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, union
library(knitr)
NJ <- read.csv("template.csv", header = TRUE, stringsAsFactors = FALSE)
View(NJ)
str(NJ)
## 'data.frame':    25 obs. of  19 variables:
##  $ Unemployment.by.race.in.2013: chr  "" "Elizabeth" "" "New Brunswick" ...
##  $ X                           : chr  "" "Women" "Men" "Women" ...
##  $ X.1                         : chr  "" "" "" "" ...
##  $ X.2                         : chr  "White non-Hispanic" "0.163" "0.069" "0.049" ...
##  $ X.3                         : chr  "\nBlack residents" "0.195" "0.182" "0.17" ...
##  $ X.4                         : chr  "" "" "" "" ...
##  $ X.5                         : chr  "Asian residents" "0.09" "0.03" "0.082" ...
##  $ X.6                         : chr  "other race residents" "0.076" "0.118" "0.088" ...
##  $ X.7                         : logi  NA NA NA NA NA NA ...
##  $ X.8                         : chr  "two or more race residents" "33.6" "0.148" "0.227" ...
##  $ X.9                         : chr  "Hispanic or Latino residents" "0.099" "0.085" "0.113" ...
##  $ X.10                        : logi  NA NA NA NA NA NA ...
##  $ X.11                        : chr  "American Indian and Alaska Native" "0.67" "0.055" "" ...
##  $ X.12                        : chr  "" "0.049847143" "0.098142857" "0.1215" ...
##  $ X.13                        : logi  NA NA NA NA NA NA ...
##  $ X.14                        : chr  "" "Elizabeth" "" "New Brunswick" ...
##  $ X.15                        : chr  "" "" "" "" ...
##  $ X.16                        : logi  NA NA NA NA NA NA ...
##  $ X.17                        : chr  "" "" "" "" ...
BigNJ <- read.csv("NewJersey.csv", header = TRUE, stringsAsFactors = FALSE)
kable(BigNJ)
City PopulationEst2015 Population2010 PopulationCh.20102015 Female2010 White2010 Black2010 Asian Hispanic White Foreignborn HousingUnits Medianrent2010.2014 Households PerHousehold X1YrSameResidence OtherLanguageAt.Home Highschoolgradorhigher Bachelor.s PersonswoutInsurance Incivilianlaborforce CivilianlaborforceFemale TotalManufacturersShipments MerchantWholesalerSales TotalRetailSales Total.retail.sales.per.capita Mean.travel.time.to.work.minutes Median.household.income.2014 Per.capita.income2014 PersonsInPoverty All.firms2012 Men.owned.firms2012 Women.owned.firms2012 Minority.owned.firms2012 Nonminority.owned.firms2012 Population.per.square.mile..2010 Land.area.in.square.miles..2010 MurderPer100K Rapes Robberies Assaults Burglaries Thefts CityCrimeIndex ChangeCIin10Yr
New Brunswick 57035 54578 4.5 48.8 45.4 16.0 7.6 49.9 26.8 38.6 15053 1370 13866 3.48 73.0 57.4 62.4 20.5 31.2 60.0 52.9 381357 751088 179461 3196 24.9 38399 14119 34.9 2126 1325 590 1077 875 10556.9 5.23 7.1 32 0.11 0.09 0.26 0.49 346.8 -0.1148545
Trenton 84225 84910 -0.8 48.4 26.6 52.0 1.2 33.7 13.5 23.6 33035 991 27998 2.83 84.0 36.9 71.3 10.7 25.0 61.6 58.8 NA 745596 341612 4044 23.6 35647 17021 28.4 4006 2036 1604 2392 1341 11102.6 7.65 37.9 27.3 478 561 971 1032 496.3 -0.2951285
Jersey City 264290 247643 6.7 50.6 32.7 25.8 23.7 27.6 21.5 39.8 108720 1187 96634 2.62 85.4 52.5 85.0 42.5 20.5 68.8 62.1 857582 12481747 2568076 10093 35.6 58907 32791 19.0 23681 12383 9196 13600 8993 16736.3 14.79 9.2 13.5 239 270 341 1078 249.5 -0.5452889
Elizabeth 129007 124969 3.2 50.4 54.6 21.1 2.1 59.5 18.2 47.1 45516 1069 39273 3.18 85.2 75.1 73.1 11.8 29.6 70.7 65.6 935403 2824024 1489177 11776 26.7 43966 19069 19.2 11483 6524 4105 7727 3319 10144.4 12.32 NA NA
Newark 281944 277149 1.7 50.5 26.3 52.4 1.6 33.8 11.6 27.9 109520 978 91771 2.89 86.2 46.1 71.4 13.3 28.2 63.3 61.2 3139443 5373530 2173876 7827 33.9 34012 16828 29.9 22800 10637 10369 16113 5745 11458.2 24.19 NA NA
