# Load the packages that you will need
require(foreign)
## Loading required package: foreign
require(dplyr)
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
require(ggvis)
## Loading required package: ggvis
require(magrittr)
## Loading required package: magrittr
# The packages must be installed, if not already
# installed, in your instance of R
# Read the SPSS .SAV file in to R
GSSdata <- read.spss("GSS.SAV",
max.value.labels=TRUE, to.data.frame=FALSE,
trim.factor.names=FALSE,
reencode=NA, use.missings=to.data.frame)
# Read the newly-read data from GSS.SAV
# into a data frame in R
GSSdata <- data.frame(GSSdata)
# Convert the data frame to a table frame through
# the dplyr function, tbl_df
GSSdata <- tbl_df(GSSdata)
head(GSSdata)
## Source: local data frame [6 x 1,061]
##
## YEAR ID WRKSTAT HRS1 HRS2 EVWORK WRKSLF WRKGOVT OCC10 INDUS10
## (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 2012 1 2 15 -1 0 2 2 4800 7470
## 2 2012 2 2 30 -1 0 2 2 2900 770
## 3 2012 3 1 60 -1 0 2 2 1320 6070
## 4 2012 4 8 -1 -1 1 2 2 800 6990
## 5 2012 5 5 -1 -1 1 2 1 3800 9470
## 6 2012 6 8 -1 -1 1 2 2 1300 7290
## Variables not shown: MARITAL (dbl), DIVORCE (dbl), WIDOWED (dbl), SPWRKSTA
## (dbl), SPHRS1 (dbl), SPHRS2 (dbl), SPEVWORK (dbl), SPWRKSLF (dbl),
## SPOCC10 (dbl), SPIND10 (dbl), PAWRKSLF (dbl), PAOCC10 (dbl), PAIND10
## (dbl), MAWRKSLF (dbl), MAOCC10 (dbl), MAIND10 (dbl), SIBS (dbl), CHILDS
## (dbl), AGE (dbl), AGEKDBRN (dbl), EDUC (dbl), PAEDUC (dbl), MAEDUC
## (dbl), SPEDUC (dbl), DEGREE (dbl), PADEG (dbl), MADEG (dbl), SPDEG
## (dbl), MAJOR1 (dbl), MAJOR2 (dbl), DIPGED (dbl), SPDIPGED (dbl), WHENHS
## (dbl), WHENCOL (dbl), SEX (dbl), RACE (dbl), RES16 (dbl), REG16 (dbl),
## MOBILE16 (dbl), FAMILY16 (dbl), FAMDIF16 (dbl), MAWRKGRW (dbl), INCOM16
## (dbl), BORN (dbl), PARBORN (dbl), GRANBORN (dbl), HOMPOP (dbl), BABIES
## (dbl), PRETEEN (dbl), TEENS (dbl), ADULTS (dbl), UNRELAT (dbl), EARNRS
## (dbl), INCOME (dbl), RINCOME (dbl), INCOME06 (dbl), RINCOM06 (dbl),
## REGION (dbl), XNORCSIZ (dbl), SRCBELT (dbl), SIZE (dbl), PARTYID (dbl),
## VOTE08 (dbl), PRES08 (dbl), IF08WHO (dbl), POLVIEWS (dbl), NATSPAC
## (dbl), NATENVIR (dbl), NATHEAL (dbl), NATCITY (dbl), NATCRIME (dbl),
## NATDRUG (dbl), NATEDUC (dbl), NATRACE (dbl), NATARMS (dbl), NATAID
## (dbl), NATFARE (dbl), NATROAD (dbl), NATSOC (dbl), NATMASS (dbl),
## NATPARK (dbl), NATCHLD (dbl), NATSCI (dbl), NATENRGY (dbl), NATSPACY
## (dbl), NATENVIY (dbl), NATHEALY (dbl), NATCITYY (dbl), NATCRIMY (dbl),
## NATDRUGY (dbl), NATEDUCY (dbl), NATRACEY (dbl), NATARMSY (dbl), NATAIDY
## (dbl), NATFAREY (dbl), EQWLTH (dbl), TAX (dbl), SPKATH (dbl), COLATH
## (dbl), LIBATH (dbl), SPKRAC (dbl), COLRAC (dbl), LIBRAC (dbl), SPKCOM
## (dbl), COLCOM (dbl), LIBCOM (dbl), SPKMIL (dbl), COLMIL (dbl), LIBMIL
## (dbl), SPKHOMO (dbl), COLHOMO (dbl), LIBHOMO (dbl), SPKMSLM (dbl),
## COLMSLM (dbl), LIBMSLM (dbl), CAPPUN (dbl), GUNLAW (dbl), COURTS (dbl),
## GRASS (dbl), RELIG (dbl), DENOM (dbl), OTHER (dbl), OTHJEW (dbl), JEW
## (dbl), JEWAJ (dbl), FUND (dbl), ATTEND (dbl), RELITEN (dbl), RELITENA
## (dbl), POSTLIFE (dbl), PRAY (dbl), POPESPKS (dbl), RELIG16 (dbl),
## DENOM16 (dbl), OTH16 (dbl), OTHJEW16 (dbl), JEW16 (dbl), JEW16AJ (dbl),
## FUND16 (dbl), SPREL (dbl), SPDEN (dbl), SPOTHER (dbl), SPOTHJEW (dbl),
## SPJEW (dbl), SPJEWAJ (dbl), SPFUND (dbl), SPREL16 (dbl), SPDEN16 (dbl),
## SPOTH16 (dbl), SPJEW16 (dbl), PRAYER (dbl), BIBLE (dbl), RACOPEN (dbl),
## RACLIVE (dbl), AFFRMACT (dbl), WRKWAYUP (dbl), CLOSEBLK (dbl), CLOSEWHT
## (dbl), HAPPY (dbl), HAPMAR (dbl), HAPCOHAB (dbl), HEALTH (dbl), LIFE
## (dbl), HELPFUL (dbl), FAIR (dbl), TRUST (dbl), CONFINAN (dbl), CONBUS
## (dbl), CONCLERG (dbl), CONEDUC (dbl), CONFED (dbl), CONLABOR (dbl),
## CONPRESS (dbl), CONMEDIC (dbl), CONTV (dbl), CONJUDGE (dbl), CONSCI
## (dbl), CONLEGIS (dbl), CONARMY (dbl), OBEY (dbl), POPULAR (dbl),
## THNKSELF (dbl), WORKHARD (dbl), HELPOTH (dbl), SOCREL (dbl), SOCOMMUN
## (dbl), SOCFREND (dbl), SOCBAR (dbl), AGED (dbl), WEEKSWRK (dbl),
## PARTFULL (dbl), JOBLOSE (dbl), JOBFIND (dbl), SATJOB (dbl), RICHWORK
## (dbl), JOBINC (dbl), JOBSEC (dbl), JOBHOUR (dbl), JOBPROMO (dbl),
## JOBMEANS (dbl), CLASS (dbl), RANK (dbl), SATFIN (dbl), FINALTER (dbl),
## FINRELA (dbl), WKSUB (dbl), WKSUBS (dbl), WKSUP (dbl), WKSUPS (dbl),
## UNEMP (dbl), UNION (dbl), GETAHEAD (dbl), PARSOL (dbl), KIDSSOL (dbl),
## FEPOL (dbl), ABDEFECT (dbl), ABNOMORE (dbl), ABHLTH (dbl), ABPOOR (dbl),
## ABRAPE (dbl), ABSINGLE (dbl), ABANY (dbl), CHLDIDEL (dbl), PILLOK (dbl),
## SEXEDUC (dbl), DIVLAW (dbl), PREMARSX (dbl), TEENSEX (dbl), XMARSEX
## (dbl), HOMOSEX (dbl), PORNLAW (dbl), XMOVIE (dbl), SPANKING (dbl),
## LETDIE1 (dbl), SUICIDE1 (dbl), SUICIDE2 (dbl), SUICIDE3 (dbl), SUICIDE4
## (dbl), POLHITOK (dbl), POLABUSE (dbl), POLMURDR (dbl), POLESCAP (dbl),
## POLATTAK (dbl), FEAR (dbl), OWNGUN (dbl), PISTOL (dbl), SHOTGUN (dbl),
## RIFLE (dbl), ROWNGUN (dbl), TICKET (dbl), ARREST (dbl), CONVICTD (dbl),
## LOCKEDUP (dbl), HUNT (dbl), NEWS (dbl), TVHOURS (dbl), PHONE (dbl), COOP
## (dbl), COMPREND (dbl), FORM (dbl), FECHLD (dbl), FEPRESCH (dbl), FEFAM
## (dbl), RACDIF1 (dbl), RACDIF2 (dbl), RACDIF3 (dbl), RACDIF4 (dbl),
## HELPPOOR (dbl), HELPNOT (dbl), HELPSICK (dbl), HELPBLK (dbl), MARELIG
## (dbl), MAJEW (dbl), PARELIG (dbl), PAJEW (dbl), GOD (dbl), REBORN (dbl),
## SAVESOUL (dbl), WLTHWHTS (dbl), WLTHBLKS (dbl), WORKWHTS (dbl), WORKBLKS
## (dbl), INTLWHTS (dbl), INTLBLKS (dbl), LIVEBLKS (dbl), LIVEWHTS (dbl),
## MARBLK (dbl), MARASIAN (dbl), MARHISP (dbl), MARWHT (dbl), RACWORK
## (dbl), DISCAFF (dbl), FEJOBAFF (dbl), DISCAFFM (dbl), DISCAFFW (dbl),
## FEHIRE (dbl), RELPERSN (dbl), SPRTPRSN (dbl), OTHLANG (dbl), OTHLANG1
## (dbl), OTHLANG2 (dbl), SPKLANG (dbl), BETRLANG (dbl), COMPUSE (dbl),
## WEBMOB (dbl), EMAILMIN (dbl), EMAILHR (dbl), USEWWW (dbl), WWWHR (dbl),
## WWWMIN (dbl), WKVSFAM (dbl), SUPCARES (dbl), JOBSECOK (dbl), TRYNEWJB
## (dbl), WKAGEISM (dbl), WKRACISM (dbl), HEALTH1 (dbl), MNTLHLTH (dbl),
## GIVBLOOD (dbl), GIVHMLSS (dbl), RETCHNGE (dbl), CUTAHEAD (dbl), VOLCHRTY
## (dbl), GIVCHRTY (dbl), GIVSEAT (dbl), HELPAWAY (dbl), CARRIED (dbl),
## DIRECTNS (dbl), LOANITEM (dbl), SELFLESS (dbl), ACCPTOTH (dbl), OTHSHELP
## (dbl), CARESELF (dbl), PEOPTRBL (dbl), SELFFRST (dbl), VOLMONTH (dbl),
## DIFSTAND (dbl), OTHCREDT (dbl), PUTDOWN (dbl), LACKINFO (dbl), ACTUPSET
## (dbl), SHOUT (dbl), TREATRES (dbl), LOOKAWAY (dbl), WKSTRESS (dbl),
## NUMEMPS (dbl), IGNORWK (dbl), RUMORWK (dbl), JOKESWK (dbl), EHARASWK
## (dbl), RUDEWK (dbl), LIEDCWKR (dbl), DENYRAIS (dbl), WKBHVRS (dbl),
## WKRSPNS (dbl), DRINK4 (dbl), NEWSFROM (dbl), SCIFROM (dbl), SEEKSCI
## (dbl), NEXTGEN (dbl), TOOFAST (dbl), ADVFRONT (dbl), ASTROLGY (dbl),
## ASTROSCI (dbl), SCIBNFTS (dbl), BALPOS (dbl), BALNEG (dbl), SCISTUDY
## (dbl), SCITEXT (dbl), EXPDESGN (dbl), EXPTEXT (dbl), ODDS1 (dbl), ODDS2
## (dbl), HOTCORE (dbl), RADIOACT (dbl), BOYORGRL (dbl), LASERS (dbl),
## ELECTRON (dbl), VIRUSES (dbl), BIGBANG (dbl), CONDRIFT (dbl), EVOLVED
## (dbl), EARTHSUN (dbl), SOLARREV (dbl), INTRHOME (dbl), COLDEG1 (dbl),
## MAJORCOL (dbl), COLSCI (dbl), COLSCINM (dbl), HSMATH (dbl), HSBIO (dbl),
## HSCHEM (dbl), HSPHYS (dbl), SOCSCI (dbl), PHYSCSCI (dbl), HISTSCI (dbl),
## ACCNTSCI (dbl), BIOSCI (dbl), ECONSCI (dbl), MEDSCI (dbl), ENGNRSCI
## (dbl), INTINTL (dbl), INTFARM (dbl), INTEDUC (dbl), INTSCI (dbl),
## INTECON (dbl), INTTECH (dbl), INTMED (dbl), INTSPACE (dbl), INTENVIR
## (dbl), INTMIL (dbl), VISART (dbl), VISNHIST (dbl), VISZOO (dbl), VISSCI
## (dbl), VISLIB (dbl), SCINEWS1 (dbl), SCINEWS2 (dbl), SCINEWS3 (dbl),
## SCIENTDA (dbl), SCIENTSN (dbl), SCIENTR (dbl), SCIENTDO (dbl), SCIENTAL
## (dbl), SCIENTDN (dbl), SCIENTGO (dbl), SCIENTFU (dbl), SCIENTHE (dbl),
## SCIENTOD (dbl), SCIENTBE (dbl), SCIENTRE (dbl), SCIENTWK (dbl), SCIENTMO
## (dbl), SCIENTBR (dbl), ENGDA (dbl), ENGSON (dbl), ENGRESP (dbl), ENGDO
## (dbl), ENGLONE (dbl), ENGDGR (dbl), ENGGOOD (dbl), ENGFUN (dbl), ENGPROB
## (dbl), ENGODD (dbl), ENGBTR (dbl), ENGREL (dbl), ENGINT (dbl), ENGEARN
## (dbl), ENGBRNG (dbl), FARMING (dbl), JOURNLSM (dbl), FIREFTNG (dbl),
## MARRCOUN (dbl), MEDTREAT (dbl), ARCHITCT (dbl), LAWENFRC (dbl), ENGNRING
## (dbl), SLSMNSHP (dbl), CMPRGMNG (dbl), FINLCOUN (dbl), BIGBANG1 (dbl),
## EVOLVED1 (dbl), SPJOTH16 (dbl), MAJWOTH (dbl), PAJWOTH (dbl), BMITZVAH
## (dbl), SYNMEM (dbl), KD1RELIG (dbl), KD2RELIG (dbl), KD3RELIG (dbl),
## KD4RELIG (dbl), KD5RELIG (fctr), KD6RELIG (fctr), KD7RELIG (fctr),
## KD8RELIG (fctr), KD1JWOTH (dbl), KD2JWOTH (dbl), KD3JWOTH (fctr),
## KD4JWOTH (fctr), KD5JWOTH (fctr), SEXSEX18 (dbl), TOLDWORK (dbl),
## EVLOSEJB (dbl), LOSEJOB5 (dbl), EVNEGJOB (dbl), NEGJOB5 (dbl), EVHARJB
## (dbl), HARJOB5 (dbl), EVDWELL (dbl), DWELL5 (dbl), PRESPOP (dbl),
## VOLACTYR (dbl), VOLACTY2 (dbl), POLEFY3 (dbl), POLEFY11 (dbl), POLEFY13
## (dbl), POLEFY15 (dbl), POLEFY16 (dbl), POLEFY17 (dbl), RATETONE (dbl),
## POLEFF3 (dbl), POLEFF11 (dbl), POLEFF13 (dbl), POLEFF15 (dbl), POLEFF16
## (dbl), POLEFF17 (dbl), POSSLQ (dbl), POSSLQY (dbl), KIDNUM (dbl),
## HELPHWRK (dbl), LENTTO (dbl), TALKEDTO (dbl), HELPJOB (dbl), GOODLIFE
## (dbl), MAWRKWRM (dbl), KIDSUFFR (dbl), FAMSUFFR (dbl), HOMEKID (dbl),
## HOUSEWRK (dbl), WRKBABY (dbl), WRKSCH (dbl), MARHAPPY (dbl), MARLEGIT
## (dbl), MARHOMO (dbl), NUMKIDS (dbl), KIDJOY (dbl), KIDNOFRE (dbl),
## MAWORK14 (dbl), HUBBYWK1 (dbl), MEOVRWRK (dbl), SINGLPAR (dbl), COHABOK
## (dbl), DIVBEST (dbl), FAMBUDGT (dbl), LAUNDRY1 (dbl), REPAIRS1 (dbl),
## CARESIK1 (dbl), SHOP1 (dbl), COOKING1 (dbl), RHHWORK (dbl), SPHHWORK
## (dbl), HHWKFAIR (dbl), DECKIDS (dbl), HAPPY7 (dbl), SATJOB7 (dbl),
## SATFAM7 (dbl), TWOINCS1 (dbl), EARNSHH (dbl), SSFCHILD (dbl), SSMCHILD
## (dbl), KIDFINBU (dbl), KIDJOB (dbl), KIDSOCST (dbl), ELDERSUP (dbl),
## PAIDLV (dbl), PAIDLV1 (dbl), PAIDLVPY (dbl), PAIDLVDV (dbl), FAMWKBST
## (dbl), FAMWKLST (dbl), CAREPROV (dbl), CARECOST (dbl), ELDHELP (dbl),
## ELDCOST (dbl), HHCLEAN1 (dbl), WKNDACT (dbl), TIREDHM1 (dbl), JOBVSFA1
## (dbl), TIREDWK1 (dbl), FAMVSWK1 (dbl), WKKIDSCL (dbl), WKYNGSCL (dbl),
## WKKIDSCS (dbl), WKYNGSCS (dbl), SPFALOOK (dbl), SPLIVE (dbl), BOSSEMPS
## (dbl), LOCALNUM (dbl), RELACTIV (dbl), LETIN1 (dbl), PARTNERS (dbl),
## MATESEX (dbl), FRNDSEX (dbl), ACQNTSEX (dbl), PIKUPSEX (dbl), PAIDSEX
## (dbl), OTHERSEX (dbl), SEXSEX (dbl), SEXFREQ (dbl), NUMWOMEN (dbl),
## NUMMEN (dbl), PARTNRS5 (dbl), SEXSEX5 (dbl), EVPAIDSX (dbl), EVSTRAY
## (dbl), CONDOM (dbl), RELATSEX (dbl), EVIDU (dbl), IDU30 (dbl), EVCRACK
## (dbl), CRACK30 (dbl), HIVTEST (dbl), HIVTEST1 (dbl), HIVTEST2 (dbl),
## SEXORNT (dbl), REALINC (dbl), REALRINC (dbl), ETHNIC (dbl), ETH1 (dbl),
## ETH2 (dbl), ETH3 (dbl), ETHNUM (dbl), HISPANIC (dbl), RACECEN1 (dbl),
## RACECEN2 (dbl), RACECEN3 (dbl), USCITZN (dbl), FUCITZN (dbl), VETYEARS
## (dbl), DWELLING (dbl), DWELOWN (dbl), WORDA (dbl), WORDB (dbl), WORDC
## (dbl), WORDD (dbl), WORDE (dbl), WORDF (dbl), WORDG (dbl), WORDH (dbl),
## WORDI (dbl), WORDJ (dbl), WORDSUM (dbl), RELATE1 (fctr), GENDER1 (dbl),
## OLD1 (dbl), MAR1 (dbl), AWAY1 (dbl), WHERE1 (dbl), RELATE2 (dbl),
## GENDER2 (dbl), OLD2 (dbl), MAR2 (dbl), AWAY2 (dbl), WHERE2 (dbl),
## RELATE3 (dbl), GENDER3 (dbl), OLD3 (dbl), MAR3 (dbl), AWAY3 (dbl),
## WHERE3 (dbl), RELATE4 (dbl), GENDER4 (dbl), OLD4 (dbl), MAR4 (dbl),
## AWAY4 (dbl), WHERE4 (dbl), RELATE5 (dbl), GENDER5 (dbl), OLD5 (dbl),
## MAR5 (dbl), AWAY5 (dbl), WHERE5 (dbl), RELATE6 (dbl), GENDER6 (dbl),
## OLD6 (dbl), MAR6 (dbl), AWAY6 (dbl), WHERE6 (dbl), RELATE7 (dbl),
## GENDER7 (dbl), OLD7 (dbl), MAR7 (dbl), AWAY7 (dbl), WHERE7 (dbl),
## RELATE8 (dbl), GENDER8 (dbl), OLD8 (dbl), MAR8 (dbl), AWAY8 (dbl),
## WHERE8 (dbl), RELATE9 (dbl), GENDER9 (dbl), OLD9 (dbl), MAR9 (dbl),
## AWAY9 (fctr), WHERE9 (fctr), RELATE10 (dbl), GENDER10 (dbl), OLD10
## (dbl), MAR10 (dbl), AWAY10 (fctr), WHERE10 (fctr), RELATE11 (dbl),
## GENDER11 (dbl), OLD11 (dbl), MAR11 (dbl), AWAY11 (dbl), WHERE11 (dbl),
## RELATE12 (dbl), GENDER12 (dbl), OLD12 (dbl), MAR12 (dbl), AWAY12 (dbl),
## WHERE12 (dbl), RELATE13 (dbl), GENDER13 (dbl), OLD13 (dbl), MAR13 (dbl),
## AWAY13 (fctr), WHERE13 (fctr), RELATE14 (dbl), GENDER14 (dbl), OLD14
## (dbl), MAR14 (dbl), AWAY14 (fctr), WHERE14 (fctr), RELHHD1 (fctr),
## RELHHD2 (dbl), RELHHD3 (dbl), RELHHD4 (dbl), RELHHD5 (dbl), RELHHD6
## (dbl), RELHHD7 (dbl), RELHHD8 (dbl), RELHHD9 (dbl), RELHHD10 (dbl),
## RELHHD11 (dbl), RELHHD12 (dbl), RELHHD13 (dbl), RELHHD14 (dbl), HEFINFO
## (dbl), HHRACE (dbl), RESPNUM (dbl), HHTYPE (dbl), HHTYPE1 (dbl), FAMGEN
## (dbl), RPLACE (dbl), RVISITOR (dbl), VISITORS (dbl), RELHH1 (fctr),
## RELHH2 (dbl), RELHH3 (dbl), RELHH4 (dbl), RELHH5 (dbl), RELHH6 (dbl),
## RELHH7 (dbl), RELHH8 (dbl), RELHH9 (dbl), RELHH10 (dbl), RELHH11 (dbl),
## RELHH12 (dbl), RELHH13 (dbl), RELHH14 (dbl), RELSP1 (dbl), RELSP2 (dbl),
## RELSP3 (dbl), RELSP4 (dbl), RELSP5 (dbl), RELSP6 (dbl), RELSP7 (dbl),
## RELSP8 (dbl), RELSP9 (dbl), RELSP10 (dbl), RELSP11 (dbl), RELSP12 (dbl),
## RELSP13 (dbl), RELSP14 (dbl), DATEINTV (dbl), ISCO88 (dbl), PAISCO88
## (dbl), MAISCO88 (dbl), SPISCO88 (dbl), USWAR (dbl), USWARY (dbl), COHORT
## (dbl), ZODIAC (dbl), INTHISP (dbl), INTRACE1 (dbl), INTRACE2 (dbl),
## INTRACE3 (dbl), CONINC (dbl), CONRINC (dbl), WHOELSE1 (dbl), WHOELSE2
## (dbl), WHOELSE3 (dbl), WHOELSE4 (dbl), WHOELSE5 (dbl), WHOELSE6 (dbl),
## INTID (dbl), FEEUSED (dbl), FEELEVEL (dbl), LNGTHINV (dbl), INTAGE
## (dbl), INTETHN (dbl), MODE (dbl), INTSEX (dbl), INTYRS (dbl), CONSENT
## (dbl), BALLOT (dbl), VERSION (dbl), ISSP (fctr), FORMWT (dbl), SAMPCODE
## (dbl), SAMPLE (dbl), OVERSAMP (dbl), PHASE (dbl), SPANSELF (dbl),
## SPANINT (dbl), SPANENG (dbl), RES2008 (dbl), RES2010 (dbl), CSHTYP08
## (dbl), CSHTYP10 (dbl), WTSS (dbl), WTSSNR (dbl), WTSSALL (dbl), VSTRAT
## (fctr), VPSU (fctr), WTCOMB (dbl), WTCOMBNR (dbl), SAMPTYPE (dbl),
## RACDIFY (dbl), RACDIF5 (dbl), RDSCRPT (dbl), RDSCMOST (dbl), RDSCINT
## (dbl), RDSCEDEV (dbl), RDSCMEM (dbl), RDSCLRN (dbl), RDSCPER (dbl),
## RDSCTCH (dbl), RDSCDEC (dbl), RDSCWLTH (dbl), RDSCHLTH (dbl), RDSCFUT
## (dbl), RDSCISS1 (dbl), RDSCISS2 (dbl), RDSCORG (dbl), RDSCOWN (dbl),
## RDSCUND (dbl), RDSCCLGY (dbl), RDSCLDR (dbl), RDSCBK (dbl), RDSCWWW
## (dbl), RDSCTV (dbl), RDSCBBL (dbl), BBLFAV (dbl), BBLFAV1 (dbl), BBLSTRY
## (dbl), CLMTKNOW (dbl), CLMTCHNG (dbl), VALORIG (dbl), VALRICH (dbl),
## VALEQL (dbl), VALABLE (dbl), VALSAFE (dbl), VALDIFF (dbl), VALRULE
## (dbl), VALLIST (dbl), VALMOD (dbl), VALSPL (dbl), VALFREE (dbl), VALCARE
## (dbl), VALACHV (dbl), VALDFND (dbl), VALRISK (dbl), VALPRPR (dbl),
## VALRSPT (dbl), VALDVOT (dbl), VALECO (dbl), VALTRDN (dbl), VALFUN (dbl),
## REFBNS (dbl), REFER12 (dbl), REFCNT12 (dbl), REFOCC10 (dbl), WINFIRMR
## (dbl), REFAPPLY (dbl), REFHSEX (dbl), REFHRACE (dbl), REFHAGE (dbl),
## PRFMCE (dbl), ARTEXBT (dbl), PRFMMUS (dbl), PRFMDAN (dbl), PRFMTHE
## (dbl), PRFMATT (dbl), PRFMATT1 (dbl), PRFMATT2 (dbl), PRFMATT3 (dbl),
## PRFMATT4 (dbl), PRFMATT5 (dbl), PRFMFREE (dbl), PRFMWHY (dbl), PRFMWHY1
## (dbl), PRFMWHY2 (dbl), PRFMWHY3 (dbl), PRFMWHY4 (dbl), PRFMWHY5 (dbl),
## PRFMWHY6 (dbl), PRFMWHY7 (dbl), PRFMWHY8 (dbl), ARTATT (dbl), ARTATT1
## (dbl), ARTATT2 (dbl), ARTATT3 (dbl), ARTATT4 (dbl), ARTATT5 (dbl),
## ARTFREE (dbl), ARTWHY1 (dbl), ARTWHY2 (dbl), ARTWHY3 (dbl), ARTWHY4
## (dbl), ARTWHY5 (dbl), ARTWHY6 (dbl), ARTWHY7 (dbl), ARTWHY8 (dbl),
## ARTNOGO (dbl), ARTNOGO1 (dbl), PRFMCOST (dbl), PRFMINT (dbl), PRFMTRVL
## (dbl), PRFMGST (dbl), PRFMTIME (dbl), PRFMLOC (dbl), PRFMOTHR (dbl),
## ARTCOST (dbl), ARTINT (dbl), ARTTRVL (dbl), ARTGST (dbl), ARTTIME (dbl),
## ARTLOC (dbl), ARTOTHR (dbl), REFRTWHY (dbl), YOUADULT (dbl), CONHLTH
## (dbl), HLTHCHNG (dbl), EDUCBTR (dbl), HLTHBTR (dbl), HLTHIMP (dbl),
## HLTHMORE (dbl), HLTHGOV (dbl), HLTHINF (dbl), HLTHTAX (dbl), FNDMEDCH
## (dbl), FNDAIDS (dbl), FNDOBSTY (dbl), FNDORGN (dbl), HLTHCTZN (dbl),
## HLTHDMG (dbl), HLTHNEED (dbl), HLTHBEH (dbl), HLTHENV (dbl), HLTHGENE
## (dbl), HLTHPOOR (dbl), HRTOP (dbl), HRTOP37 (dbl), HRTOPKID (dbl),
## ALTMED (dbl), ALTMEDPR (dbl), DOCTRST (dbl), DOCTLK (dbl), DOCSKLS
## (dbl), DOCEARN (dbl), DOCMSTK (dbl), HLTHPRB (dbl), HLTHPAIN (dbl),
## HLTHDEP (dbl), HLTHCONF (dbl), HLTHNOT (dbl), DOCVST (dbl), DOCALT
## (dbl), HSPOVRNT (dbl), MEDPAY (dbl), MEDCOMMT (dbl), MEDUNAV (dbl),
## MEDWTLST (dbl), MEDBEST (dbl), MEDDRCH (dbl), HLTHSAT (dbl), DOCVISIT
## (dbl), ALTSAT (dbl), HOSPSAT (dbl), SMOKEDAY (dbl), PHYSACT (dbl),
## FRTVEGS (dbl), DISBLTY (dbl), RHEIGHT (dbl), RWEIGHT (dbl), INSTYPE
## (dbl), INSCOVRG (dbl), VETFAM (dbl), CONSCHLS (dbl), GOTTHNGS (dbl),
## BUSGRN (dbl), PEOPGRN (dbl), WORKHSPS (dbl), WORK10 (dbl), WLTHHSPS
## (dbl), TOODIFME (dbl), SATLIFE (dbl), PHYSHLTH (dbl), FININD (dbl),
## FININD1 (dbl), OWNHH (dbl), OWNHH1 (dbl), EDDONE (dbl), EDDONE1 (dbl),
## FTWORK (dbl), FTWORK1 (dbl), SUPFAM (dbl), SUPFAM1 (dbl), LAW5 (dbl),
## KNWCAUSE (dbl), KNOWSOL (dbl), INTLHSPS (dbl), INFOBIZ (dbl), INFOGRN
## (dbl), INFOGOVT (dbl), INFONEWS (dbl), INFOTV (dbl), INFOCOL (dbl),
## INEQUAL3 (dbl), INEQUAL5 (dbl), INCGAP (dbl), IHLPGRN (dbl), IDEALLFE
## (dbl), CONEXCEL (dbl), HLTHENGY (dbl), HRDSHP1 (dbl), HRDSHP6 (dbl),
## HLTH10 (dbl), HLTH11 (dbl), HAVCHLD (dbl), HAVCHLD1 (dbl), GETMAR (dbl),
## GETMAR1 (dbl), HAPORNOT (dbl), GRNSIGN (dbl), GRNMONEY (dbl), GRNDEMO
## (dbl), GRNCON (dbl), FINAN4 (dbl), ENPRBUS (dbl), ENPRBFAM (dbl), I_AGE
## (dbl), I_ATTEND (dbl), I_POLITICS (dbl), I_RACE (dbl), I_RELIGION (dbl),
## I_SEX (dbl)
# Just to be sure you have the data read correctly,
# run some descriptive statistics (mean, standard
# deviation, and count of cases) for an important
# variable we will use, HRS1:
GSSdata %>%
summarise(mean_hours=mean(HRS1), sd_hours=sd(HRS1), n=n())
## Source: local data frame [1 x 3]
##
## mean_hours sd_hours n
## (dbl) (dbl) (int)
## 1 24.07573 24.19891 4820
na.omit(GSSdata$HRS1)
