Lab_6
Qi Deng
May 12, 2019
Part 1:
Produce a map using tmap. In this case, show the zip code where more people gave more cash than in kind
setwd("C:/Users/xwb/Desktop/Data Analysis/Session6")
link='https://github.com/EvansDataScience/DataDriven_ManagementAndPolicy/raw/master/Session6/contriWA_2016.RData'
load(file = url(link))
str(contriWA_2016,width = 60, strict.width = 'cut')## 'data.frame': 374584 obs. of 10 variables:
## $ id : chr "3982630.rcpt" "3982631.rcp"..
## $ contributor_state : chr "WA" "WA" "WA" "WA" ...
## $ contributor_zip : num 98683 98683 98683 98168 9850..
## $ amount : num 50 50 50 500 900 900 50 225 ..
## $ election_year : int 2016 2016 2016 2016 2016 201..
## $ party : Factor w/ 9 levels "","CONSTITUT"..
## $ cash_or_in_kind : Factor w/ 2 levels "Cash","In ki"..
## $ contributor_location: chr "(45.60817, -122.51972)" "("..
## $ Lat : num 45.6 45.6 45.6 47.5 47 ...
## $ Lon : num -123 -123 -123 -122 -123 ...library(utils)
zippedSHP= "https://github.com/EvansDataScience/data/raw/master/WAzips.zip"
temp=tempfile()
download.file(zippedSHP, temp)
unzip(temp)
library(rgdal)## Loading required package: sp## rgdal: version: 1.4-3, (SVN revision 828)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.2.3, released 2017/11/20
## Path to GDAL shared files: C:/Users/xwb/Documents/R/win-library/3.4/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
## Path to PROJ.4 shared files: C:/Users/xwb/Documents/R/win-library/3.4/rgdal/proj
## Linking to sp version: 1.3-1Mapdata <- readOGR("SAEP_ZIP_Code_Tabulation_Areas.shp",stringsAsFactors=F) ## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\xwb\Desktop\Data Analysis\Session6\SAEP_ZIP_Code_Tabulation_Areas.shp", layer: "SAEP_ZIP_Code_Tabulation_Areas"
## with 598 features
## It has 101 fields
## Integer64 fields read as strings: OBJECTID POP2010 HHP2010 GQ2010 HU2010 OHU2010library(tmap)
maps = tm_shape(Mapdata) + tm_polygons()
maps## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3
library(rmapshaper)
baseMap <- ms_dissolve(Mapdata)
Mapborder = tm_shape(baseMap) + tm_polygons(col = 'white',lwd = 1)
Mapborder
library(raster)
mapCRS=crs(Mapdata)
ccash = function(x){
return(sum(x=="Cash")/length(x))
}
ccash(contriWA_2016$cash_or_in_kind)## [1] 0.9825246library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:raster':
##
## intersect, select, union## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionWA_zip_contri= contriWA_2016 %>%
group_by(contributor_zip) %>%
summarize('morecash'=ccash(cash_or_in_kind))
WA_zip_contri## # A tibble: 848 x 2
## contributor_zip morecash
## <dbl> <dbl>
## 1 98001 0.986
## 2 98002 0.997
## 3 98003 0.973
## 4 98004 0.989
## 5 98005 0.931
## 6 98006 0.994
## 7 98007 0.996
## 8 98008 0.994
## 9 98009 0.998
## 10 98010 1
## # ... with 838 more rowsdd=data.frame(WA_zip_contri)
cc=as.numeric(WA_zip_contri$morecash)
morecashplace=dd[dd$morecash>0.5,]
