#Project one: MARYLAND CRIME STATS FROM 1975-2016 ##by Kevin Tukei ##June 18, 2019
#Summary/Background ###The following dataset contains Maryland crime data by county from 1975-2016. ###Install dplyr
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyverse)## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang## -- Attaching packages ------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.1 v readr 1.3.1
## v tibble 2.1.3 v purrr 0.3.2
## v tidyr 0.8.3 v stringr 1.4.0
## v ggplot2 3.1.1 v forcats 0.4.0## -- Conflicts ---------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()###Pull the Data from CSV
MDcrime<-read.csv(file = "MDcrime.csv")##Dimensions and Structure of the dataframe
dim(MDcrime)## [1] 1008 38str(MDcrime)## 'data.frame': 1008 obs. of 38 variables:
## $ JURISDICTION : Factor w/ 26 levels "Allegany County",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ YEAR : Factor w/ 42 levels "1/1/1975","1/1/1976",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ POPULATION : int 79655 83923 82102 79966 79721 80461 81781 81858 82605 80835 ...
## $ MURDER : int 3 2 3 1 1 2 11 1 5 2 ...
## $ RAPE : int 5 2 7 2 7 12 13 18 9 15 ...
## $ ROBBERY : int 20 24 32 18 18 26 24 18 19 6 ...
## $ AGGASSAULT : int 114 59 85 81 84 79 101 80 89 67 ...
## $ BE : int 669 581 592 539 502 541 539 447 347 361 ...
## $ LARCENY.THEFT : int 1425 1384 1390 1390 1611 1706 1697 1570 1412 1338 ...
## $ MotorvehicleTHEFT : int 93 73 102 100 99 108 88 55 67 68 ...
## $ GRAND.TOTAL : int 2329 2125 2211 2131 2322 2474 2473 2189 1948 1857 ...
## $ PERCENT.CHANGE : num NA -8.8 4 -3.6 9 6.5 0 -11.5 -11 -4.7 ...
## $ violentcrimetotal : int 142 87 127 102 110 119 149 117 122 90 ...
## $ VIOLENT.CRIME.PERCENT : num 6.1 4.1 5.7 4.8 4.7 4.8 6 5.3 6.3 4.8 ...
## $ VIOLENT.CRIME.PERCENT.CHANGE : num NA -38.7 46 -19.7 7.8 8.2 25.2 -21.5 4.3 -26.2 ...
## $ PROPERTY.CRIME.TOTALS : int 2187 2038 2084 2029 2212 2355 2324 2072 1826 1767 ...
## $ PROPERTY.CRIME.PERCENT : num 93.9 95.9 94.3 95.2 95.3 95.2 94 94.7 93.7 95.2 ...
## $ PROPERTY.CRIME.PERCENT.CHANGE : num NA -6.8 2.3 -2.6 9 6.5 -1.3 -10.8 -11.9 -3.2 ...
## $ OVERALL.CRIME.RATE.PER.100.000.PEOPLE : num 2924 2532 2693 2665 2913 ...
## $ OVERALL.PERCENT.CHANGE.PER.100.000.PEOPLE : num NA -13.4 6.4 -1 9.3 5.6 -1.7 -11.6 -11.8 -2.6 ...
## $ violentcrimerateper_100000 : num 178 104 155 128 138 ...
## $ VIOLENT.CRIME.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE : num NA -41.8 49.2 -17.5 8.2 7.2 23.2 -21.6 3.3 -24.6 ...
## $ PROPERTY.CRIME.RATE.PER.100.000.PEOPLE : num 2746 2428 2538 2537 2775 ...
## $ PROPERTY.CRIME.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE: num NA -11.6 4.5 0 9.4 5.5 -2.9 -10.9 -12.7 -1.1 ...
## $ murderper100000 : num 3.8 2.4 3.7 1.3 1.3 2.5 13.5 1.2 6.1 2.5 ...
## $ RAPE.PER.100.000.PEOPLE : num 6.3 2.4 8.5 2.5 8.8 14.9 15.9 22 10.9 18.6 ...
## $ robberyper100000 : num 25.1 28.6 39 22.5 22.6 32.3 29.3 22 23 7.4 ...
## $ AGG..ASSAULT.PER.100.000.PEOPLE : num 143.1 70.3 103.5 101.3 105.4 ...
## $ B...E.PER.100.000.PEOPLE : num 840 692 721 674 630 ...
## $ LARCENY.THEFT.PER.100.000.PEOPLE : num 1789 1649 1693 1738 2021 ...
## $ M.V.THEFT.PER.100.000.PEOPLE : num 117 87 124 125 124 ...
## $ MURDER..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE : num NA -36.7 53.3 -65.8 0.3 ...
## $ RAPE.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE : num NA -62 257.8 -70.7 251.1 ...
## $ ROBBERY.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE : num NA 13.9 36.3 -42.2 0.3 43.1 -9.2 -25.1 4.6 -67.7 ...
## $ AGG..ASSAULT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE : num NA -50.9 47.3 -2.2 4 -6.8 25.8 -20.9 10.2 -23.1 ...
## $ B...E.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE : num NA -17.6 4.2 -6.5 -6.6 6.8 -2 -17.1 -23.1 6.3 ...
## $ LARCENY.THEFT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE: num NA -7.8 2.7 2.7 16.3 4.9 -2.1 -7.6 -10.9 -3.2 ...
