Project Description
- To define whether crimes rates are related to the income levels or not, generating ideas for avert these crimes if it is.
Project data
*Crime:
- Homicide rates
- Sexual abuse and rape rates
*Economy:
- GDP rates
- Employment
- Gini index
Variables in crime: Year, gender, cause, countries, location etc.
Variables in economy: Work hours, countries, GDP, Inequality Index etc.
Dataset Analyzing
First of all, data on the subject were collected. Then, unnecessary data from these data were eliminated.Detailed information about dataset analyzing was given in the interim report.
- The datasets about crime rates are:
- Murders2005
- Murders2006
- Murders2007
- Murders2008
- Murders2009
- Murders2010
- Rapes2005
- Rapes2006
- Rapes2007
- Rapes2008
- Rapes2009
- Rapes2010
Importing datasets about income levels and renaming them
employment <- read.csv("datasets/ANHRS_31102021130035527.csv")
gpd <- read.csv("datasets/API_NY.GDP.MKTP.CD_DS2_en_csv_v2_3158872.csv")
gini <- read.csv("datasets/API_SI.POV.GINI_DS2_en_csv_v2_3158890.csv")
homicide <- read.csv("datasets/API_VC.IHR.PSRC.P5_DS2_en_csv_v2_3161002.csv")
sexualviolence <- read.csv("datasets/cts_sexual_violence_2012.csv")Dataframes
head(employment,5)## ï..COUNTRY Country EMPSTAT Employment.status FREQUENCY Frequency TIME Time
## 1 AUS Australia TE Total employment A Annual 2010 2010
## 2 AUS Australia TE Total employment A Annual 2011 2011
## 3 AUS Australia TE Total employment A Annual 2012 2012
## 4 AUS Australia TE Total employment A Annual 2013 2013
## 5 AUS Australia TE Total employment A Annual 2014 2014
## Unit.Code Unit PowerCode.Code PowerCode Reference.Period.Code
## 1 HOUR Hours 0 Units NA
## 2 HOUR Hours 0 Units NA
## 3 HOUR Hours 0 Units NA
## 4 HOUR Hours 0 Units NA
## 5 HOUR Hours 0 Units NA
## Reference.Period Value Flag.Codes Flags
## 1 NA 1778 NA NA
## 2 NA 1774 NA NA
## 3 NA 1771 NA NA
## 4 NA 1766 NA NA
## 5 NA 1755 NA NAhead(gpd,5)## ï..Data.Source World.Development.Indicators Column1
## 1
## 2 Last Updated Date 2021-10-28
## 3
## 4 Country Name Country Code Indicator Name
## 5 Aruba ABW GDP (current US$)
## X_1 X_2 X_3 X_4 X_5 X_6 X_7 X_8 X_9 X_10 X_11 X_12 X_13
## 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 Indicator Code 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971
## 5 NY.GDP.MKTP.CD NA NA NA NA NA NA NA NA NA NA NA NA
## X_14 X_15 X_16 X_17 X_18 X_19 X_20 X_21 X_22 X_23 X_24 X_25 X_26 X_27
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## X_28 X_29 X_30 X_31 X_32 X_33 X_34
## 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 1986 1987 1988 1989 1990 1991 1992
## 5 405463417 487602458 596423607 695304363 764887117 872138715 958463184
## X_35 X_36 X_37 X_38 X_39 X_40 X_41
## 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 1993 1994 1995 1996 1997 1998 1999
## 5 1082979721 1245688268 1320474860 1379960894 1531944134 1665100559 1722798883
## X_42 X_43 X_44 X_45 X_46 X_47 X_48
## 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 2000 2001 2002 2003 2004 2005 2006
## 5 1873452514 1920111732 1941340782 2021229050 2228491620 2330726257 2424581006
## X_49 X_50 X_51 X_52 X_53 X_54 X_55
## 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 2007 