Data visualization in crime data

names(crimedata)

attach(crimedata)

summary(year)

par(mfrow=c(2,2))

rate2003=rate[year==2003]

rate2004=rate[year==2004]

rate2005=rate[year==2005]

rate2006=rate[year==2006]

rate2007=rate[year==2007]

rate2008=rate[year==2008]

plot(rate2003,type="l",col=2)

lines(rate2004,type="o",col=3)

Exploratory data analysis using R

data source: https://docs.google.com/spreadsheets/d/1HEiR7hqHVv_tBcpdR1Wn67_5DH2jvT14pFsCrS3MIus/edit?usp=sharing

Ref: https://www.unodc.org/documents/data-and-analysis/Crime-statistics/Data_Table-final.xls

 # 2-Way Frequency Table
attach(mydata)
mytable <- table(A,B) # A will be rows, B will be columns
mytable # print table

margin.table(mytable, 1) # A frequencies (summed over B)
margin.table(mytable, 2) # B frequencies (summed over A)

prop.table(mytable) # cell percentages
prop.table(mytable, 1) # row percentages
prop.table(mytable, 2) # column percentages 

xtabs

The xtabs( ) function allows you to create crosstabulations using formula style input.

# 3-Way Frequency Table
mytable <- xtabs(~A+B+c, data=mydata)
ftable(mytable) # print table
summary(mytable) # chi-square test of indepedence

If a variable is included on the left side of the formula, it is assumed to be a vector of frequencies (useful if the data have already been tabulated).

 

 FREQUENCY:   In statistics, the frequency (or absolute frequency) of an event is the number of times the event occurred in an experiment or study. Frequency is the value in numbers that shows how often a particular item occurs in the given data set. There are two types of frequency table - Grouped Frequency Distribution and Ungrouped Frequency Distribution. Data can be shown using graphs like histograms, bar graphs, frequency polygons, and so on.

Frequency Distribution Graphs

There is another way to show data that is in the form of graphs and it can be done by using a frequency distribution graph. The graphs help us to understand the collected data in an easy way. The graphical representation of a frequency distribution can be shown using the following:

• Bar Graphs: Bar graphs represent data using rectangular bars of uniform width along with equal spacing between the rectangular bars.
• Histograms: A histogram is a graphical presentation of data using rectangular bars of different heights. In a histogram, there is no space between the rectangular bars.
• Pie Chart: A pie chart is a type of graph that visually displays data in a circular chart. It records data in a circular manner and then it is further divided into sectors that show a particular part of data out of the whole part.
• Frequency Polygon: A frequency polygon is drawn by joining the mid-points of the bars in a histogram.

Types of Frequency Distribution

There are four types of frequency distribution under statistics which are explained below:

• Ungrouped frequency distribution: It shows the frequency of an item in each separate data value rather than groups of data values.Ungrouped Frequency Distribution Table: In the ungrouped frequency distribution table, we don't make class intervals, we write the accurate frequency of individual data. Considering the above example, the ungrouped table will be like this. Given below table shows two columns: one is of marks obtained in the test and the second is of frequency (no. of students).

  Marks obtained in Test	No. of Students
  5	3
  10	4
  15	5
  18	4
  20	4
  Total	20

• Grouped frequency distribution: In this type, the data is arranged and separated into groups called class intervals. The frequency of data belonging to each class interval is noted in a frequency distribution table. The grouped frequency table shows the distribution of frequencies in class intervals.

> summary(rate$V1)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 

  0.000   1.400   3.300   8.634  10.075  72.800 

>hist(rate$V1)

par(mfrow=c(1,2))

hist(rate$V1)

> hist(rate$V1,breaks=2)

> table(ratecut)

ratecut

   least     less moderate     high 

     287      308      312      294 

> barplot(table(ratecut))

> 

• Relative frequency distribution: It tells the proportion of the total number of observations associated with each category
• > barplot(table(ratecut))
• > barplot(table(ratecut))
• > lines(ratecut)
• > table(ratecut)

1. ratecut
2.    least     less moderate     high 
3.      287      308      312      294 

• > barplot(table(ratecut))
• > sum(table(ratecut))
• [1] 1201
• > table(ratecut)/1201
• ratecut
•     least      less  moderate      high 
• 0.2389675 0.2564530 0.2597835 0.2447960 
• > (table(ratecut)/1201)*100
• ratecut
•    least     less moderate     high 
• 23.89675 25.64530 25.97835 24.47960 
• > pie(table(ratecut))
• 
  
  
  
• 
  
• 
  
• .
• Cumulative frequency distribution: It is the sum of the first frequency and all frequencies below it in a frequency distribution. You have to add a value with the next value then add the sum with the next value again and so on till the last. The last cumulative frequency will be the total sum of all frequencies.

