Construcción de Índices
Ejemplo 1: Indice de Legitimidad de la protesta
Encuesta nacional sobre conflictos sociales y representación política del IOP-PUCP, 2012.
Cargar Datos
Descargar base de datos y cuestionario de PAIDEIA e importar datos al R
library(foreign)
iop12 <- read.spss("IOP_1112_01_B.sav", to.data.frame = T)Variables del índice
El índice se construye a partir de las preguntas: P31A, P31B, P31C y P31D
Distribución de frecuencia de las preguntas
prop.table(table(iop12$P31A))*100##
## Muy de acuerdo De acuerdo En desacuerdo Muy en desacuerdo
## 19.950125 58.852868 15.710723 1.163757
## NS / NR
## 4.322527prop.table(table(iop12$P31B))*100##
## Muy de acuerdo De acuerdo En desacuerdo Muy en desacuerdo
## 9.226933 50.207814 32.668329 2.244389
## NS / NR
## 5.652535prop.table(table(iop12$P31C))*100##
## Muy de acuerdo De acuerdo En desacuerdo Muy en desacuerdo
## 17.955112 48.877805 27.098919 2.493766
## NS / NR
## 3.574397prop.table(table(iop12$P31D))*100##
## Muy de acuerdo De acuerdo En desacuerdo Muy en desacuerdo
## 22.776392 53.200333 18.204489 2.078138
## NS / NR
## 3.740648Preparación de las variables del índice
Paso 1: Convertirlas en variables numéricas
p31ar <- as.numeric(iop12$P31A)
p31br <- as.numeric(iop12$P31B)
p31cr <- as.numeric(iop12$P31C)
p31dr <- as.numeric(iop12$P31D)
table(p31ar)## p31ar
## 1 2 3 4 5
## 240 708 189 14 52Paso 2: Marcar los valores perdidos
p31ar[p31ar==5] <- NA
p31br[p31br==5] <- NA
p31cr[p31cr==5] <- NA
p31dr[p31dr==5] <- NAPaso 3: “Voltear” los valores
Cambiar el sentido de los valores de algunos ítems del índice para que todos midan la legitimidad en el mismo sentido: valores bajos = baja legitimidad; valores altos = alta legitimidad
Ello implica voltear las preguntas P31A y P31D:
p31ar <- (p31ar-5)*(-1)
p31dr <- (p31dr-5)*(-1)Paso 4: Calcular el índice
iop12$legit.prot <- (p31ar+p31br+p31cr+p31dr)-4
table(iop12$legit.prot)##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 2 8 30 63 148 328 235 169 77 28 8 2Describimos el índice
Para toda la muestra:
summary(iop12$legit.prot)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 6.000 6.000 6.512 8.000 12.000 105sd(iop12$legit.prot, na.rm=T)## [1] 1.621513Por grupos:
- Por ámbito de residencia
library(doBy)
leg.1 <- summaryBy(legit.prot~AMBITOS, data = iop12,
FUN = function(x) {c(Mediana = median(x, na.rm=T),
Media = mean(x, na.rm=T),
Desv.Est = sd(x, na.rm=T))})
leg.1## AMBITOS legit.prot.Mediana legit.prot.Media legit.prot.Desv.Est
## 1 Lima-Callao 6 6.278729 1.599805
## 2 Interior Urbano 7 6.704082 1.696919
## 3 Interior Rural 6 6.517588 1.406494- Por nivel socioeconómico
leg.2 <- summaryBy(legit.prot~NSEGrup, data = iop12,
FUN = function(x) {c(Mediana = median(x, na.rm=T),
Media = mean(x, na.rm=T),
Desv.Est = sd(x, na.rm=T))})
leg.2## NSEGrup legit.prot.Mediana legit.prot.Media legit.prot.Desv.Est
## 1 A/B 6 6.118143 1.703302
## 2 C 6 6.637168 1.648356
## 3 D/E 6 6.609195 1.539172Ejemplo 2: Índice de Tolerancia en la EMV
Identificar las variables:
load("wvs_peru.rdata")
names(wvs.peru)## [1] "S003" "S001" "S002" "S002EVS" "S003A"
## [6] "S004" "S006" "S007" "S007_01" "S008"
## [11] "S009" "S009A" "S010" "S010_01" "S010_02"
## [16] "S010_03" "S010_04" "S011" "S012" "S013"
## [21] "S013B" "S014" "S015" "S016" "S017"
## [26] "S017A" "S018" "S018A" "S019" "S019A"
## [31] "S020" "S021" "S021A" "S022" "S023"
## [36] "S024" "S024A" "S025" "S025A" "S026"
## [41] "S027" "S028" "A001" "A001_CO" "A002"
## [46] "A002_CO" "A003" "A003_CO" "A004" "A004_CO"
## [51] "A005" "A005_CO" "A006" "A006_CO" "A007"
## [56] "A008" "A009" "A010" "A011" "A012"
## [61] "A013" "A014" "A015" "A016" "A017"
## [66] "A018" "A019" "A020" "A021" "A022"
## [71] "A023" "A024" "A025" "A026" "A026_01"
## [76] "A027" "A028" "A029" "A030" "A031"
## [81] "A032" "A033" "A034" "A035" "A036"
## [86] "A037" "A038" "A039" "A040" "A041"
## [91] "A042" "A043" "A043_01" "A043_01F" "A043_F"
## [96] "A043B" "A044" "A045" "A046" "A047"
## [101] "A048" "A049" "A050" "A050_01" "A050_02"
## [106] "A050_03" "A050_04" "A051" "A052" "A053"
## [111] "A054" "A055" "A056" "A057" "A058"
## [116] "A059" "A060" "A061" "A062" "A063"
## [121] "A064" "A065" "A066" "A067" "A068"
## [126] "A069" "A070" "A071" "A071B" "A071C"
## [131] "A072" "A073" "A074" "A075" "A076"
## [136] "A077" "A078" "A079" "A080" "A080_F"
## [141] "A081" "A082" "A083" "A084" "A085"
## [146] "A086" "A087" "A088" "A088B" "A088C"
## [151] "A089" "A090" "A091" "A092" "A093"
## [156] "A094" "A095" "A096" "A097" "A097_F"
## [161] "A098" "A099" "A100" "A101" "A102"
## [166] "A103" "A104" "A105" "A106" "A106B"
## [171] "A106C" "A107" "A108" "A109" "A110"
## [176] "A111" "A112" "A113" "A114" "A115"
## [181] "A116" "A117" "A118" "A119" "A120"
## [186] "A121" "A122" "A123" "A124_01" "A124_02"
## [191] "A124_03" "A124_04" "A124_05" "A124_06" "A124_07"
## [196] "A124_08" "A124_09" "A124_10" "A124_11" "A124_12"
## [201] "A124_13" "A124_14" "A124_15" "A124_16" "A124_17"
## [206] "A124_18" "A124_19" "A124_20" "A124_21" "A124_22"
## [211] "A124_23" "A124_24" "A124_25" "A124_26" "A124_27"
## [216] "A124_28" "A124_29" "A124_30" "A124_31" "A124_32"
## [221] "A124_33" "A124_34" "A124_35" "A124_36" "A124_37"
## [226] "A124_38" "A124_39" "A124_40" "A124_41" "A124_42"
## [231] "A124_43" "A124_44" "A124_45" "A124_46" "A124_47"
## [236] "A124_48" "A124_49" "A124_50" "A124_51" "A124_52"
## [241] "A124_53" "A124_54" "A124_55" "A124_56" "A124_57"
## [246] "A124_58" "A124_59" "A124_60" "A124_61" "A165"
## [251] "A166" "A167" "A168" "A168_01" "A168A"
## [256] "A169" "A170" "A171" "A172" "A173"
## [261] "A174" "A189" "A190" "A191" "A192"
## [266] "A193" "A194" "A195" "A196" "A197"
## [271] "A198" "A199" "A200" "A201" "A202"
## [276] "A203" "A204" "A205" "A206" "A207"
## [281] "A208" "A209" "A210" "A211" "A212"
## [286] "A222" "A213" "A214" "A215" "A216"
## [291] "A217" "A218" "A219" "A220" "A221"
## [296] "B001" "B002" "B003" "B004" "B005"
