The following data “State/UT-wise Under trial Prisoners by Type of Offences under Indian Penal Code (IPC) as on 31st December, 2020” has been taken from data.gov.in. In the following sections we will see EDA through which inferences are taken and some visualization of this data set.
[1] 36 29
Summary
Sl..No...Col..1. State.UT..Col..2. S.U
Min. : 1.00 Length:36 Length:36
1st Qu.: 9.75 Class :character Class :character
Median :18.50 Mode :character Mode :character
Mean :18.50
3rd Qu.:27.25
Max. :36.00
Offences.affecting.the.Human.Body...Murder....Col..3.
Min. : 0.0
1st Qu.: 73.5
Median : 588.0
Mean : 2022.4
3rd Qu.: 3308.0
Max. :12669.0
Offences.affecting.the.Human.Body...C.H..not.amounting.to.Murder....Col..4.
Min. : 0.00
1st Qu.: 2.75
Median : 70.50
Mean : 240.31
3rd Qu.: 226.00
Max. :3510.00
Offences.affecting.the.Human.Body...Dowry.Deaths....Col..5.
Min. : 0.0
1st Qu.: 0.0
Median : 42.5
Mean : 401.8
3rd Qu.: 189.8
Max. :8584.0
Offences.affecting.the.Human.Body...Attempt.to.Murder....Col..6.
Min. : 0.0
1st Qu.: 19.0
Median : 266.0
Mean : 814.6
3rd Qu.:1127.0
Max. :5857.0
Offences.affecting.the.Human.Body...Kidnapping...Abduction....Col..7.
Min. : 0.0
1st Qu.: 7.5
Median : 124.0
Mean : 428.8
3rd Qu.: 522.0
Max. :4067.0
Offences.affecting.the.Human.Body...Rape....Col..8.
Min. : 0.00
1st Qu.: 57.75
Median : 377.50
Mean :1126.25
3rd Qu.:1576.75
Max. :9030.00
Offences.affecting.the.Human.Body...Assault.on.Women.with.Intent.to.Outrage.her.Modesty....Col..9.
Min. : 0.00
1st Qu.: 2.75
Median : 39.00
Mean : 135.69
3rd Qu.: 146.75
Max. :1402.00
Total.Offences.affecting.the.Human.Body....Col..10.
Min. : 0.0
1st Qu.: 178.2
Median : 1679.5
Mean : 5169.8
3rd Qu.: 7758.8
Max. :45119.0
Offences.against.Public.Tranquility...Riots....Col..11.
Min. : 0.00
1st Qu.: 0.00
Median : 13.00
Mean : 61.58
3rd Qu.: 83.25
Max. :525.00
Offences.against.Property...Thefts....Col..12.
Min. : 0.0
1st Qu.: 31.0
Median : 506.5
Mean : 829.9
3rd Qu.:1257.0
Max. :6415.0
Offences.against.Property...Extortion....Col..13.
Min. : 0.00
1st Qu.: 1.00
Median : 23.00
Mean : 68.94
3rd Qu.: 67.75
Max. :637.00
Offences.against.Property...Robbery....Col..14
Min. : 0.00
1st Qu.: 5.75
Median : 121.00
Mean : 353.06
3rd Qu.: 399.75
Max. :3382.00
Offences.against.Property...Dacoity....Col..15.
Min. : 0.0
1st Qu.: 1.0
Median : 66.0
Mean : 307.0
3rd Qu.: 477.2
Max. :2175.0
Offences.against.Property...Prep..and.Assembly.for.Dacoity....Col..16.
Min. : 0.0
1st Qu.: 0.0
Median : 15.0
Mean : 147.5
3rd Qu.: 157.0
Max. :1811.0
Offences.against.Property...Criminal.Breach.of.Trust....Col..17.
Min. : 0.00
1st Qu.: 0.75
Median : 24.00
Mean : 80.11
3rd Qu.: 84.25
Max. :612.00
Offences.against.Property...Cheating....Col..18.
