Mostrar codi.
ceo <- readr::read_delim(
"https://upceo.ceo.gencat.cat/wsceop/9668/Microdades_anonimitzades_1119.csv",
delim = ";",
locale = readr::locale(encoding = "ISO-8859-1")
)Activitat 3.2
Activitat 1
En aquesta activitat utilitzarem un marc de dades que conté informació del Baròmetre d’Opinió Pública del CEO. És una enquesta que es realitza periòdicament a Catalunya i conté diverses variables relacionades amb opinions polítiques i socials. Amb aquestes dades farem un senzill anàlisi de l’opinió pública a Catalunya. Aprofitarem que les dades estan penjades a la web del CEO per importar-les directament d’allà. Per fer-ho copiarem el link on es troben aquestes dades dins la funció read_delim. Aquesta funció permet importar fitxers de text com a bases de dades.
- Realitza una exploració ràpida del mateix amb les funcions que coneixes, identificant el nombre d’observacions, quines variables té com estan codificades i la unitat d’observació.
Mostrar codi.
head(ceo)# A tibble: 6 × 285
PONDERA ORDRE ORDRE_REVISADA REO METODOLOGIA BOP_NUM ANY MES DIA
<dbl> <dbl> <chr> <dbl> <chr> <chr> <dbl> <chr> <dbl>
1 1 1 " " 1119 Presencial Feb. 25 - 11… 2025 Febr… 14
2 1 2 " " 1119 Presencial Feb. 25 - 11… 2025 Febr… 14
3 1 3 " " 1119 Presencial Feb. 25 - 11… 2025 Febr… 14
4 1 4 " " 1119 Presencial Feb. 25 - 11… 2025 Febr… 14
5 1 5 " " 1119 Presencial Feb. 25 - 11… 2025 Febr… 14
6 1 6 " " 1119 Presencial Feb. 25 - 11… 2025 Febr… 14
# ℹ 276 more variables: DATA_INI <chr>, HORA_INI <chr>, DATA_FIN <chr>,
# HORA_FIN <chr>, DURADA <dbl>, GRAVACIO <chr>, TIPUS_GRAV <chr>, FASE <chr>,
# ENQUESTADOR_CODI <dbl>, ENQUESTADOR_SEXE <chr>, ENQUESTADOR_EDAT <dbl>,
# ENQUESTADOR_ESTUDIS <chr>, ENQUESTADOR_NACIONALITAT <chr>,
# NUM_QUEST_CAMP <chr>, PROVINCIA <chr>, HABITAT <chr>, MUNICIPI <chr>,
# COMARCA <chr>, CLUSTER_24 <dbl>, ID_RUTA <dbl>, SECCIO_TEORICA <dbl>,
# CONF_SECC <chr>, SECCIO_REAL <dbl>, DOMICILI_PARTICULAR <chr>, …Mostrar codi.
dim(ceo)[1] 2000 285Mostrar codi.
names(ceo) [1] "PONDERA" "ORDRE"
[3] "ORDRE_REVISADA" "REO"
[5] "METODOLOGIA" "BOP_NUM"
[7] "ANY" "MES"
[9] "DIA" "DATA_INI"
[11] "HORA_INI" "DATA_FIN"
[13] "HORA_FIN" "DURADA"
[15] "GRAVACIO" "TIPUS_GRAV"
[17] "FASE" "ENQUESTADOR_CODI"
[19] "ENQUESTADOR_SEXE" "ENQUESTADOR_EDAT"
[21] "ENQUESTADOR_ESTUDIS" "ENQUESTADOR_NACIONALITAT"
[23] "NUM_QUEST_CAMP" "PROVINCIA"
[25] "HABITAT" "MUNICIPI"
[27] "COMARCA" "CLUSTER_24"
[29] "ID_RUTA" "SECCIO_TEORICA"
[31] "CONF_SECC" "SECCIO_REAL"
[33] "DOMICILI_PARTICULAR" "LLENGUA_ENQUESTA"
[35] "LLENGUA_SISTEMA" "RESIDENCIA"
[37] "CIUTADANIA" "GENERE"
[39] "GENERE_LITERALS" "SEXE"
[41] "EDAT" "EDAT_GR"
[43] "EDAT_CEO" "LLOC_NAIX"
[45] "HORA_PRIMERA_PREGUNTA" "PRE_PROBLEMES"
[47] "PROBLEMES_LITERALS" "PROBLEMES_E_1"
