2020 Los Angeles Crime Analysis

Top crime categories

y2020 <- data %>% filter(Year=='2020')
y2021 <- data %>% filter(Year=='2021')



count21 <- summarise(group_by(y2021, Crm.Cd.Desc), Counts=length(Crm.Cd.Desc)) %>% arrange(desc(Counts))
count20 <- summarise(group_by(y2020, Crm.Cd.Desc), Counts=length(Crm.Cd.Desc)) %>% arrange(desc(Counts))

head(count20,n=20) %>% 
  kbl(caption = "Top 20 Crimes in 2020") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Top 20 Crimes in 2020
Crm.Cd.Desc	Counts
VEHICLE - STOLEN	20723
BATTERY - SIMPLE ASSAULT	16288
VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS)	12878
BURGLARY	12754
BURGLARY FROM VEHICLE	12667
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT	11518
INTIMATE PARTNER - SIMPLE ASSAULT	10787
THEFT PLAIN - PETTY ($950 & UNDER)	10767
THEFT FROM MOTOR VEHICLE - PETTY ($950 & UNDER)	9679
THEFT OF IDENTITY	7497
VANDALISM - MISDEAMEANOR ($399 OR UNDER)	6971
ROBBERY	6891
THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LIVESTK,PROD	5440
THEFT FROM MOTOR VEHICLE - GRAND ($400 AND OVER)	4763
CRIMINAL THREATS - NO WEAPON DISPLAYED	4178
SHOPLIFTING - PETTY THEFT ($950 & UNDER)	3542
BRANDISH WEAPON	3079
INTIMATE PARTNER - AGGRAVATED ASSAULT	2869
TRESPASSING	2849
VIOLATION OF RESTRAINING ORDER	2605

head(count21,n=20) %>% 
  kbl(caption = "Top 20 Crimea in 2021") %>%
  kable_classic(full_width = F,html_font = "Cambria")

Top 20 Crimea in 2021
Crm.Cd.Desc	Counts
VEHICLE - STOLEN	9323
BATTERY - SIMPLE ASSAULT	6240
VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS)	5421
BURGLARY FROM VEHICLE	5255
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT	4975
BURGLARY	4826
INTIMATE PARTNER - SIMPLE ASSAULT	4363
THEFT PLAIN - PETTY ($950 & UNDER)	4350
THEFT FROM MOTOR VEHICLE - PETTY ($950 & UNDER)	3304
THEFT OF IDENTITY	3049
THEFT FROM MOTOR VEHICLE - GRAND ($400 AND OVER)	2886
ROBBERY	2862
VANDALISM - MISDEAMEANOR ($399 OR UNDER)	2761
THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LIVESTK,PROD	2112
CRIMINAL THREATS - NO WEAPON DISPLAYED	1709
BRANDISH WEAPON	1414
TRESPASSING	1198
INTIMATE PARTNER - AGGRAVATED ASSAULT	1172
SHOPLIFTING - PETTY THEFT ($950 & UNDER)	1156
VIOLATION OF RESTRAINING ORDER	1084

head(count20,n=10) %>% ggplot( aes(x=reorder(Crm.Cd.Desc,Counts), y=Counts)) +
  geom_bar(aes(fill=Crm.Cd.Desc), stat="identity") + 
   geom_label(aes(label = Counts), size = 4) +
  coord_flip() + theme(legend.position = "none") +
  labs(title = "Top 10 crimes in 2020", x="",y="Count")

head(count21,n=10) %>% ggplot( aes(x=reorder(Crm.Cd.Desc,Counts), y=Counts)) +
  geom_bar(aes(fill=Crm.Cd.Desc), stat="identity") + 
   geom_label(aes(label = Counts), size = 4) +
  coord_flip() + theme(legend.position = "none") +
  labs(title = "Top 10 crimes in 2021", x="",y="Count")

by_hour <- aggregate(data$Hour, by = list(Hour = data$Hour), FUN=length)
colnames(by_hour) <- c("Hour", "Total")

options(scipen = 1)
ggplot(by_hour, aes(Hour, Total)) +
  geom_line(colour="Red") +
  labs(title="Crimes By Hour",
        x ="Hour of the Day", y = "Total Crimes", caption = "Lowest at 5 AM\nHighest at Noon\nHigh period at 6 to 8 PM")

