reclues

Learn new skills while solving a mystery

The reclues package mimics the SQL Murder Mystery done by Northwestern’s Knight Lab - their game is set up to use SQL to solve the mystery, reclues makes their data available in R to solve the mystery using R ;-).

A brief of the task at hand is given in their walkthrough. Want to play the SQL version of the game online? Here it is courtesy of Simon Willison!

Getting Started: Murder Mystery Prompt (@knightlab)

Whodunnit??

A crime has taken place and the detective needs your help. The detective gave you the crime scene report, but you somehow lost it. You vaguely remember that the crime was a murder that occurred sometime on Jan.15, 2018 and that it took place in SQL City. All the clues to this mystery are buried in a huge database, and you need to use your R skills to navigate through this vast network of information. Your first step to solving the mystery is to retrieve the corresponding crime scene report from the police department’s database.

Take a look at the cheatsheet (this Readme file) to learn some tips on how to do this using the tidyverse tools! From there, you can use your R skills to find the murderer. The crime may be solved with other tools besides tidy tools, please feel free to use the dataset as a learning tool for other packages in R or base R itself if that is your wish. The cheatsheet is for tidyverse tools for now as a start, but since I am learning myself I may expand this to include data.table etc. in future. Have fun!

The goal of reclues is to provide the datasets from the SQL Challenge mentioned above within R, and some cheatsheet tidyverse commands for getting you on your way to solving the mystery.

The datasets

The datasets will be available as soon as you install the package. These are the datasets available and the data contained within them.

Table Name Fields Rows
crime_scene_report date, type, description, city 1,228 rows
drivers_license id, age, height, eye_color, hair_color, gender, plate_number, car_make, car_model 10,007 rows
facebook_event_checkin person_id, event_id, event_name, date 20,011 rows
get_fit_now_check_in membership_id, check_in_date, check_in_time, check_out_time 2,703 rows
get_fit_now_member id, person_id, name, membership_start_date, membership_status 184 rows
income ssn, annual_income 7,514 rows
interview person_id, transcript 4,991 rows
person id, name, license_id, address_number, address_street_name, ssn 10,011 rows

SQLite DB

The raw SQLite database as per @knightlab is also available through the get_db() function. To use the SQLite DB for your investigation you will need the DBI package.

Installation

You can install the development version from GitHub with:

Some useful functions to view the data

Other great packages to explore your data are DataExplorer and skimr.

  • DataExplorer’screate_report(dataset) creates an html report with summary stats, missing data, graphs of categorical data etc.
  • skimr’s skim(dataset) creates a nice summary of your dataset separating the different types of data allowing you to look at summary stats by data type.

Some useful functions to explore the data

SELECT

In R dplyr’s select() works much the same as SELECT in SQL. You use it to get only specific columns you are interested in.

Let’s say I wanted a closer look at the plate_number, car_make and car_model from the drivers_license table?

plate\_number car\_make car\_model
P24L4U Acura MDX
XF02T6 Cadillac SRX
VKY5KR Scion xB
car\_make car\_model
Acura MDX
Cadillac SRX
Scion xB

SQL Equivalent is:

SELECT plate_number, car_make, car_model FROM drivers_license LIMIT 3

Here’s a snippet from the online SQL version:

LIMIT

Let’s say we wanted to see a part of the data - the head() function returns 6 observations and performs a similar functionality as the LIMIT keyword in SQL.

  • head() gives you the first 6 observations of the data in the “table”
  • tail() gives you the last 6 observations of the data in the “table”

You can also specify a number as an argument to the head() or tail() functions. For example, head(15) and tail(10) will give you the first 15, and last 10 observations respectively.

description
A Man Dressed as Spider-Man Is on a Robbery Spree
Life? Dont talk to me about life.
Mama, I killed a man, put a gun against his head…
REDACTED REDACTED REDACTED
Someone killed the guard\! He took an arrow to the knee\!
Big Bully stole my lunch money\!
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
A lone hunter stalks the night, firing arrows into the Darkness. There is no hiding, no escape. In the distance, the beast falters, tethered to the void. The killing blow comes without hesitation, without mercy.

