AS1-1 An Analytical Detective
Section 1 - Loading the Data
1.1
How many rows of data (observations) are in this dataset?
mvt=read.csv("mvtWeek1.csv")
nrow(mvt)[1] 1916411.2
How many variables are in this dataset?
mvt=read.csv("mvtWeek1.csv")
str(mvt)'data.frame': 191641 obs. of 11 variables:
$ ID : int 8951354 8951141 8952745 8952223 8951608 8950793 8950760 8951611 8951802 8950706 ...
$ Date : Factor w/ 131680 levels "1/1/01 0:01",..: 42824 42823 42823 42823 42822 42821 42820 42819 42817 42816 ...
$ LocationDescription: Factor w/ 78 levels "ABANDONED BUILDING",..: 72 72 62 72 72 72 72 72 72 72 ...
$ Arrest : logi FALSE FALSE FALSE FALSE FALSE TRUE ...
$ Domestic : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ Beat : int 623 1213 1622 724 211 2521 423 231 1021 1215 ...
$ District : int 6 12 16 7 2 25 4 2 10 12 ...
$ CommunityArea : int 69 24 11 67 35 19 48 40 29 24 ...
$ Year : int 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ...
$ Latitude : num 41.8 41.9 42 41.8 41.8 ...
$ Longitude : num -87.6 -87.7 -87.8 -87.7 -87.6 ...1.3
Using the “max” function, what is the maximum value of the variable “ID”?
max(mvt$ID)[1] 91811511.4
What is the minimum value of the variable “Beat”?
min(mvt$Beat)[1] 1111.5
How many observations have value TRUE in the Arrest variable (this is the number of crimes for which an arrest was made)?
summary(mvt) ID Date LocationDescription
Min. :1310022 5/16/08 0:00 : 11 STREET :156564
1st Qu.:2832144 10/17/01 22:00: 10 PARKING LOT/GARAGE(NON.RESID.): 14852
Median :4762956 4/13/04 21:00 : 10 OTHER : 4573
Mean :4968629 9/17/05 22:00 : 10 ALLEY : 2308
3rd Qu.:7201878 10/12/01 22:00: 9 GAS STATION : 2111
Max. :9181151 10/13/01 22:00: 9 DRIVEWAY - RESIDENTIAL : 1675
(Other) :191582 (Other) : 9558
Arrest Domestic Beat District CommunityArea Year
Mode :logical Mode :logical Min. : 111 Min. : 1.00 Min. : 0 Min. :2001
FALSE:176105 FALSE:191226 1st Qu.: 722 1st Qu.: 6.00 1st Qu.:22 1st Qu.:2003
TRUE :15536 TRUE :415 Median :1121 Median :10.00 Median :32 Median :2006
Mean :1259 Mean :11.82 Mean :38 Mean :2006
3rd Qu.:1733 3rd Qu.:17.00 3rd Qu.:60 3rd Qu.:2009
Max. :2535 Max. :31.00 Max. :77 Max. :2012
NA's :43056 NA's :24616
Latitude Longitude
Min. :41.64 Min. :-87.93
1st Qu.:41.77 1st Qu.:-87.72
Median :41.85 Median :-87.68
Mean :41.84 Mean :-87.68
3rd Qu.:41.92 3rd Qu.:-87.64
Max. :42.02 Max. :-87.52
NA's :2276 NA's :2276 1.6
How many observations have a LocationDescription value of ALLEY?