Seasonality by month. Using 2020 year: full year

y2020$Month2 <-month(y2020$DATE.OCC)
by_month <- aggregate(y2020$Month2, by = list(Month = y2020$Month2), FUN=length)

colnames(by_month) <- c("Month", "Total")
by_month$Month2 <- month.abb[by_month$Month]

ggplot(by_month, aes(reorder(Month2, Month), Total)) +
  geom_bar(fill="darkblue", stat="identity")+
   geom_label(aes(label = Total), size = 3) +
  labs(title = "2020 Crimes By Month", x="Month" , y ="Total Crimes", caption = "No general seasonality trends")

Area Sections

by_area <- aggregate(y2020$AREA.NAME, by = list(Area = y2020$AREA.NAME), FUN=length) 
colnames(by_area) <- c("Area","Total")



head(arrange(by_area,-Total),n=20) %>% ggplot(aes(reorder(Area, Total), Total)) +
  geom_bar(fill = "blue", stat="identity") + 
  geom_label(aes(label = Total), size = 3) +
  coord_flip()+
  ggtitle("Crimes By Area") +
  xlab("Area") +
  ylab("Total Crimes")

Districts

district <- aggregate(y2020$Rpt.Dist.No, by = list(District = y2020$Rpt.Dist.No), FUN=length) 
colnames(district) <- c("District","Total")



head(arrange(district,-Total),n=20) %>% ggplot(aes(reorder(District, Total), Total)) +
  geom_bar(fill = "blue", stat="identity") + 
  geom_label(aes(label = Total), size = 3) +
  coord_flip()+
  ggtitle("Crimes By District") +
  xlab("District") +
  ylab("Total ")

Crime Premise Location

location <- y2020 %>%
  filter(!is.na(Premis.Desc)) %>%
  group_by(Premis.Desc) %>%
  tally() %>%
  ungroup() %>%
  mutate(PremiseDescription = reorder(Premis.Desc,n)) %>%
  arrange(desc(n)) %>%
  head(20)

  
location %>% ggplot(aes(x = reorder(Premis.Desc,n),y = n)) +
  geom_bar(stat='identity',colour="white", fill ="blue") +
   geom_label(aes(label = n), size = 3) +
  labs(x = "Premise Description", y = "Count", 
       title = 'Premise Description') +
  coord_flip() + 
  theme_bw()

Child Abuse

child <- y2020 %>% filter(grepl("CHILD", Crm.Cd.Desc))
child20 <- summarise(group_by(child, Crm.Cd.Desc,Vict.Sex), Counts=length(Crm.Cd.Desc)) %>% arrange(desc(Counts))

## `summarise()` has grouped output by 'Crm.Cd.Desc'. You can override using the `.groups` argument.

child20 %>% 
  kbl(caption = "Child Abuse ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Child Abuse
Crm.Cd.Desc	Vict.Sex	Counts
CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	F	408
CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	M	407
CHILD ANNOYING (17YRS & UNDER)	F	175
CHILD NEGLECT (SEE 300 W.I.C.)	M	147
CHILD NEGLECT (SEE 300 W.I.C.)	F	117
CHILD ABUSE (PHYSICAL) - AGGRAVATED ASSAULT	M	73
CHILD ABUSE (PHYSICAL) - AGGRAVATED ASSAULT	F	72
CHILD STEALING	F	47
CHILD STEALING	M	47
CHILD PORNOGRAPHY	M	34
CHILD ANNOYING (17YRS & UNDER)	M	29
CHILD STEALING	X	17
LEWD/LASCIVIOUS ACTS WITH CHILD	F	17
CHILD PORNOGRAPHY	F	13
CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	X	3
CHILD ABANDONMENT	M	2
CHILD PORNOGRAPHY	X	2
CHILD ABANDONMENT	F	1
CHILD ABUSE (PHYSICAL) - AGGRAVATED ASSAULT	X	1
LEWD/LASCIVIOUS ACTS WITH CHILD	X	1

child20 <- summarise(group_by(child, Crm.Cd.Desc,Premis.Desc), Counts=length(Premis.Desc)) %>% arrange(desc(Counts))

