Homework 3
Kamil Laskowski
Libraries
library(arules)
library(arulesViz)
library(dplyr)
library(ggplot2)Data preprocessing
Loading data from csv file and printing first few rows in order to check if it was loaded correctly:
data <- read.csv("ESS11.csv")
head(data)## name essround edition proddate idno cntry dweight pspwght
## 1 ESS11e02 11 2 20.11.2024 50014 AT 1.1851145 0.3928906
## 2 ESS11e02 11 2 20.11.2024 50030 AT 0.6098981 0.3251533
## 3 ESS11e02 11 2 20.11.2024 50057 AT 1.3923296 4.0000234
## 4 ESS11e02 11 2 20.11.2024 50106 AT 0.5560615 0.1762276
## 5 ESS11e02 11 2 20.11.2024 50145 AT 0.7227953 1.0609399
## 6 ESS11e02 11 2 20.11.2024 50158 AT 0.9926053 1.3928125
## pweight anweight nwspol netusoft netustm ppltrst pplfair pplhlp polintr
## 1 0.3309145 0.13001321 90 5 180 5 5 5 1
## 2 0.3309145 0.10759795 90 5 570 10 0 1 2
## 3 0.3309145 1.32366590 30 5 30 6 9 8 2
## 4 0.3309145 0.05831629 15 1 6666 6 6 6 3
## 5 0.3309145 0.35108042 60 5 120 6 3 8 2
## 6 0.3309145 0.46090190 120 5 120 8 8 4 2
## psppsgva actrolga psppipla cptppola trstprl trstlgl trstplc trstplt trstprt
## 1 4 5 4 5 6 9 10 5 5
## 2 3 2 3 2 6 6 4 1 0
## 3 4 4 4 3 7 5 8 4 4
## 4 2 2 2 3 5 6 9 3 3
## 5 3 1 4 3 6 8 8 5 5
## 6 2 3 2 3 3 5 7 5 5
## trstep trstun vote prtvtdat prtvtebe prtvtchr prtvtccy prtvtffi prtvtffr
## 1 5 5 1 1 NA NA NA NA NA
## 2 5 5 1 5 NA NA NA NA NA
## 3 7 5 1 5 NA NA NA NA NA
## 4 4 4 2 66 NA NA NA NA NA
## 5 6 8 1 5 NA NA NA NA NA
## 6 4 6 1 7 NA NA NA NA NA
## prtvgde1 prtvgde2 prtvtegr prtvthhu prtvteis prtvteie prtvteit prtvclt1
## 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 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA
## prtvclt2 prtvclt3 prtvtinl prtvtcno prtvtfpl prtvtept prtvtbrs prtvtesk
## 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 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA
## prtvtgsi prtvtges prtvtdse prtvthch prtvtdgb contplt donprty badge sgnptit
## 1 NA NA NA NA NA 2 2 2 2
## 2 NA NA NA NA NA 2 2 1 1
## 3 NA NA NA NA NA 1 1 2 1
## 4 NA NA NA NA NA 2 2 2 2
## 5 NA NA NA NA NA 2 2 2 1
## 6 NA NA NA NA NA 2 2 2 2
## pbldmna bctprd pstplonl volunfp clsprty prtcleat prtclebe prtclbhr prtclccy
## 1 2 2 2 2 1 1 NA NA NA
## 2 1 1 2 1 1 5 NA NA NA
## 3 1 1 1 1 1 5 NA NA NA
## 4 2 2 2 2 2 66 NA NA NA
## 5 2 2 2 2 1 5 NA NA NA
## 6 2 1 2 2 2 66 NA NA NA
## prtclgfi prtclgfr prtclgde prtclegr prtclihu prtcleis prtclfie prtclfit
## 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 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA
## prtclclt prtclhnl prtclcno prtcljpl prtclgpt prtclbrs prtclesk prtclgsi
## 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 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA
## prtclhes prtcldse prtclhch prtcldgb prtdgcl lrscale stflife stfeco stfgov
## 1 NA NA NA NA 2 5 8 6 4
## 2 NA NA NA NA 2 0 9 2 5
## 3 NA NA NA NA 2 3 10 6 5
## 4 NA NA NA NA 6 5 7 4 4
## 5 NA NA NA NA 2 2 9 6 7
## 6 NA NA NA NA 6 4 8 4 2
## stfdem stfedu stfhlth gincdif freehms hmsfmlsh hmsacld euftf lrnobed loylead
## 1 6 8 8 2 2 4 3 6 4 4
## 2 7 10 10 1 1 5 1 9 2 5
## 3 6 5 8 1 1 5 1 7 5 3
## 4 6 5 2 1 2 4 3 5 2 9
## 5 8 9 9 2 2 4 2 10 1 2
## 6 3 3 7 2 2 4 2 3 2 2
## imsmetn imdfetn impcntr imbgeco imueclt imwbcnt happy sclmeet inprdsc sclact
## 1 2 2 3 7 3 5 8 4 1 3
## 2 2 2 2 6 5 9 9 7 4 4
## 3 1 1 1 9 9 8 9 4 4 3
## 4 2 2 2 6 6 5 7 6 3 3
## 5 1 1 2 10 10 10 9 5 4 3
## 6 2 3 3 5 6 6 8 6 4 2
## crmvct aesfdrk health hlthhmp atchctr atcherp rlgblg rlgdnm rlgdnbat rlgdnacy
## 1 2 2 3 3 10 5 1 1 1 NA
## 2 1 3 2 2 8 8 2 66 6666 NA
## 3 2 3 1 3 9 7 2 66 6666 NA
## 4 2 3 3 3 10 8 1 3 10 NA
## 5 2 1 2 3 10 10 1 1 1 NA
## 6 2 2 1 3 8 6 1 1 1 NA
## rlgdnafi rlgdnade rlgdnagr rlgdnhu rlgdnais rlgdnie rlgdnlt rlgdnanl rlgdnno
## 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 NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA
## rlgdnapl rlgdnapt rlgdnrs rlgdnask rlgdnase rlgdnach rlgdngb rlgblge rlgdnme
## 1 NA NA NA NA NA NA NA 6 66
## 2 NA NA NA NA NA NA NA 2 66
## 3 NA NA NA NA NA