## $ M.V.THEFT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE : num NA -25.5 42.8 0.7 -0.7 8.1 -19.8 -37.6 20.7 3.7 ...###The dataframe consists of 1008 observations and 38 variables ##Head and Tail of the dataframe
head(MDcrime)## JURISDICTION YEAR POPULATION MURDER RAPE ROBBERY AGGASSAULT BE
## 1 Allegany County 1/1/1975 79655 3 5 20 114 669
## 2 Allegany County 1/1/1976 83923 2 2 24 59 581
## 3 Allegany County 1/1/1977 82102 3 7 32 85 592
## 4 Allegany County 1/1/1978 79966 1 2 18 81 539
## 5 Allegany County 1/1/1979 79721 1 7 18 84 502
## 6 Allegany County 1/1/1980 80461 2 12 26 79 541
## LARCENY.THEFT MotorvehicleTHEFT GRAND.TOTAL PERCENT.CHANGE
## 1 1425 93 2329 NA
## 2 1384 73 2125 -8.8
## 3 1390 102 2211 4.0
## 4 1390 100 2131 -3.6
## 5 1611 99 2322 9.0
## 6 1706 108 2474 6.5
## violentcrimetotal VIOLENT.CRIME.PERCENT VIOLENT.CRIME.PERCENT.CHANGE
## 1 142 6.1 NA
## 2 87 4.1 -38.7
## 3 127 5.7 46.0
## 4 102 4.8 -19.7
## 5 110 4.7 7.8
## 6 119 4.8 8.2
## PROPERTY.CRIME.TOTALS PROPERTY.CRIME.PERCENT
## 1 2187 93.9
## 2 2038 95.9
## 3 2084 94.3
## 4 2029 95.2
## 5 2212 95.3
## 6 2355 95.2
## PROPERTY.CRIME.PERCENT.CHANGE OVERALL.CRIME.RATE.PER.100.000.PEOPLE
## 1 NA 2923.9
## 2 -6.8 2532.1
## 3 2.3 2693.0
## 4 -2.6 2664.9
## 5 9.0 2912.7
## 6 6.5 3074.8
## OVERALL.PERCENT.CHANGE.PER.100.000.PEOPLE violentcrimerateper_100000
## 1 NA 178.3
## 2 -13.4 103.7
## 3 6.4 154.7
## 4 -1.0 127.6
## 5 9.3 138.0
## 6 5.6 147.9
## VIOLENT.CRIME.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1 NA
## 2 -41.8
## 3 49.2
## 4 -17.5
## 5 8.2
## 6 7.2
## PROPERTY.CRIME.RATE.PER.100.000.PEOPLE
## 1 2745.6
## 2 2428.4
## 3 2538.3
## 4 2537.3
## 5 2774.7
## 6 2926.9
## PROPERTY.CRIME.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE murderper100000
## 1 NA 3.8
## 2 -11.6 2.4
## 3 4.5 3.7
## 4 0.0 1.3
## 5 9.4 1.3
## 6 5.5 2.5
## RAPE.PER.100.000.PEOPLE robberyper100000 AGG..ASSAULT.PER.100.000.PEOPLE
## 1 6.3 25.1 143.1
## 2 2.4 28.6 70.3
## 3 8.5 39.0 103.5
## 4 2.5 22.5 101.3
## 5 8.8 22.6 105.4
## 6 14.9 32.3 98.2
## B...E.PER.100.000.PEOPLE LARCENY.THEFT.PER.100.000.PEOPLE
## 1 839.9 1789.0
## 2 692.3 1649.1
## 3 721.1 1693.0
## 4 674.0 1738.2
## 5 629.7 2020.8
## 6 672.4 2120.3
## M.V.THEFT.PER.100.000.PEOPLE
## 1 116.8
## 2 87.0
## 3 124.2
## 4 125.1
## 5 124.2
## 6 134.2
## MURDER..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1 NA
## 2 -36.7
## 3 53.3
## 4 -65.8
## 5 0.3
## 6 98.2
## RAPE.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1 NA
## 2 -62.0
## 3 257.8
## 4 -70.7
## 5 251.1
## 6 69.9
## ROBBERY.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1 NA
## 2 13.9
## 3 36.3
## 4 -42.2
## 5 0.3
## 6 43.1
## AGG..ASSAULT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1 NA
## 2 -50.9
## 3 47.3
## 4 -2.2
## 5 4.0
## 6 -6.8
## B...E.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1 NA
## 2 -17.6
## 3 4.2
## 4 -6.5
## 5 -6.6
## 6 6.8
## LARCENY.THEFT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1 NA
## 2 -7.8
## 3 2.7
## 4 2.7
## 5 16.3
## 6 4.9
## M.V.THEFT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1 NA
## 2 -25.5
## 3 42.8
## 4 0.7
## 5 -0.7
## 6 8.1tail(MDcrime)## JURISDICTION YEAR POPULATION MURDER RAPE ROBBERY AGGASSAULT
## 1003 Worcester County 1/1/2011 51942 1 7 28 212
## 1004 Worcester County 1/1/2012 52011 1 16 31 138
## 1005 Worcester County 1/1/2013 51718 2 14 28 173
## 1006 Worcester County 1/1/2014 51756 1 14 35 140
## 1007 Worcester County 1/1/2015 51566 1 15 27 102
## 1008 Worcester County 1/1/2016 51255 3 17 39 93
## BE LARCENY.THEFT MotorvehicleTHEFT GRAND.TOTAL PERCENT.CHANGE
## 1003 423 1852 41 2564 -2.0
## 1004 432 1751 33 2402 -6.3
## 1005 356 1705 55 2333 -2.9
## 1006 445 1754 38 2427 4.0
## 1007 273 1562 28 2008 -17.3
## 1008 289 1514 32 1987 -1.0
## violentcrimetotal VIOLENT.CRIME.PERCENT VIOLENT.CRIME.PERCENT.CHANGE
## 1003 248 9.7 -5.0
## 1004 186 7.7 -25.0
## 1005 217 9.3 16.7
## 1006 190 7.8 -12.4
## 1007 145 7.2 -23.7
## 1008 152 7.6 4.8
## PROPERTY.CRIME.TOTALS PROPERTY.CRIME.PERCENT
## 1003 2316 90.3
## 1004 2216 92.3
## 1005 2116 90.7
## 1006 2237 92.2
## 1007 1863 92.8
## 1008 1835 92.4
## PROPERTY.CRIME.PERCENT.CHANGE OVERALL.CRIME.RATE.PER.100.000.PEOPLE
## 1003 -1.7 4936.3
## 1004 -4.3 4618.3
## 1005 -4.5 4511.0
## 1006 5.7 4689.3
## 1007 -16.7 3894.0
## 1008 -1.5 3876.7
## OVERALL.PERCENT.CHANGE.PER.100.000.PEOPLE violentcrimerateper_100000
## 1003 -6.4 477.5
## 1004 -6.4 357.6
## 1005 -2.3 419.6
## 1006 4.0 367.1
## 1007 -17.0 281.2
## 1008 -0.4 296.6
## VIOLENT.CRIME.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1003 -9.2
## 1004 -25.1
## 1005 17.3
## 1006 -12.5
## 1007 -23.4
## 1008 5.5
## PROPERTY.CRIME.RATE.PER.100.000.PEOPLE
## 1003 4458.8
## 1004 4260.6
## 1005 4091.4
## 1006 4322.2
## 1007 3612.8
## 1008 3580.1
## PROPERTY.CRIME.