## `summarise()` has grouped output by 'Crm.Cd.Desc'. You can override using the `.groups` argument.

head(child20,n=20) %>% 
  kbl(caption = "Location _ Child Abuse ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Location _ Child Abuse
Crm.Cd.Desc	Premis.Desc	Counts
CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	SINGLE FAMILY DWELLING	390
CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	248
CHILD NEGLECT (SEE 300 W.I.C.)	SINGLE FAMILY DWELLING	81
CHILD NEGLECT (SEE 300 W.I.C.)	STREET	81
CHILD ABUSE (PHYSICAL) - AGGRAVATED ASSAULT	SINGLE FAMILY DWELLING	70
CHILD ANNOYING (17YRS & UNDER)	SINGLE FAMILY DWELLING	65
CHILD ANNOYING (17YRS & UNDER)	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	56
CHILD STEALING	SINGLE FAMILY DWELLING	55
CHILD NEGLECT (SEE 300 W.I.C.)	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	54
CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	STREET	41
CHILD ABUSE (PHYSICAL) - AGGRAVATED ASSAULT	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	33
CHILD STEALING	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	33
CHILD PORNOGRAPHY	SINGLE FAMILY DWELLING	26
CHILD ABUSE (PHYSICAL) - AGGRAVATED ASSAULT	STREET	24
CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	SIDEWALK	24
CHILD ANNOYING (17YRS & UNDER)	STREET	22
CHILD PORNOGRAPHY	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	18
CHILD NEGLECT (SEE 300 W.I.C.)	PARKING LOT	17
CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	PARKING LOT	12
CHILD ANNOYING (17YRS & UNDER)	SIDEWALK	11

Intimate Partners

partner <- y2020 %>% filter(grepl("INTIMATE", Crm.Cd.Desc))

partner20 <- summarise(group_by(partner, Crm.Cd.Desc,Vict.Sex), Counts=length(Crm.Cd.Desc)) %>% arrange(desc(Counts))

## `summarise()` has grouped output by 'Crm.Cd.Desc'. You can override using the `.groups` argument.

partner20 %>% 
  kbl(caption = "Intimate Partner Abuse ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Intimate Partner Abuse
Crm.Cd.Desc	Vict.Sex	Counts
INTIMATE PARTNER - SIMPLE ASSAULT	F	8064
INTIMATE PARTNER - SIMPLE ASSAULT	M	2702
INTIMATE PARTNER - AGGRAVATED ASSAULT	F	2177
INTIMATE PARTNER - AGGRAVATED ASSAULT	M	689
INTIMATE PARTNER - SIMPLE ASSAULT	X	20
INTIMATE PARTNER - AGGRAVATED ASSAULT	X	3
INTIMATE PARTNER - SIMPLE ASSAULT	H	1

##----------------------
partner20 <- summarise(group_by(partner, Crm.Cd.Desc,Premis.Desc), Counts=length(Premis.Desc)) %>% arrange(desc(Counts))

