library(readr)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.2.1 --
## v ggplot2 3.2.1 v purrr 0.3.2
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 0.8.3 v stringr 1.4.0
## v ggplot2 3.2.1 v forcats 0.4.0
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(ggplot2)
library(dplyr)
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
df<-read.csv("Crime_Year_To_Date_-_2019.csv")
sum(is.na(df))
## [1] 3966
df <- df %>%
mutate(victims = replace('victims', 'victims' == "na", NA)) %>%
mutate(victims = replace('victims', 'victims' == "N/A", NA)) %>%
mutate(victims = replace('victims', 'victims' == "", NA)) %>%
mutate(victims = replace('victims', 'victims' == "?", NA)) %>%
mutate(victims = replace('victims', 'victims' == "NA", NA))
df <- df %>%
mutate(CrimeName2 = replace('Crime Name2', 'Crime Name2' == "na", NA)) %>%
mutate(CrimeName2 = replace('Crime Name2', 'Crime Name2' == "N/A", NA)) %>%
mutate(CrimeName2 = replace('Crime Name2', 'Crime Name2' == "", NA)) %>%
mutate(CrimeName2 = replace('Crime Name2', 'Crime Name2' == "?", NA)) %>%
mutate(CrimeName2 = replace('Crime Name2', 'Crime Name2' == "NA", NA))
df <- df %>%
mutate(place = replace('place', 'place' == "na", NA)) %>%
mutate(place = replace('place', 'place' == "N/A", NA)) %>%
mutate(place = replace('place', 'place' == "", NA)) %>%
mutate(place = replace('place', 'place' == "?", NA)) %>%
mutate(place = replace('place', 'place' == "NA", NA))
df <- df %>%
mutate(zipcode = replace('zip code', 'zip code' == "na", NA)) %>%
mutate(zipcode = replace('zip code', 'zip code' == "N/A", NA)) %>%
mutate(zipcode = replace('zip code', 'zip code' == "", NA)) %>%
mutate(zipcode = replace('zip code', 'zip code' == "?", NA)) %>%
mutate(zipcode = replace('zip code', 'zip code' == "NA", NA))
head(df$Crime.Name1)
## [1] Crime Against Property Crime Against Society Crime Against Society
## [4] Other Crime Against Person Crime Against Property
## 6 Levels: Crime Against Person ... Other
head(df$Crime.Name2)
## [1] Shoplifting Driving Under the Influence
## [3] Driving Under the Influence All Other Offenses
## [5] Simple Assault Identity Theft
## 54 Levels: Aggravated Assault All other Larceny ... Wire Fraud
(summary(df$Crime.Name2))
##
## 32
## Aggravated Assault
## 607
## All other Larceny
## 1854
## All Other Offenses
## 9451
## Arson
## 26
## Assisting or Promoting Prostitution
## 1
## Bad Checks
## 59
## Burglary/Breaking and Entering
## 1275
## Counterfeiting/Forgery
## 335
## Credit Card/Automatic Teller Machine Fraud
## 525
## Curfew/Loitering/Vagrancy Violations
## 1
## Destruction/Damage/Vandalism of Property
## 2478
## Disorderly Conduct
## 591
## Driving Under the Influence
## 2033
## Drug Equipment Violations
## 236
## Drug/Narcotic Violations
## 2682
## Drunkenness
## 10
## Embezzlement
## 76
## Extortion/Blackmail
## 26
## False Pretenses/Swindle/Confidence Game
## 819
## Family Offenses, NonViolent
## 107
## Fondling
## 129
## Forcible Fondling
## 38
## Forcible Rape
## 157
## Forcible Sodomy
## 50
## From Coin/Operated Machine or Device
## 5
## Human Trafficking, Commercial Sex Acts
## 5
## Identity Theft
## 1067
## Impersonation
## 34
## Intimidation
## 33