Min. : 0.00
1st Qu.: 2.75
Median : 40.00
Mean : 189.72
3rd Qu.: 232.75
Max. :1365.00
Offences.against.Property...Arson....Col..19.
Min. : 0.00
1st Qu.: 0.00
Median : 3.50
Mean : 19.25
3rd Qu.: 20.75
Max. :239.00
Offences.against.Property...Burglary....Col..20.
Min. : 0.0
1st Qu.: 5.5
Median : 74.0
Mean : 158.1
3rd Qu.: 217.8
Max. :1356.0
Total.Offences.against.Property....Col..21.
Min. : 0.0
1st Qu.: 73.5
Median : 1009.5
Mean : 2153.6
3rd Qu.: 3107.0
Max. :16970.0
Offences.relating.to.Documents...Property.Marks...Counterfeiting....Col..22.
Min. : 0.00
1st Qu.: 0.00
Median : 8.50
Mean : 39.19
3rd Qu.: 63.75
Max. :236.00
Other.Crime.Against.Women...Cruelty.by.Husband.or.Relatives.of.Husband....Col..23.
Min. : 0.0
1st Qu.: 0.0
Median : 23.0
Mean : 115.7
3rd Qu.: 126.0
Max. :1098.0
Other.Crime.Against.Women...Insult.to.the.Modesty.of.Women....Col..24.
Min. : 0.00
1st Qu.: 0.00
Median : 1.50
Mean :12.81
3rd Qu.:15.25
Max. :75.00
Total.Other.Crime.Against.Women...Col..23....Col..24.....Col..25.
Min. : 0.0
1st Qu.: 0.0
Median : 43.0
Mean : 128.5
3rd Qu.: 135.8
Max. :1170.0
Undertrials.of.offences.against.women..Total.of.Col..5...Col..8...Col..9...Col.23...Col.24.....Col..26.
Min. : 0.00
1st Qu.: 72.25
Median : 552.50
Mean : 1792.22
3rd Qu.: 2329.00
Max. :20186.00
Other.IPC.Crimes....Col..27. Total.Undertrials..IPC.Crimes.....Col..28.
Min. : 0.00 Min. : 0
1st Qu.: 20.75 1st Qu.: 332
Median : 110.00 Median : 2868
Mean : 323.83 Mean : 7877
3rd Qu.: 513.25 3rd Qu.:11979
Max. :2154.00 Max. :65897
Sl..No...Col..1.
0
State.UT..Col..2.
0
S.U
0
Offences.affecting.the.Human.Body...Murder....Col..3.
0
Offences.affecting.the.Human.Body...C.H..not.amounting.to.Murder....Col..4.
0
Offences.affecting.the.Human.Body...Dowry.Deaths....Col..5.
0
Offences.affecting.the.Human.Body...Attempt.to.Murder....Col..6.
0
Offences.affecting.the.Human.Body...Kidnapping...Abduction....Col..7.
0
Offences.affecting.the.Human.Body...Rape....Col..8.
0
Offences.affecting.the.Human.Body...Assault.on.Women.with.Intent.to.Outrage.her.Modesty....Col..9.
0
Total.Offences.affecting.the.Human.Body....Col..10.
0
Offences.against.Public.Tranquility...Riots....Col..11.
0
Offences.against.Property...Thefts....Col..12.
0
Offences.against.Property...Extortion....Col..13.
0
Offences.against.Property...Robbery....Col..14
0
Offences.against.Property...Dacoity....Col..15.
0
Offences.against.Property...Prep..and.Assembly.for.Dacoity....Col..16.
0
Offences.against.Property...Criminal.Breach.of.Trust....Col..17.
0
Offences.against.Property...Cheating....Col..18.
0
Offences.against.Property...Arson....Col..19.
0
Offences.against.Property...Burglary....Col..20.
0
Total.Offences.against.Property....Col..21.
0
Offences.relating.to.Documents...Property.Marks...Counterfeiting....Col..22.
0
Other.Crime.Against.Women...Cruelty.by.Husband.or.Relatives.of.Husband....Col..23.