NA NA 1 1
## 4 NA NA NA NA NA NA NA 6 66
## 5 NA NA NA NA NA NA NA 6 66
## 6 NA NA NA NA NA NA NA 6 66
## rlgdebat rlgdeacy rlgdeafi rlgdeade rlgdeagr rlgdehu rlgdeais rlgdeie rlgdelt
## 1 6666 NA NA NA NA NA NA NA NA
## 2 6666 NA NA NA NA NA NA NA NA
## 3 1 NA NA NA NA NA NA NA NA
## 4 6666 NA NA NA NA NA NA NA NA
## 5 6666 NA NA NA NA NA NA NA NA
## 6 6666 NA NA NA NA NA NA NA NA
## rlgdeanl rlgdeno rlgdeapl rlgdeapt rlgders rlgdeask rlgdease rlgdeach rlgdegb
## 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 NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA
## rlgdgr rlgatnd pray dscrgrp dscrrce dscrntn dscrrlg dscrlng dscretn dscrage
## 1 5 6 5 2 0 0 0 0 0 0
## 2 0 7 6 2 0 0 0 0 0 0
## 3 8 5 3 1 0 0 0 0 0 0
## 4 6 6 3 2 0 0 0 0 0 0
## 5 1 7 7 2 0 0 0 0 0 0
## 6 3 5 1 2 0 0 0 0 0 0
## dscrgnd dscrsex dscrdsb dscroth dscrdk dscrref dscrnap dscrna ctzcntr brncntr
## 1 0 0 0 0 0 0 1 0 1 1
## 2 0 0 0 0 0 0 1 0 1 1
## 3 1 0 0 0 0 0 0 0 1 1
## 4 0 0 0 0 0 0 1 0 2 2
## 5 0 0 0 0 0 0 1 0 1 1
## 6 0 0 0 0 0 0 1 0 1 2
## cntbrthd livecnta lnghom1 lnghom2 feethngr facntr fbrncntc mocntr mbrncntc
## 1 6666 6666 GER 000 1 1 6666 1 6666
## 2 6666 6666 GER ENG 8 1 6666 1 6666
## 3 6666 6666 GER ENG 1 1 6666 1 6666
## 4 RO 1989 GER RUM 2 2 RO 2 RO
## 5 6666 6666 GER 000 1 1 6666 1 6666
## 6 CZ 1965 GER ENG 1 2 CZ 2 CZ
## ccnthum ccrdprs wrclmch admrclc testjc34 testjc35 testjc36 testjc37 testjc38
## 1 4 4 4 2 66 66 66 2 2
## 2 3 10 5 3 66 66 66 6 6
## 3 4 8 5 2 66 66 66 3 4
## 4 4 6 4 2 66 66 66 4 4
## 5 5 10 4 3 66 66 66 6 6
## 6 5 8 4 3 66 66 66 6 6
## testjc39 testjc40 testjc41 testjc42 vteurmmb vteubcmb ctrlife etfruit eatveg
## 1 3 6 6 6 1 NA 8 3 3
## 2 6 3 3 2 55 NA 8 1 1
## 3 3 6 6 6 1 NA 9 4 3
## 4 4 6 6 6 55 NA 8 2 2
## 5 6 4 2 1 1 NA 9 3 3
## 6 6 3 2 0 1 NA 8 5 3
## dosprt cgtsmok alcfreq alcwkdy alcwknd icgndra alcbnge height weighta dshltgp
## 1 3 4 3 20 22 1 5 178 90 1
## 2 5 5 3 70 64 2 2 168 74 1
## 3 3 1 4 72 72 2 5 180 95 1
## 4 3 6 7 6666 6666 6 6 167 70 1
## 5 3 1 2 24 48 2 4 168 67 0
## 6 4 5 2 24 24 2 2 155 60 1
## dshltms dshltnt dshltref dshltdk dshltna medtrun medtrnp medtrnt medtroc
## 1 1 0 0 0 0 2 0 0 0
## 2 0 0 0 0 0 2 0 0 0
## 3 1 0 0 0 0 2 0 0 0
## 4 1 0 0 0 0 2 0 0 0
## 5 1 0 0 0 0 2 0 0 0
## 6 1 0 0 0 0 2 0 0 0
## medtrnl medtrwl medtrnaa medtroth medtrnap medtrref medtrdk medtrna medtrnu
## 1 0 0 0 0 1 0 0 0 1
## 2 0 0 0 0 1 0 0 0 2
## 3 0 0 0 0 1 0 0 0 1
## 4 0 0 0 0 1 0 0 0 1
## 5 0 0 0 0 1 0 0 0 1
## 6 0 0 0 0 1 0 0 0 1
## hlpfmly hlpfmhr trhltacu trhltacp trhltcm trhltch trhltos trhltho trhltht
## 1 2 66 0 0 0 0 0 0 0
## 2 1 2 0 0 0 0 0 0 0
## 3 1 1 0 0 1 0 0 0 0
## 4 2 66 0 0 0 0 0 0 0
## 5 2 66 0 0 0 0 0 1 1
## 6 1 1 0 0 0 0 0 0 1
## trhlthy trhltmt trhltpt trhltre trhltsh trhltnt trhltref trhltdk trhltna
## 1 0 0 0 0 0 1 0 0 0
## 2 0 0 0 0 0 1 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 1 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 1 1 0 0 0 0 0 0
## fltdpr flteeff slprl wrhpp fltlnl enjlf fltsd cldgng hltprhc hltprhb hltprbp
## 1 1 1 1 3 1 3 1 1 0 1 0
## 2 2 2 3 3 3 4 2 2 1 0 0
## 3 2 2 3 3 1 3 1 2 0 0 0
## 4 2 2 3 2 2 2 2 2 0 1 0
## 5 1 1 1 3 1 3 1 1 0 0 0
## 6 1 2 2 4 1 4 1 2 0 1 0
## hltpral hltprbn hltprpa hltprpf hltprsd hltprsc hltprsh hltprdi hltprnt
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 1 0 0 0 0 0
## 4 0 1 0 0 0 0 0 1 0
## 5 0 0 0 0 0 0 0 0 1
## 6 0 0 0 0 1 0 0 0 0
## hltprref hltprdk hltprna hltphhc hltphhb hltphbp hltphal hltphbn hltphpa
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 1