Paterson 147754 146199 1.1 51.7 34.7 31.7 3.3 57.6 9.2 33.1 47946 1116 43462 3.32 90.1 61.7 72.2 10.7 26.7 57.2 52.9 1167948 1075168 849783 5852 22.9 33964 16259 28.4 10323 5328 4388 7413 2657 17346.8 8.43 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA City New Brunswick Trenton Jersey City Elizabeth Newark Paterson NA New Brunswick Trenton Jersey City Elizabeth Newark Paterson NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA PopulationEst2015 57035 84225 264290 129007 281944 147754 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Population2010 54578 84910 247643 124969 277149 146199 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA PopulationCh 20102015 4.5 -0.8 6.7 3.2 1.7 1.1 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Female2010 48.8 48.4 50.6 50.4 50.5 51.7 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA White2010 45.4 26.6 32.7 54.6 26.3 34.7 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Black2010 16 52 25.8 21.1 52.4 31.7 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Asian 7.6 1.2 23.7 2.1 1.6 3.3 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Hispanic 49.9 33.7 27.6 59.5 33.8 57.6 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA White 26.8 13.5 21.5 18.2 11.6 9.2 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Foreignborn 38.6 23.6 39.8 47.1 27.9 33.1 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA HousingUnits 15053 33035 108720 45516 109520 47946 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Medianrent2010-2014 1370 991 1187 1069 978 1116 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Households 13866 27998 96634 39273 91771 43462 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA PerHousehold 3.48 2.83 2.62 3.18 2.89 3.32 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1YrSameResidence 73 84 85.4 85.2 86.2 90.1 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA OtherLanguageAt Home 57.4 36.9 52.5 75.1 46.1 61.7 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Highschoolgradorhigher 62.4 71.3 85 73.1 71.4 72.2 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Bachelor’s 20.5 10.7 42.5 11.8 13.3 10.7 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA PersonswoutInsurance 31.2 25 20.5 29.6 28.2 26.7 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Incivilianlaborforce 60 61.6 68.8 70.7 63.3 57.2 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA CivilianlaborforceFemale 52.9 58.8 62.1 65.6 61.2 52.9 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA TotalManufacturersShipments 381357 NA 857582 935403 3139443 1167948 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA MerchantWholesalerSales 751088 745596 12481747 2824024 5373530 1075168 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA TotalRetailSales 179461 341612 2568076 1489177 2173876 849783 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Total retail sales per capita 3196 4044 10093 11776 7827 5852 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Mean travel time to work minutes 24.9 23.6 35.6 26.7 33.9 22.9 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Median household income 2014 38399 35647 58907 43966 34012 33964 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Per capita income2014 14119 17021 32791 19069 16828 16259 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA PersonsInPoverty 34.9 28.4 19 19.2 29.9 28.4 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA All firms2012 2126 4006 23681 11483 22800 10323 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Men-owned firms2012 1325 2036 12383 6524 10637 5328 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Women-owned firms2012 590 1604 9196 4105 10369 