## `summarise()` has grouped output by 'Crm.Cd.Desc'. You can override using the `.groups` argument.

head(partner20,n=20) %>% 
  kbl(caption = "Location _  Abuse ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Location _ Abuse
Crm.Cd.Desc	Premis.Desc	Counts
INTIMATE PARTNER - SIMPLE ASSAULT	SINGLE FAMILY DWELLING	3668
INTIMATE PARTNER - SIMPLE ASSAULT	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	3589
INTIMATE PARTNER - SIMPLE ASSAULT	STREET	1353
INTIMATE PARTNER - AGGRAVATED ASSAULT	SINGLE FAMILY DWELLING	932
INTIMATE PARTNER - AGGRAVATED ASSAULT	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	900
INTIMATE PARTNER - SIMPLE ASSAULT	SIDEWALK	509
INTIMATE PARTNER - AGGRAVATED ASSAULT	STREET	443
INTIMATE PARTNER - SIMPLE ASSAULT	PARKING LOT	372
INTIMATE PARTNER - SIMPLE ASSAULT	HOTEL	193
INTIMATE PARTNER - SIMPLE ASSAULT	VEHICLE, PASSENGER/TRUCK	176
INTIMATE PARTNER - AGGRAVATED ASSAULT	SIDEWALK	135
INTIMATE PARTNER - SIMPLE ASSAULT	MOTEL	113
INTIMATE PARTNER - AGGRAVATED ASSAULT	PARKING LOT	83
INTIMATE PARTNER - SIMPLE ASSAULT	DRIVEWAY	77
INTIMATE PARTNER - SIMPLE ASSAULT	PARK/PLAYGROUND	65
INTIMATE PARTNER - SIMPLE ASSAULT	ALLEY	60
INTIMATE PARTNER - AGGRAVATED ASSAULT	HOTEL	58
INTIMATE PARTNER - AGGRAVATED ASSAULT	VEHICLE, PASSENGER/TRUCK	56
INTIMATE PARTNER - SIMPLE ASSAULT	OTHER BUSINESS	56
INTIMATE PARTNER - SIMPLE ASSAULT	OTHER RESIDENCE	56

##-------------------------
partnerweapon20 <- summarise(group_by(partner, Crm.Cd.Desc,Weapon.Desc), Counts=length(Weapon.Desc)) %>% arrange(desc(Counts))

## `summarise()` has grouped output by 'Crm.Cd.Desc'. You can override using the `.groups` argument.

head(partnerweapon20,n=20) %>% 
  kbl(caption = "Weapon _  Intimate Partner Cases ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Weapon _ Intimate Partner Cases
Crm.Cd.Desc	Weapon.Desc	Counts
INTIMATE PARTNER - SIMPLE ASSAULT	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	10121
INTIMATE PARTNER - AGGRAVATED ASSAULT	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	1673
INTIMATE PARTNER - SIMPLE ASSAULT	UNKNOWN WEAPON/OTHER WEAPON	462
INTIMATE PARTNER - AGGRAVATED ASSAULT	UNKNOWN WEAPON/OTHER WEAPON	207
INTIMATE PARTNER - AGGRAVATED ASSAULT	KNIFE WITH BLADE 6INCHES OR LESS	102
INTIMATE PARTNER - AGGRAVATED ASSAULT	VEHICLE	98
INTIMATE PARTNER - AGGRAVATED ASSAULT	HAND GUN	80
INTIMATE PARTNER - AGGRAVATED ASSAULT	OTHER KNIFE	72
INTIMATE PARTNER - AGGRAVATED ASSAULT	BOTTLE	61
INTIMATE PARTNER - AGGRAVATED ASSAULT	KITCHEN KNIFE	60
INTIMATE PARTNER - AGGRAVATED ASSAULT	MACE/PEPPER SPRAY	52
INTIMATE PARTNER - AGGRAVATED ASSAULT	CLUB/BAT	44
INTIMATE PARTNER - AGGRAVATED ASSAULT	FOLDING KNIFE	39
INTIMATE PARTNER - AGGRAVATED ASSAULT	STICK	37
INTIMATE PARTNER - AGGRAVATED ASSAULT	KNIFE WITH BLADE OVER 6 INCHES IN LENGTH	33
INTIMATE PARTNER - AGGRAVATED ASSAULT	SCISSORS	32
INTIMATE PARTNER - SIMPLE ASSAULT		32
INTIMATE PARTNER - AGGRAVATED ASSAULT	BLUNT INSTRUMENT	31
INTIMATE PARTNER - AGGRAVATED ASSAULT	PIPE/METAL PIPE	30
INTIMATE PARTNER - AGGRAVATED ASSAULT	SEMI-AUTOMATIC PISTOL	30