## 4 0 0 0 0 0 0 0 1 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## hltphpf hltphsd hltphsc hltphsh hltphdi hltphnt hltphnap hltphref hltphdk
## 1 0 0 0 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 1 0
## 3 1 0 0 0 0 0 0 0 0
## 4 0 0 0 0 1 0 0 0 0
## 5 0 0 0 0 0 0 1 0 0
## 6 0 0 0 0 0 1 0 0 0
## hltphna hltprca cancfre cnfpplh fnsdfml jbexpvi jbexpti jbexpml jbexpmc
## 1 0 3 6 4 5 0 0 0 0
## 2 0 3 6 2 4 0 0 0 0
## 3 0 3 6 4 2 0 0 0 0
## 4 0 3 6 4 3 0 1 1 0
## 5 0 3 6 5 5 0 0 0 0
## 6 0 3 6 4 4 0 0 0 0
## jbexpnt jbexpnap jbexpref jbexpdk jbexpna jbexevl jbexevh jbexevc jbexera
## 1 0 1 0 0 0 0 0 0 0
## 2 1 0 0 0 0 0 0 0 0
## 3 1 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 1 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 0 0 0 0
## jbexecp jbexebs jbexent jbexenap jbexeref jbexedk jbexena nobingnd likrisk
## 1 0 0 0 1 0 0 0 1 2
## 2 0 0 1 0 0 0 0 2 0
## 3 0 0 1 0 0 0 0 2 0
## 4 1 1 0 0 0 0 0 2 0
## 5 0 0 1 0 0 0 0 2 1
## 6 0 0 1 0 0 0 0 2 2
## liklead sothnds actcomp mascfel femifel impbemw trmedmw trwrkmw trplcmw
## 1 4 4 5 4 0 3 3 3 3
## 2 0 6 6 3 6 3 3 2 3
## 3 3 6 6 0 6 5 3 3 3
## 4 1 6 6 0 6 5 3 1 3
## 5 0 6 6 0 5 5 3 3 3
## 6 4 5 3 6 6 6 3 2 3
## trmdcnt trwkcnt trplcnt eqwrkbg eqpolbg eqmgmbg eqpaybg eqparep eqparlv
## 1 3 3 3 6 6 6 6 4 4
## 2 1 1 8 6 5 5 5 2 4
## 3 1 1 1 5 6 6 6 2 8
## 4 3 1 2 5 5 5 6 1 8
## 5 3 1 3 6 6 6 6 1 5
## 6 3 1 3 5 5 5 6 2 3
## freinsw fineqpy wsekpwr weasoff wlespdm wexashr wprtbym wbrgwrm hhmmb gndr
## 1 4 3 2 3 2 2 2 2 2 1
## 2 1 2 3 3 4 2 2 3 1 2
## 3 2 1 2 2 4 2 4 2 3 2
## 4 4 1 3 3 4 2 3 2 1 2
## 5 1 1 2 2 4 1 3 3 2 1
## 6 2 1 3 4 4 4 3 1 2 2
## gndr2 gndr3 gndr4 gndr5 gndr6 gndr7 gndr8 gndr9 gndr10 gndr11 gndr12 yrbrn
## 1 2 6 6 6 6 6 NA NA NA NA NA 1958
## 2 6 6 6 6 6 6 NA NA NA NA NA 2002
## 3 1 2 6 6 6 6 NA NA NA NA NA 1970
## 4 6 6 6 6 6 6 NA NA NA NA NA 1945
## 5 1 6 6 6 6 6 NA NA NA NA NA 1959
## 6 1 6 6 6 6 6 NA NA NA NA NA 1964
## agea yrbrn2 yrbrn3 yrbrn4 yrbrn5 yrbrn6 yrbrn7 yrbrn8 yrbrn9 yrbrn10 yrbrn11
## 1 65 1957 6666 6666 6666 6666 6666 NA NA NA NA
## 2 21 6666 6666 6666 6666 6666 6666 NA NA NA NA
## 3 53 1970 2001 6666 6666 6666 6666 NA NA NA NA
## 4 78 6666 6666 6666 6666 6666 6666 NA NA NA NA
## 5 64 1962 6666 6666 6666 6666 6666 NA NA NA NA
## 6 59 1956 6666 6666 6666 6666 6666 NA NA NA NA
## yrbrn12 rshipa2 rshipa3 rshipa4 rshipa5 rshipa6 rshipa7 rshipa8 rshipa9
## 1 NA 1 66 66 66 66 66 NA NA
## 2 NA 66 66 66 66 66 66 NA NA
## 3 NA 1 2 66 66 66 66 NA NA
## 4 NA 66 66 66 66 66 66 NA NA
## 5 NA 1 66 66 66 66 66 NA NA
## 6 NA 1 66 66 66 66 66 NA NA
## rshipa10 rshipa11 rshipa12 rshpsts rshpsgb lvgptnea dvrcdeva marsts marstgb
## 1 NA NA NA 1 NA 1 2 66 NA
## 2 NA NA NA 66 NA 2 2 6 NA
## 3 NA NA NA 1 NA 1 2 66 NA
## 4 NA NA NA 66 NA 1 1 4 NA
## 5 NA NA NA 1 NA 1 2 66 NA
## 6 NA NA NA 1 NA 1 1 66 NA
## maritalb chldhhe domicil paccmoro paccdwlr pacclift paccnbsh paccocrw
## 1 1 1 3 0 0 0 0 0
## 2 6 2 1 1 0 0 0 0
## 3 1 6 3 0 0 0 0 0
## 4 4 1 1 0 0 0 0 0
## 5 1 1 4 0 0 0 0 0
## 6 1 1 4 0 0 0 0 0
## paccxhoc paccnois paccinro paccnt paccref paccdk paccna edulvlb eisced
## 1 0 0 0 1 0 0 0 322 3
## 2 0 0 0 0 0 0 0 423 5
## 3 0 0 0 1 0 0 0 610 6
## 4 0 0 0 1 0 0 0 422 5
## 5 0 0 0 1 0 0 0 322 3
## 6 0 0 0 1 0 0 0 313 4
## edlveat edlvebe edlvehr edlvgcy edlvdfi edlvdfr edudde1 educde2 edlvegr
## 1 6 NA NA NA NA NA NA NA NA
## 2 10 NA NA NA NA NA NA NA NA
## 3 12 NA NA NA NA NA NA NA NA
## 4 9 NA NA NA NA NA NA NA NA
## 5 6 NA NA NA NA NA NA NA NA
## 6 8 NA NA NA NA NA NA NA NA
## edlvdahu edlvdis edlvdie edlvfit edlvdlt edlvenl edlveno edlvipl edlvept
## 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 NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA
## edlvdrs edlvdsk edlvesi edlvies edlvdse edlvdch educgb1 edubgb2 edagegb
## 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 NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA
## eduyrs pdwrk edctn uempla uempli dsbld rtrd cmsrv hswrk dngoth dngref dngdk
## 1 12 0 0 0 0 0 1 0 0 0 0 0
## 2 14 0 1 0 0 0 0 0 0 0 0 0
## 3 16 1 0 0 0 0 0 0 0 0 0 0
## 4 14 0 0 0 0 0 1 0 0 0 0 0
## 5 12 0 0 0 0 0 1 0 0 0 0 0
## 6 16 1 0 0 0 0 0 0 0 0 0 0
## dngna mainact mnactic crpdwk pdjobev pdjobyr emplrel emplno wrkctra estsz
## 1 0 66 6 2 2 6666 6 66666 6 6
## 2 0 66 2 2 1 2023 1 66666 2 2
## 3 0 66 1 6 6 6666 1 66666 1 1
## 4 0 66 6 2 1 2005 1 66666 1 5
## 5 0 66 6 2 1 2021 1 66666 1 2
## 6 0 66 1 6 6 6666 1 66666 1 5
## jbspv njbspv wkdcorga iorgact wkhct wkhtot nacer2 