summary(mvt) ID Date LocationDescription
Min. :1310022 5/16/08 0:00 : 11 STREET :156564
1st Qu.:2832144 10/17/01 22:00: 10 PARKING LOT/GARAGE(NON.RESID.): 14852
Median :4762956 4/13/04 21:00 : 10 OTHER : 4573
Mean :4968629 9/17/05 22:00 : 10 ALLEY : 2308
3rd Qu.:7201878 10/12/01 22:00: 9 GAS STATION : 2111
Max. :9181151 10/13/01 22:00: 9 DRIVEWAY - RESIDENTIAL : 1675
(Other) :191582 (Other) : 9558
Arrest Domestic Beat District CommunityArea Year
Mode :logical Mode :logical Min. : 111 Min. : 1.00 Min. : 0 Min. :2001
FALSE:176105 FALSE:191226 1st Qu.: 722 1st Qu.: 6.00 1st Qu.:22 1st Qu.:2003
TRUE :15536 TRUE :415 Median :1121 Median :10.00 Median :32 Median :2006
Mean :1259 Mean :11.82 Mean :38 Mean :2006
3rd Qu.:1733 3rd Qu.:17.00 3rd Qu.:60 3rd Qu.:2009
Max. :2535 Max. :31.00 Max. :77 Max. :2012
NA's :43056 NA's :24616
Latitude Longitude
Min. :41.64 Min. :-87.93
1st Qu.:41.77 1st Qu.:-87.72
Median :41.85 Median :-87.68
Mean :41.84 Mean :-87.68
3rd Qu.:41.92 3rd Qu.:-87.64
Max. :42.02 Max. :-87.52
NA's :2276 NA's :2276 Section 2 - Understanding Dates in R
In many datasets, like this one, you have a date field. Unfortunately, R does not automatically recognize entries that look like dates. We need to use a function in R to extract the date and time. Take a look at the first entry of Date (remember to use square brackets when looking at a certain entry of a variable).
2.1
In what format are the entries in the variable Date?
- Month/Day/Year Hour:Minute
- Day/Month/Year Hour:Minute
- Hour:Minute Month/Day/Year
- Hour:Minute Day/Month/Year
mvt$Date[1] [1] 12/31/12 23:15
131680 Levels: 1/1/01 0:01 1/1/01 0:05 1/1/01 0:30 1/1/01 1:17 1/1/01 1:50 ... 9/9/12 9:502.2
Now, let’s convert these characters into a Date object in R. In your R console, type
DateConvert = as.Date(strptime(mvt$Date, "%m/%d/%y %H:%M"))This converts the variable “Date” into a Date object in R. Take a look at the variable DateConvert using the summary function.
What is the month and year of the median date in our dataset? Enter your answer as “Month Year”, without the quotes. (Ex: if the answer was 2008-03-28, you would give the answer “March 2008”, without the quotes.)
DateConvert = as.Date(strptime(mvt$Date, "%m/%d/%y %H:%M"))
summary(DateConvert) Min. 1st Qu. Median Mean 3rd Qu. Max.
"2001-01-01" "2003-07-10" "2006-05-21" "2006-08-23" "2009-10-24" "2012-12-31" 2.3
Now, let’s extract the month and the day of the week, and add these variables to our data frame mvt. We can do this with two simple functions. Type the following commands in R:
mvt$Month = months(DateConvert)
mvt$Weekday = weekdays(DateConvert)This creates two new variables in our data frame, Month and Weekday, and sets them equal to the month and weekday values that we can extract from the Date object. Lastly, replace the old Date variable with DateConvert by typing:
mvt$Date = DateConvertUsing the table command, answer the following questions.
In which month did the fewest motor vehicle thefts occur?
mvt$Month = months(DateConvert)
mvt$Weekday = weekdays(DateConvert)
mvt$Date = DateConvert
table(mvt$Month)
一月 七月 九月 二月 八月 十一月 十二月 十月 三月 五月 六月 四月
16047 16801 16060 13511 16572 16063 16426 17086 15758 16035 16002 15280 2.4
On which weekday did the most motor vehicle thefts occur?
table(mvt$Weekday)
星期一 星期二 星期三 星期五 星期六 星期日 星期四
27397 26791 27416 29284 27118 26316 27319 2.5
Each observation in the dataset represents a motor vehicle theft, and the Arrest variable indicates whether an arrest was later made for this theft. Which month has the largest number of motor vehicle thefts for which an arrest was made?
七月 九月 二月 八月 十一月"
1, 十二月" 十月 三月 五月 六月 四月
FALSE 14612 15477 14812 12273 15243 14807 15029 15744 14460 14848 14772 14028
TRUE 1435 1324 1248 1238 1329 1256 1397 1342 1298 1187 1230 1252Section 3 - Visualizing Crime Trends
3.1
Now, let’s make some plots to help us better understand how crime has changed over time in Chicago. Throughout this problem, and in general, you can save your plot to a file. For more information, this website very clearly explains the process.