head(morecashplace)## contributor_zip morecash
## 1 98001 0.9858223
## 2 98002 0.9970674
## 3 98003 0.9727811
## 4 98004 0.9885682
## 5 98005 0.9309377
## 6 98006 0.9942787str(dd$contributor_zip)## num [1:848] 98001 98002 98003 98004 98005 ...zipcodemc=as.character(dd$contributor_zip)
zipcodemc## [1] "98001" "98002" "98003" "98004" "98005" "98006" "98007" "98008"
## [9] "98009" "98010" "98011" "98012" "98013" "98014" "98015" "98017"
## [17] "98019" "98020" "98021" "98022" "98023" "98024" "98025" "98026"
## [25] "98027" "98028" "98029" "98030" "98031" "98032" "98033" "98034"
## [33] "98035" "98036" "98037" "98038" "98039" "98040" "98041" "98042"
## [41] "98043" "98044" "98045" "98046" "98047" "98049" "98050" "98051"
## [49] "98052" "98053" "98055" "98056" "98057" "98058" "98059" "98061"
## [57] "98062" "98063" "98064" "98065" "98067" "98068" "98069" "98070"
## [65] "98071" "98072" "98073" "98074" "98075" "98076" "98077" "98080"
## [73] "98082" "98083" "98086" "98087" "98089" "98090" "98092" "98093"
## [81] "98094" "98095" "98096" "98097" "98099" "98100" "98101" "98102"
## [89] "98103" "98104" "98105" "98106" "98107" "98108" "98109" "98110"
## [97] "98111" "98112" "98113" "98114" "98115" "98116" "98117" "98118"
## [105] "98119" "98120" "98121" "98122" "98123" "98124" "98125" "98126"
## [113] "98127" "98128" "98130" "98132" "98133" "98134" "98136" "98138"
## [121] "98139" "98141" "98144" "98145" "98146" "98148" "98149" "98152"
## [129] "98154" "98155" "98156" "98158" "98159" "98160" "98161" "98162"
## [137] "98163" "98164" "98165" "98166" "98167" "98168" "98175" "98176"
## [145] "98177" "98178" "98180" "98181" "98184" "98185" "98186" "98188"
## [153] "98189" "98191" "98193" "98194" "98195" "98196" "98197" "98198"
## [161] "98199" "98200" "98201" "98202" "98203" "98204" "98205" "98206"
## [169] "98207" "98208" "98209" "98210" "98211" "98212" "98213" "98218"
## [177] "98220" "98221" "98222" "98223" "98224" "98225" "98226" "98227"
## [185] "98228" "98229" "98230" "98231" "98232" "98233" "98234" "98235"
## [193] "98236" "98237" "98238" "98239" "98240" "98241" "98243" "98244"
## [201] "98245" "98246" "98247" "98248" "98249" "98250" "98251" "98252"
## [209] "98253" "98254" "98255" "98256" "98257" "98258" "98259" "98260"
## [217] "98261" "98262" "98263" "98264" "98265" "98266" "98267" "98269"
## [225] "98270" "98271" "98272" "98273" "98274" "98275" "98276" "98277"
## [233] "98278" "98279" "98280" "98281" "98282" "98283" "98284" "98286"
## [241] "98287" "98288" "98290" "98291" "98292" "98293" "98294" "98295"
## [249] "98296" "98297" "98298" "98299" "98301" "98302" "98303" "98304"
## [257] "98305" "98307" "98308" "98310" "98311" "98312" "98313" "98315"
## [265] "98317" "98319" "98320" "98321" "98322" "98323" "98324" "98325"
## [273] "98326" "98327" "98328" "98329" "98330" "98331" "98332" "98333"
## [281] "98335" "98336" "98337" "98338" "98339" "98340" "98341" "98342"