[49] "PROBLEMES_E_2" "PROBLEMES_E_3"
[51] "PROBLEMES_E_4" "PROBLEMES_E_5"
[53] "PROBLEMES_E_6" "PROBLEMES_E_7"
[55] "PROBLEMES_E_8" "PROBLEMES_E_9"
[57] "PROBLEMES_E_10" "PROBLEMES_E_11"
[59] "PROBLEMES_E_12" "PROBLEMES_R_1"
[61] "PROBLEMES_R_2" "PROBLEMES_R_3"
[63] "PROBLEMES_R_4" "PROBLEMES_R_5"
[65] "PROBLEMES_R_6" "PROBLEMES_R_7"
[67] "PROBLEMES_R_8" "PROBLEMES_R_9"
[69] "PROBLEMES_R_10" "PROBLEMES_R_11"
[71] "PROBLEMES_R_12" "PROBLEMA"
[73] "PROBLEMA_REDUIDA" "SIT_ECO_CAT"
[75] "SIT_ECO_CAT_RETROSPECTIVA" "SIT_ECO_CAT_PROSPECTIVA"
[77] "SIT_ECO_ESP" "SIT_ECO_PERSONAL"
[79] "SIT_POL_CAT" "SIT_POL_CAT_RETROSPECTIVA"
[81] "SIT_POL_CAT_PROSPECTIVA" "SIT_POL_ESP"
[83] "RISCOS" "INTERES_POL_PUBLICS"
[85] "PREFERENCIA_PRESIDENT_CAT" "PREFERENCIA_PRESIDENT_CAT_LITERALS"
[87] "PREFERENCIA_PRESIDENT_ESP" "PREFERENCIA_PRESIDENT_ESP_LITERALS"
[89] "VAL_GOV_CAT" "VAL_GOV_ESP"
[91] "INF_POL_DIARI_FREQ" "INF_POL_TV_FREQ"
[93] "INF_POL_RADIO_FREQ" "INF_POL_XARXES_FREQ"
[95] "INF_POL_CONEGUTS_FREQ" "INTERES_MITJANS_CANVI_CLIMATIC"
[97] "INTERES_MITJANS_HABITATGE" "INTERES_MITJANS_CAT_ESP"
[99] "INTERES_MITJANS_SEGURETAT" "INTERES_MITJANS_ECONOMIA"
[101] "INTERES_MITJANS_ESPORTS" "INTERES_MITJANS_FEMINISME"
[103] "INTERES_MITJANS_GUERRES" "CONFI_TRIBUNALS"
[105] "CONFI_PARTITS" "CONFI_AJUNTAMENT"
[107] "CONFI_GOV_ESP" "CONFI_GOV_CAT"
[109] "CONFI_CONGRES" "CONFI_PARLAMENT"
[111] "CONFI_UE" "CONFI_MONARQUIA"
[113] "CONFI_SINDICATS" "CONFI_ASS_EMPRESARIALS"
[115] "CONFI_ESGLESIA" "SATIS_DEMOCRACIA"
[117] "SIMPATIA_PARTIT" "SIMPATIA_PARTIT_LITERALS"
[119] "SIMPATIA_PARTIT_PROPER" "SIMPATIA_PARTIT_PROPER_LITERALS"
[121] "IDEOL_0_10" "IDEOL_0_10_PP"
[123] "IDEOL_0_10_ERC" "IDEOL_0_10_PSC"
[125] "IDEOL_0_10_CUP" "IDEOL_0_10_JXCAT"
[127] "IDEOL_0_10_CEC" "IDEOL_0_10_VOX"
[129] "IDEOL_0_10_ALIANCA" "ESP_CAT_0_10"
[131] "ESP_CAT_0_10_PP" "ESP_CAT_0_10_ERC"
[133] "ESP_CAT_0_10_PSC" "ESP_CAT_0_10_CUP"
[135] "ESP_CAT_0_10_JXCAT" "ESP_CAT_0_10_CEC"
[137] "ESP_CAT_0_10_VOX" "ESP_CAT_0_10_ALIANCA"
[139] "AUTONOMIA_CAT" "RELACIONS_CAT_ESP"
[141] "RELACIONS_CAT_ESP_2" "ACTITUD_INDEPENDENCIA"
[143] "VAL_MESURES_GOV_CENTRAL_CAT" "ACTITUT_GRUPS_PARL_PRESSUPOSTOS"
[145] "ACTITUT_GRUPS_PARL_PRESSIO" "ACTITUT_GRUPS_PARL_NEGOCIACIO"
[147] "VAL_CORDO_SANITARI" "INT_PARLAMENT_PART_1_4"
[149] "INT_PARLAMENT_VOT" "INT_PARLAMENT_VOT_LITERALS"
[151] "INT_PARLAMENT_VOT2" "INT_PARLAMENT_VOT2_LITERALS"
[153] "INT_PARLAMENT_VOT2_RECORD" "PART_PARLAMENT"
[155] "REC_PARLAMENT_VOT" "REC_PARLAMENT_VOT_CENS"
[157] "REC_PARLAMENT_VOT_LITERALS" "INT_CONGRES_PART_1_4"
[159] "INT_CONGRES_VOT" "INT_CONGRES_VOT_LITERALS"
[161] "PART_CONGRES" "REC_CONGRES_VOT"
[163] "REC_CONGRES_VOT_CENS" "REC_CONGRES_VOT_LITERALS"