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE murderper100000
## 1003 -6.0 1.9
## 1004 -4.4 1.9
## 1005 -4.0 3.9
## 1006 5.6 1.9
## 1007 -16.4 1.9
## 1008 -16.4 5.9
## RAPE.PER.100.000.PEOPLE robberyper100000
## 1003 13.5 53.9
## 1004 30.8 59.6
## 1005 27.1 54.1
## 1006 27.1 67.6
## 1007 29.1 52.4
## 1008 33.2 76.1
## AGG..ASSAULT.PER.100.000.PEOPLE B...E.PER.100.000.PEOPLE
## 1003 408.1 814.4
## 1004 265.3 830.6
## 1005 334.5 688.3
## 1006 270.5 859.8
## 1007 197.8 529.4
## 1008 181.4 563.8
## LARCENY.THEFT.PER.100.000.PEOPLE M.V.THEFT.PER.100.000.PEOPLE
## 1003 3565.5 78.9
## 1004 3366.6 63.4
## 1005 3296.7 106.3
## 1006 3389.0 73.4
## 1007 3029.1 54.3
## 1008 2953.9 62.4
## MURDER..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1003 -76.1
## 1004 -0.1
## 1005 101.1
## 1006 -50.0
## 1007 0.4
## 1008 201.8
## RAPE.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1003 -48.5
## 1004 128.3
## 1005 -12.0
## 1006 -0.1
## 1007 7.5
## 1008 14.0
## ROBBERY.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1003 -40.5
## 1004 10.6
## 1005 -9.2
## 1006 24.9
## 1007 -22.6
## 1008 45.3
## AGG..ASSAULT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1003 1.8
## 1004 -35.0
## 1005 26.1
## 1006 -19.1
## 1007 -26.9
## 1008 -8.3
## B...E.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1003 -17.7
## 1004 2.0
## 1005 -17.1
## 1006 24.9
## 1007 -38.4
## 1008 6.5
## LARCENY.THEFT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1003 -2.6
## 1004 -5.6
## 1005 -2.1
## 1006 2.8
## 1007 -10.6
## 1008 -2.5
## M.V.THEFT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE
## 1003 -18.4
## 1004 -19.6
## 1005 67.6
## 1006 -31.0
## 1007 -26.0
## 1008 15.0##Now Lets look at the variable names
names(MDcrime)## [1] "JURISDICTION"
## [2] "YEAR"
## [3] "POPULATION"
## [4] "MURDER"
## [5] "RAPE"
## [6] "ROBBERY"
## [7] "AGGASSAULT"
## [8] "BE"
## [9] "LARCENY.THEFT"
## [10] "MotorvehicleTHEFT"
## [11] "GRAND.TOTAL"
## [12] "PERCENT.CHANGE"
## [13] "violentcrimetotal"
## [14] "VIOLENT.CRIME.PERCENT"
## [15] "VIOLENT.CRIME.PERCENT.CHANGE"
## [16] "PROPERTY.CRIME.TOTALS"
## [17] "PROPERTY.CRIME.PERCENT"
## [18] "PROPERTY.CRIME.PERCENT.CHANGE"
## [19] "OVERALL.CRIME.RATE.PER.100.000.PEOPLE"
## [20] "OVERALL.PERCENT.CHANGE.PER.100.000.PEOPLE"
## [21] "violentcrimerateper_100000"
## [22] "VIOLENT.CRIME.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE"
## [23] "PROPERTY.CRIME.RATE.PER.100.000.PEOPLE"
## [24] "PROPERTY.CRIME.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE"
## [25] "murderper100000"
## [26] "RAPE.PER.100.000.PEOPLE"
## [27] "robberyper100000"
## [28] "AGG..ASSAULT.PER.100.000.PEOPLE"
## [29] "B...E.PER.100.000.PEOPLE"
## [30] "LARCENY.THEFT.PER.100.000.PEOPLE"
## [31] "M.V.THEFT.PER.100.000.PEOPLE"
## [32] "MURDER..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE"
## [33] "RAPE.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE"
## [34] "ROBBERY.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE"
## [35] "AGG..ASSAULT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE"
## [36] "B...E.RATE.PERCENT.CHANGE.PER.100.000.PEOPLE"
## [37] "LARCENY.THEFT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE"
## [38] "M.V.THEFT..RATE.PERCENT.CHANGE.PER.100.000.PEOPLE"##Select columns to analyse
MDcrime_new<-select(MDcrime,JURISDICTION,YEAR,MURDER,ROBBERY,AGGASSAULT,BE,MotorvehicleTHEFT,violentcrimetotal,violentcrimerateper_100000,murderper100000,robberyper100000)##Lets review the DIM and STR of the new dataframe
dim(MDcrime_new)## [1] 1008 11str(MDcrime_new)## 'data.frame': 1008 obs. of 11 variables:
## $ JURISDICTION : Factor w/ 26 levels "Allegany County",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ YEAR : Factor w/ 42 levels "1/1/1975","1/1/1976",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ MURDER : int 3 2 3 1 1 2 11 1 5 2 ...
## $ ROBBERY : int 20 24 32 18 18 26 24 18 19 6 ...
## $ AGGASSAULT : int 114 59 85 81 84 79 101 80 89 67 ...
## $ BE : int 669 581 592 539 502 541 539 447 347 361 ...
## $ MotorvehicleTHEFT : int 93 73 102 100 99 108 88 55 67 68 ...
## $ violentcrimetotal : int 142 87 127 102 110 119 149 117 122 90 ...
## $ violentcrimerateper_100000: num 178 104 155 128 138 ...
## $ murderper100000 : num 3.8 2.4 3.7 1.3 1.3 2.5 13.5 1.2 6.1 2.5 ...
## $ robberyper100000 : num 25.1 28.6 39 22.5 22.6 32.3 29.3 22 23 7.4 ...##Lets view the new dataframe
head(MDcrime_new)## JURISDICTION YEAR MURDER ROBBERY AGGASSAULT BE MotorvehicleTHEFT
## 1 Allegany County 1/1/1975 3 20 114 669 93
## 2 Allegany County 1/1/1976 2 24 59 581 73
## 3 Allegany County 1/1/1977 3 32 85 592 102
## 4 Allegany County 1/1/1978 1 18 81 539 100
## 5 Allegany County 1/1/1979 1 18 84 502 99
## 6 Allegany County 1/1/1980 2 26 79 541 108
## violentcrimetotal violentcrimerateper_100000 murderper100000
## 1 142 178.3 3.8
## 2 87 103.7 2.4
## 3 127 154.7 3.7
## 4 102 127.6 1.3
## 5 110 138.0 1.3
## 6 119 147.9 2.5
## robberyper100000
## 1 25.1
## 2 28.6
## 3 39.0
## 4 22.5
## 5 22.6
## 6 32.3##Now lets look at columns from Jurisdiction to Robbery
head(select(MDcrime_new, JURISDICTION:ROBBERY))## JURISDICTION YEAR MURDER ROBBERY