## [289] "98343" "98344" "98345" "98346" "98347" "98348" "98349" "98350"
## [297] "98351" "98352" "98353" "98354" "98355" "98356" "98357" "98358"
## [305] "98359" "98360" "98361" "98362" "98363" "98364" "98365" "98366"
## [313] "98367" "98368" "98370" "98371" "98372" "98373" "98374" "98375"
## [321] "98376" "98377" "98378" "98379" "98380" "98381" "98382" "98383"
## [329] "98384" "98385" "98386" "98387" "98388" "98389" "98390" "98391"
## [337] "98392" "98393" "98394" "98395" "98396" "98397" "98400" "98401"
## [345] "98402" "98403" "98404" "98405" "98406" "98407" "98408" "98409"
## [353] "98411" "98412" "98413" "98415" "98417" "98418" "98419" "98421"
## [361] "98422" "98423" "98424" "98431" "98432" "98433" "98438" "98439"
## [369] "98442" "98443" "98444" "98445" "98446" "98448" "98454" "98460"
## [377] "98463" "98464" "98465" "98466" "98467" "98468" "98474" "98484"
## [385] "98490" "98492" "98493" "98495" "98496" "98497" "98498" "98499"
## [393] "98500" "98501" "98502" "98503" "98504" "98505" "98506" "98507"
## [401] "98508" "98509" "98511" "98512" "98513" "98516" "98520" "98522"
## [409] "98524" "98527" "98528" "98530" "98531" "98532" "98533" "98534"
## [417] "98535" "98536" "98537" "98538" "98539" "98540" "98541" "98542"
## [425] "98544" "98546" "98547" "98548" "98550" "98551" "98552" "98554"
## [433] "98555" "98556" "98557" "98558" "98560" "98561" "98562" "98563"
## [441] "98564" "98565" "98566" "98567" "98568" "98569" "98570" "98571"
## [449] "98572" "98575" "98576" "98577" "98578" "98579" "98580" "98581"
## [457] "98582" "98583" "98584" "98585" "98586" "98587" "98588" "98589"
## [465] "98590" "98591" "98592" "98593" "98594" "98595" "98596" "98597"
## [473] "98601" "98602" "98603" "98604" "98605" "98606" "98607" "98609"
## [481] "98610" "98611" "98612" "98613" "98614" "98615" "98616" "98617"
## [489] "98618" "98619" "98620" "98621" "98622" "98623" "98624" "98625"
## [497] "98626" "98627" "98628" "98629" "98630" "98631" "98632" "98635"
## [505] "98636" "98637" "98638" "98639" "98640" "98641" "98642" "98643"
## [513] "98644" "98645" "98647" "98648" "98649" "98650" "98651" "98653"
## [521] "98660" "98661" "98662" "98663" "98664" "98665" "98666" "98667"
## [529] "98668" "98670" "98671" "98672" "98673" "98674" "98675" "98680"
## [537] "98681" "98682" "98683" "98684" "98685" "98686" "98687" "98689"
## [545] "98694" "98701" "98702" "98705" "98708" "98721" "98730" "98753"
## [553] "98761" "98762" "98767" "98801" "98802" "98805" "98807" "98808"
## [561] "98810" "98812" "98813" "98814" "98815" "98816" "98817" "98818"
## [569] "98819" "98821" "98822" "98823" "98824" "98826" "98827" "98828"
## [577] "98829" "98830" "98831" "98832" "98833" "98834" "98835" "98836"
## [585] "98837" "98838" "98839" "98840" "98841" "98843" "98844" "98845"
## [593] "98846" "98847" "98848" "98849" "98850" "98851" "98852" "98853"
## [601] "98855" "98856" "98857" "98858" "98859" "98860" "98862" "98866"