## [1] 15 30 60 -1 -1 -1 -1 -1 -1 40 -1 20 -1 -1 32 53 60 40 40 -1 -1 12 40
## [24] 40 -1 75 40 -1 -1 20 -1 40 -1 40 40 47 40 40 30 40 40 -1 55 60 55 -1
## [47] -1 40 -1 -1 50 36 -1 45 40 -1 36 -1 -1 99 -1 -1 60 40 -1 -1 40 40 35
## [70] 40 50 -1 27 60 50 -1 21 -1 40 8 -1 40 55 40 50 40 -1 40 -1 60 -1 40
## [93] 7 -1 -1 15 50 -1 35 25 -1 -1 -1 80 -1 45 30 -1 -1 47 40 35 40 20 46
## [116] 38 -1 40 -1 -1 -1 48 60 40 -1 33 45 48 40 32 -1 46 -1 40 40 86 -1 -1
## [139] 40 40 25 5 -1 15 -1 -1 -1 40 40 40 30 -1 60 40 24 -1 -1 -1 41 40 -1
## [162] 45 40 60 40 -1 -1 40 20 43 -1 20 -1 -1 -1 57 16 -1 40 50 -1 40 60 -1
## [185] -1 45 40 40 40 28 -1 45 -1 50 -1 -1 -1 60 -1 40 8 30 -1 -1 45 30 40
## [208] -1 30 89 40 56 40 -1 36 45 -1 -1 20 -1 40 -1 -1 24 18 55 -1 -1 40 -1
## [231] 45 -1 -1 32 30 -1 35 50 -1 -1 65 50 99 55 -1 -1 30 -1 40 40 24 -1 40
## [254] 60 40 -1 40 38 -1 -1 -1 46 -1 35 -1 60 -1 -1 -1 40 40 45 49 -1 -1 -1
## [277] -1 55 40 60 9 70 89 -1 40 40 16 -1 40 50 42 -1 -1 -1 70 45 -1 10 40
## [300] 32 46 -1 -1 44 40 -1 60 -1 -1 40 39 -1 40 45 40 25 9 35 55 40 -1 40
## [323] 40 60 99 -1 38 85 50 8 40 72 -1 -1 48 -1 -1 50 -1 25 20 -1 -1 40 -1
## [346] 30 55 9 34 40 32 -1 -1 50 41 -1 20 37 60 -1 -1 -1 -1 40 -1 -1 -1 64
## [369] 50 -1 -1 -1 -1 60 40 -1 45 5 20 44 45 9 30 -1 40 99 -1 38 -1 -1 -1
## [392] 40 40 32 60 45 42 25 -1 50 55 45 -1 -1 40 -1 -1 50 -1 -1 20 50 12 32
## [415] -1 16 -1 9 27 40 40 -1 33 60 70 -1 -1 40 50 -1 -1 45 -1 65 50 80 55
## [438] -1 -1 -1 -1 -1 40 40 45 50 40 35 -1 -1 40 40 40 40 38 -1 12 40 40 15
## [461] 55 -1 41 45 -1 40 60 40 55 40 -1 50 16 38 -1 20 12 42 18 45 35 -1 40
## [484] -1 -1 -1 30 40 40 48 -1 40 -1 -1 60 80 48 62 48 40 -1 60 99 40 45 9
## [507] -1 60 40 -1 24 45 -1 -1 45 -1 30 8 16 -1 40 -1 -1 30 20 40 -1 20 -1
## [530] 55 70 50 35 -1 -1 60 40 20 24 -1 -1 -1 40 35 40 35 -1 40 40 32 -1 30
## [553] 45 -1 -1 -1 30 -1 40 40 -1 -1 48 89 40 36 40 -1 25 -1 40 9 -1 50 -1
## [576] -1 -1 -1 -1 -1 40 -1 57 70 80 50 40 40 -1 -1 44 47 37 50 59 -1 75 50
## [599] 42 9 40 55 -1 20 40 60 35 46 -1 24 -1 46 40 -1 24 40 -1 -1 35 -1 30
## [622] 10 45 -1 -1 -1 4 -1 22 30 60 -1 -1 -1 25 42 -1 40 40 50 -1 -1 50 43
## [645] 70 -1 36 9 -1 38 60 40 16 -1 42 45 -1 42 40 24 -1 80 18 40 19 -1 -1
## [668] -1 45 9 -1 48 40 50 42 -1 -1 -1 50 20 40 32 50 -1 60 -1 40 -1 89 -1
## [691] 42 50 60 -1 -1 60 45 9 16 40 -1 -1 -1 -1 35 48 -1 52 -1 -1 -1 41 40
## [714] 34 -1 -1 -1 40 30 -1 -1 -1 40 4 45 9 40 8 45 -1 -1 -1 -1 35 -1 -1
## [737] -1 30 46 45 20 40 -1 42 20 -1 60 36 32 40 -1 8 -1 65 -1 -1 35 -1 36
## [760] 36 80 40 32 -1 40 -1 60 70 70 48 20 30 37 9 30 42 40 39 -1 60 37 50
## [783] -1 48 80 -1 40 -1 -1 40 -1 50 9 -1 38 -1 40 20 -1 20 40 -1 -1 -1 40
## [806] -1 -1 -1 -1 60 44 -1 20 -1 50 50 -1 55 -1 -1 40 40 -1 -1 -1 50 60 -1
## [829] 35 46 -1 40 16 -1 9 43 40 20 -1 50 -1 35 -1 40 -1 40 40 9 70 -1 40
## [852] 40 52 40 -1 -1 30 40 -1 -1 -1 48 -1 -1 30 -1 40 40 20 57 16 9 30 40
## [875] -1 20 -1 40 -1 -1 40 40 39 40 40 34 40 55 40 -1 10 40 50 45 60 40 -1
## [898] -1 -1 47 49 -1 40 60 99 49 9 40 -1 44 -1 30 48 40 52 -1 15 -1 -1 -1
## [921] 35 -1 40 -1 9 45 38 32 -1 10 60 50 43 40 40 12 -1 40 -1 41 55 60 21
## [944] 40 -1 4 50 50 -1 -1 -1 40 -1 40 -1 32 43 -1 -1 -1 35 4 15 50 55 8
## [967] -1 39 37 -1 40 -1 40 20 60 5 40 20 -1 -1 60 68 40 45 45 -1 45 40 -1
## [990] -1 -1 40 70 60 -1 50 -1 50 -1 40 50 -1 36 42 -1 36 20 25 9 12 55 40
## [1013] -1 -1 59 40 40 32 -1 -1 50 -1 -1 65 40 52 50 38 -1 -1 9 45 35 30 -1
## [1036] 40 60 40 -1 9 40 -1 9 -1 -1 9 4 40 -1 -1 40 -1 -1 -1 20 -1 55 45
## [1059] 22 15 52 60 -1 8 16 24 40 -1 40 16 9 -1 -1 -1 -1 45 50 50 -1 70 35
## [1082] 40 9 52 25 20 17 35 -1 40 -1 -1 -1 24 -1 40 20 -1 -1 -1 30 40 68 20
## [1105] 42 -1 20 -1 -1 -1 -1 32 48 60 -1 25 32 -1 40 65 32 -1 -1 -1 40 -1 -1
## [1128] 65 -1 40 -1 -1 40 32 56 -1 48 -1 -1 40 40 40 -1 40 40 -1 -1 37 -1 -1
## [1151] 43 21 -1 -1 -1 32 40 37 15 -1 99 -1 -1 -1 -1 -1 99 99 40 -1 -1 -1 -1
## [1174] 50 50 40 -1 -1 -1 -1 15 -1 -1 -1 50 -1 -1 53 40 55 -1 40 32 40 20 -1
## [1197] 44 -1 -1 -1 89 -1 53 -1 44 9 40 32 -1 9 55 50 35 -1 9 25 -1 35 33
## [1220] -1 -1 -1 -1 70 -1 -1 58 -1 36 -1 50 40 -1 40 37 -1 9 40 20 -1 -1 -1
## [1243] 45 -1 -1 -1 68 -1 -1 -1 40 40 60 38 -1 36 25 40 -1 -1 -1 -1 30 40 -1
## [1266] 50 -1 -1 50 -1 -1 40 -1 9 60 -1 50 -1 20 30 40 40 63 -1 48 -1 40 -1
## [1289] -1 -1 70 -1 60 38 -1 37 60 48 -1 50 40 55 -1 -1 40 -1 -1 -1 40 55 -1
## [1312] -1 40 40 -1 -1 -1 -1 50 40 42 -1 44 -1 40 -1 70 44 -1 40 50 65 -1 19
## [1335] 40 50 -1 50 -1 -1 -1 -1 -1 -1 -1 30 -1 -1 -1 -1 60 -1 40 -1 40 50 58
## [1358] 42 -1 40 -1 -1 -1 -1 46 -1 10 40 -1 60 -1 15 -1 64 -1 -1 38 44 40 -1
## [1381] 40 1 40 37 40 -1 30 -1 -1 35 55 -1 -1 60 55 -1 -1 -1 40 -1 -1 45 55
## [1404] 40 65 -1 35 -1 46 40 -1 24 40 47 -1 60 40 40 -1 -1 -1 -1 65 -1 20 -1
## [1427] -1 -1 -1 -1 -1 -1 -1 36 50 44 45 -1 62 -1 53 58 -1 18 -1 43 -1 30 45
## [1450] 9 -1 -1 55 45 40 48 40 80 40 40 52 38 -1 40 45 46 40 40 44 -1 65 40
## [1473] 9 40 40 40 -1 40 40 40 70 15 40 60 9 -1 -1 48 25 40 42 45 40 -1 77
## [1496] 80 -1 -1 47 46 -1 40 55 38 20 -1 40 -1 40 40 80 38 40 40 40 -1 40 -1
## [1519] 40 -1 60 20 -1 -1 -1 4 -1 40 40 40 -1 23 40 99 40 50 -1 80 -1 30 30
## [1542] 30 60 -1 -1 30 -1 -1 70 80 38 40 -1 -1 