2008 2009 2010 2011 2012 2013
## 5 2615083799 2745251397 2498882682 2390502793 2549720670 2534636872 2727849721
## X_56 X_57 X_58 X_59 X_60 X_61 X_62 X_63
## 1 NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA
## 4 2014 2015 2016 2017 2018 2019 2020 NA
## 5 2790849162 2962905028 2983636872 3092430168 3202188607 NA NA NAhead(gini,5)## ï..Data.Source World.Development.Indicators
## 1
## 2 Last Updated Date 2021-10-28
## 3
## 4 Country Name Country Code
## 5 Aruba ABW
## Column1 X_1 X_2 X_3 X_4 X_5 X_6 X_7
## 1 NA NA NA NA NA NA
## 2 NA NA NA NA NA NA
## 3 NA NA NA NA NA NA
## 4 Indicator Name Indicator Code 1960 1961 1962 1963 1964 1965
## 5 Gini index (World Bank estimate) SI.POV.GINI NA NA NA NA NA NA
## X_8 X_9 X_10 X_11 X_12 X_13 X_14 X_15 X_16 X_17 X_18 X_19 X_20 X_21 X_22
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## X_23 X_24 X_25 X_26 X_27 X_28 X_29 X_30 X_31 X_32 X_33 X_34 X_35 X_36 X_37
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## X_38 X_39 X_40 X_41 X_42 X_43 X_44 X_45 X_46 X_47 X_48 X_49 X_50 X_51 X_52
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## X_53 X_54 X_55 X_56 X_57 X_58 X_59 X_60 X_61 X_62 X_63
## 1 NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA
## 4 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 NA
## 5 NA NA NA NA NA NA NA NA NA NA NAhead(homicide,5)## ï..Data.Source World.Development.Indicators
## 1
## 2 Last Updated Date 2021-10-28
## 3
## 4 Country Name Country Code
## 5 Aruba ABW
## Column1 X_1 X_2 X_3 X_4 X_5
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 Indicator Name Indicator Code 1960 1961 1962 1963
## 5 Intentional homicides (per 100,000 people) VC.IHR.PSRC.P5 NA NA NA NA
## X_6 X_7 X_8 X_9 X_10 X_11 X_12 X_13 X_14 X_15 X_16 X_17 X_18 X_19 X_20
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## X_21 X_22 X_23 X_24 X_25 X_26 X_27 X_28 X_29 X_30 X_31 X_32 X_33 X_34 X_35
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
## 5 NA NA NA NA NA NA NA NA NA NA NA 0 NA NA NA
## X_36 X_37 X_38 X_39 X_40 X_41 X_42 X_43 X_44
## 1 NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA
## 4 1994 1995.000000 1996 1997 1998 1999.000000 2000 2001.000000 2002.000000
## 5 NA 7.469748 NA NA NA 2.246964 NA 4.306076 5.263601
## X_45 X_46 X_47 X_48 X_49 X_50
## 1 NA NA NA NA NA NA
## 2 NA NA NA NA NA NA
## 3 NA NA NA NA NA NA
## 4 2003.000000 2004.00000 2005.00000 2006.000000 2007.000000 2008.000000
## 5 4.123031 2.02544 5.99832 4.958842 2.963665 4.932815
## X_51 X_52 X_53 X_54 X_55 X_56 X_57
## 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 2009.000000 2010.000000 2011.000000 2012.000000 2013.000000 2014.000000 2015
## 5 3.942751 3.934491 1.959824 3.899966 5.815926 1.927228 NA
## X_58 X_59 X_60 X_61 X_62 X_63
## 1 NA NA NA NA NA NA
## 2 NA NA NA NA NA NA
## 3 NA NA NA NA NA NA
## 4 2016 2017 2018 2019 2020 NA
## 5 NA NA NA NA NA NAhead(sexualviolence,5)## country.territory date sexual.violence rate
## 1 #country+name #date #affected #meta+rate
## 2 Albania 2003 <NA> <NA>
## 3 Albania 2004 <NA> <NA>
## 4 Albania 2005 89 2.832771023
## 5 Albania 2006 87 2.756123901- GPD, Gini and Homicide’s headers needs to be replaced with 4th row. Sexual violence’s first row needs to be deleted.