## [609] "98867" "98880" "98881" "98892" "98901" "98902" "98903" "98904"
## [617] "98905" "98906" "98907" "98908" "98909" "98911" "98916" "98920"
## [625] "98921" "98922" "98923" "98925" "98926" "98927" "98928" "98930"
## [633] "98932" "98933" "98934" "98935" "98936" "98937" "98938" "98939"
## [641] "98940" "98941" "98942" "98943" "98944" "98945" "98946" "98947"
## [649] "98948" "98950" "98951" "98952" "98953" "98956" "98957" "98958"
## [657] "98959" "98961" "98962" "98963" "98968" "98975" "98977" "98981"
## [665] "98983" "98987" "98990" "99000" "99001" "99003" "99004" "99005"
## [673] "99006" "99008" "99009" "99010" "99011" "99012" "99013" "99014"
## [681] "99016" "99017" "99019" "99020" "99021" "99022" "99023" "99025"
## [689] "99026" "99027" "99029" "99030" "99031" "99032" "99033" "99034"
## [697] "99036" "99037" "99038" "99040" "99055" "99101" "99102" "99103"
## [705] "99105" "99106" "99108" "99109" "99110" "99111" "99113" "99114"
## [713] "99115" "99116" "99117" "99118" "99119" "99121" "99122" "99123"
## [721] "99124" "99125" "99126" "99128" "99129" "99130" "99131" "99133"
## [729] "99134" "99135" "99136" "99137" "99138" "99139" "99140" "99141"
## [737] "99143" "99145" "99146" "99147" "99148" "99149" "99150" "99152"
## [745] "99153" "99154" "99155" "99156" "99157" "99158" "99159" "99161"
## [753] "99163" "99164" "99166" "99167" "99168" "99169" "99170" "99171"
## [761] "99173" "99176" "99177" "99179" "99180" "99181" "99185" "99200"
## [769] "99201" "99202" "99203" "99204" "99205" "99206" "99207" "99208"
## [777] "99209" "99210" "99211" "99212" "99213" "99214" "99215" "99216"
## [785] "99217" "99218" "99219" "99220" "99223" "99224" "99225" "99227"
## [793] "99228" "99230" "99234" "99251" "99252" "99254" "99258" "99260"
## [801] "99269" "99273" "99292" "99293" "99301" "99302" "99304" "99320"
## [809] "99321" "99322" "99323" "99324" "99325" "99326" "99328" "99329"
## [817] "99330" "99334" "99335" "99336" "99337" "99338" "99341" "99343"
## [825] "99344" "99345" "99346" "99347" "99348" "99349" "99350" "99351"
## [833] "99352" "99353" "99354" "99356" "99357" "99360" "99361" "99362"
## [841] "99363" "99365" "99371" "99380" "99382" "99401" "99402" "99403"Part 2:
Remake the plot of dimensionality reduction (where the multidemiensional scaling plot cases where colored according to the k-means output). This time, use only the variables that represent input.
library(rio)
link="https://github.com/EvansDataScience/data/raw/master/safeCitiesIndexAll.xlsx"
safe=import(link)
names(safe)## [1] "city" "D_In_PrivacyPolicy"
