows containing missing values (geom_point).
The legend tells us there are deaths there but those victims are lost in the slaughter.
We will seperate out those who were killed biking.
biker_deaths<- filter(Bike_Accidents ,Bike_Accidents$number_of_cyclist_killed>0 )dim(biker_deaths)## [1] 84 32Next we will make a seperate plots of Injured and Killed and then plot them one on top of the other as layers.
write.csv(biker_deaths, "biker_deaths.csv")biker_deaths<- read_csv("biker_deaths.csv")## Warning: Missing column names filled in: 'X1' [1]## Warning: Duplicated column names deduplicated: 'X1' => 'X1_2' [2]## Parsed with column specification:
## cols(
## .default = col_character(),
## X1 = col_integer(),
## X1_2 = col_integer(),
## X1_1 = col_integer(),
## timestamp = col_double(),
## latitude = col_double(),
## longitude = col_double(),
## number_of_cyclist_injured = col_integer(),
## number_of_cyclist_killed = col_integer(),
## survived = col_integer(),
## number_of_motorist_injured = col_integer(),
## number_of_motorist_killed = col_integer(),
## number_of_pedestrians_injured = col_integer(),
## number_of_pedestrians_killed = col_integer(),
## number_of_persons_injured = col_integer(),
## number_of_persons_killed = col_integer(),
## unique_key = col_integer(),
## zip_code = col_integer()
## )## See spec(...) for full column specifications.dim(biker_deaths)## [1] 84 33A<-ggmap(c)+
geom_point(data=biker_deaths,
aes(longitude,latitude), size=3,alpha =0.5, color="red")+
labs(x= "Long", y= "Lat" ,
title="Died")
A
biker_injuries<- filter(Bike_Accidents, Bike_Accidents$number_of_cyclist_injured>0) dim(biker_injuries)## [1] 23379 32B<-ggmap(c)+
geom_point(data=biker_injuries,
aes(longitude,latitude), size=0.01,alpha =0.5, color="yellow")+
labs(x= "Long", y= "Lat" ,
title="Injured")
B## Warning: Removed 7 rows containing missing values (geom_point).
Here the two maps are joined and the colors more apprpriate, red for injured, black for killed.
ggmap(map.NY)+
geom_point(data=biker_injuries,
aes(longitude,latitude), size=0.01,alpha =0.5, color="red")+
labs(x= "Long", y= "Lat" ,
title="P2",
color= "Survived")+
geom_point(data=biker_deaths,
aes(longitude,latitude), size=3,alpha =0.5, color="black")+
labs(x= "Long", y= "Lat" ,
title="P2",
color= "Survived")## Warning: Removed 7 rows containing missing values (geom_point).
Next we will clean up the map some , removing needless labels and adjusting point size.
Injuries_and_Deaths<-ggmap(map.NY)+
geom_point(data=biker_injuries,
aes(longitude,latitude), size=0.001,alpha =0.5, color="red")+
labs(x= "Long", y= "Lat" ,
title="P2",
color= "Survived")+
geom_point(data=biker_deaths,
aes(longitude,latitude), size=2,alpha =0.9, color="black")+
labs(x= "Long", y= "Lat" ,
title="P2",
color= "Survived")+
ggtitle(" A Sea of Red")
Injuries_and_Deaths## Warning: Removed 7 rows containing missing values (geom_point).
Injuries_and_Deaths+
theme(axis.line=element_blank(),axis.text.x=element_blank(),
axis.text.y=element_blank(),axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),legend.position="none",
panel.background=element_blank(),panel.border=element_blank(),panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),plot.background=element_blank())## Warning: Removed 7 rows containing missing values (geom_point).
Injuries_and_Deaths## Warning: Removed 7 rows containing missing values (geom_point).