The rape statistics of Indian states discussed in the hon’ble Supreme Court of India
Dr. Arvind Singla
11 August 2018
Early this week one headline that got a lot of publicity across all popular media was that as per the NCRB statistics, Madhya Pradesh and Uttar Pradesh were the top two states as far as cases of rapes were concerned. A Supreme Court Bench is reported to has this observed it during hearing on alleged sexual exploitation of girls. One such media report can be read here: http://www.rediff.com/news/report/bihar-shelter-home-case-women-being-raped-left-right-and-centre-says-sc/20180807.htm
So relavant statistics is studied here. The latest dataset I could get is for the year 2015 available at: http://ncrb.gov.in/StatPublications/CII/CII2015/FILES/CrimeInIndia2015.pdf
rape2015 <- read.csv("~/public datset/rape2015.txt", header=FALSE, stringsAsFactors=FALSE)
val2<-unlist(rape2015$V2[1:18])
val1<-unlist(rape2015$V1[1:18])
rape<-c(val1,val2)
rape## [1] "c(1 Andhra 15931 4.9 255.6 62.3"
## [2] "3 Assam 23258 7.1 157.0 148.2"
## [3] "5 ChGarh 5720 1.7 127.8 44.8"
## [4] "7 Gujarat 7762 2.4 294.7 26.3"
## [5] "9 Himachal 1289 0.4 34.4 37.4"
## [6] "11 JhKhand 6518 2.0 162.0 40.2"
## [7] "13 Kerala 9708 3.0 181.7 53.4"
## [8] "15 Maharst 31126 9.5 568.0 54.8"
## [9] "17 Meghaly 334 0.1 13.6 24.5"
## [10] "19 Nagalnd 90 0.0 11.2 8.0"
## [11] "21 Punjab 5291 1.6 133.2 39.7"
## [12] "23 Sikkim 53 0.0 3.0 17.6"
## [13] "25 Telngna 15135 4.6 182.1 * 83.1"
## [14] "27 UP 35527 10.9 1021.2 34.8"
## [15] "29 WBengal 33218 10.1 452.5 73.4"
## [16] "31 ChandiG 463 0.1 7.1 64.8"
## [17] "33 Dn&Diu 28 0.0 1.1 26.4"
## [18] "35 LakshaD 9 0.0 0.4 22.0"
## [19] " 2 Arunachal 384 0.1 6.2 62.1"
## [20] " 4 Bihar 13891 4.2 498.4 27.9"
## [21] " 6 Goa 365 0.1 9.2 39.9"
## [22] " 8 Haryana 9446 2.9 124.7 75.7"
## [23] " 10 J&K 3363 1.0 59.0 57.0"
## [24] " 12 Karntk 12705 3.9 305.7 41.6"
## [25] " 14 MP 24135 7.4 368.6 65.5"
## [26] " 16 Manipur 266 0.1 12.8 20.8"
## [27] " 18 Mizoram 158 0.0 5.1 30.9"
## [28] " 20 Odisha 17144 5.2 209.2 81.9"
## [29] " 22 Rajsthn 28165 8.6 345.6 81.5"
## [30] " 24 TamilN 5847 1.8 344.8 17.0"
## [31] " 26 Tripura 1267 0.4 18.6 68.2"
## [32] " 28 Utrkhand 1453 0.4 51.6 28.2"
## [33] " 30 A&N_Is 136 0.0 2.7 51.1"
## [34] " 32 D&N_Hv 25 0.0 1.9 12.9"
## [35] " 34 Delhi 17104 5.2 92.8 184.3"
## [36] " 36 PuduCh 80 0.0 7.4 10.9"nchar(rape)## [1] 31 29 28 29 29 30 29 31 28 26 29 25 33 28 32 27 25 25 29 29 23 30 26
## [24] 31 27 29 28 31 32 30 30 31 27 26 30 26rape[1]<-substr(rape[1],3,31)
rape[36]<-substr(rape[36],2,31)
for(i in 19:35){
rape[i] <- substr(rape[i],2,nchar(rape[i]))
}Including Plots
You can also embed plots, for example:
## [[1]]
## [1] "1" "Andhra" "15931" "4.9" "255.6" "62.3"
##
## [[2]]
## [1] "3" "Assam" "23258" "7.1" "157.0" "148.2"
##
## [[3]]
## [1] "5" "ChGarh" "5720" "1.7" "127.8" "44.8"
##
## [[4]]
## [1] "7" "Gujarat" "7762" "2.4" "294.7" "26.3"
##
## [[5]]
## [1] "9" "Himachal" "1289" "0.4" "34.4" "37.4"
##
## [[6]]
## [1] "11" "JhKhand" "6518" "2.0" "162.0" "40.2"
##
## [[7]]
## [1] "13" "Kerala" "9708" "3.0" "181.7" "53.4"
##
## [[8]]
## [1] "15" "Maharst" "31126" "9.5" "568.0" "54.8"
##
## [[9]]
## [1] "17" "Meghaly" "334" "0.1" "13.6" "24.5"
##
## [[10]]
## [1] "19" "Nagalnd" "90" "0.0" "11.2" "8.0"
##
## [[11]]
## [1] "21" "Punjab" "5291" "1.6" "133.2" "39.7"
##
## [[12]]
## [1] "23" "Sikkim" "53" "0.0" "3.0" "17.6"
##
## [[13]]
## [1] "25" "Telngna" "15135" "4.6" "182.1" "*" "83.1"
##
## [[14]]
## [1] "27" "UP" "35527" "10.9" "1021.2" "34.8"
##
## [[15]]
## [1] "29" "WBengal" "33218" "10.1" "452.5" "73.4"
##
## [[16]]
## [1] "31" "ChandiG" "463" "0.1" "7.1" "64.8"
##
## [[17]]
## [1] "33" "Dn&Diu" "28" "0.0" "1.1" "26.4"
##
## [[18]]
## [1] "35" "LakshaD" "9" "0.0" "0.4" "22.0"
##
## [[19]]
## [1] "2" "Arunachal" "384" "0.1" "6.2" "62.1"
##
## [[20]]
## [1] "4" "Bihar" "13891" "4.2" "498.4" "27.9"
##
## [[21]]
## [1] "6" "Goa" "365" "0.1" "9.2" "39.9"
##
## [[22]]
## [1] "8" "Haryana" "9446" "2.9" "124.7" "75.7"
##
## [[23]]
## [1] "10" "J&K" "3363" "1.0" "59.0" "57.0"
##
## [[24]]
## [1] "12" "Karntk" "12705" "3.9" "305.7" "41.6"
##
## [[25]]
## [1] "14" "MP" "24135" "7.4" "368.6" "65.5"
##
## [[26]]
## [1] "16" "Manipur" "266" "0.1" "12.8" "20.8"
##
## [[27]]
## [1] "18" "Mizoram" "158" "0.0" "5.1" "30.9"
##
## [[28]]
## [1] "20" "Odisha" "17144" "5.2" "209.2" "81.9"
##
## [[29]]
## [1] "22" "Rajsthn" "28165" "8.6" "345.6" "81.5"
##
## [[30]]
## [1] "24" "TamilN" "5847" "1.8" "344.8" "17.0"
##
## [[31]]
## [1] "26" "Tripura" "1267" "0.4" "18.6" "68.2"
##
## [[32]]
## [1] "28" "Utrkhand" "1453" "0.4" "51.6" "28.2"
##
## [[33]]
## [1] "30" "A&N_Is" "136" "0.0" "2.7" "51.1"
##
## [[34]]
## [1] "32" "D&N_Hv" "25" "0.0" "1.9" "12.9"
##
## [[35]]
## [1] "34" "Delhi" "17104" "5.2" "92.8" "184.3"
##
## [[36]]
## [1] "36" "PuduCh" "80" "0.0" "7.4" "10.9"## [1] "1" "Andhra" "15931" "4.9" "255.6"
## [6] "62.3" "3" "Assam" "23258" "7.1"
## [11] "157.0" "148.2" "5" "ChGarh" "5720"
## [16] "1.7" "127.8" "44.8" "7" "Gujarat"
## [21] "7762" "2.4" "294.7" "26.3" "9"
## [26] "Himachal" "1289" "0.4" "34.4" "37.4"
## [31] "11" "JhKhand" "6518" "2.0" "162.0"
## [36] "40.2" "13" "Kerala" "9708" "3.0"
## [41] "181.7" "53.4" "15" "Maharst" "31126"
## [46] "9.5" "568.0" "54.8" "17" "Meghaly"
## [51] "334" "0.1" "13.6" "24.5" "19"
## [56] "Nagalnd" "90" "0.0" "11.2" "8.0"
## [61] "21" "Punjab" "5291" "1.6" "133.2"
## [66] "39.7" "23" "Sikkim" "53" "0.0"
## [71] "3.0" "17.6" "25" "Telngna" "15135"
## [76] "4.6" "182.1" "*" "83.1" "27"
## [81] "UP" "35527" "10.9" "1021.2" "34.8"
## [86] "29" "WBengal" "33218" "10.1" "452.5"
## [91] "73.4" "31" "ChandiG" "463" "0.1"
## [96] "7.1" "64.8" "33" "Dn&Diu" "28"
## [101] "0.0" "1.1" "26.4" "35" "LakshaD"
## [106] "9" "0.0" "0.4" "22.0" "2"
## [111] "Arunachal" "384" "0.1" "6.2" "62.1"
## [116] "4" "Bihar" "13891" "4.2" "498.4"