0
Other.Crime.Against.Women...Insult.to.the.Modesty.of.Women....Col..24.
0
Total.Other.Crime.Against.Women...Col..23....Col..24.....Col..25.
0
Undertrials.of.offences.against.women..Total.of.Col..5...Col..8...Col..9...Col.23...Col.24.....Col..26.
0
Other.IPC.Crimes....Col..27.
0
Total.Undertrials..IPC.Crimes.....Col..28.
0
'data.frame': 36 obs. of 29 variables:
$ Sl..No...Col..1. : int 1 2 3 4 5 6 7 8 9 10 ...
$ State.UT..Col..2. : chr "Andhra Pradesh" "Arunachal Pradesh" "Assam" "Bihar" ...
$ S.U : chr "S" "S" "S" "S" ...
$ Offences.affecting.the.Human.Body...Murder....Col..3. : int 484 31 1741 6140 2873 113 3265 4108 398 4085 ...
$ Offences.affecting.the.Human.Body...C.H..not.amounting.to.Murder....Col..4. : int 53 4 266 508 196 0 75 189 3 220 ...
$ Offences.affecting.the.Human.Body...Dowry.Deaths....Col..5. : int 60 0 66 1639 123 0 15 389 0 779 ...
$ Offences.affecting.the.Human.Body...Attempt.to.Murder....Col..6. : int 331 1 544 4974 1383 22 420 1303 42 1611 ...
$ Offences.affecting.the.Human.Body...Kidnapping...Abduction....Col..7. : int 50 8 401 1833 294 15 502 855 32 801 ...
$ Offences.affecting.the.Human.Body...Rape....Col..8. : int 211 9 318 1537 2565 37 1651 1552 325 1793 ...
$ Offences.affecting.the.Human.Body...Assault.on.Women.with.Intent.to.Outrage.her.Modesty....Col..9. : int 25 0 57 315 290 24 92 251 10 268 ...
$ Total.Offences.affecting.the.Human.Body....Col..10. : int 1214 53 3393 16946 7724 211 6020 8647 810 9557 ...
$ Offences.against.Public.Tranquility...Riots....Col..11. : int 0 0 17 166 37 0 77 93 0 139 ...
$ Offences.against.Property...Thefts....Col..12. : int 580 23 1202 2831 483 32 613 1740 21 1441 ...
$ Offences.against.Property...Extortion....Col..13. : int 19 4 59 326 14 0 66 109 1 228 ...
$ Offences.against.Property...Robbery....Col..14 : int 103 13 127 1743 328 21 231 502 5 463 ...
$ Offences.against.Property...Dacoity....Col..15. : int 88 0 76 1289 281 3 296 550 2 555 ...
$ Offences.against.Property...Prep..and.Assembly.for.Dacoity....Col..16. : int 27 0 0 363 24 0 47 187 0 155 ...
$ Offences.against.Property...Criminal.Breach.of.Trust....Col..17. : int 23 0 25 219 76 0 319 211 4 206 ...
$ Offences.against.Property...Cheating....Col..18. : int 106 0 22 413 161 22 23 184 2 802 ...
$ Offences.against.Property...Arson....Col..19. : int 1 19 39 58 13 0 11 20 0 41 ...
$ Offences.against.Property...Burglary....Col..20. : int 97 10 270 290 373 0 152 253 8 155 ...
$ Total.Offences.against.Property....Col..21. : int 1044 69 1820 7532 1753 78 1758 3756 43 4046 ...
$ Offences.relating.to.Documents...Property.Marks...Counterfeiting....Col..22. : int 34 0 29 74 44 0 84 33 0 62 ...
$ Other.Crime.Against.Women...Cruelty.by.Husband.or.Relatives.of.Husband....Col..23. : int 102 0 159 642 32 0 138 77 5 410 ...
$ Other.Crime.Against.Women...Insult.to.the.Modesty.of.Women....Col..24. : int 8 0 1 27 14 0 0 16 0 16 ...