> attach(crimedata)
> table(region)
region
  Africa Americas     Asia   Europe  Oceania 
     167      296      289      438       30 

> table(subregion2)
subregion2
                          Australia and New Zealand                 Caribbean 
                       19                        12                        71 
          Central America              Central Asia            Eastern Africa 
                       82                        52                        58 
             Eastern Asia            Eastern Europe                 Melanesia 
                       30                       114                         6 
               Micronesia             Middle Africa           Northern Africa 
                       10                        14                        28 
          Northern Europe                 Polynesia        South-Eastern Asia 
                      120                         2                        43 
            South America           Southern Africa             Southern Asia 
                      124                        26                        50 
          Southern Europe            Western Africa              Western Asia 
                      122                        41                       114 
           Western Europe 
                       82 

> table(sourcelevel)
sourcelevel
       Police Public Health 
          846           374 

> table(sourcelevel2)
sourcelevel2
   Government International      National           NGO 
          196           992             6            26 

> table(metadata) metadata Asesinatos, homicidios 12 Assassinats, meurtres 3 Citing MoJ, Includes attempts 1 Citing National Police data 1 Citing National Police, Murder 6 Data provided by Policia de Investigaciones de Chile 6 Excluding Transdniestr 5 Homicide 11 Homicide and abetment to commit suicide 4 Homicidio 15 Homicidio doloso, denuncias registradas 5 Homicidio, infanticidio y parricidio delitos investigados 5 Homicidios 11 Homicidios dolosos 11 Homicidios dolosos, citing Policia Nacional 4 Homicidios, citing Fiscalia General de la Republica 6 Homicidios, citing National Police 4 Homicidios, citing Polica Nacional Civil 4 Homicidios, citing Policia Nacional 2 Homicidios, muertes 4 Includes attempts 23 Includes killing unwittingly 2 Intentional homicide 2 Intentional killing, reported cases 6 Intentional murder 3 Intentional murder, father kills son, and assault leading to death 1 Intentional murder, father kills son, killing a wife, and assault leading to death 2 Killing and beating till death. Includes attempts. 5 Meures violentas: homicidios y caidos en acciones legales 1 Meurtes violentas 3 Meurtes violentas: homicidios y muertos por P.N. en desempeno de sus funciones 1 Murder 76 Murder and dacoity with murder, data for 2002/03 1 Murder and dacoity with murder, data for 2003/04 1 Murder and dacoity with murder, data for 2004/05 1 Murder and dacoity with murder, data for 2005/06 1 Murder and dacoity with murder, data for 2006/07 1 Murder recorded by judicial police 3 Murder reported to police 6 Murder, citing National Police 3 Murder, infanticide 5 Murder, murder of newly born child, infanticide 5 nf 7 NULL 934 Persons killed 2 Victimas de homicidio doloso 5

>

> summary(rate)
      Rate       
 Min.   : 0.000  
 1st Qu.: 1.400  
 Median : 3.300  
 Mean   : 8.634  
 3rd Qu.:10.075  
 Max.   :72.800 

class(rate) 

hist(rate$Rate)

 > table(region$Region,sourcelevel2$Source.Level.2)
          
           Government International National NGO
  Africa           30           131        0   6
  Americas         95           178        6  17
  Asia             54           232        0   3
  Europe           12           426        0   0
  Oceania           5            25        0   0

> margin.table(mytable,1) Africa Americas Asia Europe Oceania 167 296 289 438 30 > margin.table(mytable,2) Government International National NGO 196 992 6 26

>

> prop.table(mytable,1) Government International National NGO Africa 0.17964072 0.78443114 0.00000000 0.03592814 Americas 0.32094595 0.60135135 0.02027027 0.05743243 Asia 0.18685121 0.80276817 0.00000000 0.01038062 Europe 0.02739726 0.97260274 0.00000000 0.00000000 Oceania 0.16666667 0.83333333 0.00000000 0.00000000 > prop.table(mytable,2) Government International National NGO Africa 0.15306122 0.13205645 0.00000000 0.23076923 Americas 0.48469388 0.17943548 1.00000000 0.65384615 Asia 0.27551020 0.23387097 0.00000000 0.11538462 Europe 0.06122449 0.42943548 0.00000000 0.00000000 Oceania 0.02551020 0.02520161 0.00000000 0.00000000