## 1 Allegany County 1/1/1975 3 20
## 2 Allegany County 1/1/1976 2 24
## 3 Allegany County 1/1/1977 3 32
## 4 Allegany County 1/1/1978 1 18
## 5 Allegany County 1/1/1979 1 18
## 6 Allegany County 1/1/1980 2 26##Now lets look at all columns without the BE column
head(select(MDcrime_new, -(JURISDICTION)))## YEAR MURDER ROBBERY AGGASSAULT BE MotorvehicleTHEFT
## 1 1/1/1975 3 20 114 669 93
## 2 1/1/1976 2 24 59 581 73
## 3 1/1/1977 3 32 85 592 102
## 4 1/1/1978 1 18 81 539 100
## 5 1/1/1979 1 18 84 502 99
## 6 1/1/1980 2 26 79 541 108
## violentcrimetotal violentcrimerateper_100000 murderper100000
## 1 142 178.3 3.8
## 2 87 103.7 2.4
## 3 127 154.7 3.7
## 4 102 127.6 1.3
## 5 110 138.0 1.3
## 6 119 147.9 2.5
## robberyper100000
## 1 25.1
## 2 28.6
## 3 39.0
## 4 22.5
## 5 22.6
## 6 32.3##Now lets look at the years with murders greater than 10 and Robberies greater than 20
MDcrime_newx<-filter(MDcrime_new,MURDER>10 & ROBBERY>20)
(MDcrime_newx)## JURISDICTION YEAR MURDER ROBBERY AGGASSAULT BE
## 1 Allegany County 1/1/1981 11 24 101 539
## 2 Anne Arundel County 1/1/1975 16 413 1514 5662
## 3 Anne Arundel County 1/1/1977 16 396 656 5091
## 4 Anne Arundel County 1/1/1978 17 349 710 4694
## 5 Anne Arundel County 1/1/1979 13 301 728 4430
## 6 Anne Arundel County 1/1/1980 20 352 815 5444
## 7 Anne Arundel County 1/1/1981 23 488 820 5105
## 8 Anne Arundel County 1/1/1982 19 420 844 4614
## 9 Anne Arundel County 1/1/1983 13 465 935 4392
## 10 Anne Arundel County 1/1/1984 16 416 1085 4269
## 11 Anne Arundel County 1/1/1985 16 417 1204 4675
## 12 Anne Arundel County 1/1/1986 20 369 979 5382
## 13 Anne Arundel County 1/1/1987 19 383 895 4539
## 14 Anne Arundel County 1/1/1988 18 420 975 4134
## 15 Anne Arundel County 1/1/1989 20 468 895 4009
## 16 Anne Arundel County 1/1/1990 16 462 1017 4329
## 17 Anne Arundel County 1/1/1991 12 643 1067 4259
## 18 Anne Arundel County 1/1/1992 12 655 1177 4205
## 19 Anne Arundel County 1/1/1993 22 647 1394 4029
## 20 Anne Arundel County 1/1/1994 11 692 1287 3644
## 21 Anne Arundel County 1/1/1995 17 763 1081 3498
## 22 Anne Arundel County 1/1/1996 16 771 1267 4003
## 23 Anne Arundel County 1/1/1997 12 580 1416 3817
## 24 Anne Arundel County 1/1/2000 11 646 2610 3147
## 25 Anne Arundel County 1/1/2001 11 736 2932 3206
## 26 Anne Arundel County 1/1/2002 17 754 2742 3191
## 27 Anne Arundel County 1/1/2003 24 753 2633 3107
## 28 Anne Arundel County 1/1/2004 16 767 2403 2986
## 29 Anne Arundel County 1/1/2005 16 865 2198 3122
## 30 Anne Arundel County 1/1/2006 23 943 2091 3420
## 31 Anne Arundel County 1/1/2007 20 910 2048 3412
## 32 Anne Arundel County 1/1/2008 18 810 1994 3419
## 33 Anne Arundel County 1/1/2009 16 746 2063 3067
## 34 Anne Arundel County 1/1/2010 18 661 2137 2860
## 35 Anne Arundel County 1/1/2011 15 597 1972 2259
## 36 Anne Arundel County 1/1/2012 13 538 1630 2585
## 37 Anne Arundel County 1/1/2013 15 630 1603 2522
## 38 Anne Arundel County 1/1/2014 14 634 1523 2152
## 39 Anne Arundel County 1/1/2015 17 613 1425 2084
## 40 Montgomery County 1/1/1975 21 651 367 5860
## 41 Anne Arundel County 1/1/2016 25 558 1575 1979
## 42 Baltimore City 1/1/1975 259 9055 6309 15787
## 43 Baltimore City 1/1/1976 200 7764 5788 15443
## 44 Baltimore City 1/1/1977 171 7574 6075 15463
## 45 Baltimore City 1/1/1978 199 8103 5849 15969
## 46 Baltimore City 1/1/1979 245 8492 6247 17054
## 47 Baltimore City 1/1/1980 216 10046 5805 17774
## 48 Baltimore City 1/1/1981 228 10737 6258 18604
## 49 Baltimore City 1/1/1982 227 9357 6592 16501
## 50 Baltimore City 1/1/1983 201 9176 6291 14690
## 51 Baltimore City 1/1/1984 215 8017 6830 14062
## 52 Baltimore City 1/1/1985 213 7794 6949 13988
## 53 Baltimore City 1/1/1986 240 8008 6369 14388
## 54 Baltimore City 1/1/1987 226 7485 6030 13558
## 55 Baltimore City 1/1/1988 237 7418 6592 14359
## 56 Baltimore City 1/1/1989 259 7986 6876 14446
## 57 Baltimore City 1/1/1990 305 9491 7519 14867
## 58 Baltimore City 1/1/1991 304 10793 7295 16394
## 59 Baltimore City 1/1/1992 335 12290 8481 16503
## 60 Baltimore City 1/1/1993 353 12408 8577 18076
## 61 Baltimore City 1/1/1994 321 11303 8748 16026
## 62 Baltimore City 1/1/1995 325 11397 9172 16705
## 63 Baltimore City 1/1/1996 333 10429 8216 14887
## 64 Baltimore City 1/1/1997 313 8665 8072 12841
## 65 Baltimore City 1/1/1998 315 7718 7605 13279
## 66 Baltimore City 1/1/1999 305 7462 10536 12386
## 67 Baltimore City 1/1/2000 261 6634 8774 10751
## 68 Baltimore City 1/1/2001 256 5762 8520 10960
## 69 Baltimore City 1/1/2002 253 4764 8667 8814
## 70 Baltimore City 1/1/2003 270 4364 6385 7855
## 71 Baltimore City 1/1/2004 276 4085 7199 8022
## 72 Baltimore City 1/1/2005 269 3935 6943 7388
## 73 Baltimore City 1/1/2006 276 4260 6196 7664
## 74 Baltimore City 1/1/2007 282 3926 5875 7431
## 75 Baltimore City 1/1/2008 234 4058 5703 7880
## 76 Baltimore City 1/1/2009 238 3726 5579 7856
## 77 Baltimore City 1/1/2010 223 3361 5502 7646
## 78 Baltimore City 1/1/2011 197 3485 4905 8649
## 79 Baltimore City 1/1/2012 216 3635 4657 