4388 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Minority-owned firms2012 1077 2392 13600 7727 16113 7413 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Nonminority-owned firms2012 875 1341 8993 3319 5745 2657 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Population per square mile, 2010 10556.9 11102.6 16736.3 10144.4 11458.2 17346.8 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Land area in square miles, 2010 5.23 7.65 14.79 12.32 24.19 8.43 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA MurderPer100K 7.1 37.9 9.2 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Rapes 32 27.3 13.5 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Robberies 0.11 478 239 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Assaults 0.09 561 270 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Burglaries 0.26 971 341 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Thefts 0.49 1032 1078 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA CityCrimeIndex 346.8 496.3 249.5 NA NA
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA ChangeCIin10Yr -0.114854518 -0.295128533 -0.545288865 NA NA 
library(ggvis)
Violence <- read.csv("CrimeInNJ.csv", header=TRUE, stringsAsFactors=FALSE)
kable(Violence)
City Murder_per_100K Rape_per_100K Robberies_per_100K Assaults_per_100K Burglaries_per_100K Thefts_per_100K Auto_Theft_per_100K Arson_per_100K Crime_Index_Av_275 Ten_YearChange
New Brunswick 7.1 32.0 395.1 309.6 930.7 1742.2 142.4 19.6 346.8 -0.1148545
Trenton 37.9 27.3 477.9 560.9 971.3 1031.7 403.2 30.8 496.3 -0.2951285
Jersey City 9.2 13.5 238.5 270.0 341.1 1078.4 211.1 14.2 249.5 -0.5452889
Elizabeth 10.1 26.5 494.0 359.2 651.4 1658.8 686.4 10.1 448.5 0.0099077
Newark 33.3 17.6 688.6 338.2 622.0 1365.1 864.2 14.0 525.0 -0.1539081
Paterson 16.4 17.1 418.9 363.5 792.0 1339.6 507.9 8.9 416.6 -0.0151300
E. Orange 3.1 23.2 288.8 392.2 480.2 849.3 369.1 21.6 305.6 -0.6560108 
names(Violence)
##  [1] "City"                "Murder_per_100K"     "Rape_per_100K"      
##  [4] "Robberies_per_100K"  "Assaults_per_100K"   "Burglaries_per_100K"
##  [7] "Thefts_per_100K"     "Auto_Theft_per_100K" "Arson_per_100K"     
## [10] "Crime_Index_Av_275"  "Ten_YearChange"
Violence %>% select(City, Crime_Index_Av_275,Ten_YearChange ) %>% arrange(Ten_YearChange)
##            City Crime_Index_Av_275 Ten_YearChange
## 1     E. Orange              305.6   -0.656010806
## 2   Jersey City              249.5   -0.545288865
## 3       Trenton              496.3   -0.295128533
## 4        Newark              525.0   -0.153908139
## 5 New Brunswick              346.8   -0.114854518
## 6      Paterson              416.6   -0.015130024
## 7     Elizabeth              448.5    0.009907678
Violence %>% select(City,Murder_per_100K) %>% arrange(desc(Murder_per_100K))
##            City Murder_per_100K
## 1       Trenton            37.9
## 2        Newark            33.3
## 3      Paterson            16.4
## 4     Elizabeth            10.1
## 5   Jersey City             9.2
## 6 New Brunswick             7.1
## 7     E. Orange             3.1
Violence %>% select(City,Rape_per_100K) %>% arrange(desc(Rape_per_100K))
##            City Rape_per_100K
## 1 New Brunswick          32.0
## 2       Trenton          27.3
## 3     Elizabeth          26.5
## 4     E. Orange          23.2
## 5        Newark          17.6
## 6      Paterson          17.1
## 7   Jersey City          13.5
Violence %>% select(City,Assaults_per_100K) %>% arrange(desc(Assaults_per_100K))
##            City Assaults_per_100K
## 1       Trenton             560.9
## 2     E. Orange             392.2
## 3      Paterson             363.5
## 4     Elizabeth             359.2
## 5        Newark             338.2
## 6 New Brunswick             309.6
## 7   Jersey City             270.0
Violence %>% select(City,Robberies_per_100K) %>% arrange(desc(Robberies_per_100K))