[165] "CONEIX_A_FERNANDEZ" "VAL_A_FERNANDEZ"
[167] "CONEIX_C_PUIGDEMONT" "VAL_C_PUIGDEMONT"
[169] "CONEIX_O_JUNQUERAS" "VAL_O_JUNQUERAS"
[171] "CONEIX_S_ILLA" "VAL_S_ILLA"
[173] "CONEIX_J_ALBIACH" "VAL_J_ALBIACH"
[175] "CONEIX_L_ESTRADA" "VAL_L_ESTRADA"
[177] "CONEIX_I_GARRIGA" "VAL_I_GARRIGA"
[179] "CONEIX_S_ORRIOLS" "VAL_S_ORRIOLS"
[181] "CONEIX_P_SANCHEZ" "VAL_P_SANCHEZ"
[183] "CONEIX_A_N_FEIJOO" "VAL_A_N_FEIJOO"
[185] "CONEIX_S_ABASCAL" "VAL_S_ABASCAL"
[187] "CONEIX_Y_DIAZ" "VAL_Y_DIAZ"
[189] "CONEIX_G_RUFIAN" "VAL_G_RUFIAN"
[191] "CONEIX_M_NOGUERAS" "VAL_M_NOGUERAS"
[193] "CORRUPCIO_POLITICS" "CORRUPCIO_CIUTADANIA_PAGAR"
[195] "VAL_POL_TRUMP" "VAL_POL_TRUMP_VS_SIT_ECO_CAT"
[197] "CONEIX_IA" "US_IA"
[199] "LLOC_NAIX_CCAA" "LLOC_NAIX_MON"
[201] "LLOC_NAIX_PARE" "LLOC_NAIX_PARE_JOVE"
[203] "LLOC_NAIX_PARE_GRAN" "LLOC_NAIX_MARE"
[205] "LLOC_NAIX_MARE_JOVE" "LLOC_NAIX_MARE_GRAN"
[207] "RELIGIO" "RELIGIO_LITERALS"
[209] "RELIGIO_FREQ" "SIT_LAB"
[211] "SIT_LAB_NO_ACTIU" "SIT_LAB_ACTIU"
[213] "SECTOR" "OCUPACIO_CNO11_LITERALS"
[215] "OCUPACIO_CNO11_1" "OCUPACIO_CNO11_2"
[217] "OCUPACIO_CNO11_3" "ESTUDIS_1_15"
[219] "ESTUDIS_LITERALS" "ESTAT_CIVIL_2"
[221] "RELACIO_HABITATGE" "RELACIO_HABITATGE_LITERALS"
[223] "UTILITZA_FACEBOOK" "UTILITZA_INSTAGRAM"
[225] "UTILITZA_TWITTER" "UTILITZA_TELEGRAM"
[227] "UTILITZA_TIKTOK" "UTILITZA_BLUESKY"
[229] "UTILITZA_YOUTUBE" "UTILITZA_TWITCH"
[231] "LLENGUA_PRIMERA_1_3" "LLENGUA_PRIMERA_ALTRES"
[233] "LLENGUA_PRIMERA_ALTRES_LITERALS" "LLENGUA_IDENT_1_3"
[235] "LLENGUA_IDENT_ALTRES" "LLENGUA_IDENT_ALTRES_LITERALS"
[237] "BENESTAR_PERMETRE_COTXE_PROPI" "BENESTAR_VACANCES"
[239] "BENESTAR_SUPORT_LLAR" "BENESTAR_PROPIETATS"
[241] "BENESTAR_TEMPERATURA_LLAR" "BENESTAR_FACTURES"
[243] "SENTIMENT_PERTINENCA" "PERSONES_LLAR"
[245] "DEPENDENCIA_ECO_PARES_MENORS_40" "ORIENTACIO_SEXUAL"
[247] "ORIENTACIO_SEXUAL_LITERALS" "INGRESSOS_1_15"
[249] "CLASSE_SOCIAL_SUBJECTIVA_1_7" "HORA_ULTIMA_PREGUNTA"
[251] "E2" "E3_1_4"
[253] "E4_1_4" "E5_1_4"
[255] "E6" "E7"
[257] "E81" "E82"
[259] "E83" "E84"
[261] "E85" "E86"
[263] "E898" "E9"
[265] "E10" "E11"
[267] "E11_LITERALS" "FORMAT_ENQUESTA"
[269] "MONITORATGE" "RECONTACTE"
[271] "SIMPATIA_PARTIT_R" "INT_PARLAMENT_VOT_R"
[273] "REC_PARLAMENT_VOT_R" "REC_PARLAMENT_VOT_CENS_R"
[275] "INT_CONGRES_VOT_R" "REC_CONGRES_VOT_R"
[277] "REC_CONGRES_VOT_CENS_R" "CIRCUIT_1119_1"
[279] "CIRCUIT_1119_2" "CIRCUIT_1119_3"
[281] "CIRCUIT_1119_4" "CIRCUIT_1119_5"
[283] "EGP10" "EGP6"
[285] "INDEX_BENESTAR" Mostrar codi.
str(ceo)spc_tbl_ [2,000 × 285] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ PONDERA : num [1:2000] 1 1 1 1 1 1 1 1 1 1 ...
$ ORDRE : num [1:2000] 1 2 3 4 5 6 7 8 9 10 ...
$ ORDRE_REVISADA : chr [1:2000] " " " " " " " " ...
