Crme Data Analysis 2008 & 2017
Nan Dong
August 20, 2019
summary table
Summarized Crime Data for Year 2008 and 2017 in zipcode: 68104,68111,68110 area:
data<-read.csv("all1.csv")
print(data)## X ZIP type Freq.x year.x Freq.y year.y diff
## 1 1 68104 ABUSE 104 2008 40 2017 64
## 2 2 68104 ASSAULT 433 2008 294 2017 139
## 3 3 68104 BURGLARY 280 2008 68 2017 212
## 4 4 68104 CRIMINAL MISCHIEF 569 2008 232 2017 337
## 5 5 68104 FINAN TRNSACT 8 2008 13 2017 -5
## 6 6 68104 FORGERY 20 2008 4 2017 16
## 7 7 68104 HOMICIDE 2 2008 2 2017 0
## 8 8 68104 ROBBERY 65 2008 32 2017 33
## 9 9 68104 THEFT 728 2008 477 2017 251
## 10 10 68110 ABUSE 43 2008 14 2017 29
## 11 11 68110 ASSAULT 260 2008 120 2017 140
## 12 12 68110 BURGLARY 116 2008 32 2017 84
## 13 13 68110 CRIMINAL MISCHIEF 217 2008 85 2017 132
## 14 14 68110 FINAN TRNSACT 1 2008 2 2017 -1
## 15 15 68110 FORGERY 12 2008 2 2017 10
## 16 16 68110 HOMICIDE 4 2008 2 2017 2
## 17 17 68110 ROBBERY 20 2008 10 2017 10
## 18 18 68110 STALKING 2 2008 2 2017 0
## 19 19 68110 THEFT 327 2008 160 2017 167
## 20 20 68111 ABUSE 133 2008 67 2017 66
## 21 21 68111 ASSAULT 825 2008 432 2017 393
## 22 22 68111 BURGLARY 488 2008 92 2017 396
## 23 23 68111 CRIMINAL MISCHIEF 747 2008 311 2017 436
## 24 24 68111 FINAN TRNSACT 8 2008 4 2017 4
## 25 25 68111 FORGERY 30 2008 3 2017 27
## 26 26 68111 HOMICIDE 9 2008 9 2017 0
## 27 27 68111 ROBBERY 93 2008 28 2017 65
## 28 28 68111 STALKING 1 2008 21 2017 -20
## 29 29 68111 THEFT 821 2008 344 2017 477plot for Crime types
barplot