tporgwk isco08 wrkac6m
## 1 6 66666 66 66 666 666 666 66 66666 6
## 2 2 66666 0 0 32 32 14 4 5249 2
## 3 1 4 10 9 25 25 88 6 2635 2
## 4 2 66666 5 0 39 39 87 2 2221 2
## 5 2 66666 7 7 40 35 47 4 5223 2
## 6 1 12 10 7 40 40 21 4 1112 2
## uemp3m uemp12m uemp5yr mbtru hincsrca hinctnta hincfel edulvlpb eiscedp
## 1 2 6 6 3 4 6 1 322 3
## 2 2 6 6 3 8 1 2 6666 66
## 3 2 6 6 3 1 5 1 520 5
## 4 2 6 6 3 4 2 2 6666 66
## 5 2 6 6 2 4 77 2 322 3
## 6 2 6 6 1 1 9 1 322 3
## edlvpfat edlvpebe edlvpehr edlvpgcy edlvpdfi edlvpdfr edupdde1 edupcde2
## 1 6 NA NA NA NA NA NA NA
## 2 6666 NA NA NA NA NA NA NA
## 3 11 NA NA NA NA NA NA NA
## 4 6666 NA NA NA NA NA NA NA
## 5 6 NA NA NA NA NA NA NA
## 6 6 NA NA NA NA NA NA NA
## edlvpegr edlvpdahu edlvpdis edlvpdie edlvpfit edlvpdlt edlvpenl edlvpeno
## 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 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA
## edlvphpl edlvpept edlvpdrs edlvpdsk edlvpesi edlvphes edlvpdse edlvpdch
## 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 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA
## edupcgb1 edupbgb2 edagepgb pdwrkp edctnp uemplap uemplip dsbldp rtrdp cmsrvp
## 1 NA NA NA 0 0 0 0 0 1 0
## 2 NA NA NA 0 0 0 0 0 0 0
## 3 NA NA NA 1 0 0 0 0 0 0
## 4 NA NA NA 0 0 0 0 0 0 0
## 5 NA NA NA 0 0 0 0 0 1 0
## 6 NA NA NA 1 0 0 0 0 0 0
## hswrkp dngothp dngdkp dngnapp dngrefp dngnap mnactp crpdwkp isco08p emprelp
## 1 0 0 0 0 0 0 66 2 66666 6
## 2 0 0 0 1 0 0 66 6 66666 6
## 3 0 0 0 0 0 0 66 6 2635 1
## 4 0 0 0 1 0 0 66 6 66666 6
## 5 0 0 0 0 0 0 66 2 66666 6
## 6 0 0 0 0 0 0 66 6 3321 2
## wkhtotp edulvlfb eiscedf edlvfeat edlvfebe edlvfehr edlvfgcy edlvfdfi
## 1 666 322 3 7 NA NA NA NA
## 2 666 322 3 6 NA NA NA NA
## 3 1 322 3 6 NA NA NA NA
## 4 666 322 3 6 NA NA NA NA
## 5 666 322 3 6 NA NA NA NA
## 6 1 800 7 18 NA NA NA NA
## edlvfdfr edufcde1 edufbde2 edlvfegr edlvfdahu edlvfdis edlvfdie edlvffit
## 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 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA
## edlvfdlt edlvfenl edlvfeno edlvfgpl edlvfept edlvfdrs edlvfdsk edlvfesi
## 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 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA
## edlvfges edlvfdse edlvfdch edufcgb1 edufbgb2 edagefgb emprf14 occf14b
## 1 NA NA NA NA NA NA 2 9
## 2 NA NA NA NA NA NA 2 9
## 3 NA NA NA NA NA NA 1 6
## 4 NA NA NA NA NA NA 1 5
## 5 NA NA NA NA NA NA 1 4
## 6 NA NA NA NA NA NA 1 1
## edulvlmb eiscedm edlvmeat edlvmebe edlvmehr edlvmgcy edlvmdfi edlvmdfr
## 1 212 2 3 NA NA NA NA NA
## 2 322 3 6 NA NA NA NA NA
## 3 322 3 7 NA NA NA NA NA
## 4 212 2 3 NA NA NA NA NA
## 5 212 2 3 NA NA NA NA NA
## 6 610 6 13 NA NA NA NA NA
## edumcde1 edumbde2 edlvmegr edlvmdahu edlvmdis edlvmdie edlvmfit edlvmdlt
## 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 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA
## edlvmenl edlvmeno edlvmgpl edlvmept edlvmdrs edlvmdsk edlvmesi edlvmges
## 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 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA
## edlvmdse edlvmdch edumcgb1 edumbgb2 edagemgb emprm14 occm14b atncrse anctrya1
## 1 NA NA NA NA NA 2 9 2 11010
## 2 NA NA NA NA NA 2 9 2 11010
## 3 NA NA NA NA NA 3 66 1 11010
## 4 NA NA NA NA NA 1 4 2 14090
## 5 NA NA NA NA NA 1 7 2 11010
## 6 NA NA NA NA NA 1 1 2 11010
## anctrya2 regunit region ipcrtiva impricha ipeqopta ipshabta impsafea impdiffa
## 1 555555 2 AT31 3 5 2 2 2 4
## 2 555555 2 AT22 2 4 2 4 4 4
## 3 11070 2 AT33 1 4 1 3 2 4
## 4 11010 2 AT31 3 4 2 3 1 4
## 5 11018 2 AT32 3 4 2 2 2 4
## 6 11018 2 AT33 1 2 2 1 2 2
## ipfrulea ipudrsta ipmodsta ipgdtima impfreea iphlppla ipsucesa ipstrgva
## 1 2 2 2 2 2 2 3 2
## 2 4 2 2 2 2 1 4 2
## 3 3 1 3 3 2 1 3 2
## 4 3 3 2 3 2 2 3 3
## 5 5 2 3 3 2 2 2 2
## 6 2 2 3 2 2 1 2 1
## ipadvnta ipbhprpa iprspota iplylfra impenva imptrada impfuna testji1 testji2
## 1 5 2 2 2 2 3 3 6 6
## 2 4 2 4 1 1 4 2 6 6
## 3 4 3 3 1 1 3 2 6 6
## 4 4 3 3 2 2 2 3 6 6
## 5 4 2 2 2 2 2 2 6 6
## 6 3 1 2 1 2 2 2 