names(gpd) <- as.matrix(gpd[4, ])
gpd <- gpd[-4, ]
gpd[] <- lapply(gpd, function(x) type.convert(as.character(x)))
gpd <- gpd[-1, ]
gpd <- gpd[-1, ]
gpd <- gpd[-1, ]
row.names(gpd) <- NULL
head(gpd)## Country Name Country Code Indicator Name Indicator Code
## 1 Aruba ABW GDP (current US$) NY.GDP.MKTP.CD
## 2 Africa Eastern and Southern AFE GDP (current US$) NY.GDP.MKTP.CD
## 3 Afghanistan AFG GDP (current US$) NY.GDP.MKTP.CD
## 4 Africa Western and Central AFW GDP (current US$) NY.GDP.MKTP.CD
## 5 Angola AGO GDP (current US$) NY.GDP.MKTP.CD
## 6 Albania ALB GDP (current US$) NY.GDP.MKTP.CD
## 1960 1961 1962 1963 1964 1965
## 1 NA NA NA NA NA NA
## 2 19342484576 19753490586 21526615650 25772356399 23563232195 26851350246
## 3 537777811 548888896 546666678 751111191 800000044 1006666638
## 4 10407321640 11131302981 11946843969 12680220415 13842621612 14866816737
## 5 NA NA NA NA NA NA
## 6 NA NA NA NA NA NA
## 1966 1967 1968 1969 1970 1971
## 1 NA NA NA NA NA NA
## 2 29196502382 30219070807 32927067005 37801761961 40377109505 44544318707
## 3 1399999967 1673333418 1373333367 1408888922 1748886596 1831108971
## 4 15837474343 14430648807 14884699923 16887028428 23511477700 20838908163
## 5 NA NA NA NA NA NA
## 6 NA NA NA NA NA NA
## 1972 1973 1974 1975 1976 1977
## 1 NA NA NA NA NA NA
## 2 48374959174 63079306619 78369918525 83562484550 83337002757 95133441245
## 3 1595555476 1733333264 2155555498 2366666616 2555555567 2953333418
## 4 25272340678 31282962686 44227412162 51459772973 62147555474 65334104528
## 5 NA NA NA NA NA NA
## 6 NA NA NA NA NA NA
## 1978 1979 1980 1981 1982 1983
## 1 NA NA NA NA NA NA
## 2 106508000000 124688000000 156751000000 160622000000 154905000000 160001000000
## 3 3300000109 3697940410 3641723322 3478787909 NA NA
## 4 71220525033 88654314398 112064000000 211065000000 187218000000 138156000000
## 5 NA NA 5934073604 5553824464 5553824464 5787823809
## 6 NA NA NA NA NA NA
## 1984 1985 1986 1987 1988 1989
## 1 NA NA 405463417 487602458 596423607 695304363
## 2 146244000000 130638000000 147249000000 180013000000 189291000000 194839000000
## 3 NA NA NA NA NA NA
## 4 114296000000 116541000000 107529000000 110354000000 108975000000 101799000000
## 5 6135166254 7558613008 7076793823 8089279285 8775116269 10207922517
## 6 1857338012 1897050133 2097326250 2080796250 2051236250 2253090000
## 1990 1991 1992 1993 1994 1995
## 1 764887117 872138715 958463184 1082979721 1245688268 1320474860
## 2 212659000000 221100000000 220554000000 220950000000 225100000000 253136000000
## 3 NA NA NA NA NA NA
## 4 121838000000 117491000000 118317000000 97186773684 85693055814 107403000000
## 5 11236275843 NA NA NA 3390500000 5561222222
## 6 2028553750 1099559028 652174991 1185315468 1880951520 2392764853
## 1996 1997 1998 1999 2000 2001
## 1 1379960894 1531944134 1665100559 1722798883 1873452514 1920111732
## 2 252550000000 265549000000 250378000000 247067000000 268315000000 242105000000
## 3 NA NA NA NA NA NA
## 4 119044000000 119983000000 122621000000 130199000000 134150000000 141863000000
## 5 7526963964 7649716157 6506619145 6152936539 9129634978 8936063723
## 6 3199642580 2258515610 2545967253 3212119044 3480355189 3922099471
## 2002 2003 2004 2005 