First, let’s make a histogram of the variable Date. We’ll add an extra argument, to specify the number of bars we want in our histogram. In your R console, type
hist(mvt$Date, breaks=100)
hist(mvt$Date, breaks=100)
Looking at the histogram, answer the following questions.
In general, does it look like crime increases or decreases from 2002 - 2012?
- Increases
- Decreases
In general, does it look like crime increases or decreases from 2005 - 2008?
- Increases
- Decreases
3.2
Now, let’s see how arrests have changed over time. Create a boxplot of the variable “Date”, sorted by the variable “Arrest” (if you are not familiar with boxplots and would like to learn more, check out this tutorial). In a boxplot, the bold horizontal line is the median value of the data, the box shows the range of values between the first quartile and third quartile, and the whiskers (the dotted lines extending outside the box) show the minimum and maximum values, excluding any outliers (which are plotted as circles). Outliers are defined by first computing the difference between the first and third quartile values, or the height of the box. This number is called the Inter-Quartile Range (IQR). Any point that is greater than the third quartile plus the IQR or less than the first quartile minus the IQR is considered an outlier.
Does it look like there were more crimes for which arrests were made in the first half of the time period or the second half of the time period? (Note that the time period is from 2001 to 2012, so the middle of the time period is the beginning of 2007.)
- First half
- Second half
boxplot(mvt$Date ~ mvt$Arrest)
3.3
Let’s investigate this further. Use the table function for the next few questions.
For what proportion of motor vehicle thefts in 2001 was an arrest made?
Note: in this question and many others in the course, we are asking for an answer as a proportion. Therefore, your answer should take a value between 0 and 1.
table(mvt$Arrest, mvt$Year)
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
FALSE 18517 16638 14859 15169 14956 14796 13068 13425 11327 14796 15012 13542
TRUE 2152 2115 1798 1693 1528 1302 1212 1020 840 701 625 5502152/(2152+18517)[1] 0.10411733.4
For what proportion of motor vehicle thefts in 2007 was an arrest made?
table(mvt$Arrest, mvt$Year)
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
FALSE 18517 16638 14859 15169 14956 14796 13068 13425 11327 14796 15012 13542
TRUE 2152 2115 1798 1693 1528 1302 1212 1020 840 701 625 5501212/(1212+13068)[1] 0.084873953.5
For what proportion of motor vehicle thefts in 2012 was an arrest made?
table(mvt$Arrest, mvt$Year)
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
FALSE 18517 16638 14859 15169 14956 14796 13068 13425 11327 14796 15012 13542
TRUE 2152 2115 1798 1693 1528 1302 1212 1020 840 701 625 550550/(550+13542)[1] 0.03902924Since there may still be open investigations for recent crimes, this could explain the trend we are seeing in the data. There could also be other factors at play, and this trend should be investigated further. However, since we don’t know when the arrests were actually made, our detective work in this area has reached a dead end.
Section 4 - Popular Locations
4.1
Analyzing this data could be useful to the Chicago Police Department when deciding where to allocate resources. If they want to increase the number of arrests that are made for motor vehicle thefts, where should they focus their efforts?
We want to find the top five locations where motor vehicle thefts occur. If you create a table of the LocationDescription variable, it is unfortunately very hard to read since there are 78 different locations in the data set. By using the sort function, we can view this same table, but sorted by the number of observations in each category. In your R console, type:
sort(table(mvt$LocationDescription))Which locations are the top five locations for motor vehicle thefts, excluding the “Other” category? You should select 5 of the following options.