## Justifiable Homicide
## 1
## Kidnapping/Abduction
## 6
## Liquor Law Violations
## 569
## Motor Vehicle Theft
## 795
## Murder and Nonnegligent Manslaughter
## 11
## Not Mapped
## 32
## NOT NIBRS CODE
## 25
## Peeping Tom
## 19
## Pocket/picking
## 104
## Pornography/Obscene Material
## 7
## Prostitution
## 16
## Purse-snatching
## 68
## Robbery
## 503
## Runaway
## 560
## Sexual Assault With An Object
## 38
## Shoplifting
## 2459
## Simple Assault
## 3064
## Stolen Property Offenses
## 20
## Theft from Building
## 1528
## Theft From Motor Vehicle
## 3927
## Theft of Motor Vehicle Parts or Accessories
## 728
## Trespass of Real Property
## 519
## Weapon Law Violations
## 260
## Wire Fraud
## 33
arrange(df,desc(Crime.Name2)) %>%
head(10)
## Incident.ID Offence.Code CR.Number Dispatch.Date...Time NIBRS.Code
## 1 201225120 2608 190004970 01/31/2019 05:27:20 PM 26E
## 2 201225791 2608 190005889 02/06/2019 01:30:49 PM 26E
## 3 201227204 2608 190007650 02/16/2019 09:07:12 PM 26E
## 4 201229023 2608 190009890 03/02/2019 10:23:21 PM 26E
## 5 201229588 2608 190010614 03/07/2019 12:10:36 PM 26E
## 6 201230193 2608 190011386 03/12/2019 08:50:39 AM 26E
## 7 201230547 2608 190011841 03/14/2019 03:20:00 PM 26E
## 8 201233391 2608 190014211 03/27/2019 05:27:15 PM 26E
## 9 201233637 2608 190015427 04/03/2019 11:07:09 AM 26E
## 10 201236291 2608 190018866 04/22/2019 05:08:03 PM 26E
## Victims Crime.Name1 Crime.Name2 Crime.Name3
## 1 1 Crime Against Property Wire Fraud FRAUD BY WIRE
## 2 1 Crime Against Property Wire Fraud FRAUD BY WIRE
## 3 1 Crime Against Property Wire Fraud FRAUD BY WIRE
## 4 1 Crime Against Property Wire Fraud FRAUD BY WIRE
## 5 1 Crime Against Property Wire Fraud FRAUD BY WIRE
## 6 1 Crime Against Property Wire Fraud FRAUD BY WIRE
## 7 1 Crime Against Property Wire Fraud FRAUD BY WIRE
## 8 1 Crime Against Property Wire Fraud FRAUD BY WIRE
## 9 1 Crime Against Property Wire Fraud FRAUD BY WIRE
## 10 1 Crime Against Property Wire Fraud FRAUD BY WIRE
## Police.District.Name Block.Address City State
## 1 SILVER SPRING 2400 BLK PARALLEL LA SILVER SPRING MD
## 2 GERMANTOWN 13500 BLK HAMLET SQUARE CT GERMANTOWN MD
## 3 ROCKVILLE 13200 BLK TWINBROOK PKW ROCKVILLE MD
## 4 ROCKVILLE 1300 BLK PICCARD DR ROCKVILLE MD
## 5 SILVER SPRING 300 BLK GREENWICH LA SILVER SPRING MD
## 6 BETHESDA 9800 BLK INGLEMERE DR BETHESDA MD
## 7 BETHESDA 7600 BLK WISCONSIN AVE BETHESDA MD
## 8 CITY OF TAKOMA PARK 6700 BLK ALLEGHENY AVE TAKOMA PARK MD
## 9 SILVER SPRING 500 BLK E INDIAN SPRING DR SILVER SPRING MD
## 10 GERMANTOWN 20000 BLK DUNSTABLE CIR GERMANTOWN MD
## Zip.Code Agency Place Sector Beat PRA
## 1 20904 MCPD Residence - Single Family I 3I2 379
## 2 20874 MCPD Residence -Townhouse/Duplex N 5N2 594
## 3 20851 RCPD Residence - Apartment/Condo A 1A2 301
## 4 20850 RCPD Other/Unknown A 1A3 242
## 5 20910 MCPD Residence - Single Family G 3G2 119
## 6 20817 MCPD Bank/S&L/Credit Union E 2E3 195
## 7 20814 MCPD Bank/S&L/Credit Union D 2D2 48
## 8 20912 TPPD Residence - Single Family T 8T1 802
## 9 20901 MCPD Bank/S&L/Credit Union H 3H1 672
## 10 20876 MCPD Bank/S&L/Credit Union M 5M1 470
## Address.Number Street.Prefix Street.Name Street.Suffix