2006 2007
## 1 1941340782 2021229050 2228491620 2330726257 2424581006 2615083799
## 2 247657000000 326744000000 405860000000 471743000000 533533000000 613164000000
## 3 4055179566 4515558808 5226778809 6209137625 6971285595 9747879532
## 4 170532000000 197384000000 245856000000 302111000000 384336000000 451866000000
## 5 15285594828 17812705294 23552052408 36970918699 52381006892 65266452081
## 6 4348070165 5611492283 7184681399 8052075642 8896073938 10677321490
## 2008 2009 2010 2011 2012 2013
## 1 2745251397 2498882682 2390502793 2549720670 2534636872 2727849721
## 2 668037000000 670986000000 805795000000 898605000000 915590000000 930086000000
## 3 10109225814 12439087077 15856574731 17804292964 20001598506 20561069558
## 4 553031000000 492546000000 580217000000 658428000000 716935000000 807819000000
## 5 88538611205 70307163678 83799496611 111790000000 128053000000 136710000000
## 6 12881354104 12044223353 11926928506 12890765324 12319830252 12776217195
## 2014 2015 2016 2017 2018 2019
## 1 2790849162 2962905028 2983636872 3092430168 3202188607 NA
## 2 958825000000 895440000000 856992000000 964791000000 986611000000 980372000000
## 3 20484885120 19907111419 18017749074 18869945678 18353881130 19291104008
## 4 846943000000 757492000000 687485000000 680989000000 738131000000 792079000000
## 5 145712000000 116194000000 101124000000 122124000000 101353000000 89417190341
## 6 13228144008 11386846319 11861200797 13019693451 15147020535 15286612573
## 2020 NA
## 1 NA NA
## 2 900829000000 NA
## 3 19807067268 NA
## 4 786585000000 NA
## 5 62306913444 NA
## 6 14799615097 NAnames(gini) <- as.matrix(gini[4, ])
gini <- gini[-4, ]
gini[] <- lapply(gini, function(x) type.convert(as.character(x)))
gini <- gini[-1, ]
gini <- gini[-1, ]
gini <- gini[-1, ]
row.names(gini) <- NULL
head(gini)## Country Name Country Code Indicator Name
## 1 Aruba ABW Gini index (World Bank estimate)
## 2 Africa Eastern and Southern AFE Gini index (World Bank estimate)
## 3 Afghanistan AFG Gini index (World Bank estimate)
## 4 Africa Western and Central AFW Gini index (World Bank estimate)
## 5 Angola AGO Gini index (World Bank estimate)
## 6 Albania ALB Gini index (World Bank estimate)
## Indicator Code 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971
## 1 SI.POV.GINI NA NA NA NA NA NA NA NA NA NA NA NA
## 2 SI.POV.GINI NA NA NA NA NA NA NA NA NA NA NA NA
## 3 SI.POV.GINI NA NA NA NA NA NA NA NA NA NA NA NA
## 4 SI.POV.GINI NA NA NA NA NA NA NA NA NA NA NA NA
## 5 SI.POV.GINI NA NA NA NA NA NA NA NA NA NA NA NA
## 6 SI.POV.GINI NA NA NA NA NA NA NA NA NA NA NA NA
## 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA 52 NA
## 6 NA NA NA NA NA NA NA NA NA 27 NA NA NA NA NA
## 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA 42.7 NA NA NA NA NA NA NA NA
## 6 31.7 NA NA 30.6 NA NA 30.0 NA NA NA 29 NA 34.6 32.9 33.7
## 2017 2018 2019 2020 NA
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA 51.3 NA NA NA
## 6 33.2 NA NA NA NAnames(homicide) <- as.matrix(homicide[4, ])
homicide <- homicide[-4, ]
homicide[] <- lapply(homicide, function(x) type.convert(as.character(x)))
homicide <- homicide[-1, ]
homicide <- homicide[-1, ]
homicide <- homicide[-1, ]
row.names(homicide) <- NULL