7796
## 80 Baltimore City 1/1/2013 233 3755 4471 7440
## 81 Baltimore City 1/1/2014 211 3740 4236 6966
## 82 Baltimore City 1/1/2015 344 4378 4671 7803
## 83 Baltimore City 1/1/2016 318 5288 5210 7399
## 84 Baltimore County 1/1/1975 27 787 503 8312
## 85 Baltimore County 1/1/1976 22 730 1095 8205
## 86 Baltimore County 1/1/1977 26 751 3229 8868
## 87 Baltimore County 1/1/1978 29 846 3755 9001
## 88 Baltimore County 1/1/1979 23 1085 3945 10138
## 89 Baltimore County 1/1/1980 26 1223 4456 11793
## 90 Baltimore County 1/1/1981 32 1401 4580 11964
## 91 Baltimore County 1/1/1982 34 1242 5155 9981
## 92 Baltimore County 1/1/1983 34 1272 4627 8923
## 93 Baltimore County 1/1/1984 28 1087 4900 8727
## 94 Baltimore County 1/1/1985 24 1092 5592 8617
## 95 Baltimore County 1/1/1986 31 1394 5289 9612
## 96 Baltimore County 1/1/1987 32 1419 3995 8490
## 97 Baltimore County 1/1/1988 24 1661 4714 8703
## 98 Baltimore County 1/1/1989 33 1810 5240 8468
## 99 Baltimore County 1/1/1990 34 1709 5000 7996
## 100 Baltimore County 1/1/1991 24 2151 5221 8308
## 101 Baltimore County 1/1/1992 43 2307 5090 8322
## 102 Baltimore County 1/1/1993 36 2311 4864 8050
## 103 Baltimore County 1/1/1994 31 2180 4761 7353
## 104 Baltimore County 1/1/1995 38 2473 4935 7637
## 105 Baltimore County 1/1/1996 34 2427 4690 6972
## 106 Baltimore County 1/1/1997 23 2482 4572 7266
## 107 Baltimore County 1/1/1998 20 1878 4005 6993
## 108 Baltimore County 1/1/1999 30 1708 4171 6194
## 109 Baltimore County 1/1/2000 33 1587 4340 5586
## 110 Baltimore County 1/1/2001 31 1728 4576 6246
## 111 Baltimore County 1/1/2002 29 1705 4589 5719
## 112 Baltimore County 1/1/2003 31 1611 4356 5425
## 113 Baltimore County 1/1/2004 29 1565 4388 4942
## 114 Baltimore County 1/1/2005 40 1769 3663 4629
## 115 Baltimore County 1/1/2006 35 2090 3445 4812
## 116 Baltimore County 1/1/2007 36 1787 3407 4949
## 117 Baltimore County 1/1/2008 31 1730 3018 4448
## 118 Baltimore County 1/1/2009 32 1471 2898 4316
## 119 Baltimore County 1/1/2010 20 1341 2862 4119
## 120 Baltimore County 1/1/2011 30 1457 2654 4295
## 121 Baltimore County 1/1/2012 23 1368 2643 4079
## 122 Baltimore County 1/1/2013 20 1511 2544 3949
## 123 Baltimore County 1/1/2014 25 1512 2405 3493
## 124 Baltimore County 1/1/2015 32 1525 2626 3587
## 125 Baltimore County 1/1/2016 35 1498 2564 3695
## 126 Charles County 1/1/1996 11 116 477 838
## 127 Harford County 1/1/1989 11 105 416 1422
## 128 Montgomery County 1/1/1976 28 638 360 5584
## 129 Montgomery County 1/1/1977 15 520 462 5766
## 130 Montgomery County 1/1/1978 12 595 598 5932
## 131 Montgomery County 1/1/1979 17 606 670 6522
## 132 Montgomery County 1/1/1980 17 852 553 8348
## 133 Montgomery County 1/1/1981 13 955 733 7135
## 134 Montgomery County 1/1/1982 20 729 879 5960
## 135 Montgomery County 1/1/1983 16 665 716 5047
## 136 Montgomery County 1/1/1984 14 636 845 5112
## 137 Montgomery County 1/1/1985 12 728 892 5430
## 138 Montgomery County 1/1/1987 17 738 928 5338
## 139 Montgomery County 1/1/1988 19 812 973 5529
## 140 Montgomery County 1/1/1989 21 866 900 4505
## 141 Montgomery County 1/1/1990 25 863 1106 4973
## 142 Montgomery County 1/1/1991 26 1045 1281 5263
## 143 Montgomery County 1/1/1992 21 993 1296 4971
## 144 Montgomery County 1/1/1993 30 903 1135 4648
## 145 Montgomery County 1/1/1994 34 937 1145 4625
## 146 Montgomery County 1/1/1995 21 1088 1190 4817
## 147 Montgomery County 1/1/1996 13 1038 1075 4670
## 148 Montgomery County 1/1/1997 23 913 1171 4144
## 149 Montgomery County 1/1/1998 13 796 1038 4218
## 150 Montgomery County 1/1/1999 13 713 830 3873
## 151 Montgomery County 1/1/2000 12 813 871 3777
## 152 Montgomery County 1/1/2001 19 895 912 3707
## 153 Montgomery County 1/1/2002 32 977 950 3996
## 154 Montgomery County 1/1/2003 23 1076 1015 4273
## 155 Montgomery County 1/1/2004 18 856 1037 3893
## 156 Montgomery County 1/1/2005 21 1109 909 3729
## 157 Montgomery County 1/1/2006 19 1261 879 3932
## 158 Montgomery County 1/1/2007 20 1189 865 3708
## 159 Montgomery County 1/1/2008 21 1192 882 3760
## 160 Montgomery County 1/1/2009 13 1062 957 3142
## 161 Montgomery County 1/1/2010 16 975 683 3486
## 162 Montgomery County 1/1/2011 15 878 681 3203
## 163 Montgomery County 1/1/2012 14 878 876 2685
## 164 Montgomery County 1/1/2014 18 661 934 2442
## 165 Montgomery County 1/1/2015 29 641 1087 1946
## 166 Montgomery County 1/1/2016 15 698 783 1961
## 167 Prince George's County 1/1/1975 52 2445 2024 11342
## 168 Prince George's County 1/1/1976 38 2205 1962 11448
## 169 Prince George's County 1/1/1977 54 2204 1915 11939
## 170 Prince George's County 1/1/1978 37 2295 2022 12325
## 171 Prince George's County 1/1/1979 51 2613 2314 12417
## 172 Prince George's County 1/1/1980 56 3314 2261 14298
## 173 Prince George's County 1/1/1981 61 3841 2185 14643
## 174 Prince George's County 1/1/1982 76 2998 2257 12622
## 175 Prince George's County 1/1/1983 58 2742 2305 10323
## 176 Prince George's County 1/1/1984 42 2400 2576 9784
## 177 Prince