6 6
## testji3 testji4 testji5 testji6 testji7 testji8 testji9 respc19a symtc19
## 1 6 3 3 2 66 66 66 1 2
## 2 6 6 6 6 10 2 2 1 2
## 3 6 8 8 8 66 66 66 1 2
## 4 6 1 1 1 66 66 66 1 2
## 5 6 6 6 6 8 4 3 3 6
## 6 6 6 6 6 7 4 1 1 2
## symtnc19 vacc19 recon inwds ainws
## 1 6 1 2 2023-11-12 14:49:50 2023-11-12 14:49:50
## 2 6 1 1 2023-10-18 09:56:32 2023-10-18 09:56:32
## 3 6 1 1 2023-09-30 13:22:49 2023-09-30 13:22:49
## 4 6 1 2 2023-06-30 14:31:46 2023-06-30 14:31:46
## 5 6 1 2 2023-07-11 10:11:00 2023-07-11 10:11:00
## 6 6 1 2 2023-10-16 08:35:24 2023-10-16 08:35:24
## ainwe binwe cinwe
## 1 2023-11-12 14:50:23 2023-11-12 15:01:14 2023-11-12 15:03:07
## 2 2023-10-18 09:58:32 2023-10-18 10:07:21 2023-10-18 10:11:42
## 3 2023-09-30 13:24:24 2023-09-30 13:34:23 2023-09-30 13:41:50
## 4 2023-06-30 14:32:40 2023-06-30 14:42:30 2023-06-30 14:50:31
## 5 2023-07-11 10:13:36 2023-07-11 10:18:33 2023-07-11 10:23:56
## 6 2023-10-16 08:37:00 2023-10-16 08:49:01 2023-10-16 08:55:53
## dinwe einwe finwe
## 1 2023-11-12 15:08:28 2023-11-12 15:14:58 2023-11-12 15:18:41
## 2 2023-10-18 10:19:27 2023-10-18 10:25:47 2023-10-18 10:32:34
## 3 2023-09-30 13:50:25 2023-09-30 13:56:10 2023-09-30 14:03:15
## 4 2023-06-30 14:53:56 2023-06-30 14:57:00 2023-06-30 15:04:03
## 5 2023-07-11 10:43:23 2023-07-11 10:56:16 2023-07-11 11:03:31
## 6 2023-10-16 09:07:39 2023-10-16 09:14:29 2023-10-16 09:29:26
## hinwe iinwe kinwe rinwe
## 1 2023-11-12 15:20:19 2023-11-12 15:20:37 2023-11-12 15:20:46
## 2 2023-10-18 10:36:39 2023-10-18 10:37:18 2023-10-18 10:37:49
## 3 2023-09-30 14:05:12 2023-09-30 14:05:27 2023-09-30 14:05:41
## 4 2023-06-30 15:05:22 2023-06-30 15:05:41 2023-06-30 15:06:02
## 5 2023-07-11 11:07:49 2023-07-11 11:08:12 2023-07-11 11:08:26
## 6 2023-10-16 09:32:29 2023-10-16 09:33:12 2023-10-16 09:33:34
## inwde jinws jinwe inwtm mode domain
## 1 2023-11-12 15:26:55 2023-11-12 15:21:28 2023-11-12 15:26:55 30 1 2
## 2 2023-10-18 10:44:18 2023-10-18 10:42:22 2023-10-18 10:44:18 40 1 1
## 3 2023-09-30 14:13:33 2023-09-30 14:08:31 2023-09-30 14:13:33 42 1 2
## 4 2023-06-30 15:11:21 2023-06-30 15:08:05 2023-06-30 15:11:21 34 1 1
## 5 2023-07-11 11:14:03 2023-07-11 11:10:02 2023-07-11 11:14:03 57 1 2
## 6 2023-10-16 09:42:52 2023-10-16 09:38:14 2023-10-16 09:42:52 57 1 2
## prob stratum psu
## 1 0.0005786479 107 317
## 2 0.0011243912 69 128
## 3 0.0004925300 18 418
## 4 0.0012332522 101 295
## 5 0.0009487666 115 344
## 6 0.0006908728 7 373The dataset contains many variables. I am selecting only those that I believe are most likely to influence hincfel:
- hincfel - Feeling about household’s income nowadays
- uempla - Doing last 7 days: unemployed, actively looking for job
- uempli - Doing last 7 days: unemployed, not actively looking for job
- gndr - Gender
- hinctnta - Household’s total net income, all sources
- health - Subjective general health
- stflife - How satisfied with life as a whole
- ppltrst - Most people can be trusted or you can’t be too careful
data <- data[, c("hincfel", "uempla", "uempli", "gndr", "hinctnta", "health", "stflife", "ppltrst")]
data <- na.omit(data)
head(data)## hincfel uempla uempli gndr hinctnta health stflife ppltrst
## 1 1 0 0 1 6 3 8 5
## 2 2 0 0 2 1 2 9 10
## 3 1 0 0 2 5 1 10 6
## 4 2 0 0 2 2 3 7 6
## 5 2 0 0 1 77 2 9 6
## 6 1 0 0 2 9 1 8 8Since association rule mining works best with categorical data, I convert numerical values into meaningful categories. It will also help with further rules analysis. I took categories directly from ESS11 variable description
data$hincfel <- ifelse(data$hincfel == 1, "Living comfortably",
ifelse(data$hincfel == 2, "Coping",
ifelse(data$hincfel == 3, "Difficult",
ifelse(data$hincfel == 4, "Very difficult", NA))))
data$gndr <- ifelse(data$gndr == 1, "Male",
ifelse(data$gndr == 2, "Female", NA))
data$hinctnta <- ifelse(data$hinctnta %in% c(1,2), "Low income",
ifelse(data$hinctnta %in% c(3,4,5), "Lower-middle income",
ifelse(data$hinctnta %in% c(6,7,8), "Upper-middle income",
ifelse(data$hinctnta %in% c(9,10), "High income", NA))))