$ REO : num [1:2000] 1119 1119 1119 1119 1119 ...
$ METODOLOGIA : chr [1:2000] "Presencial" "Presencial" "Presencial" "Presencial" ...
$ BOP_NUM : chr [1:2000] "Feb. 25 - 1119" "Feb. 25 - 1119" "Feb. 25 - 1119" "Feb. 25 - 1119" ...
$ ANY : num [1:2000] 2025 2025 2025 2025 2025 ...
$ MES : chr [1:2000] "Febrer" "Febrer" "Febrer" "Febrer" ...
$ DIA : num [1:2000] 14 14 14 14 14 14 14 14 14 14 ...
$ DATA_INI : chr [1:2000] "2/14/2025" "2/14/2025" "2/14/2025" "2/14/2025" ...
$ HORA_INI : chr [1:2000] " " " " " " " " ...
$ DATA_FIN : chr [1:2000] "2/14/2025" "2/14/2025" "2/14/2025" "2/14/2025" ...
$ HORA_FIN : chr [1:2000] " " " " " " " " ...
$ DURADA : num [1:2000] 1565 1248 1261 1648 1636 ...
$ GRAVACIO : chr [1:2000] "Sí" "Sí" "Sí" "Sí" ...
$ TIPUS_GRAV : chr [1:2000] "Enquesta gravada" "Enquesta gravada" "Enquesta gravada" "Enquesta gravada" ...
$ FASE : chr [1:2000] "Camp" "Camp" "Camp" "Camp" ...
$ ENQUESTADOR_CODI : num [1:2000] 524 524 524 83 83 83 83 83 83 83 ...
$ ENQUESTADOR_SEXE : chr [1:2000] "Home" "Home" "Home" "Home" ...
$ ENQUESTADOR_EDAT : num [1:2000] 68 68 68 51 51 51 51 51 51 51 ...
$ ENQUESTADOR_ESTUDIS : chr [1:2000] "Batxillerat, BUP, FP2, COU, Mòdul professional 2 i 3, Batxiller superior" "Batxillerat, BUP, FP2, COU, Mòdul professional 2 i 3, Batxiller superior" "Batxillerat, BUP, FP2, COU, Mòdul professional 2 i 3, Batxiller superior" "Batxillerat, BUP, FP2, COU, Mòdul professional 2 i 3, Batxiller superior" ...
$ ENQUESTADOR_NACIONALITAT : chr [1:2000] "Espanya" "Espanya" "Espanya" "Espanya" ...
$ NUM_QUEST_CAMP : chr [1:2000] " " " " " " " " ...
$ PROVINCIA : chr [1:2000] "Barcelona" "Barcelona" "Barcelona" "Barcelona" ...
$ HABITAT : chr [1:2000] ">1.000.000 habitants" ">1.000.000 habitants" ">1.000.000 habitants" "2.001-10.000 habitants" ...
$ MUNICIPI : chr [1:2000] "Barcelona (ciutat)" "Barcelona (ciutat)" "Barcelona (ciutat)" " " ...
$ COMARCA : chr [1:2000] "Barcelonès" "Barcelonès" "Barcelonès" "Alt Penedès" ...
$ CLUSTER_24 : num [1:2000] 1 1 1 4 4 4 4 4 4 4 ...
$ ID_RUTA : num [1:2000] 7 7 7 78 78 78 78 78 78 78 ...
$ SECCIO_TEORICA : num [1:2000] 232 232 232 2340 2340 2340 2340 2340 2340 2340 ...
$ CONF_SECC : chr [1:2000] "Sí" "Sí" "Sí" "Sí" ...
$ SECCIO_REAL : num [1:2000] 232 232 232 2340 2340 2340 2340 2340 2340 2340 ...
$ DOMICILI_PARTICULAR : chr [1:2000] "Sí" "Sí" "Sí" "Sí" ...
$ LLENGUA_ENQUESTA : chr [1:2000] "Català" "Castellà" "Castellà" "Castellà" ...
$ LLENGUA_SISTEMA : chr [1:2000] "Català" "Castellà" "Castellà" "Castellà" ...
$ RESIDENCIA : chr [1:2000] "Sí" "Sí" "Sí" "Sí" ...
$ CIUTADANIA : chr [1:2000] "Sí, tinc la ciutadania (espanyola)" "Sí, tinc la ciutadania (espanyola)" "Sí, tinc la ciutadania (espanyola)" "Sí, tinc la ciutadania (espanyola)" ...
$ GENERE : chr [1:2000] "Home" "Home" "Dona" "Home" ...
$ GENERE_LITERALS : chr [1:2000] " " " " " " " " ...
$ SEXE : chr [1:2000] "Masculí" "Masculí" "Femení" "Masculí" ...
$ EDAT : num [1:2000] 25 44 44 50 97 68 62 69 69 48 ...
$ EDAT_GR : chr [1:2000] "De 25 a 34 anys" "De 35 a 49 anys" "De 35 a 49 anys" "De 50 a 64 anys" ...