## [301] "B006" "B007" "B008" "B009" "B010"
## [306] "B011" "B012" "B013" "B014" "B015"
## [311] "B016" "B017" "B018" "B019" "B020"
## [316] "B021" "B022" "B023" "B024" "B025"
## [321] "B026" "B027" "B028" "B029" "B030"
## [326] "B031" "C001" "C002" "C003" "C004"
## [331] "C005" "C006" "C007" "C008" "C009"
## [336] "C010" "C011" "C012" "C013" "C014"
## [341] "C015" "C016" "C017" "C018" "C019"
## [346] "C020" "C021" "C022" "C023" "C024"
## [351] "C025" "C026" "C027" "C027_1" "C027_2"
## [356] "C027_3" "C027_4" "C028" "C028_F" "C029"
## [361] "C030" "C031" "C032" "C033" "C034"
## [366] "C035" "C036" "C037" "C038" "C039"
## [371] "C040" "C041" "C042B1" "C042B2" "C042B3"
## [376] "C042B4" "C042B5" "C042B6" "C042B7" "C049"
## [381] "C050" "C051" "C052" "C053" "C054"
## [386] "C055" "C056" "C057" "C057_F" "C058"
## [391] "C059" "C060" "C061" "C062" "C063"
## [396] "C064" "D001" "D001_B" "D002" "D003"
## [401] "D004" "D005" "D006" "D007" "D008"
## [406] "D008_F" "D009" "D010" "D011" "D012"
## [411] "D013" "D014" "D015" "D015_F" "D016"
## [416] "D017" "D018" "D019" "D020" "D021"
## [421] "D022" "D023" "D024" "D025" "D026"
## [426] "D026_01" "D026_02" "D026_03" "D026_04" "D026_05"
## [431] "D027" "D028" "D029" "D030" "D031"
## [436] "D032" "D033" "D034" "D035" "D036"
## [441] "D037" "D038" "D039" "D040" "D041"
## [446] "D042" "D043" "D043_01" "D044" "D044A"
## [451] "D045" "D046" "D047" "D048" "D049"
## [456] "D050" "D051" "D052" "D053" "D053_F"
## [461] "D054" "D055" "D056" "D057" "D058"
## [466] "D059" "D060" "D061" "D062" "D063"
## [471] "D063_B" "D064" "D064_01" "D065" "D066"
## [476] "D066_B" "D067" "D068" "D069" "D070"
## [481] "D071" "D072" "D073" "D074" "D075"
## [486] "D076" "D077" "D078" "D079" "D080"
## [491] "E001" "E001_F" "E001_HK" "E002" "E002_HK"
## [496] "E003" "E003_F" "E004" "E005" "E005_F"
## [501] "E005_HK" "E006" "E006_HK" "E007" "E008"
## [506] "E009" "E010" "E011" "E012" "E013"
## [511] "E014" "E015" "E016" "E017" "E018"
## [516] "E019" "E020" "E021" "E022" "E023"
## [521] "E024" "E025" "E025B" "E026" "E026B"
## [526] "E027" "E028" "E028B" "E029" "E030"
## [531] "E031" "E032" "E033" "E034" "E035"
## [536] "E036" "E037" "E038" "E039" "E040"
## [541] "E041" "E042" "E043" "E044" "E045"
## [546] "E046" "E047" "E048" "E049" "E050"
## [551] "E051" "E052" "E053" "E054" "E055"
## [556] "E056" "E056_F" "E057" "E058" "E059"
## [561] "E060" "E061" "E062" "E063" "E064"
## [566] "E065" "E066" "E067" "E068" "E069_01"
## [571] "E069_02" "E069_03" "E069_04" "E069_05" "E069_06"
## [576] "E069_07" "E069_08" "E069_09" "E069_10" "E069_11"
## [581] "E069_12" "E069_13" "E069_14" "E069_15" "E069_16"
## [586] "E069_17" "E069_18" "E069_19" "E069_20" "E069_21"
## [591] "E069_22" "E069_23" "E069_24" "E069_25" "E069_26"
## [596] "E069_27" "E069_28" "E069_29" "E069_30" "E069_31"
## [601] "E069_32" "E069_33" "E069_34" "E069_35" "E069_36"
## [606] "E069_37" "E069_38" "E069_39" "E069_40" "E069_41"
## [611] "E069_42" "E069_43" "E069_44" "E069_45" "E069_46"