Street.Type
## 1 2400 PARALLEL LA
## 2 13500 HAMLET SQUARE CT
## 3 13200 TWINBROOK PKW
## 4 1300 PICCARD DR
## 5 300 GREENWICH LA
## 6 9800 INGLEMERE DR
## 7 7600 WISCONSIN AVE
## 8 6700 ALLEGHENY AVE
## 9 500 E INDIAN SPRING DR
## 10 20000 DUNSTABLE CIR
## Start_Date_Time End_Date_Time Latitude Longitude
## 1 01/31/2019 05:27:00 PM 39.08218 -76.96528
## 2 01/01/2019 01:30:00 PM 02/06/2019 12:00:00 PM 39.16157 -77.27879
## 3 02/16/2019 10:00:00 AM 02/16/2019 06:00:00 PM 39.07177 -77.11339
## 4 02/26/2019 12:00:00 AM 02/27/2019 11:59:00 PM 39.10192 -77.17717
## 5 02/01/2019 12:10:00 PM 03/07/2019 12:58:00 PM 39.00128 -77.01598
## 6 02/25/2019 01:00:00 AM 39.01724 -77.13320
## 7 03/11/2019 02:30:00 PM 38.98599 -77.09456
## 8 03/09/2019 12:00:00 PM 03/25/2019 02:59:00 PM 38.97209 -77.00706
## 9 04/03/2019 11:07:00 AM 04/03/2019 11:54:00 AM 39.01508 -77.00017
## 10 04/22/2019 05:08:00 PM 39.18313 -77.23006
## Police.District.Number Location victims CrimeName2 place
## 1 3D (39.0822, -76.9653) victims Crime Name2 place
## 2 5D (39.1616, -77.2788) victims Crime Name2 place
## 3 1D (39.0718, -77.1134) victims Crime Name2 place
## 4 1D (39.1019, -77.1772) victims Crime Name2 place
## 5 3D (39.0013, -77.016) victims Crime Name2 place
## 6 2D (39.0172, -77.1332) victims Crime Name2 place
## 7 2D (38.986, -77.0946) victims Crime Name2 place
## 8 8D (38.9721, -77.0071) victims Crime Name2 place
## 9 3D (39.0151, -77.0002) victims Crime Name2 place
## 10 5D (39.1831, -77.2301) victims Crime Name2 place
## zipcode
## 1 zip code
## 2 zip code
## 3 zip code
## 4 zip code
## 5 zip code
## 6 zip code
## 7 zip code
## 8 zip code
## 9 zip code
## 10 zip code
top5<-df %>%
group_by(Crime.Name2) %>%
summarize(sum=sum(Victims)) %>%
arrange(desc(sum)) %>%
top_n(n=5)
## Selecting by sum
top5
## # A tibble: 5 x 2
## Crime.Name2 sum
## <fct> <int>
## 1 All Other Offenses 9549
## 2 Theft From Motor Vehicle 3927
## 3 Simple Assault 3799
## 4 Drug/Narcotic Violations 2682
## 5 Destruction/Damage/Vandalism of Property 2478
arrange(df,desc(Police.District.Name)) %>%
head(10)
## Incident.ID Offence.Code CR.Number Dispatch.Date...Time NIBRS.Code
## 1 201225013 2404 190004832 01/31/2019 07:10:59 AM 240
## 2 201225013 2305 190004832 01/31/2019 07:10:59 AM 23F
## 3 201225011 2902 190004847 01/31/2019 08:28:20 AM 290
## 4 201225011 2305 190004847 01/31/2019 08:28:20 AM 23F
## 5 201225038 2902 190004876 01/31/2019 10:56:55 AM 290
## 6 201225038 2305 190004876 01/31/2019 10:56:55 AM 23F
## 7 201225136 2305 190004881 01/31/2019 11:47:55 AM 23F
## 8 201225051 1214 190004895 01/31/2019 12:58:37 PM 120
## 9 201225050 2601 190004894 01/31/2019 12:59:23 PM 26A
## 10 201225066 2303 190004911 01/31/2019 02:30:17 PM 23C
## Victims Crime.Name1 Crime.Name2
## 1 1 Crime Against Property Motor Vehicle Theft
## 2 1 Crime Against Property Theft From Motor Vehicle
## 3 1 Crime Against Property Destruction/Damage/Vandalism of Property
## 4 1 Crime Against Property Theft From Motor Vehicle
## 5 1 Crime Against Property Destruction/Damage/Vandalism of Property
## 6 1 Crime Against Property Theft From Motor Vehicle
## 7 1 Crime Against Property Theft From Motor Vehicle
## 8 1 Crime