data$health <- ifelse(data$health == 1, "Very good",
ifelse(data$health == 2, "Good",
ifelse(data$health == 3, "Fair",
ifelse(data$health == 4, "Bad",
ifelse(data$health == 5, "Very bad", NA)))))
data$stflife <- ifelse(data$stflife %in% c(0,1,2,3), "Dissatisfied",
ifelse(data$stflife %in% c(4,5,6), "Neutral",
ifelse(data$stflife %in% c(7,8,9,10), "Satisfied", NA)))
data$ppltrst <- ifelse(data$ppltrst %in% c(0,1,2,3), "Low trust",
ifelse(data$ppltrst %in% c(4,5,6), "Moderate trust",
ifelse(data$ppltrst %in% c(7,8,9,10), "High trust", NA)))Here I am changing all variables to factor data type:
data[] <- lapply(data, as.factor)
str(data)## 'data.frame': 40156 obs. of 8 variables:
## $ hincfel : Factor w/ 4 levels "Coping","Difficult",..: 3 1 3 1 1 3 1 3 1 1 ...
## $ uempla : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ uempli : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ gndr : Factor w/ 2 levels "Female","Male": 2 1 1 1 2 1 1 1 1 1 ...
## $ hinctnta: Factor w/ 4 levels "High income",..: 4 2 3 2 NA 1 3 1 4 3 ...
## $ health : Factor w/ 5 levels "Bad","Fair","Good",..: 2 3 5 2 3 5 3 3 3 3 ...
## $ stflife : Factor w/ 3 levels "Dissatisfied",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ ppltrst : Factor w/ 3 levels "High trust","Low trust",..: 3 1 3 3 3 1 1 1 1 2 ...summary(data)## hincfel uempla uempli gndr
## Coping :18108 0:39069 0:39441 Female:21396
## Difficult : 5862 1: 1087 1: 715 Male :18760
## Living comfortably:14309
## Very difficult : 1426
## NA's : 451
##
## hinctnta health stflife
## High income : 5502 Bad : 2418 Dissatisfied: 2358
## Low income : 5186 Fair : 9928 Neutral : 8791
## Lower-middle income:10284 Good :17079 Satisfied :28714
## Upper-middle income:10754 Very bad : 448 NA's : 293
## NA's : 8430 Very good:10230
## NA's : 53
## ppltrst
## High trust :13682
## Low trust :10125
## Moderate trust:16250
## NA's : 99
##
## I see that 451 respondents did not provide their feeling about household income, so we are removing these rows:
data <- data[!is.na(data$hincfel), ]Association measures - Apriori algorithm
In order to proceed with Apriori alogirthm we need to convert the dataset to transactions:
transactions <- as(data, "transactions")summary(transactions)## transactions as itemMatrix in sparse format with
## 39705 rows (elements/itemsets/transactions) and
## 25 columns (items) and a density of 0.3115275
##
## most frequent items:
## uempli=0 uempla=0 stflife=Satisfied gndr=Female
## 39003 38633 28437 21140
## gndr=Male (Other)
## 18565 163452
##
## element (itemset/transaction) length distribution:
## sizes
## 5 6 7 8
## 3 129 8143 31430
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.000 8.000 8.000 7.788 8.000 8.000
##
## includes extended item information - examples:
## labels variables levels
## 1 hincfel=Coping hincfel Coping
## 2 hincfel=Difficult hincfel Difficult
## 3 hincfel=Living comfortably hincfel Living comfortably
##
## includes extended transaction information - examples:
## transactionID
## 1 1
## 2 2
## 3 3inspect(head(transactions, 5))## items transactionID
## [1] {hincfel=Living comfortably,
## uempla=0,
## uempli=0,
## gndr=Male,
## hinctnta=Upper-middle income,
## health=Fair,
## stflife=Satisfied,
## ppltrst=Moderate trust} 1
## [2] {hincfel=Coping,
## uempla=0,
## uempli=0,
## gndr=Female,
## hinctnta=Low income,
## health=Good,
## stflife=Satisfied,
## ppltrst=High trust} 2
## [3] {hincfel=Living comfortably,
## uempla=0,
## uempli=0,
## gndr=Female,
## hinctnta=Lower-middle income,
## health=Very good,
## stflife=Satisfied,
## ppltrst=Moderate trust} 3
## [4] {hincfel=Coping,
## uempla=0,
## uempli=0,
## gndr=Female,
## hinctnta=Low income,
## health=Fair,
## stflife=Satisfied,
## ppltrst=Moderate trust} 4
## [5] {hincfel=Coping,
## uempla=0,
## uempli=0,
## gndr=Male,
## health=Good,
## stflife=Satisfied,
## ppltrst=Moderate trust} 5The transactions dataset consists of 39705 transactions (respondents) and 25 unique items (categorical attributes).