$ Total.Other.Crime.Against.Women...Col..23....Col..24.....Col..25. : int 110 0 160 669 46 0 138 93 5 426 ...
$ Undertrials.of.offences.against.women..Total.of.Col..5...Col..8...Col..9...Col.23...Col.24.....Col..26.: int 406 9 601 4160 3024 61 1896 2285 340 3266 ...
$ Other.IPC.Crimes....Col..27. : int 345 4 197 503 732 43 297 0 57 134 ...
$ Total.Undertrials..IPC.Crimes.....Col..28. : int 2747 126 5616 25890 10336 332 8374 12622 915 14364 ...
[1] "Sl.no"
[2] "State/UT"
[3] "SoU"
[4] "Murder"
[5] "C.H"
[6] "Dowry"
[7] "Attempt to murder"
[8] "Kidnapping & Abduction"
[9] "Rape"
[10] "Assault on Women with Intent to Outrage her Modesty"
[11] "Total Offences affecting the Human Body"
[12] "Riots"
[13] "Thefts"
[14] "Extortion"
[15] "Robbery"
[16] "Dacoity"
[17] "Prep. and Assembly for Dacoity"
[18] "Criminal Breach of Trust"
[19] "Cheating"
[20] "Arson"
[21] "Burglary"
[22] "Total Offences against Property"
[23] "Counterfeiting"
[24] "Cruelty by Husband or Relatives of Husband"
[25] "Insult to the Modesty of Women"
[26] "Total Other Crime Against Women"
[27] "Undertrials of offences against women"
[28] "Other IPC Crimes"
[29] "Total_Undertrials"
Upon looking at the histogram plots it is observed that all the
plots here are right skewed and also we can see that there’s a state
having more number of cases under trial for crime against woman.
Sl.no State/UT
1 4 Bihar
2 5 Chhattisgarh
3 8 Haryana
4 10 Jharkhand
5 13 Madhya Pradesh
6 14 Maharashtra
7 19 Odisha
8 21 Rajasthan
9 26 Uttar Pradesh
10 28 West Bengal
Here dataset has been filtered such that it has all states which
have cases that are greater than the mean of Total_under trials This
plot helps us to see that UP tops in the list of having more number of
criminal cases against women
Sl.no State/UT
1 4 Bihar
2 5 Chhattisgarh
3 8 Haryana
4 10 Jharkhand
5 13 Madhya Pradesh
6 14 Maharashtra
7 19 Odisha
8 21 Rajasthan
9 26 Uttar Pradesh
10 28 West Bengal
With the before filtered data set and with the help of this plot
we can see UP topping the list of having more number of cases against
property
[1] "Rape"
[2] "Assault on Women with Intent to Outrage her Modesty"
[3] "Cruelty by Husband or Relatives of Husband"
[4] "Insult to the Modesty of Women"
---
title: "Prison"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
social: menu
theme: united
source_code: embed
storyboard: TRUE
---
```{r setup, include=FALSE}
libraries= c("flexdashboard","lattice","DT","dplyr","corrplot","ggplot2")
lapply(libraries,require,character.only=TRUE)
data2=read.csv("D:/BU/Sem 2/R/Rdata/prison.csv",header = TRUE)
attach(data2)
```
# Home
The following data "State/UT-wise Under trial Prisoners by Type of Offences under Indian Penal Code (IPC) as on 31st December, 2020" has been taken from data.gov.in. In the following sections we will see EDA through which inferences are taken and some visualization of this data set.