- Bank
- Gas Station
- Hotel/Motel
- Street
- Car Wash
- Restaurant
- Parking Lot/Garage (Non-Residential)
- Alley
- Driveway (Residential)
- Vacant Lot/Land
sort(table(mvt$LocationDescription))
AIRPORT BUILDING NON-TERMINAL - SECURE AREA AIRPORT EXTERIOR - SECURE AREA
1 1
ANIMAL HOSPITAL APPLIANCE STORE
1 1
CTA TRAIN JAIL / LOCK-UP FACILITY
1 1
NEWSSTAND BRIDGE
1 2
COLLEGE/UNIVERSITY RESIDENCE HALL CURRENCY EXCHANGE
2 2
BOWLING ALLEY CLEANING STORE
3 3
MEDICAL/DENTAL OFFICE ABANDONED BUILDING
3 4
AIRPORT BUILDING NON-TERMINAL - NON-SECURE AREA BARBERSHOP
4 4
LAKEFRONT/WATERFRONT/RIVERBANK LIBRARY
4 4
SAVINGS AND LOAN AIRPORT TERMINAL UPPER LEVEL - NON-SECURE AREA
4 5
CHA APARTMENT DAY CARE CENTER
5 5
FIRE STATION FOREST PRESERVE
5 6
BANK CONVENIENCE STORE
7 7
DRUG STORE OTHER COMMERCIAL TRANSPORTATION
8 8
ATHLETIC CLUB AIRPORT VENDING ESTABLISHMENT
9 10
AIRPORT PARKING LOT SCHOOL, PRIVATE, BUILDING
11 14
TAVERN/LIQUOR STORE FACTORY/MANUFACTURING BUILDING
14 16
BAR OR TAVERN WAREHOUSE
17 17
MOVIE HOUSE/THEATER RESIDENCE PORCH/HALLWAY
18 18
NURSING HOME/RETIREMENT HOME TAXICAB
21 21
DEPARTMENT STORE HIGHWAY/EXPRESSWAY
22 22
SCHOOL, PRIVATE, GROUNDS VEHICLE-COMMERCIAL
23 23
AIRPORT EXTERIOR - NON-SECURE AREA OTHER RAILROAD PROP / TRAIN DEPOT
24 28
SMALL RETAIL STORE CONSTRUCTION SITE
33 35
CAR WASH COLLEGE/UNIVERSITY GROUNDS
44 47
GOVERNMENT BUILDING/PROPERTY RESTAURANT
48 49
CHURCH/SYNAGOGUE/PLACE OF WORSHIP GROCERY FOOD STORE
56 80
HOSPITAL BUILDING/GROUNDS SCHOOL, PUBLIC, BUILDING
101 114
HOTEL/MOTEL COMMERCIAL / BUSINESS OFFICE
124 126
CTA GARAGE / OTHER PROPERTY SPORTS ARENA/STADIUM
148 166
APARTMENT SCHOOL, PUBLIC, GROUNDS
184 206
PARK PROPERTY POLICE FACILITY/VEH PARKING LOT
255 266
AIRPORT/AIRCRAFT CHA PARKING LOT/GROUNDS
363 405
SIDEWALK VEHICLE NON-COMMERCIAL
462 817
VACANT LOT/LAND RESIDENCE-GARAGE
985 1176
RESIDENCE RESIDENTIAL YARD (FRONT/BACK)
1302 1536
DRIVEWAY - RESIDENTIAL GAS STATION
1675 2111
ALLEY OTHER
2308 4573
PARKING LOT/GARAGE(NON.RESID.) STREET
14852 156564 4.2
Create a subset of your data, only taking observations for which the theft happened in one of these five locations, and call this new data set “Top5”. To do this, you can use the | symbol. In lecture, we used the & symbol to use two criteria to make a subset of the data. To only take observations that have a certain value in one variable or the other, the | character can be used in place of the & symbol. This is also called a logical “or” operation.
Alternately, you could create five different subsets, and then merge them together into one data frame using rbind.
How many observations are in Top5?
Top5 = subset(mvt, LocationDescription=="STREET" | LocationDescription=="PARKING LOT/GARAGE(NON.RESID.)" | LocationDescription=="ALLEY" | LocationDescription=="GAS STATION" | LocationDescription=="DRIVEWAY - RESIDENTIAL")4.3
R will remember the other categories of the LocationDescription variable from the original dataset, so running table(Top5$LocationDescription) will have a lot of unnecessary output. To make our tables a bit nicer to read, we can refresh this factor variable. In your R console, type:
Top5$LocationDescription = factor(Top5$LocationDescription)If you run the str or table function on Top5 now, you should see that LocationDescription now only has 5 values, as we expect.
Use the Top5 data frame to answer the remaining questions.