The most frequent items in respondents’ data are:
- uempli=0 (wanting a job but not actively looking for a job) – 39003 occurrences
- uempla=0 (Unemployed and not actively looking for a job) – 38633 occurrences
- stflife=Satisfied (Satisfied with life) – 28437 occurrences
- gndr=Female – 21140 occurrences
- gndr=Male – 18565 occurrences
summary(data$hincfel)## Coping Difficult Living comfortably Very difficult
## 18108 5862 14309 1426The hincfel variable represents respondents’ perceptions of their household income, ranging from Living comfortably, Coping, Difficult, and Very difficult. The majority of respondents fall into the Coping category with 18108 responses, followed by Living comfortably with 14309 responses. A total of 5862 respondents reported experiencing financial difficulties, while only 1426 respondents indicated that they find it Very difficult to manage on their current income. This distribution suggests that while many respondents feel financially stable, a significant portion still faces financial struggles.
Applying the Apriori algorithm
First of all, we begin with creating rules for all categories of hincfel. We will extract rules that meet the minimum support of 4% and confidence of 40%. This ensures that the rule appears in at least 4% of all transactions and that the consequent product is strongly associated with the antecedent in 40% of relevant cases.
rules <- apriori(transactions,
parameter = list(supp = 0.04, conf = 0.4),
appearance = list(rhs= c("hincfel=Living comfortably", "hincfel=Coping", "hincfel=Difficult","hincfel=Very difficult"), default = "lhs"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.4 0.1 1 none FALSE TRUE 5 0.04 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 1588
##
## set item appearances ...[4 item(s)] done [0.00s].
## set transactions ...[25 item(s), 39705 transaction(s)] done [0.01s].
## sorting and recoding items ... [21 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 5 6 done [0.00s].
## writing ... [474 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(rules)## set of 474 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4 5 6
## 1 20 103 184 132 34
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 4.114 5.000 6.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.04020 Min. :0.4022 Min. :0.05516 Min. :0.8819
## 1st Qu.:0.05142 1st Qu.:0.4438 1st Qu.:0.10439 1st Qu.:1.0169
## Median :0.06352 Median :0.4771 Median :0.13406 Median :1.1377
## Mean :0.08594 Mean :0.4957 Mean :0.18080 Mean :1.2125
## 3rd Qu.:0.10228 3rd Qu.:0.5190 3rd Qu.:0.20965 3rd Qu.:1.3112
## Max. :0.45606 Max. :0.7708 Max. :1.00000 Max. :2.1387
## count
## Min. : 1596
## 1st Qu.: 2042
## Median : 2522
## Mean : 3412
## 3rd Qu.: 4061
## Max. :18108
##
## mining info:
## data ntransactions support confidence
## transactions 39705 0.04 0.4
## call
## apriori(data = transactions, parameter = list(supp = 0.04, conf = 0.4), appearance = list(rhs = c("hincfel=Living comfortably", "hincfel=Coping", "hincfel=Difficult", "hincfel=Very difficult"), default = "lhs"))plot(rules, measure = c("support", "confidence"), shading = "lift")
A total of 474 rules were generated, with support values ranging from 0.04 to 0.45 and confidence varying between 0.4 and 0.75. Lift values are mostly concentrated around 1, with a small cluster of stronger associations appearing in the top-left region.
plot(rules, method="graph")## Warning: Too many rules supplied. Only plotting the best 100 using 'lift'
## (change control parameter max if needed).
Hincfel Categories Analysis
rules_living_comfortably <- apriori(transactions,
parameter = list(supp = 0.04, conf = 0.4),
appearance = list(rhs= c("hincfel=Living comfortably"), default = "lhs"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.4 0.1 1 none FALSE TRUE 5 0.04 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 1588
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[25 item(s), 39705 transaction(s)] done [0.01s].
## sorting and recoding items ... [21 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 5 6 done [0.00s].
## writing ... [193 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].plot(rules_living_comfortably, measure = c("support", "confidence"), shading = "lift")
rules_living_comfortably_sorted<-sort(rules_living_comfortably, by="confidence", decreasing=TRUE)
inspect(head(rules_living_comfortably_sorted))## lhs rhs support confidence coverage lift count
## [1] {uempla=0,
## uempli=0,
## hinctnta=High income,
## stflife=Satisfied,
## ppltrst=High trust} => {hincfel=Living comfortably} 0.04606473 0.7707543 0.05976577 2.138710 1829
## [2] {uempli=0,
## hinctnta=High income,
## stflife=Satisfied,
## ppltrst=High trust} => {hincfel=Living comfortably} 0.04639214 0.7700669 0.06024430 2.136802 1842
## [3] {uempla=0,
## hinctnta=High income,
## stflife=Satisfied,
## ppltrst=High trust} => {hincfel=Living comfortably} 0.04626621 0.7699078 0.06009319 2.136361 1837
## [4] {hinctnta=High income,
## stflife=Satisfied,
## ppltrst=High trust} => {hincfel=Living comfortably} 0.04659363 0.7692308 0.06057172 2.134482 1850
## [5] {uempla=0,
## gndr=Male,
## hinctnta=High income,
## stflife=Satisfied} => {hincfel=Living comfortably} 0.04782773 0.7629570 0.06268732 2.117074 1899
## [6] {uempla=0,
## uempli=0,
## gndr=Male,
## hinctnta=High income,
## stflife=Satisfied} => {hincfel=Living comfortably} 0.04765143 0.7629032 0.06246065 2.116924 1892The rules indicate that individuals who feel financially comfortable are most often characterized by:
- high household income
- satisfaction with life
- high trust in people
- employment stability
rules_coping <- apriori(transactions,
parameter = list(supp = 0.04, conf = 0.4),
appearance = list(rhs= c("hincfel=Coping"), default = "lhs"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.4 0.1 1 none FALSE TRUE 5 0.04 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 1588
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[25 item(s), 39705 transaction(s)] done [0.01s].