## [616] "E069_47" "E069_48" "E069_49" "E069_50" "E069_51"
## [621] "E069_52" "E069_54" "E069_55" "E069_56" "E069_57"
## [626] "E069_58" "E069_59" "E069_60" "E104" "E105"
## [631] "E106" "E107" "E108" "E109" "E110"
## [636] "E111" "E112" "E113" "E114" "E114_MX"
## [641] "E115" "E115_MX" "E116" "E116_MX" "E117"
## [646] "E117_IQA" "E117_IQB" "E117_MX" "E118" "E119"
## [651] "E120" "E121" "E122" "E123" "E124"
## [656] "E125" "E127" "E128" "E129" "E129A"
## [661] "E129B" "E129C" "E129D" "E130" "E131"
## [666] "E132" "E133" "E134" "E135" "E136"
## [671] "E137" "E138" "E139" "E140" "E141"
## [676] "E142" "E143" "E144" "E145" "E146"
## [681] "E147" "E148" "E149" "E150" "E151"
## [686] "E152" "E153" "E154" "E155" "E156"
## [691] "E157" "E158" "E159" "E160" "E161"
## [696] "E162" "E162_01" "E163" "E164" "E165"
## [701] "E166" "E167" "E168" "E169" "E170"
## [706] "E171" "E172" "E173" "E174" "E175"
## [711] "E176" "E177" "E178" "E178_01" "E179"
## [716] "E179_01" "E179_F" "E179WVS" "E180" "E180WVS"
## [721] "E181" "E181_01" "E182" "E183" "E184"
## [726] "E185" "E186" "E187" "E188" "E189"
## [731] "E190" "E191" "E192" "E193" "E194"
## [736] "E195" "E196" "E197" "E198" "E203"
## [741] "E204" "E205" "E206" "E207" "E208"
## [746] "E209" "E211" "E212" "E213" "E214"
## [751] "E215" "E216" "E217" "E218" "E219"
## [756] "E220" "E221B" "E222" "E222B" "E224"
## [761] "E225" "E226" "E227" "E228" "E229"
## [766] "E230" "E231" "E232" "E233" "E233A"
## [771] "E233B" "E234" "E235" "E236" "E237"
## [776] "E238" "E238_ES" "E239" "E239_ES" "E240"
## [781] "E240_ES" "E241" "E241_ES" "E242" "E243"
## [786] "E244" "E245" "E246" "E247" "E248"
## [791] "E248B" "E249" "E250" "E250B" "E251"
## [796] "E252" "E253" "E254" "E254B" "E255"
## [801] "E256" "E257" "E258" "E258B" "E259"
## [806] "E259B" "E260" "E260B" "E261" "E261B"
## [811] "E262" "E262B" "E263" "E264" "E265_01"
## [816] "E265_02" "E265_03" "E265_04" "E265_05" "E265_06"
## [821] "E265_07" "E265_08" "E265_09" "E266" "E267"
## [826] "F001" "F002" "F003" "F004" "F005"
## [831] "F006" "F007" "F008" "F009" "F010"
## [836] "F011" "F012" "F014" "F015" "F016"
## [841] "F017" "F018" "F019" "F020" "F021"
## [846] "F022" "F022_01" "F023" "F024" "F025"
## [851] "F025_01" "F026" "F027" "F027_01" "F028"
## [856] "F028B" "F029" "F030" "F031" "F032"
## [861] "F033" "F034" "F035" "F036" "F037"
## [866] "F038" "F039" "F040" "F041" "F042"
## [871] "F043" "F044" "F045" "F046" "F047"
## [876] "F048" "F049" "F050" "F051" "F052"
## [881] "F053" "F054" "F055" "F056" "F057"
## [886] "F058" "F059" "F060" "F061" "F062"
## [891] "F062_01" "F062_02" "F062_03" "F063" "F064"
## [896] "F065" "F066" "F067" "F068" "F069"
## [901] "F070" "F071" "F072" "F073" "F074"
## [906] "F075" "F076" "F077" "F078" "F079"
## [911] "F080" "F081" "F082" "F083" "F084"
## [916] "F085" "F086" "F087" "F088" "F089"
## [921] "F090" "F091" "F092" "F093" "F094"
## [926] "F095" "F096" "F097" "F098" "F099"
## [931] "F100" "F101" "F102" "F103" "F104"