One of the locations has a much higher arrest rate than the other locations. Which is it? Please enter the text in exactly the same way as how it looks in the answer options for Problem 4.1.
table(Top5$LocationDescription, Top5$Arrest)
FALSE TRUE
ABANDONED BUILDING 0 0
AIRPORT BUILDING NON-TERMINAL - NON-SECURE AREA 0 0
AIRPORT BUILDING NON-TERMINAL - SECURE AREA 0 0
AIRPORT EXTERIOR - NON-SECURE AREA 0 0
AIRPORT EXTERIOR - SECURE AREA 0 0
AIRPORT PARKING LOT 0 0
AIRPORT TERMINAL UPPER LEVEL - NON-SECURE AREA 0 0
AIRPORT VENDING ESTABLISHMENT 0 0
AIRPORT/AIRCRAFT 0 0
ALLEY 2059 249
ANIMAL HOSPITAL 0 0
APARTMENT 0 0
APPLIANCE STORE 0 0
ATHLETIC CLUB 0 0
BANK 0 0
BAR OR TAVERN 0 0
BARBERSHOP 0 0
BOWLING ALLEY 0 0
BRIDGE 0 0
CAR WASH 0 0
CHA APARTMENT 0 0
CHA PARKING LOT/GROUNDS 0 0
CHURCH/SYNAGOGUE/PLACE OF WORSHIP 0 0
CLEANING STORE 0 0
COLLEGE/UNIVERSITY GROUNDS 0 0
COLLEGE/UNIVERSITY RESIDENCE HALL 0 0
COMMERCIAL / BUSINESS OFFICE 0 0
CONSTRUCTION SITE 0 0
CONVENIENCE STORE 0 0
CTA GARAGE / OTHER PROPERTY 0 0
CTA TRAIN 0 0
CURRENCY EXCHANGE 0 0
DAY CARE CENTER 0 0
DEPARTMENT STORE 0 0
DRIVEWAY - RESIDENTIAL 1543 132
DRUG STORE 0 0
FACTORY/MANUFACTURING BUILDING 0 0
FIRE STATION 0 0
FOREST PRESERVE 0 0
GAS STATION 1672 439
GOVERNMENT BUILDING/PROPERTY 0 0
GROCERY FOOD STORE 0 0
HIGHWAY/EXPRESSWAY 0 0
HOSPITAL BUILDING/GROUNDS 0 0
HOTEL/MOTEL 0 0
JAIL / LOCK-UP FACILITY 0 0
LAKEFRONT/WATERFRONT/RIVERBANK 0 0
LIBRARY 0 0
MEDICAL/DENTAL OFFICE 0 0
MOVIE HOUSE/THEATER 0 0
NEWSSTAND 0 0
NURSING HOME/RETIREMENT HOME 0 0
OTHER 0 0
OTHER COMMERCIAL TRANSPORTATION 0 0
OTHER RAILROAD PROP / TRAIN DEPOT 0 0
PARK PROPERTY 0 0
PARKING LOT/GARAGE(NON.RESID.) 13249 1603
POLICE FACILITY/VEH PARKING LOT 0 0
RESIDENCE 0 0
RESIDENCE-GARAGE 0 0
RESIDENCE PORCH/HALLWAY 0 0
RESIDENTIAL YARD (FRONT/BACK) 0 0
RESTAURANT 0 0
SAVINGS AND LOAN 0 0
SCHOOL, PRIVATE, BUILDING 0 0
SCHOOL, PRIVATE, GROUNDS 0 0
SCHOOL, PUBLIC, BUILDING 0 0
SCHOOL, PUBLIC, GROUNDS 0 0
SIDEWALK 0 0
SMALL RETAIL STORE 0 0
SPORTS ARENA/STADIUM 0 0
STREET 144969 11595
TAVERN/LIQUOR STORE 0 0
TAXICAB 0 0
VACANT LOT/LAND 0 0
VEHICLE-COMMERCIAL 0 0
VEHICLE NON-COMMERCIAL 0 0
WAREHOUSE 0 04.4
On which day of the week do the most motor vehicle thefts at gas stations happen? (Monday~Sunday)
4.5
On which day of the week do the fewest motor vehicle thefts in residential driveways happen?(Monday~Sunday)