99 -1 40 40 -1 40 -1 -1 45 -1
## [1565] 64 45 40 40 40 99 -1 40 35 -1 35 48 -1 -1 40 -1 32 40 -1 43 40 40 30
## [1588] -1 30 24 40 38 -1 -1 -1 -1 40 20 -1 -1 -1 -1 45 1 -1 -1 -1 -1 65 20
## [1611] 28 -1 -1 -1 -1 40 41 -1 -1 -1 -1 40 9 40 -1 -1 -1 -1 -1 29 -1 40 45
## [1634] 60 -1 -1 -1 -1 -1 35 16 60 58 20 -1 -1 19 -1 -1 -1 -1 40 -1 -1 -1 40
## [1657] -1 -1 40 -1 -1 17 -1 -1 34 60 -1 40 -1 30 -1 50 50 40 89 -1 -1 -1 -1
## [1680] -1 72 40 48 -1 40 40 30 12 43 60 48 -1 -1 -1 16 28 -1 -1 36 -1 40 50
## [1703] 36 89 55 60 -1 -1 -1 -1 -1 55 -1 40 70 -1 9 -1 89 40 70 -1 24 -1 40
## [1726] 48 -1 32 -1 40 -1 -1 40 80 65 30 -1 -1 45 9 -1 -1 -1 70 -1 -1 -1 -1
## [1749] 34 44 -1 32 37 31 40 31 99 80 40 -1 -1 60 44 -1 40 -1 30 -1 -1 -1 -1
## [1772] -1 -1 -1 40 -1 70 25 40 -1 22 60 60 9 72 55 2 40 35 60 45 -1 56 48
## [1795] -1 -1 38 40 50 -1 -1 -1 40 26 -1 40 -1 11 -1 -1 -1 -1 40 -1 70 -1 25
## [1818] 25 -1 60 -1 10 20 -1 -1 50 50 40 8 50 40 32 14 -1 -1 50 30 -1 20 46
## [1841] 40 45 -1 -1 60 40 45 40 -1 53 -1 24 -1 -1 40 15 -1 38 -1 48 -1 -1 -1
## [1864] -1 60 -1 -1 48 -1 42 -1 -1 -1 48 16 -1 8 -1 40 40 9 38 9 -1 40 -1
## [1887] 80 -1 30 20 60 -1 -1 60 -1 -1 40 -1 48 40 -1 50 34 40 -1 40 60 -1 40
## [1910] -1 -1 -1 40 42 8 -1 -1 -1 30 15 36 40 40 -1 40 -1 40 45 24 -1 -1 -1
## [1933] -1 46 20 50 -1 55 -1 56 9 -1 -1 65 -1 43 36 60 -1 45 18 40 62 40 -1
## [1956] 45 60 40 60 -1 30 -1 25 40 40 -1 -1 -1 -1 40 -1 40 35 -1 40 -1 40 -1
## [1979] 35 -1 -1 40 40 30 32 -1 -1 40 67 33 20 -1 20 60 20 -1 -1 -1 45 9 35
## [2002] 55 25 35 20 50 40 45 40 60 -1 40 16 50 -1 -1 -1 50 25 40 -1 -1 40 40
## [2025] -1 68 -1 30 30 -1 60 60 38 35 -1 48 37 -1 30 9 24 -1 -1 40 45 -1 50
## [2048] 45 35 60 50 40 40 10 42 45 40 45 45 1 -1 41 40 45 20 -1 48 -1 37 -1
## [2071] 32 45 45 12 47 -1 40 50 40 -1 24 -1 52 -1 -1 50 20 9 37 35 -1 -1 40
## [2094] 60 32 -1 40 -1 35 -1 36 -1 40 -1 60 9 34 30 -1 40 8 40 45 -1 40 46
## [2117] 47 -1 -1 40 -1 -1 -1 60 -1 25 48 40 36 40 -1 -1 40 46 99 60 -1 50 -1
## [2140] 40 40 80 40 -1 31 -1 25 -1 27 -1 -1 -1 -1 30 40 40 25 9 56 20 80 40
## [2163] -1 89 40 -1 32 39 1 40 -1 20 -1 -1 40 70 50 9 50 25 16 25 -1 -1 46
## [2186] 75 -1 35 12 40 -1 35 9 35 -1 -1 52 -1 -1 48 9 32 -1 40 65 -1 40 40
## [2209] 60 40 -1 40 40 40 99 46 -1 25 50 99 65 -1 40 15 48 -1 -1 -1 38 -1 -1
## [2232] -1 40 -1 -1 -1 40 89 40 77 33 55 -1 80 56 38 80 40 -1 -1 89 30 35 -1
## [2255] -1 35 35 -1 -1 40 40 -1 12 -1 -1 -1 50 10 80 40 65 -1 44 50 40 -1 41
## [2278] -1 35 -1 50 -1 -1 -1 10 -1 50 -1 42 -1 50 40 -1 -1 18 36 44 50 32 40
## [2301] 40 9 30 -1 -1 -1 55 7 40 55 20 -1 30 40 40 52 -1 -1 42 42 -1 -1 -1
## [2324] -1 -1 42 9 70 63 -1 -1 -1 -1 62 40 9 39 -1 40 -1 9 25 -1 40 20 -1
## [2347] 45 30 -1 60 50 -1 40 45 60 9 -1 -1 37 -1 50 41 -1 40 38 52 65 40 40
## [2370] 60 -1 -1 -1 40 4 40 45 40 58 -1 -1 48 30 -1 -1 89 -1 20 -1 -1 -1 40
## [2393] -1 -1 60 -1 -1 50 -1 -1 40 40 65 -1 40 55 44 -1 60 29 -1 -1 -1 -1 60
## [2416] 45 40 72 60 -1 40 44 -1 -1 40 37 50 -1 46 50 26 55 32 45 10 40 -1 50
## [2439] -1 26 40 -1 -1 -1 52 -1 -1 36 35 40 -1 30 60 -1 -1 80 60 32 40 -1 -1
## [2462] 40 -1 -1 -1 60 40 40 25 60 45 40 40 23 60 40 9 -1 50 15 50 40 35 40
## [2485] 50 31 15 40 32 -1 40 -1 50 -1 40 -1 40 40 -1 -1 -1 36 -1 -1 -1 -1 -1
## [2508] -1 -1 -1 40 41 52 -1 -1 -1 55 60 43 -1 37 -1 45 40 -1 -1 46 40 -1 56
## [2531] -1 -1 -1 -1 -1 -1 44 -1 -1 32 -1 -1 50 40 50 50 -1 -1 32 -1 -1 65 -1
## [2554] 35 -1 -1 42 40 68 -1 -1 40 40 50 9 63 60 -1 40 -1 -1 -1 57 40 -1 40
## [2577] -1 -1 -1 -1 40 45 -1 -1 46 30 -1 40 -1 72 -1 -1 -1 -1 40 30 10 45 40
## [2600] 40 -1 -1 50 98 -1 -1 -1 55 40 -1 -1 -1 -1 57 -1 40 -1 -1 40 -1 40 -1
## [2623] 48 40 48 50 40 -1 40 20 -1 -1 45 -1 37 42 -1 -1 -1 -1 50 55 -1 40 -1
## [2646] 45 -1 -1 58 67 -1 -1 -1 40 42 -1 37 -1 -1 40 -1 -1 40 15 -1 55 -1 40
## [2669] 40 65 37 -1 55 50 -1 75 -1 50 -1 40 38 27 -1 40 99 -1 40 -1 -1 -1 36
## [2692] -1 40 30 65 -1 40 36 -1 40 -1 40 -1 40 52 -1 -1 -1 10 40 45 40 60 -1
## [2715] 45 50 68 -1 -1 99 40 -1 -1 60 -1 -1 -1 40 -1 -1 -1 70 50 48 -1 -1 50
## [2738] 30 32 -1 48 40 35 -1 15 60 40 32 89 -1 40 50 30 50 45 48 20 60 -1 40
## [2761] 40 -1 26 -1 25 -1 40 -1 -1 45 -1 40 40 -1 60 40 40 56 10 60 -1 40 40
## [2784] -1 -1 -1 -1 -1 32 -1 41 40 30 30 12 -1 40 -1 86 50 -1 43 -1 36 -1 35
## [2807] -1 -1 -1 55 -1 52 -1 -1 -1 40 5 40 60 -1 50 80 9 -1 40 36 32 44 36
## [2830] 30 -1 44 -1 -1 36 -1 -1 40 -1 -1 70 40 40 40 5 40 -1 60 89 -1 -1 -1
## [2853] 25 40 45 40 55 55 -1 -1 25 30 40 -1 -1 37 -1 -1 -1 55 40 50 35 -1 45
## [2876] 40 40 -1 -1 44 48 14 6 -1 -1 45 -1 -1 30 40 -1 -1 -1 40 9 30 40 -1
## [2899] 40 8 -1 6 15 50 20 35 -1 -1 40 -1 40 