load in data from data.police.uk

load_CamCrimeData.r

setwd("Directoy where the data is saved")

library(plyr) # used to read in csvs
library(psych) # has the describe function

################
# import data  #
################

# Data is in monthly CSV's, so loop over them pulling in data
  
  # CAREFUL this will get all .csv names in directory
  temp_filenames = list.files(pattern="*.csv") 
  
  #Now we have a list of the csv names, read them into one data frame
  crime_data <- ldply(temp_filenames, read.csv)
  #could do in full like below, but prefer the shorter method above using plyr
  #crime_data <- do.call("rbind", lapply(temp, read.csv, header = TRUE)) 

# take a gander!
  head(crime_data)
  summary(crime_data)
  describe(crime_data)
  str(crime_data)
  # so 228, 949 crimes are in the database, 
  # seems most the crime ID stuff came in later, and ties into the seperate
  # outcome data. Same with context. For now I'll focus on location, 
  # time and type of crime

  #DATA ISSUES. Time is a factor variable.