##            City Robberies_per_100K
## 1        Newark              688.6
## 2     Elizabeth              494.0
## 3       Trenton              477.9
## 4      Paterson              418.9
## 5 New Brunswick              395.1
## 6     E. Orange              288.8
## 7   Jersey City              238.5
Violence %>% ggvis(x = ~ City, y = ~ Murder_per_100K) %>% layer_bars()
library(tidyr)
V2 <- gather(Violence, "Crime", "Number", Murder_per_100K:Arson_per_100K) %>% select(City, Crime, Number,  Crime_Index_Av_275, Ten_YearChange) %>% arrange(desc(Crime))
kable(V2)
City Crime Number Crime_Index_Av_275 Ten_YearChange
New Brunswick Thefts_per_100K 1742.2 346.8 -0.1148545
Trenton Thefts_per_100K 1031.7 496.3 -0.2951285
Jersey City Thefts_per_100K 1078.4 249.5 -0.5452889
Elizabeth Thefts_per_100K 1658.8 448.5 0.0099077
Newark Thefts_per_100K 1365.1 525.0 -0.1539081
Paterson Thefts_per_100K 1339.6 416.6 -0.0151300
E. Orange Thefts_per_100K 849.3 305.6 -0.6560108
New Brunswick Robberies_per_100K 395.1 346.8 -0.1148545
Trenton Robberies_per_100K 477.9 496.3 -0.2951285
Jersey City Robberies_per_100K 238.5 249.5 -0.5452889
Elizabeth Robberies_per_100K 494.0 448.5 0.0099077
Newark Robberies_per_100K 688.6 525.0 -0.1539081
Paterson Robberies_per_100K 418.9 416.6 -0.0151300
E. Orange Robberies_per_100K 288.8 305.6 -0.6560108
New Brunswick Rape_per_100K 32.0 346.8 -0.1148545
Trenton Rape_per_100K 27.3 496.3 -0.2951285
Jersey City Rape_per_100K 13.5 249.5 -0.5452889
Elizabeth Rape_per_100K 26.5 448.5 0.0099077
Newark Rape_per_100K 17.6 525.0 -0.1539081
Paterson Rape_per_100K 17.1 416.6 -0.0151300
E. Orange Rape_per_100K 23.2 305.6 -0.6560108
New Brunswick Murder_per_100K 7.1 346.8 -0.1148545
Trenton Murder_per_100K 37.9 496.3 -0.2951285
Jersey City Murder_per_100K 9.2 249.5 -0.5452889
Elizabeth Murder_per_100K 10.1 448.5 0.0099077
Newark Murder_per_100K 33.3 525.0 -0.1539081
Paterson Murder_per_100K 16.4 416.6 -0.0151300
E. Orange Murder_per_100K 3.1 305.6 -0.6560108
New Brunswick Burglaries_per_100K 930.7 346.8 -0.1148545
Trenton Burglaries_per_100K 971.3 496.3 -0.2951285
Jersey City Burglaries_per_100K 341.1 249.5 -0.5452889
Elizabeth Burglaries_per_100K 651.4 448.5 0.0099077
Newark Burglaries_per_100K 622.0 525.0 -0.1539081
Paterson Burglaries_per_100K 792.0 416.6 -0.0151300
E. Orange Burglaries_per_100K 480.2 305.6 -0.6560108
New Brunswick Auto_Theft_per_100K 142.4 346.8 -0.1148545
Trenton Auto_Theft_per_100K 403.2 496.3 -0.2951285
Jersey City Auto_Theft_per_100K 211.1 249.5 -0.5452889
Elizabeth Auto_Theft_per_100K 686.4 448.5 0.0099077
Newark Auto_Theft_per_100K 864.2 525.0 -0.1539081
Paterson Auto_Theft_per_100K 507.9 416.6 -0.0151300
E. Orange Auto_Theft_per_100K 369.1 305.6 -0.6560108
New Brunswick Assaults_per_100K 309.6 346.8 -0.1148545
Trenton Assaults_per_100K 560.9 496.3 -0.2951285
Jersey City Assaults_per_100K 270.0 249.5 -0.5452889
Elizabeth Assaults_per_100K 359.2 448.5 0.0099077
Newark Assaults_per_100K 338.2 525.0 -0.1539081
Paterson Assaults_per_100K 363.5 416.6 -0.0151300
E. Orange Assaults_per_100K 392.2 305.6 -0.6560108
New Brunswick Arson_per_100K 19.6 346.8 -0.1148545
Trenton Arson_per_100K 30.8 496.3 -0.2951285
Jersey City Arson_per_100K 14.2 249.5 -0.5452889
Elizabeth Arson_per_100K 10.1 448.5 0.0099077
Newark Arson_per_100K 14.0 525.0 -0.1539081
Paterson Arson_per_100K 8.9 416.6 -0.0151300
E. Orange Arson_per_100K 21.6 305.6 -0.6560108 
library(ggplot2)
## 
## Attaching package: 'ggplot2'
## The following object is masked from 'package:ggvis':
## 
##     resolution
Index <- read.csv("OverallCrimeNJ.csv", header = TRUE, stringsAsFactors = FALSE)