George's County 1/1/1985 47 2567 2944 10156
## 178 Prince George's County 1/1/1986 52 2441 3329 10028
## 179 Prince George's County 1/1/1987 94 2574 3444 10065
## 180 Prince George's County 1/1/1988 101 2856 3532 10190
## 181 Prince George's County 1/1/1989 122 3517 3468 9981
## 182 Prince George's County 1/1/1990 112 4033 3879 9989
## 183 Prince George's County 1/1/1991 147 4227 3837 9838
## 184 Prince George's County 1/1/1992 134 3786 3917 9882
## 185 Prince George's County 1/1/1993 141 4400 3993 10323
## 186 Prince George's County 1/1/1994 127 3984 3815 10352
## 187 Prince George's County 1/1/1995 137 4403 4031 10231
## 188 Prince George's County 1/1/1996 142 4078 4499 9319
## 189 Prince George's County 1/1/1997 83 3263 3579 9203
## 190 Prince George's County 1/1/1998 107 3146 4310 8913
## 191 Prince George's County 1/1/1999 95 2474 3827 7985
## 192 Prince George's County 1/1/2000 72 2937 4172 7042
## 193 Prince George's County 1/1/2001 109 3916 3974 8914
## 194 Prince George's County 1/1/2002 141 4056 4007 8561
## 195 Prince George's County 1/1/2003 135 4148 3439 8483
## 196 Prince George's County 1/1/2004 146 4100 3649 7460
## 197 Prince George's County 1/1/2005 164 5172 3856 7445
## 198 Prince George's County 1/1/2006 130 4094 3970 6903
## 199 Prince George's County 1/1/2007 141 3831 3276 7188
## 200 Prince George's County 1/1/2008 122 3765 3263 8209
## 201 Prince George's County 1/1/2009 96 3324 2980 8219
## 202 Prince George's County 1/1/2010 91 3126 2781 8055
## 203 Prince George's County 1/1/2011 95 2545 2346 6985
## 204 Prince George's County 1/1/2012 63 2450 2260 6046
## 205 Prince George's County 1/1/2013 56 2012 2246 5733
## 206 Prince George's County 1/1/2014 56 1846 2020 4767
## 207 Prince George's County 1/1/2015 78 1704 2079 3667
## 208 Prince George's County 1/1/2016 88 1534 1620 2951
## 209 Wicomico County 1/1/1980 13 64 156 1082
## MotorvehicleTHEFT violentcrimetotal violentcrimerateper_100000
## 1 88 149 182.2
## 2 1986 2017 608.6
## 3 1436 1186 341.3
## 4 1161 1144 315.0
## 5 1264 1126 311.3
## 6 1353 1304 352.3
## 7 1276 1416 376.4
## 8 1128 1366 362.8
## 9 951 1484 390.6
## 10 941 1614 415.3
## 11 1121 1750 445.6
## 12 1533 1501 376.1
## 13 1676 1412 342.3
## 14 1801 1496 354.0
## 15 1805 1507 355.3
## 16 2197 1626 380.6
## 17 2084 1848 425.6
## 18 2101 1980 451.5
## 19 1854 2204 494.4
## 20 1806 2122 472.1
## 21 1863 1972 429.2
## 22 1797 2140 463.0
## 23 1760 2098 452.0
## 24 1398 3384 691.1
## 25 1258 3798 764.3
## 26 1484 3607 714.8
## 27 1441 3503 689.5
## 28 1631 3296 644.8
## 29 1524 3167 618.0
## 30 1654 3167 618.2
## 31 1667 3068 603.7
## 32 1581 2931 573.4
## 33 1134 2920 564.0
## 34 1236 2929 555.5
## 35 832 2675 492.9
## 36 888 2281 415.0
## 37 677 2319 417.6
## 38 702 2263 403.4
## 39 629 2195 389.3
## 40 2029 1131 197.9
## 41 622 2312 408.4
## 42 7602 16086 1861.6
## 43 5903 14212 1650.7
## 44 6213 14319 1730.4
## 45 5909 14705 1881.1
## 46 6807 15549 1966.0
## 47 5661 16633 2120.1
## 48 5331 17789 2230.8
## 49 4462 16729 2095.9
## 50 4470 16168 2007.1
## 51 5419 15628 1981.7
## 52 6027 15550 2016.6
## 53 6884 15277 1949.7
## 54 7495 14336 1874.2
## 55 8468 14766 1933.0
## 56 8188 15664 2052.6
## 57 9939 18006 2446.4
## 58 10618 19094 2552.3
## 59 11332 21860 2893.4
## 60 10672 22006 3002.3
## 61 13603 21011 2842.5
## 62 11210 21578 3029.7
## 63 11186 19621 2738.7
## 64 8856 17530 2436.1
## 65 7375 16108 2432.3
## 66 7255 18677 2870.8
## 67 7871 16035 2462.6
## 68 8199 14837 2245.2
## 69 6572 13863 2065.9
## 70 6874 11227 1741.8
## 71 6731 11742 1851.2
## 72 6232 11309 1764.0
## 73 6276 10871 1705.1
## 74 5821 10229 1638.6
## 75 5518 10132 1596.7
## 76 4632 9701 1518.7
## 77 4421 9352 1461.4
## 78 4207 8928 1424.3
## 79 3987 8825 1410.9
## 80 4462 8757 1406.4
## 81 4504 8432 1352.3
## 82 5541 9680 1558.1
## 83 5336 11115 1797.4
## 84 2992 1470 228.9
## 85 2834 2002 311.7
## 86 2892 4175 648.9
## 87 2720 4815 751.0
## 88 3306 5249 820.3
## 89 3287 5895 905.1
## 90 3203 6205 937.3
## 91 2941 6597 995.6
## 92 2774 6083 909.7
## 93 2775 6191 921.8
## 94 3064 6891 1013.8
## 95 3691 6914 1001.1
## 96 3863 5653 830.0
## 97 4606 6593 951.8
## 98 4480 7308 1043.9
## 99 5098 7005 1012.1
## 100 5913 7674 1090.7
## 101 5619 7743 1089.8
## 102 5477 7494 1052.5
## 103 6289 7260 1011.3
## 104 5406 7697 1073.6
## 105 4751 7427 1029.8
## 106 4185 7333 1012.4
## 107 3367 6129 843.7
## 108 2953 6099 838.9
## 109 3418 6200 822.0
## 110 3297 6572 858.5
## 111 3491 6559 843.8
## 112 3341 6208 798.5
## 113 2936 6180 788.1
## 114 3046 5650 718.1
## 115 3463 5713 724.8
## 116 3372 5381 685.0
## 117 2940 4931 626.1
## 118 2443 4555 574.5
## 119 1880 4349 545.1
## 120 1710 4288 527.6
## 121 1572 4181 511.3
## 122 1463 4200 510.4
## 123 1499 4042 487.7
## 124 1770 4517 544.5
## 125 2070 4416 531.2
## 126 420 646 583.4
## 127 317 570 329.4
## 128 1655 1122 196.7
## 129 1739 1117 194.2
## 130 1731 1327 227.8
## 131 2163 1430 247.9
## 132 1938 1573 274.0
## 133 2000 1868 320.1
## 134 