##-------------------------
partnerrace20 <- summarise(group_by(partner, Crm.Cd.Desc,Vict.Descent), Counts=length(Vict.Descent)) %>% arrange(desc(Counts))

## `summarise()` has grouped output by 'Crm.Cd.Desc'. You can override using the `.groups` argument.

head(partnerrace20,n=20) %>% 
  kbl(caption = "Demographic factor: Race _  Intimate Partner Cases ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Demographic factor: Race _ Intimate Partner Cases
Crm.Cd.Desc	Vict.Descent	Counts
INTIMATE PARTNER - SIMPLE ASSAULT	H	5587
INTIMATE PARTNER - SIMPLE ASSAULT	B	2629
INTIMATE PARTNER - SIMPLE ASSAULT	W	1780
INTIMATE PARTNER - AGGRAVATED ASSAULT	H	1282
INTIMATE PARTNER - AGGRAVATED ASSAULT	B	1014
INTIMATE PARTNER - SIMPLE ASSAULT	O	526
INTIMATE PARTNER - AGGRAVATED ASSAULT	W	422
INTIMATE PARTNER - SIMPLE ASSAULT	A	195
INTIMATE PARTNER - AGGRAVATED ASSAULT	O	89
INTIMATE PARTNER - AGGRAVATED ASSAULT	A	51
INTIMATE PARTNER - SIMPLE ASSAULT	X	39
INTIMATE PARTNER - SIMPLE ASSAULT	K	23
INTIMATE PARTNER - AGGRAVATED ASSAULT	K	5
INTIMATE PARTNER - AGGRAVATED ASSAULT	X	5
INTIMATE PARTNER - SIMPLE ASSAULT	F	3
INTIMATE PARTNER - SIMPLE ASSAULT	I	2
INTIMATE PARTNER - AGGRAVATED ASSAULT	F	1
INTIMATE PARTNER - SIMPLE ASSAULT	G	1
INTIMATE PARTNER - SIMPLE ASSAULT	J	1
INTIMATE PARTNER - SIMPLE ASSAULT	U	1

##------------------
partnerage20 <- summarise(group_by(partner, Vict.Age), Counts=length(Vict.Age)) %>% arrange(Vict.Age)
partnerage20 %>% 
  kbl(caption = "Demographic factor: Age _  Intimate Partner Cases ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Demographic factor: Age _ Intimate Partner Cases
Vict.Age	Counts
0	135
4	2
7	1
8	1
9	2
12	2
13	2
14	3
15	12
16	31
17	71
18	159
19	258
20	311
21	325
22	408
23	456
24	511
25	515
26	578
27	543
28	541
29	562
30	514
31	516
32	467
33	464
34	481
35	412
36	394
37	365
38	377
39	335
40	309
41	288
42	284
43	234
44	221
45	209
46	227
47	204
48	177
49	147
50	155
51	170
52	113
53	122
54	101
55	110
56	114
57	85
58	87
59	93
60	68
61	49
62	49
63	45
64	37
65	26
66	21
67	10
68	21
69	14
70	16
71	12
72	3
73	14
74	11
75	7
76	3
77	2
78	9
79	3
80	10
81	3
82	1
83	3
84	2
85	1
87	1
88	2
96	1
98	1
99	7

Rape

rape <- y2020 %>% filter(grepl("RAPE", Crm.Cd.Desc))
rape20 <- summarise(group_by(rape, Crm.Cd.Desc,Vict.Sex), Counts=length(Crm.Cd.Desc)) %>% arrange(desc(Counts))

## `summarise()` has grouped output by 'Crm.Cd.Desc'. You can override using the `.groups` argument.

rape20 %>% 
  kbl(caption = "RAPE ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

RAPE
Crm.Cd.Desc	Vict.Sex	Counts
RAPE, FORCIBLE	F	793
RAPE, ATTEMPTED	F	69
RAPE, FORCIBLE	M	5

rape20 <- summarise(group_by(rape, Crm.Cd.Desc,Premis.Desc), Counts=length(Premis.Desc)) %>% arrange(desc(Counts))