V3 <- gather(Index, "Measure", "Number", Crime_Index_Av_275:Ten_YearChange)
V3
##             City            Measure Number
## 1  New Brunswick Crime_Index_Av_275  346.8
## 2        Trenton Crime_Index_Av_275  496.3
## 3     Jerey City Crime_Index_Av_275  249.5
## 4      Elizabeth Crime_Index_Av_275  448.5
## 5         Newark Crime_Index_Av_275    525
## 6       Paterson Crime_Index_Av_275  416.6
## 7      E. Orange Crime_Index_Av_275  305.6
## 8  New Brunswick     Ten_YearChange   -11%
## 9        Trenton     Ten_YearChange   -30%
## 10    Jerey City     Ten_YearChange   -55%
## 11     Elizabeth     Ten_YearChange     1%
## 12        Newark     Ten_YearChange   -15%
## 13      Paterson     Ten_YearChange    -2%
## 14     E. Orange     Ten_YearChange   -66%
ggplot(V3, aes(factor(City), Number, fill =Measure))+ 
  geom_bar(stat="identity", position = "dodge") + 
  scale_fill_brewer(palette = "Set1")

ViolCr <- read.csv("NJViolentCrime.csv", header = TRUE, stringsAsFactors = FALSE)
VC <- gather(ViolCr, "Crime", "Number", Murder_per_100K:Assaults_per_100K)
VC
##             City              Crime Number
## 1  New Brunswick    Murder_per_100K    7.1
## 2        Trenton    Murder_per_100K   37.9
## 3    Jersey City    Murder_per_100K    9.2
## 4      Elizabeth    Murder_per_100K   10.1
## 5         Newark    Murder_per_100K   33.3
## 6       Paterson    Murder_per_100K   16.4
## 7      E. Orange    Murder_per_100K    3.1
## 8  New Brunswick      Rape_per_100K   32.0
## 9        Trenton      Rape_per_100K   27.3
## 10   Jersey City      Rape_per_100K   13.5
## 11     Elizabeth      Rape_per_100K   26.5
## 12        Newark      Rape_per_100K   17.6
## 13      Paterson      Rape_per_100K   17.1
## 14     E. Orange      Rape_per_100K   23.2
## 15 New Brunswick Robberies_per_100K  395.1
## 16       Trenton Robberies_per_100K  477.9
## 17   Jersey City Robberies_per_100K  238.5
## 18     Elizabeth Robberies_per_100K  494.0
## 19        Newark Robberies_per_100K  688.6
## 20      Paterson Robberies_per_100K  418.9
## 21     E. Orange Robberies_per_100K  288.8
## 22 New Brunswick  Assaults_per_100K  309.6
## 23       Trenton  Assaults_per_100K  560.9
## 24   Jersey City  Assaults_per_100K  270.0
## 25     Elizabeth  Assaults_per_100K  359.2
## 26        Newark  Assaults_per_100K  338.2
## 27      Paterson  Assaults_per_100K  363.5
## 28     E. Orange  Assaults_per_100K  392.2
ggplot(VC, aes(factor(City), Number, fill =Crime))+ 
  geom_bar(stat="identity", position = "dodge") + 
  scale_fill_brewer(palette = "Set1")

library(ggplot2)
ggplot(V2, aes(factor(City), Number, fill = Crime)) + 
  geom_bar(stat="identity", position = "dodge") + 
  scale_fill_brewer(palette = "Set1")

NJSt <- read.csv("MasterCopy.csv", header = TRUE, stringsAsFactors = FALSE) %>% rename(City = People)

kable(head(NJSt), digits = 3)
City NewBrunswick Trenton JerseyCity Elizabeth Newark Paterson EastOrange
Population2015 57035 84225 264290 129007 281944 147754 64949
Population2010 55181 84913 247597 124969 277140 146199 64270
Populationchange2010-2015 4.5 -0.8 6.7 3.2 1.7 1.1 1.3
PersonsUnder5_2010 7.2 7.9 7.1 8 7.5 8 7.2
PersonsUnder_18 years2010 21.1 25.1 21.1 25.6 25.6 27.9 25.7
PercentFemales 0.488 0.484 0.506 0.504 0.505 0.517 0.552 
TrMC <- t(NJSt)
kable(head(TrMC))