62 -1 -1 50 50 -1 55 42 40 40
## [2922] -1 45 46 -1 -1 -1 26 -1 -1 -1 40 -1 22 25 55 40 -1 32 55 -1 40 70 -1
## [2945] 40 -1 20 40 -1 40 50 -1 -1 9 50 44 40 42 -1 50 22 -1 36 -1 -1 9 89
## [2968] 35 32 40 -1 13 60 -1 -1 55 50 55 15 -1 35 -1 40 -1 -1 -1 32 40 -1 40
## [2991] 20 -1 -1 -1 -1 -1 -1 60 -1 40 46 -1 -1 -1 22 -1 -1 40 40 40 40 37 -1
## [3014] -1 40 -1 -1 -1 40 -1 -1 -1 -1 -1 -1 -1 -1 -1 50 -1 40 -1 -1 48 45 99
## [3037] -1 40 35 -1 40 50 40 -1 -1 9 -1 -1 85 35 -1 -1 40 40 -1 -1 -1 -1 40
## [3060] 40 -1 -1 15 48 50 -1 55 32 50 50 -1 50 -1 18 74 -1 -1 -1 -1 -1 25 -1
## [3083] 55 40 -1 80 -1 8 24 -1 60 36 -1 36 40 -1 -1 -1 58 -1 -1 40 40 -1 -1
## [3106] -1 40 32 44 -1 33 -1 -1 -1 20 40 -1 -1 32 40 -1 -1 -1 30 40 13 55 35
## [3129] 60 40 1 26 40 -1 25 32 -1 -1 48 -1 40 35 -1 28 60 40 -1 40 -1 40 55
## [3152] 40 20 -1 -1 40 15 -1 -1 -1 9 -1 50 40 40 60 48 -1 -1 -1 50 9 89 -1
## [3175] 25 40 50 -1 -1 40 60 52 -1 -1 60 -1 40 -1 -1 20 20 40 48 -1 -1 51 30
## [3198] 9 -1 -1 37 40 -1 -1 34 -1 -1 99 -1 38 60 24 -1 89 -1 -1 -1 48 35 40
## [3221] 9 -1 40 32 -1 -1 -1 -1 18 -1 16 70 48 -1 40 40 20 14 -1 50 40 40 -1
## [3244] 40 -1 40 -1 -1 5 50 40 -1 -1 -1 65 -1 48 40 -1 -1 25 40 -1 40 -1 -1
## [3267] 43 40 -1 43 45 -1 -1 -1 45 -1 37 60 -1 -1 40 -1 -1 -1 15 50 14 -1 -1
## [3290] -1 -1 -1 55 -1 70 55 2 40 -1 -1 40 50 9 20 40 60 -1 50 50 -1 40 35
## [3313] -1 -1 15 36 25 -1 60 40 40 42 60 48 -1 40 45 -1 -1 60 50 -1 50 30 13
## [3336] 43 40 56 -1 -1 41 40 40 9 70 40 50 9 28 40 60 20 36 40 40 -1 45 -1
## [3359] 40 15 40 40 20 40 40 40 20 60 50 -1 5 16 -1 60 45 40 -1 38 70 65 -1
## [3382] 45 40 40 -1 50 40 -1 12 45 -1 32 60 60 40 9 30 55 50 45 60 89 50 48
## [3405] -1 -1 70 -1 40 40 40 -1 -1 46 -1 -1 57 25 37 -1 -1 -1 55 -1 50 7 40
## [3428] 20 -1 40 50 12 70 -1 -1 50 32 50 -1 36 -1 44 40 9 9 -1 40 -1 16 -1
## [3451] -1 32 38 32 -1 40 24 65 45 20 -1 60 40 85 60 -1 45 -1 20 -1 -1 36 40
## [3474] 4 -1 60 -1 -1 20 60 50 50 -1 -1 32 9 45 20 40 46 -1 -1 -1 -1 40 -1
## [3497] 9 50 40 -1 -1 -1 40 99 -1 40 -1 40 40 56 -1 -1 -1 40 -1 -1 40 -1 48
## [3520] 20 -1 45 55 45 52 45 40 38 32 54 -1 -1 40 58 -1 -1 40 58 40 -1 40 -1
## [3543] 40 52 -1 50 80 -1 30 46 42 8 -1 -1 -1 -1 8 -1 -1 16 -1 -1 -1 -1 60
## [3566] 40 -1 -1 -1 47 -1 32 -1 -1 -1 99 56 -1 50 40 30 -1 -1 -1 40 -1 -1 40
## [3589] 44 45 -1 40 40 35 -1 40 -1 -1 40 45 -1 50 12 -1 5 45 -1 30 55 45 40
## [3612] -1 20 21 50 40 -1 50 -1 30 45 -1 46 -1 38 -1 40 50 6 50 -1 60 45 20
## [3635] -1 52 -1 -1 -1 -1 50 -1 40 9 -1 30 -1 32 50 33 -1 55 -1 -1 24 6 -1
## [3658] 60 40 40 -1 37 72 -1 -1 -1 12 45 45 99 45 62 -1 40 -1 40 -1 40 -1 -1
## [3681] -1 30 -1 70 -1 20 -1 -1 40 -1 13 32 45 -1 40 48 -1 -1 -1 40 40 9 40
## [3704] 24 30 -1 -1 20 -1 50 45 40 -1 60 20 50 70 48 40 40 -1 -1 40 56 50 24
## [3727] -1 10 38 55 -1 25 -1 -1 -1 16 -1 -1 -1 40 21 -1 -1 -1 37 99 40 40 40
## [3750] 55 45 -1 30 -1 -1 -1 44 37 43 -1 -1 1 -1 -1 40 -1 42 27 20 -1 -1 -1
## [3773] 80 40 40 40 40 12 14 41 40 50 17 8 50 -1 40 -1 35 -1 40 55 72 50 -1
## [3796] 10 8 -1 30 -1 -1 -1 55 60 -1 99 70 60 50 -1 -1 20 -1 -1 39 66 20 -1
## [3819] 50 45 45 -1 -1 39 45 -1 40 70 40 20 40 55 -1 40 40 -1 45 45 -1 53 -1
## [3842] 30 16 -1 45 70 -1 -1 -1 40 40 25 31 60 50 51 -1 50 24 -1 28 -1 50 -1
## [3865] -1 12 55 -1 45 -1 -1 40 31 -1 -1 48 9 -1 -1 -1 45 20 35 40 7 -1 -1
## [3888] 11 -1 40 -1 40 60 30 70 16 40 4 40 -1 50 -1 68 -1 98 48 37 82 -1 -1
## [3911] 40 35 -1 -1 -1 75 45 32 -1 48 32 -1 40 -1 -1 40 25 -1 50 40 36 25 -1
## [3934] 65 35 40 -1 40 36 44 50 52 -1 45 40 -1 -1 40 -1 -1 -1 6 50 40 22 45
## [3957] 60 -1 -1 40 40 40 40 50 -1 44 10 40 -1 32 -1 -1 30 37 -1 40 12 50 72
## [3980] -1 -1 -1 40 50 52 -1 -1 -1 -1 -1 -1 -1 58 40 40 30 55 40 -1 35 45 -1
## [4003] -1 50 -1 -1 -1 -1 24 -1 -1 48 -1 37 -1 32 -1 42 -1 60 65 38 55 55 -1
## [4026] 40 -1 1 -1 -1 45 -1 50 60 35 40 30 40 9 9 -1 44 -1 -1 43 30 37 -1
## [4049] 35 -1 50 50 70 80 -1 60 -1 40 40 -1 40 -1 50 40 20 40 -1 40 40 -1 55
## [4072] -1 40 40 -1 20 52 -1 -1 40 60 40 -1 40 40 -1 -1 30 42 38 -1 -1 -1 35
## [4095] -1 -1 -1 70 -1 -1 40 68 57 55 -1 15 37 38 27 44 -1 40 -1 -1 -1 -1 50
## [4118] -1 40 55 -1 40 -1 9 -1 40 -1 -1 40 40 60 -1 47 9 40 -1 -1 -1 40 50
## [4141] 9 -1 -1 40 60 42 40 60 32 50 28 -1 40 -1 -1 -1 -1 4 -1 -1 -1 24 40
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## attr(,"value.labels")
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## 9 8 7 6 5
## 4 3 2 1 Inapplicable
## 4 3 2 1 -1
#1. Visualizations. Using the ggvis package, create two plots ~
#a. One histogram of HRS1; and
hist(GSSdata$HRS1)
#b. Another histogram of the natural logarithm (ln) of HRS1, or ln(HRS1).