## `summarise()` has grouped output by 'Crm.Cd.Desc'. You can override using the `.groups` argument.

head(rape20,n=20) %>% 
  kbl(caption = "Location _  Rape ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Location _ Rape
Crm.Cd.Desc	Premis.Desc	Counts
RAPE, FORCIBLE	SINGLE FAMILY DWELLING	235
RAPE, FORCIBLE	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	203
RAPE, FORCIBLE	STREET	84
RAPE, FORCIBLE	VEHICLE, PASSENGER/TRUCK	36
RAPE, FORCIBLE	HOTEL	29
RAPE, FORCIBLE	PARKING LOT	28
RAPE, FORCIBLE	MOTEL	27
RAPE, FORCIBLE	SIDEWALK	26
RAPE, ATTEMPTED	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	17
RAPE, FORCIBLE	PARK/PLAYGROUND	15
RAPE, ATTEMPTED	SINGLE FAMILY DWELLING	14
RAPE, FORCIBLE	ALLEY	14
RAPE, ATTEMPTED	STREET	13
RAPE, ATTEMPTED	SIDEWALK	10
RAPE, FORCIBLE	OTHER PREMISE	9
RAPE, FORCIBLE	MOBILE HOME/TRAILERS/CONSTRUCTION TRAILERS/RV’S/MOTORHOME	8
RAPE, FORCIBLE	OTHER BUSINESS	8
RAPE, FORCIBLE	OTHER RESIDENCE	7
RAPE, FORCIBLE	TRANSIENT ENCAMPMENT	7
RAPE, FORCIBLE	OTHER/OUTSIDE	6

rapeweapon20 <- summarise(group_by(rape, Crm.Cd.Desc,Weapon.Desc), Counts=length(Weapon.Desc)) %>% arrange(desc(Counts))

## `summarise()` has grouped output by 'Crm.Cd.Desc'. You can override using the `.groups` argument.

head(rapeweapon20,n=20) %>% 
  kbl(caption = "Weapon _  Rape Cases ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Weapon _ Rape Cases
Crm.Cd.Desc	Weapon.Desc	Counts
RAPE, FORCIBLE	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	744
RAPE, ATTEMPTED	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	57
RAPE, FORCIBLE	UNKNOWN WEAPON/OTHER WEAPON	25
RAPE, FORCIBLE	HAND GUN	10
RAPE, FORCIBLE	KNIFE WITH BLADE 6INCHES OR LESS	4
RAPE, FORCIBLE	OTHER KNIFE	4
RAPE, ATTEMPTED	KNIFE WITH BLADE 6INCHES OR LESS	3
RAPE, FORCIBLE	VERBAL THREAT	3
RAPE, ATTEMPTED	FOLDING KNIFE	2
RAPE, ATTEMPTED	HAND GUN	2
RAPE, ATTEMPTED	UNKNOWN WEAPON/OTHER WEAPON	2
RAPE, ATTEMPTED	VERBAL THREAT	2
RAPE, FORCIBLE	UNKNOWN FIREARM	2
RAPE, ATTEMPTED	OTHER KNIFE	1
RAPE, FORCIBLE	FOLDING KNIFE	1
RAPE, FORCIBLE	KITCHEN KNIFE	1
RAPE, FORCIBLE	KNIFE WITH BLADE OVER 6 INCHES IN LENGTH	1
RAPE, FORCIBLE	OTHER FIREARM	1
RAPE, FORCIBLE	PHYSICAL PRESENCE	1
RAPE, FORCIBLE	PIPE/METAL PIPE	1

raperace20 <- summarise(group_by(rape, Crm.Cd.Desc,Vict.Descent), Counts=length(Vict.Descent)) %>% arrange(desc(Counts))