head(homicide)## Country Name Country Code
## 1 Aruba ABW
## 2 Africa Eastern and Southern AFE
## 3 Afghanistan AFG
## 4 Africa Western and Central AFW
## 5 Angola AGO
## 6 Albania ALB
## Indicator Name Indicator Code 1960 1961 1962 1963
## 1 Intentional homicides (per 100,000 people) VC.IHR.PSRC.P5 NA NA NA NA
## 2 Intentional homicides (per 100,000 people) VC.IHR.PSRC.P5 NA NA NA NA
## 3 Intentional homicides (per 100,000 people) VC.IHR.PSRC.P5 NA NA NA NA
## 4 Intentional homicides (per 100,000 people) VC.IHR.PSRC.P5 NA NA NA NA
## 5 Intentional homicides (per 100,000 people) VC.IHR.PSRC.P5 NA NA NA NA
## 6 Intentional homicides (per 100,000 people) VC.IHR.PSRC.P5 NA NA NA NA
## 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992
## 1 NA NA NA NA NA NA NA NA NA NA NA 0 NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA 4.005069
## 1993 1994 1995 1996 1997 1998 1999 2000
## 1 NA NA 7.469748 NA NA NA 2.246964 NA
## 2 NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 5.977724 3.55949 8.320154 8.777877 43.03577 21.2171 17.036539 4.186312
## 2001 2002 2003 2004 2005 2006 2007 2008
## 1 4.306076 5.263601 4.123031 2.025440 5.998320 4.958842 2.963665 4.932815
## 2 NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 7.029425 6.909384 5.323897 4.219147 4.988969 3.101513 3.460786 3.097230
## 2009 2010 2011 2012 2013 2014 2015 2016
## 1 3.942751 3.934491 1.959824 3.899966 5.815926 1.927228 NA NA
## 2 NA NA NA 15.100000 NA 10.500000 10.300000 NA
## 3 3.926774 3.368110 4.087337 6.251328 NA NA 9.783922 6.551163
## 4 NA NA NA 13.300000 NA 8.100000 8.100000 NA
## 5 NA NA 4.355786 4.847075 NA NA NA NA
## 6 2.859023 4.307963 4.848732 5.387615 4.270284 4.039627 2.214131 2.736948
## 2017 2018 2019 2020 NA
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 6.678401 6.655561 NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 2.010978 2.289492 NA NA NAsexualviolence <- sexualviolence[-1, ]
head(sexualviolence)## country.territory date sexual.violence rate
## 2 Albania 2003 <NA> <NA>
## 3 Albania 2004 <NA> <NA>
## 4 Albania 2005 89 2.832771023
## 5 Albania 2006 87 2.756123901
## 6 Albania 2007 101 3.186456613
## 7 Albania 2008 76 2.388887649Importing datasets about crime rates and renaming them
Murders2005 <- read.csv("data/Murders2005.csv")
Murders2006 <- read.csv("data/Murders2006.csv")
Murders2007 <- read.csv("data/Murders2007.csv")
Murders2008 <- read.csv("data/Murders2008.csv")
Murders2009 <- read.csv("data/Murders2009.csv")
Murders2010 <- read.csv("data/Murders2010.csv")
Rapes2005 <- read.csv("data/Rapes2005.csv")
Rapes2006 <- read.csv("data/Rapes2006.csv")
Rapes2007 <- read.csv("data/Rapes2007.csv")
Rapes2008 <- read.csv("data/Rapes2008.csv")
Rapes2009 <- read.csv("data\\Rapes2009.csv")
Rapes2010 <- read.csv("data/Rapes2010.csv")Jointing datasets by using merge function
df05= merge(x = Murders2005, y = Rapes2005, by = "Country")
head(df05)## Country Amount.x Date.x Amount.y Date.y
## 1 Albania 131 2005 49 2005
## 2 Argentina 2,115 2005 3,154 2005
## 3 Armenia 58 2005 15 2005
## 4 Austria 54 2005 678 2005
## 5 Bahrain 4 2005 25 2005
## 6 Bangladesh 3,592 2005 11,291 2005df06= merge(x = Murders2006, y = Rapes2006, by = "Country")
head(df06)## Country Amount.x Date.x Amount.y Date.y
