It's available as part of the data.gov open data project. The results were really neat, but it got me thinking - we have latitude and longitude within the dataset, so why don't we create some geographical graphs?
Y'all know I'm a sucker for a beautiful data visualizations and I know just the package to do this: GGMAP! The ggmap package extends the ever-amazing ggplot core features to allow for spatial and geograhic visualization.
So let's get going!
R packages contain a grouping of R data functions and code that can be used to perform your analysis. We need to install and load them in your environment so that we can call upon them later.
# Install the relevant libraries - do this one time
# Load the relevant libraries - do this every time
3) Load Data File and Assign Variables
Rather than loading the data set right from the data.gov website , I've decided to pre-download and sample the data for you all. Map based graphs can take a while to render and we don't want to compound this issue with a large data set. The resulting dataset can be loaded as step A below.
In step B, I found a website which outlined their opinion on the most dangerous Seattle cities. I thought it would be fun to layer this data on top of actual 911 meta data to test how many dangerous crimes these neighborhoods actually see.
#A) Download the main crime incident dataset
incidents = fread('https://raw.githubusercontent.com/lgellis/MiscTutorial/master/ggmap/i2Sample.csv', stringsAsFactors = FALSE)
#B) Download the extra dataset with the most dangerous Seattle cities as per:
n <- fread('https://raw.githubusercontent.com/lgellis/MiscTutorial/master/ggmap/n.csv', stringsAsFactors = FALSE)
# Create some color variables for graphing later
col1 = "#011f4b"
col2 = "#6497b1"
col3 = "#b3cde0"
col4 = "#CC0000"
We are going to do a few quick transformations to calculate the year and then subset the years to just 2017 and 2018. We are then going filter out any rows with missing data. Finally, we are creating a display label to use when marking the dangerous neighborhoods from the housely website review.
#add year to the incidents data frame
incidents$year <- year(incidents$ymd)
#Create a more manageable data frame with only 2017 and 2018 data
i2 <- incidents[year>=2017 & year<=2018, ]
#Only include complete cases
#create a display label to the n data frame (dangerous neighbourhoods)
n$label <-paste(Rank, Location, sep="-")
5) Start Making Maps!
Map 1: Incident occurrences color coded by group
In this map we are simply creating the ggmap object called p which contains a Google map of Seattle. We are then adding a classic ggplot layer (geom_point) to plot all of the rows in our i2 data set.
##1) Create a map with all of the crime locations plotted.
p <- ggmap(get_googlemap(center = c(lon = -122.335167, lat = 47.608013),
zoom = 11, scale = 2,
color = 'color'))
p + geom_point(aes(x = Longitude, y = Latitude, colour = Initial.Type.Group), data = i2, size = 0.5) +