## [3] "D_In_AwarenessDigitalThreats" "D_In_PubPrivPartnerships"
## [5] "D_In_TechnologyEmployed" "D_In_CyberSecurity"
## [7] "D_Out_IdentityTheft" "D_Out_CompInfected"
## [9] "D_Out_InternetAccess" "H_In_EnvironmentPolicies"
## [11] "H_In_AccessHealthcare" "H_In_Beds_1000"
## [13] "H_In_Doctors_1000" "H_In_AccessFood"
## [15] "H_In_QualityHealthServ" "H_Out_AirQuality"
## [17] "H_Out_WaterQuality" "H_Out_LifeExpectY"
## [19] "H_Out_InfMortality" "H_Out_CancerMortality"
## [21] "H_Out_AttacksBioChemRad" "I_In_EnforceTransportSafety"
## [23] "I_In_PedestrianFriendliness" "I_In_QualityRoad"
## [25] "I_In_QualityElectricity" "I_In_DisasterManagement"
## [27] "I_Out_DeathsDisaster" "I_Out_VehicularAccidents"
## [29] "I_Out_PedestrianDeath" "I_Out_LiveSlums"
## [31] "I_Out_AttacksInfrastructure" "P_In_PoliceEngage"
## [33] "P_In_CommunityPatrol" "P_In_StreetCrimeData"
## [35] "P_In_TechForCrime" "P_In_PrivateSecurity"
## [37] "P_In_GunRegulation" "P_In_PoliticalStability"
## [39] "P_Out_PettyCrime" "P_Out_ViolentCrime"
## [41] "P_Out_OrganisedCrime" "P_Out_Corruption"
## [43] "P_Out_DrugUse" "P_Out_TerroristAttacks"
## [45] "P_Out_SeverityTerrorist" "P_Out_GenderSafety"
## [47] "P_Out_PerceptionSafety" "P_Out_ThreaTerrorism"
## [49] "P_Out_ThreatMilitaryConf" "P_Out_ThreatCivUnrest"input=safe[c(1:6,10:15,22:26,32,38)]
input## city D_In_PrivacyPolicy D_In_AwarenessDigitalThreats
## 1 Abu Dhabi 50 66.66667
## 2 Amsterdam 100 100.00000
## 3 Athens 75 100.00000
## 4 Bangkok 25 66.66667
## 5 Barcelona 100 100.00000
## 6 Beijing 75 66.66667
## 7 Bogota 50 33.33333
## 8 Brussels 100 100.00000
## 9 Buenos Aires 75 33.33333
## 10 Cairo 50 33.33333
## 11 Caracas 25 33.33333
## 12 Casablanca 75 33.33333
## 13 Chicago 100 100.00000
## 14 Dallas 100 100.00000
## 15 Delhi 25 66.66667
## 16 Dhaka 0 33.33333
## 17 Doha 50 66.66667
## 18 Frankfurt 100 100.00000
## 19 Ho Chi Minh City 25 33.33333
## 20 Hong Kong 100 100.00000
## 21 Istanbul 25 100.00000
## 22 Jakarta 25 66.66667
## 23 Jeddah 50 66.66667
## 24 Johannesburg 50 66.66667
## 25 Karachi 25 100.00000
## 26 Kuala Lumpur 50 66.66667
## 27 Kuwait City 50 33.33333
## 28 Lima 50 66.66667
## 29 London 100 100.00000
## 30 Los Angeles 100 100.00000
## 31 Madrid 100 100.00000
## 32 Manila 25 33.33333
## 33 Melbourne 100 100.00000
## 34 Mexico City 50 66.66667
## 35 Milan 100 66.66667
## 36 Moscow 50 33.33333
## 37 Mumbai 25 66.66667
## 38 New York 100 100.00000
## 39 Osaka 75 66.66667
## 40 Paris 100 66.66667
## 41 Quito 0 66.66667
## 42 Rio de Janeiro 25 66.66667
## 43 Riyadh 50 66.66667
## 44 Rome 100 100.00000
## 45 San Francisco 100 100.00000
## 46 Santiago 50 66.66667
## 47 Sao Paulo 25 66.66667
## 48 Seoul 100 33.33333
## 49 Shanghai 75 66.66667
## 50 Singapore 75 100.00000
## 51 Stockholm 75 100.00000
## 52 Sydney 100 100.00000
## 53 Taipei 75 66.66667
## 54 Tehran 0 33.33333
## 55 Tokyo 75 66.66667
## 56 Toronto 100 100.00000
## 57 Washington DC 100 100.00000
## 58 Wellington 75 66.66667
## 59 Yangon 0 33.33333
## 60 Zurich 75 100.00000
## D_In_PubPrivPartnerships D_In_TechnologyEmployed D_In_CyberSecurity
## 1 50 100 50
## 2 50 100 50
## 3 0 75 50
## 4 0 0 50
## 5 50 100 50
## 6 0 75 100
## 7 0 75 50
## 8 50 