## `summarise()` has grouped output by 'Crm.Cd.Desc'. You can override using the `.groups` argument.

head(raperace20,n=20) %>% 
  kbl(caption = "Demographic factor: Race _  Rape Cases ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Demographic factor: Race _ Rape Cases
Crm.Cd.Desc	Vict.Descent	Counts
RAPE, FORCIBLE	H	347
RAPE, FORCIBLE	B	202
RAPE, FORCIBLE	W	190
RAPE, FORCIBLE	O	30
RAPE, ATTEMPTED	H	28
RAPE, ATTEMPTED	B	23
RAPE, FORCIBLE	A	21
RAPE, ATTEMPTED	W	11
RAPE, ATTEMPTED	O	5
RAPE, FORCIBLE	K	3
RAPE, FORCIBLE	X	3
RAPE, ATTEMPTED	A	2
RAPE, FORCIBLE	C	1
RAPE, FORCIBLE	F	1

rapeage20 <- summarise(group_by(rape, Vict.Age), Counts=length(Vict.Age)) %>% arrange(Vict.Age)

rapeage20 %>% 
  kbl(caption = "Demographic factor: Age _  Rape Cases ") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Demographic factor: Age _ Rape Cases
Vict.Age	Counts
0	8
5	1
6	1
7	3
8	1
9	2
10	7
11	8
12	17
13	16
14	24
15	35
16	33
17	26
18	29
19	43
20	46
21	21
22	26
23	43
24	25
25	34
26	21
27	25
28	26
29	27
30	17
31	16
32	20
33	20
34	14
35	15
36	14
37	11
38	12
39	11
40	14
41	5
42	9
43	14
44	14
45	6
46	7
47	10
48	8
49	12
50	7
51	5
52	7
53	3
54	5
55	4
56	1
57	3
58	2
59	1
60	4
61	5
62	1
63	2
64	2
65	5
66	2
67	3
69	1
74	2
78	2
80	2
82	1

Victims by gender

gender <- y2020 %>%  
  group_by(`Vict.Sex`, `Crm.Cd.Desc`) %>%
 
  summarise(total = n())

## `summarise()` has grouped output by 'Vict.Sex'. You can override using the `.groups` argument.

gender$Vict.Sex[gender$Vict.Sex==""] <- NA
 
gender <- na.omit(gender) %>% filter(total>1000)  %>% arrange(desc(total)) 



top_n(gender,n=10) %>%
  ggplot(aes(reorder(x = `Crm.Cd.Desc`, total), y = total)) +
  geom_col(fill = 'lightgreen') +
  geom_text(aes(label=total), color='black', hjust = 0.8, size = 3) +
  coord_flip() +
  facet_wrap(~ `Vict.Sex`) +
  labs(x = 'Crime', 
       y = "Total")

## Selecting by total

Ages of Victims

Note : 48,076 crime activities with no information on victim’s age

y2020$Vict.Age[y2020$Vict.Age==0] <- NA  
age <- y2020 %>% 
  group_by(`Vict.Age`) %>%
  summarise(total = n()) 


age <- age %>% filter( Vict.Age !='NA') 

age %>%
  ggplot(aes(x = `Vict.Age`, y = total)) +
  geom_line(group = 1,color="blue") +
  geom_point(size = 1,color="red") +
  labs(title = "Ages Most Likely To Become Crime Victim", 
       x = "Victim Age", 
       y = "Total")

2020 Los Angeles Crime Analysis

Joe Long

5/16/2021

Top crime categories

Seasonality by month. Using 2020 year: full year

Area Sections

Districts

Crime Premise Location

Child Abuse

Intimate Partners

Rape

Victims by gender

Ages of Victims

Note : 48,076 crime activities with no information on victim’s age

Vict.Age	Counts
0	135
4	2
7	1
8	1
9	2
12	2
13	2
14	3
15	12
16	31
17	71
18	159
19	258
20	311
21	325
22	408
23	456
24	511
25	515
26	578
27	543
28	541
29	562
30	514
31	516
32	467
33	464
34	481
35	412
36	394
37	365
38	377
39	335
40	309
41	288
42	284
43	234
44	221
45	209
46	227
47	204
48	177
49	147
50	155
51	170
52	113
53	122
54	101
55	110
56	114
57	85
58	87
59	93
60	68
61	49
62	49
63	45
64	37
65	26
66	21
67	10
68	21
69	14
70	16
71	12
72	3
73	14
74	11
75	7
76	3
77	2
78	9
79	3
80	10
81	3
82	1
83	3
84	2
85	1
87	1
88	2
96	1
98	1
99	7