## DD{.tabset}
### Summary
Dimension
```{r}
dim(data2)
```
Summary
```{r}
summary(data2)
```
### Null Check
```{r}
colSums(is.na(data2))
```
### Structure
```{r}
str(data2)
```
### Renaming the attributes
```{r}
names(data2)=c("Sl.no","State/UT","SoU","Murder","C.H","Dowry",
"Attempt to murder","Kidnapping & Abduction","Rape",
"Assault on Women with Intent to Outrage her Modesty" ,
"Total Offences affecting the Human Body",
"Riots","Thefts","Extortion","Robbery","Dacoity",
"Prep. and Assembly for Dacoity","Criminal Breach of Trust",
"Cheating","Arson","Burglary","Total Offences against Property",
"Counterfeiting","Cruelty by Husband or Relatives of Husband",
"Insult to the Modesty of Women","Total Other Crime Against Women",
"Undertrials of offences against women","Other IPC Crimes",
"Total_Undertrials")
names(data2)
attach(data2)
```
# Visualization
## DV{.tabset}
### Histogram
```{r}
par( mfrow=c(2,2), mar=c(4,4,1,0) )
hist(`Total_Undertrials`, breaks=10 , col=rgb(0,0,1,0.5) ,
xlab="Total_Undertrials" , ylab="" , main="")
hist(`Undertrials of offences against women`, breaks=30 , col=rgb(0,0,1,0.5) , xlab="Undertrials of offences against women" , ylab="" , main="")
hist(`Assault on Women with Intent to Outrage her Modesty`, breaks=20 , col=rgb(0,0,1,0.5) ,
xlab="Assault on Women with Intent to Outrage her Modesty" , ylab="" , main="")
hist(`Total Other Crime Against Women`, breaks=25 , col=rgb(0,0,1,0.5) ,
xlab="Total Other Crime Against Women" , ylab="" , main="")
```
```{r}
par( mfrow=c(2,2), mar=c(4,4,1,0) )
hist(`Rape`, breaks=20 , col=rgb(0,0,1,0.5) ,
xlab="Rape" , ylab="" , main="")
hist(`Dowry`, breaks=30, col=rgb(0,0,1,0.5) ,
xlab="Dowry" , ylab="" , main="")
hist(`Cruelty by Husband or Relatives of Husband`, breaks=25 , col=rgb(0,0,1,0.5) ,
xlab="Cruelty by Husband or Relatives of Husband" , ylab="" , main="")
hist(`Insult to the Modesty of Women`, breaks=30 , col=rgb(0,0,1,0.5) ,
xlab="Insult to the Modesty of Women" , ylab="" , main="")
```
```{r}
#par( mfrow=c(2,2), mar=c(4,4,1,0) )
#hist(`Undertrials of offences against women`, breaks=20 , col=rgb(0,0,1,0.5) , xlab="Undertrials of offences against women" , ylab="" , main="")
```
Upon looking at the histogram plots it is observed that all the plots here are right skewed and also we can see that there's a state having more number of cases under trial for crime against woman.
### Scatterplot
```{r}
U=filter(data2,SoU=="S")
a=mean(U$Total_Undertrials)
A=filter(U,Total_Undertrials > a)
State=as.factor(A$Sl.no)
ggplot(A)+geom_point(mapping = aes(x=State,y=`Undertrials of offences against women`))+
ggtitle(label = "States vs Undertrials of offences against women")
```
```{r}
d=subset(A,select = c(1,2))
d
```
Here dataset has been filtered such that it has all states which have cases that are greater than the mean of Total_under trials
This plot helps us to see that UP tops in the list of having more number of criminal cases against women
### Barplot
```{r}
State=as.factor(A$Sl.no)
ggplot(A, aes(State,`Total Offences against Property`)) +
geom_bar(stat = "identity", color = "purple") +
theme(axis.text.x = element_text(angle = 70, vjust = 0.5, color = "green")) +
ggtitle("State`V`Total Offences against Property") + theme_bw()
```
```{r}
d=subset(A,select = c(1,2))
d
```
With the before filtered data set and with the help of this plot we can see UP topping the list of having more number of cases against property
### Correlation
```{r}
b=subset(A,select = c(9,10,24,25))
names(b)
```
```{r}
names(b)=c("R","A","C","I")
corrplot(cor(b),method = "number",shade.col=NA, tl.col="black",tl.srt=0, mar=c(4,2,4,0))
```
# Download
```{r}
datatable(data2,extensions='Buttons',options=list(dom="Bftrip",buttons=c('copy','print','csv','pdf')))
```