100 50
## 9 50 75 100
## 10 0 0 50
## 11 50 75 50
## 12 0 75 50
## 13 100 100 100
## 14 100 100 100
## 15 50 75 100
## 16 50 0 50
## 17 50 75 50
## 18 50 100 50
## 19 0 0 50
## 20 100 100 100
## 21 50 75 50
## 22 0 0 50
## 23 0 75 100
## 24 50 75 50
## 25 0 0 50
## 26 50 75 50
## 27 0 75 50
## 28 50 75 50
## 29 50 100 50
## 30 100 100 100
## 31 50 100 50
## 32 50 0 50
## 33 50 100 100
## 34 50 75 50
## 35 100 100 50
## 36 0 75 50
## 37 50 75 100
## 38 100 100 100
## 39 50 100 100
## 40 50 100 50
## 41 50 75 50
## 42 0 75 50
## 43 0 75 50
## 44 50 100 50
## 45 100 100 100
## 46 50 75 50
## 47 0 75 50
## 48 50 100 50
## 49 0 75 50
## 50 100 100 100
## 51 50 100 50
## 52 50 100 100
## 53 100 100 100
## 54 0 0 50
## 55 100 100 100
## 56 50 100 100
## 57 50 100 100
## 58 50 100 50
## 59 0 0 50
## 60 50 100 50
## H_In_EnvironmentPolicies H_In_AccessHealthcare H_In_Beds_1000
## 1 62.790698 73.33333 8.265146
## 2 97.674419 100.00000 38.019671
## 3 41.860465 73.33333 38.846186
## 4 72.093023 80.00000 16.530292
## 5 81.395349 93.33333 24.795438
## 6 79.069767 86.66667 47.899860
## 7 62.790698 73.33333 11.571204
## 8 97.674419 100.00000 52.896934
## 9 90.697674 93.33333 38.019671
## 10 46.511628 60.00000 3.306058
## 11 23.255814 53.33333 6.612117
## 12 62.790698 60.00000 6.612117
## 13 90.697674 93.33333 19.836350
## 14 74.418605 86.66667 18.183321
## 15 90.697674 80.00000 21.572031
## 16 4.651163 46.66667 4.132573
## 17 27.906977 80.00000 9.091660
## 18 90.697674 100.00000 66.947682
## 19 62.790698 73.33333 15.703777
## 20 97.674419 93.33333 42.152244
## 21 72.093023 60.00000 19.836350
## 22 44.186047 73.33333 6.612117
## 23 32.558140 73.33333 11.188475
## 24 86.046512 80.00000 19.836350
## 25 13.953488 66.66667 4.132573
## 26 62.790698 73.33333 14.877263
## 27 0.000000 80.00000 17.356806
## 28 69.767442 86.66667 11.571204
## 29 83.720930 86.66667 23.142408
## 30 95.348837 93.33333 14.050748
## 31 81.395349 93.33333 24.795438
## 32 27.906977 73.33333 7.438631
## 33 81.395349 100.00000 31.407554
## 34 86.046512 86.66667 11.571204
## 35 83.720930 93.33333 27.274981
## 36 65.116279 80.00000 89.180927
## 37 32.558140 86.66667 26.448467
## 38 95.348837 93.33333 21.489379
## 39 90.697674 100.00000 100.000000
## 40 97.674419 100.00000 52.070419
## 41 23.255814 60.00000 12.397719
## 42 60.465116 73.33333 18.183321
## 43 32.558140 80.00000 12.704341
## 44 69.767442 93.33333 27.274981
## 45 90.697674 93.33333 14.050748
## 46 48.837209 80.00000 16.530292
## 47 60.465116 80.00000 18.183321
## 48 93.023256 93.33333 84.304488
## 49 79.069767 86.66667 44.544961
## 50 97.674419 93.33333 15.703777
## 51 86.046512 86.66667 21.489379
## 52 86.046512 100.00000 31.407554
## 53 97.674419 86.66667 75.328540
## 54 62.790698 66.66667 0.000000
## 55 86.046512 100.00000 77.551864
## 56 95.348837 100.00000 21.489379
## 57 100.000000 93.33333 13.224233
## 58 86.046512 93.33333 18.183321