City Population2015 Population2010 Populationchange2010-2015 PersonsUnder5_2010 PersonsUnder_18 years2010 PercentFemales NumberFemales White2010 Percent White Count TotalWhiteCount Black2010 PercentBlack TotalBlackCount AmericanIndian2010 PercentAmericanIndian AmericanIndianCount AsianPercent2010 PercentAsian AsianCount TwoorMoreRaces 2010 PercentR2Races TwoRacesCount Hispanic2010 White alone, not Hispanic or Latino, percent, April 1, 2010 Total ForeignBorn2014 HousingUnits2010 Owner-occupied2014Rate MedianValueOwnerOcc2014 Median selected monthly owner costs -without a mortgage, 2010-2014 MedianGrossRent Households, 2010-2014 Persons per household, 2010-2014 Living in same house 1 year ago, percent of persons age 1 year+, 2010-2014 Language other than English spoken at home, percent of persons age 5 years+, 2010-2014 HSGradPercentunder25Yrs BAUnder25Yrs With a disability, under age 65 years, percent, 2010-2014 Persons without health insurance, under age 65 years, percent In civilian labor force, total, percent of population age 16 years+, 2010-2014 In civilian labor force, female, percent of population age 16 years+, 2010-2014 Total accommodation and food services sales, 2012 ($1,000) (c) Total health care and social assistance receipts/revenue, 2012 ($1,000) (c) Total manufacturers shipments, 2012 ($1,000) (c) Total merchant wholesaler sales, 2012 ($1,000) (c) Total retail sales, 2012 ($1,000) (c) Total retail sales per capita, 2012 (c) Mean travel time to work (minutes), workers age 16 years+, 2010-2014 Median household income (in 2014 dollars), 2010-2014 Per capita income in past 12 months (in 2014 dollars), 2010-2014 Persons in poverty, percent All firms, 2012 Men-owned firms, 2012 Women-owned firms, 2012 Minority-owned firms, 2012 Nonminority-owned firms, 2012 Veteran-owned firms, 2012 Nonveteran-owned firms, 2012 Population per square mile, 2010 Land area in square miles, 2010 FEMALE UNEMPLOYMENT
NewBrunswick 57035 55181 4.5 7.2 21.1 0.488 26,634.06 45.4 0.454 25,052.17 16 0.16 8,732.48 0.9 0.009 491.2 7.6 0.076 4147.928 4.4 0.044 2401.432 49.9 26.8 15,878.93 38.6 15053 21 240700 2107 1370 13866 3.48 73 0.574 0.624 20.5 3.9 31.2 60 52.9 127075 1458287 381357 751088 179461 3196 24.9 38399 14119 34.9 2126 1325 590 1077 875 124 1815 10556.9 5.23 0.107
Trenton 84225 84913 -0.8 7.9 25.1 0.484 41,096.44 26.6 0.266 22,586.86 52 0.52 44,153.20 0.7 0.007 594.37 1.2 0.012 1018.92 4.1 0.041 3481.31 33.7 13.5 49,353.58 23.6 33035 38 113100 1472 991 27998 2.83 84 0.369 0.713 10.7 11.3 25 61.6 58.8 NA 735324 NA 745596 341612 4044 23.6 35647 17021 28.4 4006 2036 1604 2392 1341 373 3350 11102.6 7.65 0.1199
JerseyCity 264290 247597 6.7 7.1 21.1 0.506 125,307.36 32.7 0.327 80,964.22 25.8 0.258 63,891.89 0.5 0.005 1,238.22 23.7 0.237 58691.391 4.4 0.044 10896.292 27.6 21.5 134,821.84 39.8 108720 29.9 323800 2651 1187 96634 2.62 85.4 0.525 0.85 42.5 6.7 20.5 68.8 62.1 422529 1073908 857582 12481747 2568076 10093 35.6 58907 32791 19 23681 12383 9196 13600 8993 1204 21509 16736.3 14.79 0.1157
Elizabeth 129007 124969 3.2 8 25.6 0.504 62,984.38 54.6 0.546 68,233.07 21.1 0.211 26,368.46 0.8 0.008 999.75 2.1 0.021 2624.349 4.6 0.046 5748.574 59.5 18.2 35,847.72 47.1 45516 27.1 271400 2486 1069 39273 3.18 85.2 0.751 0.731 11.8 6.3 29.6 70.7 65.6 206476 554858 935403 2824024 1489177 11776 26.7 43966 19069 19.2 11483 6524 4105 7727 3319 592 10518 10144.4 12.32 0.0695
Newark 281944 277140 1.7 7.5 25.6 0.505 139,960.25 26.3 0.263 72,887.82 52.4 0.524 145,226.08 0.6 0.006 1,662.89 1.6 0.016 4434.384 3.8 0.038 10531.662 33.8 11.6 161,959.40 27.9 109520 22.3 229600 2224 978 91771 2.89 86.2 0.461 0.714 13.3 13.1 28.2 63.3 61.2 582379 2119780 3139443 5373530 2173876 7827 33.9 34012 16828 29.9 22800 10637 10369 16113 5745 1398 20441 11458.2 24.19 0.0591 
names(TrMC)
## NULL
library(ggvis)
Violence %>% ggvis(x = ~ City, y = ~ Assaults_per_100K)%>% layer_bars(stroke := "red", fill := "purple", width = .4)
VC %>%
  ggvis(~City, ~Number, stroke = ~Crime) %>% layer_lines() %>% layer_points()