$ EDAT_CEO : chr [1:2000] "De 25 a 29 anys" "De 40 a 44 anys" "De 40 a 44 anys" "De 50 a 54 anys" ...
$ LLOC_NAIX : chr [1:2000] "Fora d'Espanya" "Fora d'Espanya" "Catalunya" "Catalunya" ...
$ HORA_PRIMERA_PREGUNTA : chr [1:2000] " " " " " " " " ...
$ PRE_PROBLEMES : chr [1:2000] "Contesta" "Contesta" "Contesta" "Contesta" ...
$ PROBLEMES_LITERALS : chr [1:2000] "VIOLENCIA / ROBATORI" "INMIGRACION ILEGAL / PRECIO VIVIENDA / -INSEGURIDAD" "INMIGRACIN / -DELINCUENCIA" "ODIO EN GNAL" ...
$ PROBLEMES_E_1 : chr [1:2000] "Violència (en general)" "Massa immigrants sense papers" "Immigració" "Altres problemes" ...
$ PROBLEMES_E_2 : chr [1:2000] "Robatoris / estafes" "Habitatge car" "Massa delinqüència" " " ...
$ PROBLEMES_E_3 : chr [1:2000] " " "Inseguretat ciutadana" " " " " ...
$ PROBLEMES_E_4 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_E_5 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_E_6 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_E_7 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_E_8 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_E_9 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_E_10 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_E_11 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_E_12 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_R_1 : chr [1:2000] "Incivisme i violència" "Immigració" "Immigració" "Altres problemes" ...
$ PROBLEMES_R_2 : chr [1:2000] "Inseguretat ciutadana" "Accés a l'habitatge" "Inseguretat ciutadana" " " ...
$ PROBLEMES_R_3 : chr [1:2000] " " "Inseguretat ciutadana" " " " " ...
$ PROBLEMES_R_4 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_R_5 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_R_6 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_R_7 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_R_8 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_R_9 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_R_10 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_R_11 : chr [1:2000] " " " " " " " " ...
$ PROBLEMES_R_12 : chr [1:2000] " " " " " " " " ...
$ PROBLEMA : chr [1:2000] "Robatoris / estafes" "Massa immigrants sense papers" "Immigració" "Altres problemes" ...
$ PROBLEMA_REDUIDA : chr [1:2000] "Inseguretat ciutadana" "Immigració" "Immigració" "Altres problemes" ...
$ SIT_ECO_CAT : chr [1:2000] "Ni bona ni dolenta" "Bona" "Molt dolenta" "Bona" ...
$ SIT_ECO_CAT_RETROSPECTIVA : chr [1:2000] "Millor" "Pitjor" "Pitjor" "No ho sap" ...
$ SIT_ECO_CAT_PROSPECTIVA : chr [1:2000] "Es quedarà igual" "Empitjorarà" "Empitjorarà" "Millorarà" ...
$ SIT_ECO_ESP : chr [1:2000] "Ni bona ni dolenta" "Dolenta" "Molt dolenta" "Bona" ...
$ SIT_ECO_PERSONAL : chr [1:2000] "Millor" "Pitjor" "Pitjor" "Igual" ...
$ SIT_POL_CAT : chr [1:2000] "Dolenta" "Molt dolenta" "Molt dolenta" "Bona" ...
$ SIT_POL_CAT_RETROSPECTIVA : chr [1:2000] "Igual" "Pitjor" "Pitjor" "Millor" ...
$ SIT_POL_CAT_PROSPECTIVA : chr [1:2000] "Millorarà" "Empitjorarà" "Empitjorarà" "Millorarà" ...
$ SIT_POL_ESP : chr [1:2000] "Ni bona ni dolenta" "Dolenta" "Molt dolenta" "Dolenta" ...
$ RISCOS : chr [1:2000] "8" "Totalment disposat/ada" "Totalment disposat/ada" "Totalment disposat/ada" ...
$ INTERES_POL_PUBLICS : chr [1:2000] "Molt" "Molt" "Molt" "Bastant" ...
$ PREFERENCIA_PRESIDENT_CAT : chr [1:2000] "Salvador Illa" "Ignacio Garriga" "Salvador Illa" "Salvador Illa" ...
$ PREFERENCIA_PRESIDENT_CAT_LITERALS: chr [1:2000] " " " " " " " " ...
$ PREFERENCIA_PRESIDENT_ESP : chr [1:2000] "Yolanda Díaz" "Santiago Abascal" "Santiago Abascal" "Pedro Sánchez" ...
$ PREFERENCIA_PRESIDENT_ESP_LITERALS: chr [1:2000] " " " " " " " " ...
$ VAL_GOV_CAT : chr [1:2000] "5" "6" "6" "8" ...
$ VAL_GOV_ESP : chr [1:2000] "4" "4" "0 Molt dolenta" "7" ...
$ INF_POL_DIARI_FREQ : chr [1:2000] "5-6 dies per setmana" "Tots els dies" "Tots els dies" "Mai" ...
$ INF_POL_TV_FREQ : chr [1:2000] "3-4 dies per setmana" "Tots els dies" "Mai" "Mai" ...