hist(log(GSSdata$HRS1))
## Warning in log(GSSdata$HRS1): NaNs produced
#2. Analyses. Conduct two ordinary least squares regression analyses ~
HRS1_factors <- lm(HRS1 ~ INCOME + WEEKSWRK + AGED, data=GSSdata)
HRS1_factors #print equation
##
## Call:
## lm(formula = HRS1 ~ INCOME + WEEKSWRK + AGED, data = GSSdata)
##
## Coefficients:
## (Intercept) INCOME WEEKSWRK AGED
## 1.60199 -0.02946 0.72056 0.51653
summary(HRS1_factors) # information about fit and nature of equation
##
## Call:
## lm(formula = HRS1 ~ INCOME + WEEKSWRK + AGED, data = GSSdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.413 -3.798 -2.248 6.254 99.769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.60199 0.52881 3.029 0.00246 **
## INCOME -0.02946 0.01332 -2.212 0.02700 *
## WEEKSWRK 0.72056 0.01029 70.030 < 2e-16 ***
## AGED 0.51653 0.21558 2.396 0.01661 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17 on 4816 degrees of freedom
## Multiple R-squared: 0.5067, Adjusted R-squared: 0.5064
## F-statistic: 1649 on 3 and 4816 DF, p-value: < 2.2e-16
confint(HRS1_factors)
## 2.5 % 97.5 %
## (Intercept) 0.56529161 2.638695361
## INCOME -0.05557417 -0.003353566
## WEEKSWRK 0.70038519 0.740728362
## AGED 0.09389274 0.939176557
#a. Use HRS1 as the dependent variable and at least three variables from the
rg1<-lm(GSSdata$HRS1~ GSSdata$INCOME+GSSdata$WEEKSWRK+GSSdata$AGED)
summary(rg1)
##
## Call:
## lm(formula = GSSdata$HRS1 ~ GSSdata$INCOME + GSSdata$WEEKSWRK +
## GSSdata$AGED)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.413 -3.798 -2.248 6.254 99.769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.60199 0.52881 3.029 0.00246 **
## GSSdata$INCOME -0.02946 0.01332 -2.212 0.02700 *
## GSSdata$WEEKSWRK 0.72056 0.01029 70.030 < 2e-16 ***
## GSSdata$AGED 0.51653 0.21558 2.396 0.01661 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17 on 4816 degrees of freedom
## Multiple R-squared: 0.5067, Adjusted R-squared: 0.5064
## F-statistic: 1649 on 3 and 4816 DF, p-value: < 2.2e-16
confint(rg1)
## 2.5 % 97.5 %
## (Intercept) 0.56529161 2.638695361
## GSSdata$INCOME -0.05557417 -0.003353566
## GSSdata$WEEKSWRK 0.70038519 0.740728362
## GSSdata$AGED 0.09389274 0.939176557
# α= .05,then the p-value, 2.2e-16, is less than $\alpha$. Therefore, we reject the null hypothesis that there is no relationship between the dependent variable and the entire sent of independent variables.
#b. Use ln(HRS1) as the dependent variable and the same set of independent variables that you chose for 2.a.
rg2<-lm(log(GSSdata$HRS1) ~ GSSdata$INCOME+GSSdata$WEEKSWRK+GSSdata$AGED)
## Warning in log(GSSdata$HRS1): NaNs produced
summary(rg2)
##
## Call:
## lm(formula = log(GSSdata$HRS1) ~ GSSdata$INCOME + GSSdata$WEEKSWRK +
## GSSdata$AGED)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9991 -0.0685 0.0651 0.2749 1.5503
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2317640 0.0412877 78.274 <2e-16 ***
## GSSdata$INCOME -0.0020931 0.0006768 -3.093 0.002 **
## GSSdata$WEEKSWRK 0.0080225 0.0007892 10.165 <2e-16 ***
## GSSdata$AGED 0.0181345 0.0091268 1.987 0.047 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5496 on 2881 degrees of freedom
## (1935 observations deleted due to missingness)
## Multiple R-squared: 0.03913, Adjusted R-squared: 0.03813
## F-statistic: 39.11 on 3 and 2881 DF, p-value: < 2.2e-16
confint(rg2)
## 2.5 % 97.5 %
## (Intercept) 3.1508075823 3.3127204262
## GSSdata$INCOME -0.0034200576 -0.0007661096
## GSSdata$WEEKSWRK 0.0064750656 0.0095700229
## GSSdata$AGED 0.0002386979 0.0360303158
# α= .05, then the p-value, 2.2e-16, is less than α. Therefore, we reject the null hypothesis that there is no relationship between the dependent variable and the entire sent of independent variables.
#3. Reports. Build two APA–correct tables ~
HRS1 <- tbl_df(GSSdata)
HRS1 %>%
summarise(mean_HRS1=mean(HRS1), mean_INCOME=mean(INCOME), mean_WEEKSWRK=mean(WEEKSWRK),mean_AGED = mean(AGED))
## Source: local data frame [1 x 4]
##
## mean_HRS1 mean_INCOME mean_WEEKSWRK mean_AGED
## (dbl) (dbl) (dbl) (dbl)
## 1 24.07573 15.11846 30.96909 1.16971
#a. One table reporting the regression of HRS1 that you conducted in 2.a.; and
#b. Another table reporting the regression of ln(HRS1) that you conducted in 2.b.
HRS1 %>%
summarise(sd_HRS1=sd(HRS1), sd_INCOME=sd(INCOME), sd_WEEKSWRKt=sd(WEEKSWRK),
sd_AGED = sd(AGED))
## Source: local data frame [1 x 4]
##
## sd_HRS1 sd_INCOME sd_WEEKSWRKt sd_AGED
## (dbl) (dbl) (dbl) (dbl)
## 1 24.19891 18.42523 23.87458 1.137486