Against Property Robbery
## 9 1 Crime Against Property False Pretenses/Swindle/Confidence Game
## 10 1 Crime Against Property Shoplifting
## Crime.Name3 Police.District.Name
## 1 AUTO THEFT - VEHICLE THEFT WHEATON
## 2 LARCENY - FROM AUTO WHEATON
## 3 DAMAGE PROPERTY - PRIVATE WHEATON
## 4 LARCENY - FROM AUTO WHEATON
## 5 DAMAGE PROPERTY - PRIVATE WHEATON
## 6 LARCENY - FROM AUTO WHEATON
## 7 LARCENY - FROM AUTO WHEATON
## 8 ROBBERY - KNIFE WHEATON
## 9 FRAUD - CONFIDENCE GAME WHEATON
## 10 LARCENY - SHOPLIFTING WHEATON
## Block.Address City State Zip.Code Agency
## 1 11500 BLK NAIRN RD SILVER SPRING MD 20902 MCPD
## 2 11500 BLK NAIRN RD SILVER SPRING MD 20902 MCPD
## 3 14200 BLK GRAND PRE RD SILVER SPRING MD 20906 MCPD
## 4 14200 BLK GRAND PRE RD SILVER SPRING MD 20906 MCPD
## 5 12100 BLK EDGEMONT ST SILVER SPRING MD 20902 MCPD
## 6 12100 BLK EDGEMONT ST SILVER SPRING MD 20902 MCPD
## 7 SILVER SPRING MD 20906 MCPD
## 8 800 BLK UNIVERSITY BLV W SILVER SPRING MD 20901 MCPD
## 9 1100 BLK N BELGRADE RD SILVER SPRING MD 20902 MCPD
## 10 11100 BLK VEIRS MILL RD SILVER SPRING MD 20902 MCPD
## Place Sector Beat PRA Address.Number
## 1 Street - Alley L 4L1 359 11500
## 2 Street - In vehicle L 4L1 359 11500
## 3 Street - In vehicle K 4K1 534 14200
## 4 Street - In vehicle K 4K1 534 14200
## 5 Street - Residential L 4L1 331 12100
## 6 Street - Residential L 4L1 331 12100
## 7 Street - Residential K 4K1 347 NA
## 8 School/College L 4L2 158 800
## 9 Residence - Single Family L 4L1 535 1100
## 10 Retail - Department/Discount Store L 4L2 321 11100
## Street.Prefix Street.Name Street.Suffix Street.Type
## 1 NAIRN RD
## 2 NAIRN RD
## 3 GRAND PRE RD
## 4 GRAND PRE RD
## 5 EDGEMONT ST
## 6 EDGEMONT ST
## 7 CHESTER MILL RD
## 8 UNIVERSITY W BLV
## 9 N BELGRADE RD
## 10 VEIRS MILL RD
## Start_Date_Time End_Date_Time Latitude Longitude
## 1 01/30/2019 08:00:00 PM 01/31/2019 05:50:00 AM 39.04510 -77.04303
## 2 01/30/2019 08:00:00 PM 01/31/2019 05:50:00 AM 39.04510 -77.04303
## 3 01/31/2019 08:28:00 AM 39.08866 -77.07517
## 4 01/31/2019 08:28:00 AM 39.08866 -77.07517
## 5 01/30/2019 07:30:00 PM 01/31/2019 07:30:00 AM 39.05457 -77.07334
## 6 01/30/2019 07:30:00 PM 01/31/2019 07:30:00 AM 39.05457 -77.07334
## 7 01/31/2019 11:47:00 AM 39.11772 -77.03618
## 8 01/31/2019 11:00:00 AM 01/31/2019 12:10:00 PM 39.03138 -77.02231
## 9 01/31/2019 12:59:00 PM 39.04221 -77.02728
## 10 01/31/2019 02:30:00 PM 39.03737 -77.05166
## Police.District.Number Location victims CrimeName2 place
## 1 4D (39.0451, -77.043) victims Crime Name2 place
## 2 4D (39.0451, -77.043) victims Crime Name2 place
## 3 4D (39.0887, -77.0752) victims Crime Name2 place
## 4 4D (39.0887, -77.0752) victims Crime Name2 place
## 5 4D (39.0546, -77.0733) victims Crime Name2 place
## 6 4D (39.0546, -77.0733) victims Crime Name2 place
## 7 4D (39.1177, -77.0362) victims Crime Name2 place
## 8 4D (39.0314, -77.0223) victims Crime Name2 place
## 9 4D (39.0422, -77.0273) victims Crime Name2 place
## 10 4D (39.0374, -77.0517) victims Crime Name2 place
## zipcode
## 1 zip code
## 2 zip code
## 3 zip code
## 4 zip code
## 5 zip code
## 6 zip code
## 7 zip code
## 8 zip code
## 9 zip code
## 10 zip code