## 59 37.209302 60.00000 4.959088
## 60 86.046512 100.00000 40.499215
## H_In_Doctors_1000 H_In_AccessFood H_In_QualityHealthServ
## 1 16.365171 99.7 75.0
## 2 38.000482 100.0 100.0
## 3 73.010130 100.0 62.5
## 4 2.327545 98.2 62.5
## 5 43.632417 100.0 100.0
## 6 44.609262 96.2 62.5
## 7 16.485769 92.8 62.5
## 8 33.405692 100.0 100.0
## 9 42.945007 99.2 75.0
## 10 7.392668 99.5 37.5
## 11 20.791124 94.2 37.5
## 12 5.028944 87.7 37.5
## 13 30.318379 99.3 87.5
## 14 23.299566 99.3 87.5
## 15 6.319344 82.5 62.5
## 16 2.267246 76.5 37.5
## 17 21.261457 87.5 75.0
## 18 47.322721 100.0 100.0
## 19 11.806561 85.5 37.5
## 20 20.489629 96.2 87.5
## 21 18.668596 100.0 50.0
## 22 0.000000 76.9 37.5
## 23 24.213421 97.5 62.5
## 24 6.825856 94.3 50.0
## 25 7.296189 48.1 50.0
## 26 13.024602 98.5 62.5
## 27 21.080560 99.2 87.5
## 28 11.034732 88.8 62.5
## 29 31.415822 100.0 87.5
## 30 29.232996 99.3 87.5
## 31 43.632417 100.0 87.5
## 32 10.962373 93.1 62.5
## 33 38.265798 100.0 100.0
## 34 22.551857 96.7 50.0
## 35 45.151954 100.0 75.0
## 36 37.445731 97.4 75.0
## 37 6.319344 82.5 37.5
## 38 40.243608 99.3 87.5
## 39 30.692233 100.0 100.0
## 40 36.493005 100.0 100.0
## 41 17.655572 89.0 37.5
## 42 19.910757 98.4 62.5
## 43 23.202224 97.5 62.5
## 44 45.151954 100.0 75.0
## 45 29.232996 99.3 87.5
## 46 10.033767 99.2 62.5
## 47 19.910757 98.4 62.5
## 48 24.481428 99.7 87.5
## 49 28.931500 96.2 62.5
## 50 20.646406 100.0 100.0
## 51 47.105644 100.0 87.5
## 52 38.265798 100.0 100.0
## 53 14.063372 96.2 87.5
## 54 15.557164 84.9 62.5
## 55 36.577424 100.0 100.0
## 56 27.448143 99.8 100.0
## 57 100.000000 99.3 87.5
## 58 31.970574 100.0 87.5
## 59 4.425953 71.2 37.5
## 60 47.190063 100.0 100.0
## I_In_EnforceTransportSafety I_In_PedestrianFriendliness
## 1 100.0 100
## 2 70.0 100
## 3 60.0 100
## 4 52.5 50
## 5 82.5 100
## 6 77.5 50
## 7 40.0 100
## 8 67.5 100
## 9 62.5 100
## 10 60.0 50
## 11 40.0 0
## 12 57.5 0
## 13 77.5 100
## 14 77.5 50
## 15 37.5 0
## 16 30.0 0
## 17 77.5 50
## 18 75.0 100
## 19 65.0 50
## 20 77.5 100
## 21 30.0 100
## 22 65.0 0
## 23 60.0 0
## 24 35.0 50
## 25 30.0 50
## 26 50.0 50
## 27 62.5 50
## 28 37.5 0
## 29 87.5 100
## 30 77.5 100
## 31 82.5 100
## 32 42.5 50
## 33 77.5 100
## 34 42.5 100
## 35 72.5 100
## 36 67.5 100
## 37 37.5 0
## 38 77.5 100
## 39 82.5 100
## 40 87.5 100
## 41 55.0 0
## 42 70.0 100
## 43 60.0 0
## 44 72.5 100
## 45 77.5 100
## 46 45.0 100
## 47 70.0 100
## 48 72.5 100
## 49 77.5 50
## 50 82.5 100
## 51 75.0 100
## 52 77.5 100
## 53 77.5 100
## 54 67.5 100
## 55 82.5 100
## 56 77.5 100
## 57 77.5 100
## 58 87.5 100
## 59 50.0 0
## 60 75.0 100
## I_In_QualityRoad I_In_QualityElectricity I_In_DisasterManagement
## 1 100 75 75
## 2 100 100 100
## 3 50 75 75
## 4 25 100 75
## 5 100 100 100
## 6 75 100 50
## 7 50 75 50
## 8 75 100 100
## 9 75 100 100
## 10 50 75 50
## 11 50 50 50
## 12 50 75 75
## 13 75 100 100
## 