$ INF_POL_RADIO_FREQ : chr [1:2000] "Mai" "Tots els dies" "3-4 dies per setmana" "Mai" ...
$ INF_POL_XARXES_FREQ : chr [1:2000] "Tots els dies" "Tots els dies" "Tots els dies" "Mai" ...
$ INF_POL_CONEGUTS_FREQ : chr [1:2000] "5-6 dies per setmana" "Tots els dies" "Tots els dies" "Mai" ...
$ INTERES_MITJANS_CANVI_CLIMATIC : chr [1:2000] "Poc" "Gens" "Molt" "Molt" ...
$ INTERES_MITJANS_HABITATGE : chr [1:2000] "Bastant" "Molt" "Molt" "Molt" ...
$ INTERES_MITJANS_CAT_ESP : chr [1:2000] "Poc" "Molt" "Molt" "Gens" ...
$ INTERES_MITJANS_SEGURETAT : chr [1:2000] "Bastant" "Molt" "Molt" "Molt" ...
[list output truncated]
- attr(*, "spec")=
.. cols(
.. PONDERA = col_double(),
.. ORDRE = col_double(),
.. ORDRE_REVISADA = col_character(),
.. REO = col_double(),
.. METODOLOGIA = col_character(),
.. BOP_NUM = col_character(),
.. ANY = col_double(),
.. MES = col_character(),
.. DIA = col_double(),
.. DATA_INI = col_character(),
.. HORA_INI = col_character(),
.. DATA_FIN = col_character(),
.. HORA_FIN = col_character(),
.. DURADA = col_double(),
.. GRAVACIO = col_character(),
.. TIPUS_GRAV = col_character(),
.. FASE = col_character(),
.. ENQUESTADOR_CODI = col_double(),
.. ENQUESTADOR_SEXE = col_character(),
.. ENQUESTADOR_EDAT = col_double(),
.. ENQUESTADOR_ESTUDIS = col_character(),
.. ENQUESTADOR_NACIONALITAT = col_character(),
.. NUM_QUEST_CAMP = col_character(),
.. PROVINCIA = col_character(),
.. HABITAT = col_character(),
.. MUNICIPI = col_character(),
.. COMARCA = col_character(),
.. CLUSTER_24 = col_double(),
.. ID_RUTA = col_double(),
.. SECCIO_TEORICA = col_double(),
.. CONF_SECC = col_character(),
.. SECCIO_REAL = col_double(),
.. DOMICILI_PARTICULAR = col_character(),
.. LLENGUA_ENQUESTA = col_character(),
.. LLENGUA_SISTEMA = col_character(),
.. RESIDENCIA = col_character(),
.. CIUTADANIA = col_character(),
.. GENERE = col_character(),
.. GENERE_LITERALS = col_character(),
.. SEXE = col_character(),
.. EDAT = col_double(),
.. EDAT_GR = col_character(),
.. EDAT_CEO = col_character(),
.. LLOC_NAIX = col_character(),
.. HORA_PRIMERA_PREGUNTA = col_character(),
.. PRE_PROBLEMES = col_character(),
.. PROBLEMES_LITERALS = col_character(),
.. PROBLEMES_E_1 = col_character(),
.. PROBLEMES_E_2 = col_character(),
.. PROBLEMES_E_3 = col_character(),
.. PROBLEMES_E_4 = col_character(),
.. PROBLEMES_E_5 = col_character(),
.. PROBLEMES_E_6 = col_character(),
.. PROBLEMES_E_7 = col_character(),
.. PROBLEMES_E_8 = col_character(),
.. PROBLEMES_E_9 = col_character(),
.. PROBLEMES_E_10 = col_character(),
.. PROBLEMES_E_11 = col_character(),
.. PROBLEMES_E_12 = col_character(),
.. PROBLEMES_R_1 = col_character(),
.. PROBLEMES_R_2 = col_character(),
.. PROBLEMES_R_3 = col_character(),
.. PROBLEMES_R_4 = col_character(),
.. PROBLEMES_R_5 = col_character(),
.. PROBLEMES_R_6 = col_character(),
.. PROBLEMES_R_7 = col_character(),
.. PROBLEMES_R_8 = col_character(),
.. PROBLEMES_R_9 = col_character(),
.. PROBLEMES_R_10 = col_character(),
.. PROBLEMES_R_11 = col_character(),
.. PROBLEMES_R_12 = col_character(),
.. PROBLEMA = col_character(),
.. PROBLEMA_REDUIDA = col_character(),
.. SIT_ECO_CAT = col_character(),
.. SIT_ECO_CAT_RETROSPECTIVA = col_character(),
.. SIT_ECO_CAT_PROSPECTIVA = col_character(),
.. SIT_ECO_ESP = col_character(),
.. SIT_ECO_PERSONAL = col_character(),
.. SIT_POL_CAT = col_character(),
.. SIT_POL_CAT_RETROSPECTIVA = col_character(),
.. SIT_POL_CAT_PROSPECTIVA = col_character(),