## [936] "F105" "F106" "F107" "F108" "F109"
## [941] "F110" "F111" "F112" "F113" "F114"
## [946] "F114_01" "F114_02" "F114_03" "F115" "F116"
## [951] "F117" "F118" "F119" "F120" "F121"
## [956] "F122" "F123" "F124" "F125" "F126"
## [961] "F127" "F128" "F129" "F130" "F131"
## [966] "F132" "F133" "F134" "F135" "F135A"
## [971] "F136" "F137" "F138" "F139" "F140"
## [976] "F141" "F142" "F143" "F144" "F144_01"
## [981] "F144_02" "F145" "F146" "F147" "F148"
## [986] "F149" "F150" "F151" "F152" "F153"
## [991] "F154" "F155" "F156" "F157" "F158"
## [996] "F159" "F160" "F161" "F163" "F164"
## [1001] "F165" "F166" "F167" "F168" "F169"
## [1006] "F170" "F171" "F172" "F173" "F174"
## [1011] "F175" "F176" "F177" "F178" "F179"
## [1016] "F186" "F187" "F188" "F189" "F190"
## [1021] "F191" "F191_F" "F192" "F193" "F194"
## [1026] "F195" "F196" "F197" "F198" "F199"
## [1031] "F200" "F201" "F202" "F203" "F204"
## [1036] "F205" "G001" "G001_F" "G001CS" "G001CS_F"
## [1041] "G002" "G002CS" "G003" "G003CS" "G004"
## [1046] "G005" "G006" "G007_01" "G007_02" "G007_03"
## [1051] "G007_04" "G007_05" "G007_06" "G007_07" "G007_08"
## [1056] "G007_09" "G007_10" "G007_11" "G007_12" "G007_13"
## [1061] "G007_14" "G007_15" "G007_16" "G007_17" "G007_18"
## [1066] "G007_18_B" "G007_19" "G007_20" "G007_21" "G007_22"
## [1071] "G007_23" "G007_24" "G007_25" "G007_26" "G007_27"
## [1076] "G007_28" "G007_29" "G007_30" "G007_31" "G007_32"
## [1081] "G007_33" "G007_33_B" "G007_34" "G007_34_B" "G007_35"
## [1086] "G007_35_B" "G007_36" "G007_36_B" "G007_37" "G007_38"
## [1091] "G007_39" "G007_40" "G007_41" "G007_42" "G007_43"
## [1096] "G007_44" "G007_45" "G007_46" "G007_47" "G007_48"
## [1101] "G007_49" "G007_50" "G007_51" "G007_52" "G007_53"
## [1106] "G007_54" "G007_55" "G007_56" "G007_57" "G007_58"
## [1111] "G007_59" "G007_60" "G007_61" "G007_62" "G007_63"
## [1116] "G007_64" "G007_65" "G007_66" "G007_67" "G014"
## [1121] "G015" "G015B" "G016" "G017" "G018"
## [1126] "G019" "G020" "G021" "G022A" "G022B"
## [1131] "G022C" "G022D" "G022E" "G022F" "G022G"
## [1136] "G022H" "G022I" "G022J" "G022K" "G022L"
## [1141] "G022M" "G022N" "G022O" "G022P" "G022Q"
## [1146] "G022R" "G022S" "G023" "G024" "G025"
## [1151] "G026" "G026_01" "G027" "G027_01" "G027A"
## [1156] "G027B" "G028" "G029" "G030" "G031"
## [1161] "G032" "G033" "G034" "G035" "G036"
## [1166] "G037" "G038" "G039" "G040" "G041"
## [1171] "G042" "G043" "G044" "G045" "G046"
## [1176] "G047" "G048" "G049" "G050" "G051"
## [1181] "H001" "H002_01" "H002_02" "H002_03" "H002_04"
## [1186] "H002_05" "H003_01" "H003_02" "H003_03" "H004"
## [1191] "H005" "H006_01" "H006_02" "H006_03" "H006_04"
## [1196] "H006_05" "H006_06" "H007" "H008_01" "H008_02"
## [1201] "H008_03" "H008_04" "I001" "I002" "U001A"
## [1206] "U001B" "U002A" "U002B" "U003A" "U003B"
## [1211] "U004A" "U004B" "U005A" "U005B" "U006A"
## [1216] "U006B" "V001" "V001A" "V002" "V002A"
## [1221] "V003" "V004A" "V004B" "V004C" "V004D"