## 1 Albania 87 2006 40 2006
## 2 Argentina 2,052 2006 3,264 2006
## 3 Armenia 79 2006 7 2006
## 4 Austria 61 2006 700 2006
## 5 Bahrain 7 2006 17 2006
## 6 Bangladesh 4,166 2006 11,682 2006df07= merge(x = Murders2007, y = Rapes2007, by = "Country")
head(df07)## Country Amount.x Date.x Amount.y Date.y
## 1 Albania 105 2007 41 2007
## 2 Algeria 438 2007 836 2007
## 3 Argentina 2,071 2007 3,276 2007
## 4 Armenia 77 2007 5 2007
## 5 Austria 45 2007 710 2007
## 6 Azerbaijan 189 2007 34 2007df08= merge(x = Murders2008, y = Rapes2008, by = "Country")
head(df08)## Country Amount.x Date.x Amount.y Date.y
## 1 Albania 93 2008 39 2008
## 2 Algeria 516 2008 812 2008
## 3 Argentina 2,305 2008 3,367 2008
## 4 Armenia 83 2008 15 2008
## 5 Australia 261 2008 6,382 2008
## 6 Austria 45 2008 693 2008df09= merge(x = Murders2009, y = Rapes2009, by = "Country")
head(df09)## Country Amount.x Date.x Amount.y Date.y
## 1 Albania 85 2009 34 2009
## 2 Armenia 83 2009 15 2009
## 3 Australia 263 2009 6,343 2009
## 4 Austria 43 2009 779 2009
## 5 Barbados 19 2009 68 2009
## 6 Belarus 473 2009 218 2009df10= merge(x = Murders2010, y = Rapes2010, by = "Country")
head(df10)## Country Amount.x Date.x Amount.y Date.y
## 1 Albania 127 2010 24 2010
## 2 Armenia 44 2010 11 2010
## 3 Australia 229 2010 6,378 2010
## 4 Austria 53 2010 875 2010
## 5 Azerbaijan 206 2010 16 2010
## 6 Belgium 180 2010 2,991 2010Changing the column names and removing the year
MR2005<-subset(df05, select=-c(Date.x,Date.y))
MR2006<-subset(df06, select=-c(Date.x,Date.y))
MR2007<-subset(df07, select=-c(Date.x,Date.y))
MR2008<-subset(df08, select=-c(Date.x,Date.y))
MR2009<-subset(df09, select=-c(Date.x,Date.y))
MR2010<-subset(df10, select=-c(Date.x,Date.y))
MR0506<-merge(MR2005,MR2006,by='Country')
MR0708<-merge(MR2007,MR2008,by='Country')
MR0910<-merge(MR2009,MR2010,by='Country')
MR1<-merge(MR0506,MR0708,by='Country')
MR<-merge(MR1,MR0910,by='Country')
MR<-rename(MR, Murders2005=Amount.x.x.x, Rape2005=Amount.y.x.x,
Murders2006=Amount.x.y.x, Rape2006=Amount.y.y.x,
Murders2007=Amount.x.x.y, Rape2007=Amount.y.x.y,
Murders2008=Amount.x.y.y, Rape2008=Amount.y.y.y,
Murders2009=Amount.x.x, Rape2009=Amount.y.x,
Murders2010=Amount.x.y, Rape2010=Amount.y.y)The final results
head(MR)## Country Murders2005 Rape2005 Murders2006 Rape2006 Murders2007
## 1 Albania 131 49 87 40 105
## 2 Armenia 58 15 79 7 77
## 3 Austria 54 678 61 700 45
## 4 Belgium 221 3,029 221 3,194 211
## 5 Bolivia 596 1,137 530 1,137 723
## 6 Bosnia and Herzegovina 70 39 73 40 66
## Rape2007 Murders2008 Rape2008 Murders2009 Rape2009 Murders2010 Rape2010
## 1 41 93 39 85 34 127 24
## 2 5 83 15 83 15 44 11
## 3 710 45 693 43 779 53 875
## 4 3,232 200 3,126 186 2,953 180 2,991
## 5 1,437 724 1,596 673 1,989 884 2,587
## 6 44 66 35 67 29 56 46- The economical data
employmentcopy<-employment
employmentcopy<-filter(employment, Employment.status == 'Total employment')
EMP<-subset(employmentcopy, select=c(Country,Time,Value))
EMP2010 <- filter(EMP, Time == 2010)
EMP2010<-subset(EMP2010, select=-c(Time))
EMP2010<-rename(EMP2010, Unemployment2010=Value)
GPD2005<-data.frame(gpd[, c(1,50)])