.. SIT_POL_ESP = col_character(),
.. RISCOS = col_character(),
.. INTERES_POL_PUBLICS = col_character(),
.. PREFERENCIA_PRESIDENT_CAT = col_character(),
.. PREFERENCIA_PRESIDENT_CAT_LITERALS = col_character(),
.. PREFERENCIA_PRESIDENT_ESP = col_character(),
.. PREFERENCIA_PRESIDENT_ESP_LITERALS = col_character(),
.. VAL_GOV_CAT = col_character(),
.. VAL_GOV_ESP = col_character(),
.. INF_POL_DIARI_FREQ = col_character(),
.. INF_POL_TV_FREQ = col_character(),
.. INF_POL_RADIO_FREQ = col_character(),
.. INF_POL_XARXES_FREQ = col_character(),
.. INF_POL_CONEGUTS_FREQ = col_character(),
.. INTERES_MITJANS_CANVI_CLIMATIC = col_character(),
.. INTERES_MITJANS_HABITATGE = col_character(),
.. INTERES_MITJANS_CAT_ESP = col_character(),
.. INTERES_MITJANS_SEGURETAT = col_character(),
.. INTERES_MITJANS_ECONOMIA = col_character(),
.. INTERES_MITJANS_ESPORTS = col_character(),
.. INTERES_MITJANS_FEMINISME = col_character(),
.. INTERES_MITJANS_GUERRES = col_character(),
.. CONFI_TRIBUNALS = col_character(),
.. CONFI_PARTITS = col_character(),
.. CONFI_AJUNTAMENT = col_character(),
.. CONFI_GOV_ESP = col_character(),
.. CONFI_GOV_CAT = col_character(),
.. CONFI_CONGRES = col_character(),
.. CONFI_PARLAMENT = col_character(),
.. CONFI_UE = col_character(),
.. CONFI_MONARQUIA = col_character(),
.. CONFI_SINDICATS = col_character(),
.. CONFI_ASS_EMPRESARIALS = col_character(),
.. CONFI_ESGLESIA = col_character(),
.. SATIS_DEMOCRACIA = col_character(),
.. SIMPATIA_PARTIT = col_character(),
.. SIMPATIA_PARTIT_LITERALS = col_character(),
.. SIMPATIA_PARTIT_PROPER = col_character(),
.. SIMPATIA_PARTIT_PROPER_LITERALS = col_character(),
.. IDEOL_0_10 = col_character(),
.. IDEOL_0_10_PP = col_character(),
.. IDEOL_0_10_ERC = col_character(),
.. IDEOL_0_10_PSC = col_character(),
.. IDEOL_0_10_CUP = col_character(),
.. IDEOL_0_10_JXCAT = col_character(),
.. IDEOL_0_10_CEC = col_character(),
.. IDEOL_0_10_VOX = col_character(),
.. IDEOL_0_10_ALIANCA = col_character(),
.. ESP_CAT_0_10 = col_character(),
.. ESP_CAT_0_10_PP = col_character(),
.. ESP_CAT_0_10_ERC = col_character(),
.. ESP_CAT_0_10_PSC = col_character(),
.. ESP_CAT_0_10_CUP = col_character(),
.. ESP_CAT_0_10_JXCAT = col_character(),
.. ESP_CAT_0_10_CEC = col_character(),
.. ESP_CAT_0_10_VOX = col_character(),
.. ESP_CAT_0_10_ALIANCA = col_character(),
.. AUTONOMIA_CAT = col_character(),
.. RELACIONS_CAT_ESP = col_character(),
.. RELACIONS_CAT_ESP_2 = col_character(),
.. ACTITUD_INDEPENDENCIA = col_character(),
.. VAL_MESURES_GOV_CENTRAL_CAT = col_character(),
.. ACTITUT_GRUPS_PARL_PRESSUPOSTOS = col_character(),
.. ACTITUT_GRUPS_PARL_PRESSIO = col_character(),
.. ACTITUT_GRUPS_PARL_NEGOCIACIO = col_character(),
.. VAL_CORDO_SANITARI = col_character(),
.. INT_PARLAMENT_PART_1_4 = col_character(),
.. INT_PARLAMENT_VOT = col_character(),
.. INT_PARLAMENT_VOT_LITERALS = col_character(),
.. INT_PARLAMENT_VOT2 = col_character(),
.. INT_PARLAMENT_VOT2_LITERALS = col_character(),
.. INT_PARLAMENT_VOT2_RECORD = col_character(),
.. PART_PARLAMENT = col_character(),
.. REC_PARLAMENT_VOT = col_character(),
.. REC_PARLAMENT_VOT_CENS = col_character(),
.. REC_PARLAMENT_VOT_LITERALS = col_character(),
.. INT_CONGRES_PART_1_4 = col_character(),
.. INT_CONGRES_VOT = col_character(),
.. INT_CONGRES_VOT_LITERALS = col_character(),
.. PART_CONGRES = col_character(),
.. REC_CONGRES_VOT = col_character(),
.. REC_CONGRES_VOT_CENS = col_character(),