## [1226] "V004E" "V004R" "V005" "V006" "V006_2"
## [1231] "V006_3" "V006_4" "V007A" "V007B" "V007C"
## [1236] "V007D" "V008" "V009" "V010" "V011"
## [1241] "V012" "V013" "V014" "V015" "V016"
## [1246] "V017" "V018" "W001" "W001A" "W002A"
## [1251] "W002B" "W002C" "W002D" "W002E" "W002R"
## [1256] "W003" "W004" "W005" "W005_2" "W005_3"
## [1261] "W005_4" "W006A" "W006B" "W006C" "W006D"
## [1266] "W007" "W008" "W009" "W010" "W011"
## [1271] "X001" "X002" "X002_01" "X002_01A" "X002_02"
## [1276] "X002_02A" "X002_03" "X003" "X003R" "X003R2"
## [1281] "X004" "X005" "X006" "X006_01" "X006_02"
## [1286] "X007" "X007_01" "X007_02" "X008" "X009"
## [1291] "X009_01" "X010" "X011" "X011_01" "X011_02"
## [1296] "X011A" "X012" "X013" "X014" "X015"
## [1301] "X016" "X017" "X018" "X019" "X020"
## [1306] "X021" "X022" "X022_01" "X022_02A" "X022_02B"
## [1311] "X022_03A" "X022_03B" "X022_04A" "X022_04B" "X022_05A"
## [1316] "X022_05B" "X022_06A" "X022_06B" "X023" "X023R"
## [1321] "X024" "X024B" "X025" "X025A" "X025B"
## [1326] "X025C" "X025CS" "X025CSWVS" "X025LIT" "X025R"
## [1331] "X026" "X027" "X028" "X028_01" "X029"
## [1336] "X030" "X031" "X032" "X032R" "X032R_01"
## [1341] "X033" "X033R" "X034" "X034R" "X034R_01"
## [1346] "X035_2" "X035_3" "X035_4" "X036" "X036A"
## [1351] "X036B" "X036C" "X036D" "X037" "X037_01"
## [1356] "X037_02" "X038" "X039" "X040" "X041"
## [1361] "X042_2" "X042_3" "X042_4" "X043" "X044"
## [1366] "X045" "X045B" "X046" "X047" "X047A"
## [1371] "X047A_01" "X047B" "X047B_01" "X047C" "X047C_01"
## [1376] "X047CS" "X047D" "X047R" "X048" "X048A"
## [1381] "X048B" "X048C" "X048D" "X048E" "X048F"
## [1386] "X048G" "X048WVS" "X049" "X049CS" "X050"
## [1391] "X051" "X052" "X053" "X054" "X055"
## [1396] "Y001" "Y002" "Y003" "Y010" "Y011"
## [1401] "Y012" "Y013" "Y014" "Y020" "Y021"
## [1406] "Y022" "Y023" "Y024" "TRADRAT5" "survself"
## [1411] "nompais" "oleada"summary(wvs.peru[, c(189:249)])## A124_01 A124_02 A124_03 A124_04
## Min. :-4.0000 Min. :0.00000 Min. :0.0000 Min. :-4.000
## 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:-4.000
## Median : 0.0000 Median :0.00000 Median :1.0000 Median : 0.000
## Mean :-0.4736 Mean :0.09351 Mean :0.5527 Mean :-1.822
## 3rd Qu.: 1.0000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.: 0.000
## Max. : 1.0000 Max. :1.00000 Max. :1.0000 Max. : 1.000
## A124_05 A124_06 A124_07 A124_08
## Min. :-4.000 Min. :0.00000 Min. :0.000 Min. :0.0000
## 1st Qu.:-4.000 1st Qu.:0.00000 1st Qu.:0.000 1st Qu.:1.0000
## Median :-4.000 Median :0.00000 Median :0.000 Median :1.0000
## Mean :-2.855 Mean :0.09277 Mean :0.306 Mean :0.7632
## 3rd Qu.: 0.000 3rd Qu.:0.00000 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. : 1.000 Max. :1.00000 Max. :1.000 Max. :1.0000
## A124_09 A124_10 A124_11 A124_12 A124_13
## Min. :0.0000 Min. :-4 Min. :-4 Min. :-4.000 Min. :-4
## 1st Qu.:0.0000 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4.000 1st Qu.:-4
## Median :0.0000 Median :-4 Median :-4 