## sorting and recoding items ... [21 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 5 6 done [0.00s].
## writing ... [281 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].plot(rules_coping, measure = c("support", "confidence"), shading = "lift")
rules_coping_sorted<-sort(rules_coping, by="confidence", decreasing=TRUE)
inspect(head(rules_coping_sorted))## lhs rhs support confidence coverage lift count
## [1] {uempla=0,
## hinctnta=Lower-middle income,
## health=Fair} => {hincfel=Coping} 0.04070016 0.5526676 0.07364312 1.211822 1616
## [2] {uempli=0,
## hinctnta=Lower-middle income,
## health=Fair} => {hincfel=Coping} 0.04070016 0.5517241 0.07376905 1.209753 1616
## [3] {hinctnta=Lower-middle income,
## health=Fair} => {hincfel=Coping} 0.04153129 0.5505843 0.07543131 1.207254 1649
## [4] {uempla=0,
## uempli=0,
## hinctnta=Lower-middle income,
## ppltrst=Moderate trust} => {hincfel=Coping} 0.05649163 0.5500245 0.10270747 1.206026 2243
## [5] {uempla=0,
## hinctnta=Lower-middle income,
## ppltrst=Moderate trust} => {hincfel=Coping} 0.05739831 0.5488921 0.10457121 1.203543 2279
## [6] {uempli=0,
## hinctnta=Lower-middle income,
## ppltrst=Moderate trust} => {hincfel=Coping} 0.05782647 0.5474487 0.10562901 1.200378 2296The rules indicate that individuals who feel coping financially are most often characterized by:
- lower-middle household income
- fair health
- moderate trust in people
- employment stability
rules_difficult <- apriori(transactions,
parameter = list(supp = 0.02, conf = 0.2),
appearance = list(rhs= c("hincfel=Difficult"), default = "lhs"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.2 0.1 1 none FALSE TRUE 5 0.02 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 794
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[25 item(s), 39705 transaction(s)] done [0.01s].
## sorting and recoding items ... [23 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 5 6 7 done [0.00s].
## writing ... [42 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].plot(rules_difficult, measure = c("support", "confidence"), shading = "lift")
rules_difficult_sorted<-sort(rules_difficult, by="confidence", decreasing=TRUE)
inspect(head(rules_difficult_sorted))## lhs rhs support confidence coverage lift count
## [1] {gndr=Female,
## hinctnta=Low income} => {hincfel=Difficult} 0.02647022 0.3223926 0.08210553 2.183657 1051
## [2] {uempla=0,
## gndr=Female,
## hinctnta=Low income} => {hincfel=Difficult} 0.02493389 0.3196642 0.07800025 2.165177 990
## [3] {uempli=0,
## gndr=Female,
## hinctnta=Low income} => {hincfel=Difficult} 0.02533686 0.3192637 0.07936028 2.162464 1006
## [4] {hinctnta=Low income} => {hincfel=Difficult} 0.04135499 0.3172334 0.13036142 2.148712 1642
## [5] {uempla=0,
## uempli=0,
## gndr=Female,
## hinctnta=Low income} => {hincfel=Difficult} 0.02382571 0.3164938 0.07528019 2.143703 946
## [6] {uempli=0,
## hinctnta=Low income} => {hincfel=Difficult} 0.03908828 0.3135354 0.12466944 2.123664 1552The rules indicate that individuals who feel financially struggling are most often characterized by:
- low household income
- being female
- not actively seeking employment
These factors suggest that women with low income are more likely to experience financial difficulties than men with low income.
rules_very_difficult <- apriori(transactions,
parameter = list(supp = 0.01, conf = 0.1),
appearance = list(rhs= c("hincfel=Very difficult"), default = "lhs"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.1 0.1 1 none FALSE TRUE 5 0.01 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 397
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[25 item(s), 39705 transaction(s)] done [0.01s].
## sorting and recoding items ... [25 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 5 6 7 8 done [0.00s].
## writing ... [6 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].plot(rules_very_difficult, measure = c("support", "confidence"), shading = "lift")
rules_very_difficult_sorted<-sort(rules_very_difficult, by="confidence", decreasing=TRUE)
inspect(head(rules_very_difficult_sorted))## lhs rhs support confidence coverage lift count
## [1] {gndr=Female,
## hinctnta=Low income} => {hincfel=Very difficult} 0.01077950 0.1312883 0.08210553 3.655543 428
## [2] {hinctnta=Low income} => {hincfel=Very difficult} 0.01687445 0.1294436 0.13036142 3.604178 670
## [3] {uempli=0,
## gndr=Female,
## hinctnta=Low income} => {hincfel=Very difficult} 0.01002393 0.1263091 0.07936028 3.516903 398
## [4] {uempli=0,
## hinctnta=Low income} => {hincfel=Very difficult} 0.01538849 0.1234343 0.12466944 3.436859 611
## [5] {uempla=0,
## hinctnta=Low income} => {hincfel=Very difficult} 0.01468329 0.1202062 0.12215086 3.346975 583
## [6] {uempla=0,
## uempli=0,
## hinctnta=Low income} => {hincfel=Very difficult} 0.01319733 0.1132483 0.11653444 3.153243 524This results are very similar to the results for individuals who feel financially struggling (Difficult).
Conclusions
The association rules reveal clear patterns in financial well-being, highlighting the influence of income, employment status, health, and social trust on individuals perceptions of their financial situation.
Individuals who feel financially comfortable are most often characterized by high income, life satisfaction and strong social trust.
Those who are coping financially typically have a lower-middle income, fair health and moderate social trust.
Individuals struggling financially (Difficult and Very difficult) are strongly linked to low income and not actively seeking employment.
The findings emphasize the role of economic stability, employment opportunities, health status and social trust in shaping financial well-being.