GPD2005<-rename(GPD2005, Country=Country.Name, GPD2005=X2005)One can see that only years 2005 and 2010 are common in merged data frames. Only the data of 2005 and 2010 were kept and the data of the other years were deleted
MR0510<-subset(MR, select=c(Country,Murders2005,Rape2005,Murders2010,Rape2010))
head(MR0510)## Country Murders2005 Rape2005 Murders2010 Rape2010
## 1 Albania 131 49 127 24
## 2 Armenia 58 15 44 11
## 3 Austria 54 678 53 875
## 4 Belgium 221 3,029 180 2,991
## 5 Bolivia 596 1,137 884 2,587
## 6 Bosnia and Herzegovina 70 39 56 46Merging with the economical values
- This is the final data frame to make analyzes from.
prefinal<-merge(EMP2010,MR0510, by='Country')
FINAL<-merge(prefinal,GPD2005, by='Country')
head(FINAL)## Country Unemployment2010 Murders2005 Rape2005 Murders2010 Rape2010
## 1 Austria 1552.0 54 678 53 875
## 2 Belgium 1574.0 221 3,029 180 2,991
## 3 Canada 1715.0 663 565 554 576
## 4 Estonia 1785.0 113 179 70 81
## 5 Finland 1585.0 119 593 118 818
## 6 Germany 1425.7 869 8,133 690 7,724
## GPD2005
## 1 3.159740e+11
## 2 3.855710e+11
## 3 1.173110e+12
## 4 1.410679e+10
## 5 2.048090e+11
## 6 2.845800e+12Before continuing, checking the types of data stored in FINAL
sapply(FINAL, mode)## Country Unemployment2010 Murders2005 Rape2005
## "character" "numeric" "character" "character"
## Murders2010 Rape2010 GPD2005
## "character" "character" "numeric"Converting characters to numerical values
cols.num <- c("Murders2005","Rape2005","Murders2010","Rape2010")
FINAL[cols.num] <- sapply(FINAL[cols.num],as.numeric)
sapply(FINAL, mode)## Country Unemployment2010 Murders2005 Rape2005
## "character" "numeric" "numeric" "numeric"
## Murders2010 Rape2010 GPD2005
## "numeric" "numeric" "numeric"- Result Although the data is concentrated in a certain region, a positive correlation is observed between murder counts and GPD. That is, as the number of murders increases, so does the GPD.
ggplot(FINAL, aes(x=GPD2005, y=Murders2005, color=Country)) +
geom_point(size=4) + xlim(0, 3*10^12)
Graphing 2
- Result Although there is not much difference between the GPDs, positive correlation is also seen in this graph. Canada is located here in a separate position from other countries in terms of GPD.
ggplot(FINAL, aes(x=GPD2005, y=Rape2005, color=Country)) +
geom_point(size=4) + xlim(0, 125*10^10)
Graphing 3
- Result Positive correlation is seen in this graph. Germany has a very high value in homicides and low in unemployment. For this reason, it is in a different position from other countries.
ggplot(FINAL, aes(x=Unemployment2010, y=Murders2010, color=Country)) +
geom_point(size=4)
Graphing 4
- Result Unlike other graphs, there is no positive correlation in this graph. The employment rate increases as the rape rate decreases.
ggplot(FINAL, aes(x=Unemployment2010, y=Rape2010, color=Country)) +
geom_point(size=4)
Conclusion.
With the exception of the third chart, similar results are obtained with the other charts. The relationship between GPD and crime rates is surprising. In 2010, it was observed that as unemployment rates increased, murder increased, but rape rates decreased. In the past 5 years, it has been observed that there is an awareness of rape in high-income countries.