Median :-4.000 Median :-4
## Mean :0.4703 Mean :-4 Mean :-4 Mean :-1.953 Mean :-4
## 3rd Qu.:1.0000 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.: 0.000 3rd Qu.:-4
## Max. :1.0000 Max. :-4 Max. :-4 Max. : 1.000 Max. :-4
## A124_14 A124_15 A124_16 A124_17 A124_18
## Min. :-4 Min. :-4 Min. :-4 Min. :-4 Min. :-4.000
## 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4.000
## Median :-4 Median :-4 Median :-4 Median :-4 Median : 0.000
## Mean :-4 Mean :-4 Mean :-4 Mean :-4 Mean :-1.807
## 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.: 0.000
## Max. :-4 Max. :-4 Max. :-4 Max. :-4 Max. : 1.000
## A124_19 A124_20 A124_21 A124_22 A124_23
## Min. :-4 Min. :-4 Min. :-4 Min. :-4 Min. :-4
## 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4
## Median :-4 Median :-4 Median :-4 Median :-4 Median :-4
## Mean :-4 Mean :-4 Mean :-4 Mean :-4 Mean :-4
## 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4
## Max. :-4 Max. :-4 Max. :-4 Max. :-4 Max. :-4
## A124_24 A124_25 A124_26 A124_27 A124_28
## Min. :-4 Min. :-4 Min. :-4 Min. :-4 Min. :-4
## 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4
## Median :-4 Median :-4 Median :-4 Median :-4 Median :-4
## Mean :-4 Mean :-4 Mean :-4 Mean :-4 Mean :-4
## 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4
## Max. :-4 Max. :-4 Max. :-4 Max. :-4 Max. :-4
## A124_29 A124_30 A124_31 A124_32 A124_33
## Min. :-4 Min. :-4 Min. :-4 Min. :-4 Min. :-4.000
## 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4.000
## Median :-4 Median :-4 Median :-4 Median :-4 Median :-4.000
## Mean :-4 Mean :-4 Mean :-4 Mean :-4 Mean :-3.078
## 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4.000
## Max. :-4 Max. :-4 Max. :-4 Max. :-4 Max. : 1.000
## A124_34 A124_35 A124_36 A124_37 A124_38
## Min. :-4 Min. :-4 Min. :-4 Min. :-4 Min. :-4
## 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4
## Median :-4 Median :-4 Median :-4 Median :-4 Median :-4
## Mean :-4 Mean :-4 Mean :-4 Mean :-4 Mean :-4
## 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4
## Max. :-4 Max. :-4 Max. :-4 Max. :-4 Max. :-4
## A124_39 A124_40 A124_41 A124_42 A124_43
## Min. :-4 Min. :-4 Min. :-4 Min. :-4.000 Min. :-4.000
## 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4.000 1st Qu.:-4.000
## Median :-4 Median :-4 Median :-4 Median :-4.000 Median :-4.000
## Mean :-4 Mean :-4 Mean :-4 Mean :-1.963 Mean :-1.964
## 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. :-4 Max. :-4 Max. :-4 Max. : 1.000 Max. : 1.000
## A124_44 A124_45 A124_46 A124_47 A124_48
## Min. :-4 Min. :-4 Min. :-4 Min. :-4 Min. :-4
## 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4
## Median :-4 Median :-4 Median :-4 Median :-4 Median :-4
## Mean :-4 Mean :-4 Mean :-4 Mean :-4 Mean :-4
## 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4
## Max. :-4 Max. :-4 Max. :-4 Max. :-4 Max. :-4
## A124_49 A124_50 A124_51 A124_52 A124_53
## Min. :-4 Min. :-4 Min. :-4 Min. :-4 Min. :-4
## 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4