14 75 100 100
## 15 50 50 50
## 16 0 50 25
## 17 75 75 100
## 18 100 100 100
## 19 25 50 25
## 20 100 100 100
## 21 50 75 75
## 22 50 50 50
## 23 75 100 0
## 24 50 50 75
## 25 25 25 0
## 26 75 100 75
## 27 75 100 75
## 28 75 100 75
## 29 75 100 100
## 30 75 100 100
## 31 100 100 100
## 32 50 75 25
## 33 100 100 100
## 34 50 50 75
## 35 75 100 100
## 36 75 100 75
## 37 0 75 50
## 38 75 100 100
## 39 100 100 100
## 40 75 100 100
## 41 50 75 25
## 42 50 100 75
## 43 50 75 0
## 44 100 100 100
## 45 75 100 100
## 46 75 100 100
## 47 75 75 75
## 48 75 100 100
## 49 75 100 50
## 50 100 100 100
## 51 100 100 100
## 52 100 100 100
## 53 75 75 75
## 54 50 50 75
## 55 100 100 100
## 56 75 100 100
## 57 75 100 100
## 58 100 100 100
## 59 25 50 25
## 60 100 100 100
## P_In_PoliceEngage P_In_PoliticalStability
## 1 100 55
## 2 100 80
## 3 50 70
## 4 50 40
## 5 100 70
## 6 50 45
## 7 50 65
## 8 50 75
## 9 50 60
## 10 50 50
## 11 50 25
## 12 100 40
## 13 100 80
## 14 100 80
## 15 100 75
## 16 100 45
## 17 100 60
## 18 100 85
## 19 0 45
## 20 100 60
## 21 50 35
## 22 50 60
## 23 50 45
## 24 50 65
## 25 100 40
## 26 50 65
## 27 50 40
## 28 50 65
## 29 100 75
## 30 100 80
## 31 100 70
## 32 50 50
## 33 100 90
## 34 50 50
## 35 50 60
## 36 0 40
## 37 100 75
## 38 100 80
## 39 100 85
## 40 50 65
## 41 50 50
## 42 50 60
## 43 50 45
## 44 50 60
## 45 100 80
## 46 50 80
## 47 50 60
## 48 100 55
## 49 50 45
## 50 100 80
## 51 50 85
## 52 100 90
## 53 100 65
## 54 50 30
## 55 100 85
## 56 100 95
## 57 100 80
## 58 100 85
## 59 50 50
## 60 50 85library(reshape2)
safeinput=melt(input,
id.vars = 'city')
head(safeinput)## city variable value
## 1 Abu Dhabi D_In_PrivacyPolicy 50
## 2 Amsterdam D_In_PrivacyPolicy 100
## 3 Athens D_In_PrivacyPolicy 75
## 4 Bangkok D_In_PrivacyPolicy 25
## 5 Barcelona D_In_PrivacyPolicy 100
## 6 Beijing D_In_PrivacyPolicy 75library(ggplot2)
meltgraph=ggplot(data = safeinput, aes(x = variable,
y =city))
graph1= meltgraph + geom_tile(aes(fill = value))
graph1
library(ggiraph)
library(ggiraphExtra)
distanceAmong <- dist(input[,-1])
resultMDS <- cmdscale(distanceAmong,eig=TRUE, k=2)
dim1 <- resultMDS$points[,1]
dim2 <- resultMDS$points[,2]
coordinates=data.frame(dim1,dim2,city=input$city)
head(coordinates)## dim1 dim2 city
## 1 -21.580506 2.664374 Abu Dhabi
## 2 -84.364052 8.461246 Amsterdam
## 3 7.040038 49.402597 Athens
## 4 88.942349 26.868937 Bangkok
## 5 -78.393608 8.069760 Barcelona
## 6 15.879587 19.141895 Beijingbase= ggplot(coordinates,aes(x=dim1, y=dim2,label=city))
base + geom_text(size=2)
library(cluster)
set.seed(123)
resultKM = kmeans(input[,-c(1)],
centers = 3)
coordinates$cluster=as.factor(resultKM$cluster)
base = ggplot(coordinates, aes(x=dim1, y=dim2, label=city, color=cluster))
base + geom_text(size=2)
Part 3 (For final project):
Prepare a report of the status of your final project, informing if the data collection process is over.