1687 1801 308.4
## 135 1401 1529 259.4
## 136 1601 1641 271.1
## 137 1986 1759 279.2
## 138 2640 1856 274.6
## 139 3218 1988 283.0
## 140 3010 1987 277.5
## 141 3023 2198 290.3
## 142 3378 2572 334.3
## 143 3134 2499 321.6
## 144 3159 2280 289.1
## 145 3370 2315 291.1
## 146 3388 2521 311.8
## 147 3329 2286 281.1
## 148 3226 2296 279.3
## 149 2815 2014 241.7
## 150 2667 1692 199.8
## 151 2904 1873 214.5
## 152 3353 1972 222.5
## 153 3958 2104 233.8
## 154 3676 2262 246.2
## 155 2730 2060 222.2
## 156 2671 2196 236.5
## 157 2640 2304 247.7
## 158 2634 2207 236.6
## 159 2384 2231 236.6
## 160 1824 2162 224.3
## 161 1530 1797 182.0
## 162 1278 1689 172.2
## 163 1073 1874 187.5
## 164 753 1738 168.9
## 165 768 2032 195.1
## 166 872 1834 175.1
## 167 4722 4809 706.8
## 168 4172 4513 657.9
## 169 3521 4470 661.7
## 170 4044 4703 700.5
## 171 4466 5371 809.9
## 172 4458 6058 921.1
## 173 4565 6497 971.9
## 174 4587 5706 852.8
## 175 4321 5446 806.5
## 176 4574 5415 794.4
## 177 5920 5924 868.3
## 178 7324 6251 901.7
## 179 7921 6571 949.1
## 180 10035 6909 982.9
## 181 10391 7409 1040.7
## 182 10199 8517 1167.9
## 183 9973 8712 1175.3
## 184 10210 8289 1107.3
## 185 9344 8993 1186.5
## 186 9477 8339 1091.3
## 187 10864 8976 1173.0
## 188 11644 9063 1177.4
## 189 9287 7272 947.0
## 190 9836 7868 1012.8
## 191 8619 6661 850.3
## 192 9881 7409 924.4
## 193 13670 8254 1014.7
## 194 16991 8478 1026.4
## 195 17628 8009 952.5
## 196 18482 8188 967.6
## 197 17242 9497 1118.1
## 198 12944 8481 999.6
## 199 11646 7515 893.3
## 200 9743 7400 895.8
## 201 7266 6621 800.7
## 202 6700 6202 736.1
## 203 5943 5169 593.0
## 204 5092 4946 562.3
## 205 4293 4490 505.6
## 206 4278 4128 459.7
## 207 3431 4139 454.1
## 208 3371 3519 385.3
## 209 156 248 381.7
## murderper100000 robberyper100000
## 1 13.5 29.3
## 2 4.8 124.6
## 3 4.6 113.9
## 4 4.7 96.1
## 5 3.6 83.2
## 6 5.4 95.1
## 7 6.1 129.7
## 8 5.0 111.5
## 9 3.4 122.4
## 10 4.1 107.0
## 11 4.1 106.2
## 12 5.0 92.5
## 13 4.6 92.8
## 14 4.3 99.4
## 15 4.7 110.3
## 16 3.7 108.1
## 17 2.8 148.1
## 18 2.7 149.4
## 19 4.9 145.1
## 20 2.4 154.0
## 21 3.7 166.1
## 22 3.5 166.8
## 23 2.6 124.9
## 24 2.2 131.9
## 25 2.2 148.1
## 26 3.4 149.4
## 27 4.7 148.2
## 28 3.1 150.1
## 29 3.1 168.8
## 30 4.5 184.1
## 31 3.9 179.1
## 32 3.5 158.5
## 33 3.1 144.1
## 34 3.4 125.4
## 35 2.8 110.0
## 36 2.4 97.9
## 37 2.7 113.4
## 38 2.5 113.0
## 39 3.0 108.7
## 40 3.7 113.9
## 41 4.4 98.6
## 42 30.0 1047.9
## 43 23.2 901.8
## 44 20.7 915.3
## 45 25.5 1036.5
## 46 31.0 1073.7
## 47 27.5 1280.5
## 48 28.6 1346.5
## 49 28.4 1172.3
## 50 25.0 1139.1
## 51 27.3 1016.6
## 52 27.6 1010.8
## 53 30.6 1022.0
## 54 29.5 978.6
## 55 31.0 971.1
## 56 33.9 1046.5
## 57 41.4 1289.5
## 58 40.6 1442.7
## 59 44.3 1626.7
## 60 48.2 1692.8
## 61 43.4 1529.1
## 62 45.6 1600.2
## 63 46.5 1455.7
## 64 43.5 1204.2
## 65 47.6 1165.4
## 66 46.9 1147.0
## 67 40.1 1018.8
## 68 38.7 871.9
## 69 37.7 710.0
## 70 41.9 677.1
## 71 43.5 644.0
## 72 42.0 613.8
## 73 43.3 668.2
## 74 45.2 628.9
## 75 36.9 639.5
## 76 37.3 583.3
## 77 34.8 525.2
## 78 31.4 556.0
## 79 34.5 581.2
## 80 37.4 603.0
## 81 33.8 599.8
## 82 55.4 704.7
## 83 51.4 855.1
## 84 4.2 122.6
## 85 3.4 113.6
## 86 4.0 116.7
## 87 4.5 132.0
## 88 3.6 169.6
## 89 4.0 187.8
## 90 4.8 211.6
## 91 5.1 187.4
## 92 5.1 190.2
## 93 4.2 161.9
## 94 3.5 160.7
## 95 4.5 201.8
## 96 4.7 208.3
## 97 3.5 239.8
## 98 4.7 258.5
## 99 4.9 246.9
## 100 3.4 305.7
## 101 6.1 324.7
## 102 5.1 324.6
## 103 4.3 303.7
## 104 5.3 345.0
## 105 4.7 336.5
## 106 3.2 342.7
## 107 2.8 258.5
## 108 4.1 234.9
## 109 4.4 210.4
## 110 4.0 225.7
## 111 3.7 219.3
## 112 4.0 207.2
## 113 3.7 199.6
## 114 5.1 224.8
## 115 4.4 265.1
## 116 4.6 227.5
## 117 3.9 219.7
## 118 4.0 185.5
## 119 2.5 168.1
## 120 3.7 179.3
## 121 2.8 167.3
## 122 2.4 183.6
## 123 3.0 182.4
## 124 3.9 183.8
## 125 4.2 180.2
## 126 9.9 104.8
## 127 6.4 60.7
## 128 4.9 111.9
## 129 2.6 90.4
## 130 2.1 102.2
## 131 2.9 105.1
## 132 3.0 148.4
## 133 2.2 163.7
## 134 3.4 124.8
## 135 2.7 112.8
## 136 2.3 105.1
## 137 1.9 115.5
## 138 2.5 109.2
## 139 2.7 115.6
## 140 2.9 121.0
## 141 3.3 114.0
## 142 3.4 135.8
## 143 2.7 127.8
## 144 3.8 114.5
## 145 4.3 117.8
## 146 2.6 134.6
## 147 1.6 127.6
## 148 2.8 111.1
## 149 1.6 95.5
## 150 1.5 84.2
## 151 1.4 93.1
## 152 2.1 101.0
## 153 3.6 108.6
## 154 2.5 117.1
## 155 1.9 92.3
## 156 2.3 119.4
## 157 2.0 135.6
## 158 2.1 127.5
## 159 2.2 126.4
## 160 1.3 110.2
## 161 1.6 98.8
## 162 1.5 89.5
## 163 1.4 87.9
## 164 1.7 64.2
## 165 2.8 61.6
## 166 1.4 66.6
## 167 7.6 359.4
## 168 5.5 321.5
## 169 8.0 326.3
## 170 5.5 341.9
## 171 7.7 394.0
## 172 8.5 503.9
## 173 9.1 574.6
## 174 11.4 448.0
## 175 8.6 406.1
## 176 6.2 352.1
## 177 6.9 376.3
## 178 7.5 352.1
## 179 13.6 371.8
## 180 14.4 406.3
## 181 17.1 494.0
## 182 15.4 553.0
## 183 19.8 570.3
## 184 17.9 505.8
## 185 18.6 580.5
## 186 16.6 521.4
## 187 17.9 575.4
## 188 18.4 529.8
## 189 10.8 424.9
## 190 13.8 405.0
## 191 12.1 315.8
## 192 9.0 366.4
## 193 13.4 481.4
## 194 17.1 491.1
## 195 16.1 493.3
## 196 17.3 484.5
## 197 19.3 608.9
## 198 15.3 482.5
## 199 16.8 455.4
## 200 14.8 455.8
## 201 11.6 402.0
## 202 10.8 371.0
## 203 10.9 292.0
## 204 7.2 278.5
## 205 6.3 226.6
## 206 6.2 205.6
## 207 8.6 186.9
## 208 9.6 168.0
## 209 20.0 98.5##Lets Rename the BE column
MDcrime_new<-rename(MDcrime_new, breakingANDentering = BE)