slicing the data > head(crimedata) V1 V1.1 V1.2 V1.3 V1.4 V1.5 V1.6 1 Africa Not available 37.4 2004 Public Health WHO NULL 2 Africa Not available 11.9 2004 Public Health WHO NULL 3 Africa Not available 3.4 2004 Public Health WHO NULL 4 Africa Not available 16.1 2004 Public Health WHO NULL 5 Africa Not available 6.4 2004 Police Interpol NULL 6 Africa Not available 20.5 2004 Public Health WHO NULL > names(crimedata)=c("region","subregion","rate","year","source1","source2","metadata") > head(crimedata) region subregion rate year source1 source2 metadata 1 Africa Not available 37.4 2004 Public Health WHO NULL 2 Africa Not available 11.9 2004 Public Health WHO NULL 3 Africa Not available 3.4 2004 Public Health WHO NULL 4 Africa Not available 16.1 2004 Public Health WHO NULL 5 Africa Not available 6.4 2004 Police Interpol NULL 6 Africa Not available 20.5 2004 Public Health WHO NULL

> crimedata[2,]
  region     subregion rate year       source1 source2 metadata
2 Africa Not available 11.9 2004 Public Health     WHO     NULL

> crimedata[2:3,]
  region     subregion rate year       source1 source2 metadata
2 Africa Not available 11.9 2004 Public Health     WHO     NULL
3 Africa Not available  3.4 2004 Public Health     WHO     NULL

> crimedata[3:6,]
  region     subregion rate year       source1  source2 metadata
3 Africa Not available  3.4 2004 Public Health      WHO     NULL
4 Africa Not available 16.1 2004 Public Health      WHO     NULL
5 Africa Not available  6.4 2004        Police Interpol     NULL
6 Africa Not available 20.5 2004 Public Health      WHO     NULL

> crimedata[3:6,3]
[1]  3.4 16.1  6.4 20.5

> crimedata[3:6,3,drop=F]
  rate
3  3.4
4 16.1
5  6.4
6 20.5

subset(crimedata,year==2004)

subset(crimedata$region,year==2004)

table(crimedata$Region,crimedata$Source.Level.1)
          
           Police Public Health
  Africa      115            52
  Americas    197            99
  Asia        228            61
  Europe      289           149
  Oceania      17            13

barplot(table(crimedata$Region,crimedata$Source.Level.1),col=c(1:5))

> my.table=table(crimedata$Region,crimedata$Source.Level.1)
> barplot(my.table)

barplot(my.table,legend=rownames(my.table),col=c(1:5),main="Vertical Distribution of data sources")

> 

>barplot(my.table,legend=rownames(my.table),col=c(1:5),beside=T,main="Vertical Distribution of data sources")

> ratecut=cut(rate$Rate,breaks=2,labels = c("low","high"))
> table(ratecut)
ratecut
 low high 
1155   65 

 table(crimedata1$Region,crimedata1$ratecut)
          
           low high
  Africa   151   16
  Americas 247   49
  Asia     289    0
  Europe   438    0
  Oceania   30    0

ratecut=cut(rate$Rate,breaks=c(0,4,10,72),labels=c("very.low","medium","very.high"))
> table(ratecut)
ratecut
 very.low    medium very.high 
      644       252       304 
> crimedata1=data.frame(crimedata,ratecut)
> table(crimedata1$Region,crimedata1$ratecut)
          
           very.low medium very.high
  Africa         77     23        66
  Americas       21     79       195
  Asia          193     69        26
  Europe        331     75        16
  Oceania        22      6         1

>my.table=table(crimedata1$Region,crimedata1$ratecut)
> margin.table(my.table,1)

  Africa Americas     Asia   Europe  Oceania 
     166      295      288      422       29 
> margin.table(my.table,2)

 very.low    medium very.high 

      644       252       304

>round(prop.table(my.table,1),2)

          
           very.low medium very.high
  Africa       0.46   0.14      0.40
  Americas     0.07   0.27      0.66
  Asia         0.67   0.24      0.09
  Europe       0.78   0.18      0.04
  Oceania      0.76   0.21      0.03
> round(prop.table(my.table,2),2)
          
           very.low medium very.high
  Africa       0.12   0.09      0.22
  Americas     0.03   0.31      0.64
  Asia         0.30   0.27      0.09
  Europe       0.51   0.30      0.05
  Oceania      0.03   0.02      0.00