Day with most Homicides



In [14]:

    
library(httr)

# Pull the NewsroomDB URL from an environment variable so we don't expose our super-secret data feed ;-)
kNewsroomDbUrl <- Sys.getenv("NEWSROOMDB_URL")

# Download homicides CSV

r <- GET(paste0(kNewsroomDbUrl, "table/csv/homicides"))

# Load CSV into a dataframe
homicide.victims.csv <- content(r, "text")
con <- textConnection(homicide.victims.csv)
homicide.victims <- read.csv(con)
close(con)

# Parse some of the columns

# Convert the Date column to a date object
homicide.victims$Occ.Date <- as.Date(homicide.victims$Occ.Date, format="%Y-%m-%d")

# Extract the year from the date
homicide.victims$Year <- as.numeric(format(homicide.victims$Occ.Date,'%Y'))



In [15]:

    
library(dplyr)

# Get only 2016 homicide victims
homicide.victims.2016 <- filter(homicide.victims, Year == 2016)

# Group them by day
homicide.victims.2016.by.date <- group_by(homicide.victims.2016, Occ.Date)

# Count the homicide victims each day and sort in descending order
homicide.victims.2016.date.counts <- summarise(homicide.victims.2016.by.date, count = n()) %>%
  arrange(desc(count))



In [16]:

    
homicide.victims.2016.date.counts









    





Occ.Date count

	2016-08-08 9         
	2016-09-05 9         
	2016-02-02 8         
	2016-08-23 8         
	2016-06-18 6         
	2016-08-14 6         
	2016-08-21 6         
	2016-01-25 5         
	2016-05-09 5         
	2016-05-15 5         
	2016-06-04 5         
	2016-06-16 5         
	2016-07-17 5         
	2016-07-28 5         
	2016-08-16 5         
	2016-08-28 5         
	2016-09-11 5         
	2016-09-18 5         
	2016-01-05 4         
	2016-01-08 4         
	2016-01-23 4         
	2016-01-31 4         
	2016-02-04 4         
	2016-02-13 4         
	2016-03-11 4         
	2016-03-15 4         
	2016-03-31 4         
	2016-04-07 4         
	2016-04-24 4         
	2016-04-30 4         
	⋮ ⋮
	2016-05-29 1         
	2016-06-02 1         
	2016-06-15 1         
	2016-06-22 1         
	2016-06-28 1         
	2016-06-30 1         
	2016-07-03 1         
	2016-07-09 1         
	2016-07-10 1         
	2016-07-15 1         
	2016-07-19 1         
	2016-07-23 1         
	2016-07-26 1         
	2016-08-01 1         
	2016-08-15 1         
	2016-08-17 1         
	2016-08-22 1         
	2016-09-01 1         
	2016-09-02 1         
	2016-09-07 1         
	2016-09-08 1         
	2016-09-09 1         
	2016-09-12 1         
	2016-09-14 1         
	2016-09-15 1         
	2016-09-19 1         
	2016-09-20 1         
	2016-09-22 1         
	2016-09-25 1         
	2016-09-27 1



In [ ]:

Occ.Date	count
2016-08-08	9
2016-09-05	9
2016-02-02	8
2016-08-23	8
2016-06-18	6
2016-08-14	6
2016-08-21	6
2016-01-25	5
2016-05-09	5
2016-05-15	5
2016-06-04	5
2016-06-16	5
2016-07-17	5
2016-07-28	5
2016-08-16	5
2016-08-28	5
2016-09-11	5
2016-09-18	5
2016-01-05	4
2016-01-08	4
2016-01-23	4
2016-01-31	4
2016-02-04	4
2016-02-13	4
2016-03-11	4
2016-03-15	4
2016-03-31	4
2016-04-07	4
2016-04-24	4
2016-04-30	4
⋮	⋮
2016-05-29	1
2016-06-02	1
2016-06-15	1
2016-06-22	1
2016-06-28	1
2016-06-30	1
2016-07-03	1
2016-07-09	1
2016-07-10	1
2016-07-15	1
2016-07-19	1
2016-07-23	1
2016-07-26	1
2016-08-01	1
2016-08-15	1
2016-08-17	1
2016-08-22	1
2016-09-01	1
2016-09-02	1
2016-09-07	1
2016-09-08	1
2016-09-09	1
2016-09-12	1
2016-09-14	1
2016-09-15	1
2016-09-19	1
2016-09-20	1
2016-09-22	1
2016-09-25	1
2016-09-27	1