Vict.Age	Counts
0	8
5	1
6	1
7	3
8	1
9	2
10	7
11	8
12	17
13	16
14	24
15	35
16	33
17	26
18	29
19	43
20	46
21	21
22	26
23	43
24	25
25	34
26	21
27	25
28	26
29	27
30	17
31	16
32	20
33	20
34	14
35	15
36	14
37	11
38	12
39	11
40	14
41	5
42	9
43	14
44	14
45	6
46	7
47	10
48	8
49	12
50	7
51	5
52	7
53	3
54	5
55	4
56	1
57	3
58	2
59	1
60	4
61	5
62	1
63	2
64	2
65	5
66	2
67	3
69	1
74	2
78	2
80	2
82	1

Vict.Age	Counts
0	135
4	2
7	1
8	1
9	2
12	2
13	2
14	3
15	12
16	31
17	71
18	159
19	258
20	311
21	325
22	408
23	456
24	511
25	515
26	578
27	543
28	541
29	562
30	514
31	516
32	467
33	464
34	481
35	412
36	394
37	365
38	377
39	335
40	309
41	288
42	284
43	234
44	221
45	209
46	227
47	204
48	177
49	147
50	155
51	170
52	113
53	122
54	101
55	110
56	114
57	85
58	87
59	93
60	68
61	49
62	49
63	45
64	37
65	26
66	21
67	10
68	21
69	14
70	16
71	12
72	3
73	14
74	11
75	7
76	3
77	2
78	9
79	3
80	10
81	3
82	1
83	3
84	2
85	1
87	1
88	2
96	1
98	1
99	7

Vict.Age	Counts
0	8
5	1
6	1
7	3
8	1
9	2
10	7
11	8
12	17
13	16
14	24
15	35
16	33
17	26
18	29
19	43
20	46
21	21
22	26
23	43
24	25
25	34
26	21
27	25
28	26
29	27
30	17
31	16
32	20
33	20
34	14
35	15
36	14
37	11
38	12
39	11
40	14
41	5
42	9
43	14
44	14
45	6
46	7
47	10
48	8
49	12
50	7
51	5
52	7
53	3
54	5
55	4
56	1
57	3
58	2
59	1
60	4
61	5
62	1
63	2
64	2
65	5
66	2
67	3
69	1
74	2
78	2
80	2
82	1

Vict.Age	Counts
0	135
4	2
7	1
8	1
9	2
12	2
13	2
14	3
15	12
16	31
17	71
18	159
19	258
20	311
21	325
22	408
23	456
24	511
25	515
26	578
27	543
28	541
29	562
30	514
31	516
32	467
33	464
34	481
35	412
36	394
37	365
38	377
39	335
40	309
41	288
42	284
43	234
44	221
45	209
46	227
47	204
48	177
49	147
50	155
51	170
52	113
53	122
54	101
55	110
56	114
57	85
58	87
59	93
60	68
61	49
62	49
63	45
64	37
65	26
66	21
67	10
68	21
69	14
70	16
71	12
72	3
73	14
74	11
75	7
76	3
77	2
78	9
79	3
80	10
81	3
82	1
83	3
84	2
85	1
87	1
88	2
96	1
98	1
99	7

Vict.Age	Counts
0	8
5	1
6	1
7	3
8	1
9	2
10	7
11	8
12	17
13	16
14	24
15	35
16	33
17	26
18	29
19	43
20	46
21	21
22	26
23	43
24	25
25	34
26	21
27	25
28	26
29	27
30	17
31	16
32	20
33	20
34	14
35	15
36	14
37	11
38	12
39	11
40	14
41	5
42	9
43	14
44	14
45	6
46	7
47	10
48	8
49	12
50	7
51	5
52	7
53	3
54	5
55	4
56	1
57	3
58	2
59	1
60	4
61	5
62	1
63	2
64	2
65	5
66	2
67	3
69	1
74	2
78	2
80	2
82	1