## Median :-4 Median :-4 Median :-4 Median :-4 Median :-4
## Mean :-4 Mean :-4 Mean :-4 Mean :-4 Mean :-4
## 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4
## Max. :-4 Max. :-4 Max. :-4 Max. :-4 Max. :-4
## A124_54 A124_55 A124_56 A124_57 A124_58
## Min. :-4 Min. :-4 Min. :-4 Min. :-4 Min. :-4
## 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4
## Median :-4 Median :-4 Median :-4 Median :-4 Median :-4
## Mean :-4 Mean :-4 Mean :-4 Mean :-4 Mean :-4
## 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4
## Max. :-4 Max. :-4 Max. :-4 Max. :-4 Max. :-4
## A124_59 A124_60 A124_61
## Min. :-4 Min. :-4 Min. :-4
## 1st Qu.:-4 1st Qu.:-4 1st Qu.:-4
## Median :-4 Median :-4 Median :-4
## Mean :-4 Mean :-4 Mean :-4
## 3rd Qu.:-4 3rd Qu.:-4 3rd Qu.:-4
## Max. :-4 Max. :-4 Max. :-4Las variables a utilizar serán:
A124_01 : Gente con antecedentes criminales A124_02 : Gente de raza diferente A124_03 : Gente que toma mucho alcohol A124_06 : Inmigrantes A124_07 : Gente que tiene SIDA A124_08 : Drogadictos A124_09 : Homosexuales
Paso 1: Marcar los valores perdidos en los casos que corresponda:
wvs.peru$a124.1.r <- wvs.peru$A124_01
wvs.peru$a124.1.r[wvs.peru$a124.1.r < 0] <- NAPaso 2: Calcular el índice
wvs.peru$tolera <- wvs.peru$a124.1.r + wvs.peru$A124_02 + wvs.peru$A124_03 +
wvs.peru$A124_06 + wvs.peru$A124_07 + wvs.peru$A124_08 + wvs.peru$A124_09
table(wvs.peru$tolera)##
## 0 1 2 3 4 5 6 7
## 391 666 850 899 732 496 118 60Vamos a acondicional las variables para el análisis: recodificando y/o etiquetando los valores; asignando los Missing Values (NA)
# Sexo del entrevistado
wvs.peru$sex <- as.factor(wvs.peru$X001)
levels(wvs.peru$sex) <- c("Masculino", "Femenino")
# Grupos de edad
wvs.peru$gedad <- as.factor(wvs.peru$X003R2)
levels(wvs.peru$gedad) <- c("15 a 29", "30 a 49", "50 a más")
# Importancia de la religión
wvs.peru$a006r <- wvs.peru$A006
wvs.peru$a006r[wvs.peru$a006r < 0] <- NA
wvs.peru$a006r <- factor(wvs.peru$a006r)
levels(wvs.peru$a006r) <- c("Muy importante", "Algo importante",
"Poco importante", "Nada importante")Tolerancia según oleada
tol.1 <- summaryBy(tolera~oleada, data = wvs.peru,
FUN = function(x) {c(Mediana = median(x, na.rm=T),
Media = mean(x, na.rm=T),
Desv.Est = sd(x, na.rm=T))})
tol.1## oleada tolera.Mediana tolera.Media tolera.Desv.Est
## 1 1995-1998 3 3.049546 1.667637
## 2 1999-2004 3 2.707528 1.727543
## 3 2005-2009 2 2.561333 1.530076
## 4 2010-2014 NA NaN NAGráfico de cajas:
library(ggplot2)
ggplot(subset(wvs.peru, oleada=="2005-2009"), aes(x=gedad, y=tolera)) +
geom_boxplot()
ggplot(wvs.peru, aes(x=gedad, y=tolera, fill=oleada)) +
geom_boxplot() + scale_fill_manual(values = c("#FFFFFF", "#CCCCCC", "#999999")) +
xlab("Grupo de Edad") + ylab("Índice de tolerancia") +
ggtitle("Encuesta Mundial de Valores: Índice de Tolerancia, según\n Grupos de Edad, por Oleada de la Encuesta") +
theme_bw()## Warning: Removed 1210 rows containing non-finite values (stat_boxplot).