head(MDcrime_new)## JURISDICTION YEAR MURDER ROBBERY AGGASSAULT breakingANDentering
## 1 Allegany County 1/1/1975 3 20 114 669
## 2 Allegany County 1/1/1976 2 24 59 581
## 3 Allegany County 1/1/1977 3 32 85 592
## 4 Allegany County 1/1/1978 1 18 81 539
## 5 Allegany County 1/1/1979 1 18 84 502
## 6 Allegany County 1/1/1980 2 26 79 541
## MotorvehicleTHEFT violentcrimetotal violentcrimerateper_100000
## 1 93 142 178.3
## 2 73 87 103.7
## 3 102 127 154.7
## 4 100 102 127.6
## 5 99 110 138.0
## 6 108 119 147.9
## murderper100000 robberyper100000
## 1 3.8 25.1
## 2 2.4 28.6
## 3 3.7 39.0
## 4 1.3 22.5
## 5 1.3 22.6
## 6 2.5 32.3##Subsetting: Lets view Montgomery county crime
MDcrime_new<-filter(MDcrime_new, JURISDICTION == "Montgomery County")
head(MDcrime_new, 42)## JURISDICTION YEAR MURDER ROBBERY AGGASSAULT
## 1 Montgomery County 1/1/1975 21 651 367
## 2 Montgomery County 1/1/1976 28 638 360
## 3 Montgomery County 1/1/1977 15 520 462
## 4 Montgomery County 1/1/1978 12 595 598
## 5 Montgomery County 1/1/1979 17 606 670
## 6 Montgomery County 1/1/1980 17 852 553
## 7 Montgomery County 1/1/1981 13 955 733
## 8 Montgomery County 1/1/1982 20 729 879
## 9 Montgomery County 1/1/1983 16 665 716
## 10 Montgomery County 1/1/1984 14 636 845
## 11 Montgomery County 1/1/1985 12 728 892
## 12 Montgomery County 1/1/1986 8 695 900
## 13 Montgomery County 1/1/1987 17 738 928
## 14 Montgomery County 1/1/1988 19 812 973
## 15 Montgomery County 1/1/1989 21 866 900
## 16 Montgomery County 1/1/1990 25 863 1106
## 17 Montgomery County 1/1/1991 26 1045 1281
## 18 Montgomery County 1/1/1992 21 993 1296
## 19 Montgomery County 1/1/1993 30 903 1135
## 20 Montgomery County 1/1/1994 34 937 1145
## 21 Montgomery County 1/1/1995 21 1088 1190
## 22 Montgomery County 1/1/1996 13 1038 1075
## 23 Montgomery County 1/1/1997 23 913 1171
## 24 Montgomery County 1/1/1998 13 796 1038
## 25 Montgomery County 1/1/1999 13 713 830
## 26 Montgomery County 1/1/2000 12 813 871
## 27 Montgomery County 1/1/2001 19 895 912
## 28 Montgomery County 1/1/2002 32 977 950
## 29 Montgomery County 1/1/2003 23 1076 1015
## 30 Montgomery County 1/1/2004 18 856 1037
## 31 Montgomery County 1/1/2005 21 1109 909
## 32 Montgomery County 1/1/2006 19 1261 879
## 33 Montgomery County 1/1/2007 20 1189 865
## 34 Montgomery County 1/1/2008 21 1192 882
## 35 Montgomery County 1/1/2009 13 1062 957
## 36 Montgomery County 1/1/2010 16 975 683
## 37 Montgomery County 1/1/2011 15 878 681
## 38 Montgomery County 1/1/2012 14 878 876
## 39 Montgomery County 1/1/2013 9 812 813
## 40 Montgomery County 1/1/2014 18 661 934
## 41 Montgomery County 1/1/2015 29 641 1087
## 42 Montgomery County 1/1/2016 15 698 783
## breakingANDentering MotorvehicleTHEFT violentcrimetotal
## 1 5860 2029 1131
## 2 5584 1655 1122
## 3 5766 1739 1117
## 4 5932 1731 1327
## 5 6522 2163 1430
## 6 8348 1938 1573
## 7 7135 2000 1868
## 8 5960 1687 1801
## 9 5047 1401 1529
## 10 5112 1601 1641
## 11 5430 1986 1759
## 12 5010 2291 1748
## 13 5338 2640 1856
## 14 5529 3218 1988
## 15 4505 3010 1987
## 16 4973 3023 2198
## 17 5263 3378 2572
## 18 4971 3134 2499
## 19 4648 3159 2280
## 20 4625 3370 2315
## 21 4817 3388 2521
## 22 4670 3329 2286
## 23 4144 3226 2296
## 24 4218 2815 2014
## 25 3873 2667 1692
## 26 3777 2904 1873
## 27 3707 3353 1972
## 28 3996 3958 2104
## 29 4273 3676 2262
## 30 3893 2730 2060
## 31 3729 2671 2196
## 32 3932 2640 2304
## 33 3708 2634 2207
## 34 3760 2384 2231
## 35 3142 1824 2162
## 36 3486 1530 1797
## 37 3203 1278 1689
## 38 2685 1073 1874
## 39 2728 913 1769
## 40 2442 753 1738
## 41 1946 768 2032
## 42 1961 872 1834
## violentcrimerateper_100000 murderper100000 robberyper100000
## 1 197.9 3.7 113.9
## 2 196.7 4.9 111.9
## 3 194.2 2.6 90.4
## 4 227.8 2.1 102.2
## 5 247.9 2.9 105.1
## 6 274.0 3.0 148.4
## 7 320.1 2.2 163.7
## 8 308.4 3.4 124.8
## 9 259.4 2.7 112.8
## 10 271.1 2.3 105.1
## 11 279.2 1.9 115.5
## 12 273.0 1.2 108.5
## 13 274.6 2.5 109.2
## 14 283.0 2.7 115.6
## 15 277.5 2.9 121.0
## 16 290.3 3.3 114.0
## 17 334.3 3.4 135.8
## 18 321.6 2.7 127.8
## 19 289.1 3.8 114.5
## 20 291.1 4.3 117.8
## 21 311.8 2.6 134.6
## 22 281.1 1.6 127.6
## 23 279.3 2.8 111.1
## 24 241.7 1.6 95.5
## 25 199.8 1.5 84.2
## 26 214.5 1.4 93.1
## 27 222.5 2.1 101.0
## 28 233.8 3.6 108.6
## 29 246.2 2.5 117.1
## 30 222.2 1.9 92.3
## 31 236.5 2.3 119.4
## 32 247.7 2.0 135.6
## 33 236.6 2.1 127.5
## 34 236.6 2.2 126.4
## 35 224.3 1.3 110.2
## 36 182.0 1.6 98.8
## 37 172.2 1.5 89.5
## 38 187.5 1.4 87.9
## 39 174.0 0.9 79.9
## 40 168.9 1.7 64.2
## 41 195.1 2.8 61.6
## 42 175.1 1.4 66.6