SQL Equivalent is:

SELECT description FROM crime_scene_report LIMIT 8

Here’s a snippet from the online SQL version:

Maybe I am interested in having a look at all variables associated with a person but I just want to have a look at the data not bring back all 10,011 rows.

id name license\_id address\_number address\_street\_name ssn
10000 Christoper Peteuil 993845 624 Bankhall Ave 747714076
10007 Kourtney Calderwood 861794 2791 Gustavus Blvd 477972044
10010 Muoi Cary 385336 741 Northwestern Dr 828638512
10016 Era Moselle 431897 1987 Wood Glade St 614621061

SQL Equivalent is:

SELECT * FROM person LIMIT 4;

Here’s a snippet from the online SQL version:

DISTINCT

Let’s say we wanted to see the different types of criminal activity the reports identify ….
The type field in the crime scene reports table seems to contain this info. We will use the distinct function from dplyr.

type
robbery
murder
theft
fraud
arson
bribery
assault
smuggling
blackmail

SQL Equivalent is:

SELECT DISTINCT(type) FROM crime_scene_report

Here’s a snippet from the online SQL version:

COUNT DISTINCT

Let’s say we were wondering which city has the highest number of crimes

  • here we want the city and a count of the times that city is mentioned in the crime scene report …
city n
Murfreesboro 9
SQL City 9
Duluth 8
Evansville 8
Jersey City 8
Toledo 8
Dallas 7
Hollywood 7
Kissimmee 7
Lancaster 7
Little Rock 7
Newark 7
Portsmouth 7
Reno 7
Waterbury 7
Wilmington 7

Hhmmm looks like SQL City is quite notorious for crime!

SQL Equivalent is:

SELECT city, count(city) AS n
FROM crime_scene_report
GROUP BY city
ORDER BY n DESC

Here’s a snippet from the online SQL version:

Magnify long pieces of text

Sometimes there are fields like crime_scene_report.description which are hard to see because the text runs over several lines. Even using View() or printing just the description to the screen sometimes does not help.

Enter pull() from the dplyr package which extracts a column from the data.

Hint: You will need something like this to read some of the textual description and transcript information.

value
A Man Dressed as Spider-Man Is on a Robbery Spree
Life? Dont talk to me about life.
Mama, I killed a man, put a gun against his head…
REDACTED REDACTED REDACTED
Someone killed the guard\! He took an arrow to the knee\!
Big Bully stole my lunch money\!
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
A lone hunter stalks the night, firing arrows into the Darkness. There is no hiding, no escape. In the distance, the beast falters, tethered to the void. The killing blow comes without hesitation, without mercy.
value
I was hired by a woman with a lot of money. I don’t know her name but I know she’s around 5’5" (65“) or 5’7” (67"). She has red hair and she drives a Tesla Model S. I know that she attended the SQL Symphony Concert 3 times in December 2017.

LIKE

Let’s say we’re interested in finding the people that start with a Z. We will use the stringr package for this. The str_detect() function can be used in conjunction with regular expressions - here we looking for names that start with (^) Z.

id name license\_id address\_number address\_street\_name ssn
10452 Zachary Angeloro 702210 3713 Currant Ave 965949567
10797 Zack Pentecost 150590 1839 Rushwood St 532875652
11264 Zada Laban 808317 1663 Testa Circle 983015244
14182 Zack Karwoski 964918 1546 W Middleton Way 482427372
14930 Zella Pietrzyk 440964 3680 Sandgate Circle 443068729

SQL Equivalent is:

SELECT * FROM person
WHERE name LIKE ‘Z%’

Here’s a snippet from the online SQL version:

JOINS

dplyr has joining functions such as inner_join(), left_join() etc. for joining one table to another. This mimics the SQL INNER JOIN etc.
You will notice that the person table has a field called id and the interview table has a person_id field. Let’s join these tables and see what we get.

id name license\_id address\_number address\_street\_name ssn transcript
10027 Antione Godbolt 439509 2431 Zelham Dr 491650087 nearer to watch them, and just as she came up to them she heard one of
10034 Kyra Buen 920494 1873 Sleigh Dr 332497972 a kind of serpent, that’s all I can say.’
10039 Francesco Agundez 278151 736 Buswell Dr 861079251 Beau–ootiful Soo–oop\!

SELECT * FROM person
INNER JOIN interview ON person.id = interview.person_id
WHERE address_street_name LIKE ‘%Dr%’
LIMIT 3

Here’s a snippet from the online SQL version:

Think you solved it?

Check via @knightlab’s Solution Checker

Head over to ‘The SQL Murder Mystery Page’ OR ‘The SQL Murder Mystery Walkthrough’ to check your solution! At the bottom of both pages there is a Check your solution section where you enter the name of the individual you suspect committed the crime.

Check in R

Run the following commands in R once you think you’ve solved the problem. You will need the DBI package and if you’ve been using the datasets to solve the mystery and not the SQLite database (i.e. the individual dataframes of person, drivers_license etc.) then uncomment the first line to make a connection to the database, run the queries below after you’ve put in the culprit you suspect, and then disconnect from the database.

Resources for learning

SQL

  • Brandon Rohrer (@_brohrer_) has a curated list of resources here

R