## [121] "27.9" "6" "Goa" "365" "0.1"
## [126] "9.2" "39.9" "8" "Haryana" "9446"
## [131] "2.9" "124.7" "75.7" "10" "J&K"
## [136] "3363" "1.0" "59.0" "57.0" "12"
## [141] "Karntk" "12705" "3.9" "305.7" "41.6"
## [146] "14" "MP" "24135" "7.4" "368.6"
## [151] "65.5" "16" "Manipur" "266" "0.1"
## [156] "12.8" "20.8" "18" "Mizoram" "158"
## [161] "0.0" "5.1" "30.9" "20" "Odisha"
## [166] "17144" "5.2" "209.2" "81.9" "22"
## [171] "Rajsthn" "28165" "8.6" "345.6" "81.5"
## [176] "24" "TamilN" "5847" "1.8" "344.8"
## [181] "17.0" "26" "Tripura" "1267" "0.4"
## [186] "18.6" "68.2" "28" "Utrkhand" "1453"
## [191] "0.4" "51.6" "28.2" "30" "A&N_Is"
## [196] "136" "0.0" "2.7" "51.1" "32"
## [201] "D&N_Hv" "25" "0.0" "1.9" "12.9"
## [206] "34" "Delhi" "17104" "5.2" "92.8"
## [211] "184.3" "36" "PuduCh" "80" "0.0"
## [216] "7.4" "10.9"output <- matrix(rp, ncol=6, nrow=36,byrow = TRUE)
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, unionrape<-output
rape<-data.frame("s"=rape[,1],"State/UT"=rape[,2],"Number Of Cases"=as.numeric(rape[,3]),"Fraction(%) of All India Total"=as.numeric(rape[,4]),"Population(F) in Lakhs"=as.numeric(rape[,5]),"Rate/Incidence"=as.numeric(rape[,6]),stringsAsFactors = FALSE)
names(rape)<-c("s","State","Number","Fraction","Population","Rate")
rape=rape%>%select(State,Number,Fraction,Population,Rate)index_byRate=order(rape$Rate)
rape_sortedBy_Rate=rape[index_byRate,]
rape_sortedBy_Rate=rape_sortedBy_Rate[36:1,]
####
index_by_Total=order(rape$Number)
rape_by_total=rape[index_by_Total,]
rape_by_total=rape_by_total[36:1,]Rate or Incidence:
Barplot of Rate or Incidence of Rape in 2015 in the States in India
The statistics to be used in this case is total number of cases of crime per unit population that is called incidence or rate and not just gross number of cases in two states of grossly different population sizes.
bp=barplot(round(rape_sortedBy_Rate$Rate,0),col = heat.colors(100)[c(seq(1,12,2),seq(21,80,3),seq(81,100,2))],main = "States in India by Incidence of Rape:2015",ylab = "Rape Incidence",axes = FALSE,names.arg = rape_sortedBy_Rate$State,las=2)
text(bp,rape_sortedBy_Rate$Rate,unlist(round(rape_sortedBy_Rate$Rate,0)),cex = 0.7,pos = 3)
rape_sortedBy_Rate$State[1:7]## [1] "Delhi" "Assam" "Telngna" "Odisha" "Rajsthn" "Haryana" "WBengal"which(rape_sortedBy_Rate$State=="UP")## [1] 23Obiviously Top seven states with the worst rating wrt Incidence of Rape cases are: Delhi, Assam, Telangana, Odisha, Rajasthan, Haryana and West Bengal, in that order. The UP comes at number 23 from the upper worst end in the list of 36 and 14th from below. With apologies to the Supreme Court these data, per NCRB statistcs(2015) doesn’t support the observation that Madhya Pradesh and Uttar Pradesh were the top two states as far as cases of rapes were concerned.
It is disturbing that a lots of people, even very eminent people needs to be more careful to be able to interpret simple statistics.