.. REC_CONGRES_VOT_LITERALS = col_character(),
.. CONEIX_A_FERNANDEZ = col_character(),
.. VAL_A_FERNANDEZ = col_character(),
.. CONEIX_C_PUIGDEMONT = col_character(),
.. VAL_C_PUIGDEMONT = col_character(),
.. CONEIX_O_JUNQUERAS = col_character(),
.. VAL_O_JUNQUERAS = col_character(),
.. CONEIX_S_ILLA = col_character(),
.. VAL_S_ILLA = col_character(),
.. CONEIX_J_ALBIACH = col_character(),
.. VAL_J_ALBIACH = col_character(),
.. CONEIX_L_ESTRADA = col_character(),
.. VAL_L_ESTRADA = col_character(),
.. CONEIX_I_GARRIGA = col_character(),
.. VAL_I_GARRIGA = col_character(),
.. CONEIX_S_ORRIOLS = col_character(),
.. VAL_S_ORRIOLS = col_character(),
.. CONEIX_P_SANCHEZ = col_character(),
.. VAL_P_SANCHEZ = col_character(),
.. CONEIX_A_N_FEIJOO = col_character(),
.. VAL_A_N_FEIJOO = col_character(),
.. CONEIX_S_ABASCAL = col_character(),
.. VAL_S_ABASCAL = col_character(),
.. CONEIX_Y_DIAZ = col_character(),
.. VAL_Y_DIAZ = col_character(),
.. CONEIX_G_RUFIAN = col_character(),
.. VAL_G_RUFIAN = col_character(),
.. CONEIX_M_NOGUERAS = col_character(),
.. VAL_M_NOGUERAS = col_character(),
.. CORRUPCIO_POLITICS = col_character(),
.. CORRUPCIO_CIUTADANIA_PAGAR = col_character(),
.. VAL_POL_TRUMP = col_character(),
.. VAL_POL_TRUMP_VS_SIT_ECO_CAT = col_character(),
.. CONEIX_IA = col_character(),
.. US_IA = col_character(),
.. LLOC_NAIX_CCAA = col_character(),
.. LLOC_NAIX_MON = col_character(),
.. LLOC_NAIX_PARE = col_character(),
.. LLOC_NAIX_PARE_JOVE = col_character(),
.. LLOC_NAIX_PARE_GRAN = col_character(),
.. LLOC_NAIX_MARE = col_character(),
.. LLOC_NAIX_MARE_JOVE = col_character(),
.. LLOC_NAIX_MARE_GRAN = col_character(),
.. RELIGIO = col_character(),
.. RELIGIO_LITERALS = col_character(),
.. RELIGIO_FREQ = col_character(),
.. SIT_LAB = col_character(),
.. SIT_LAB_NO_ACTIU = col_character(),
.. SIT_LAB_ACTIU = col_character(),
.. SECTOR = col_character(),
.. OCUPACIO_CNO11_LITERALS = col_character(),
.. OCUPACIO_CNO11_1 = col_character(),
.. OCUPACIO_CNO11_2 = col_character(),
.. OCUPACIO_CNO11_3 = col_character(),
.. ESTUDIS_1_15 = col_character(),
.. ESTUDIS_LITERALS = col_character(),
.. ESTAT_CIVIL_2 = col_character(),
.. RELACIO_HABITATGE = col_character(),
.. RELACIO_HABITATGE_LITERALS = col_character(),
.. UTILITZA_FACEBOOK = col_character(),
.. UTILITZA_INSTAGRAM = col_character(),
.. UTILITZA_TWITTER = col_character(),
.. UTILITZA_TELEGRAM = col_character(),
.. UTILITZA_TIKTOK = col_character(),
.. UTILITZA_BLUESKY = col_character(),
.. UTILITZA_YOUTUBE = col_character(),
.. UTILITZA_TWITCH = col_character(),
.. LLENGUA_PRIMERA_1_3 = col_character(),
.. LLENGUA_PRIMERA_ALTRES = col_character(),
.. LLENGUA_PRIMERA_ALTRES_LITERALS = col_character(),
.. LLENGUA_IDENT_1_3 = col_character(),
.. LLENGUA_IDENT_ALTRES = col_character(),
.. LLENGUA_IDENT_ALTRES_LITERALS = col_character(),
.. BENESTAR_PERMETRE_COTXE_PROPI = col_character(),
.. BENESTAR_VACANCES = col_character(),
.. BENESTAR_SUPORT_LLAR = col_character(),
.. BENESTAR_PROPIETATS = col_character(),
.. BENESTAR_TEMPERATURA_LLAR = col_character(),
.. BENESTAR_FACTURES = col_character(),
.. SENTIMENT_PERTINENCA = col_character(),
.. PERSONES_LLAR = col_character(),
.. DEPENDENCIA_ECO_PARES_MENORS_40 = col_character(),
.. ORIENTACIO_SEXUAL = col_character(),
.. ORIENTACIO_SEXUAL_LITERALS = col_character(),
.. INGRESSOS_1_15 = col_character(),
.. CLASSE_SOCIAL_SUBJECTIVA_1_7 = col_character(),
.. HORA_ULTIMA_PREGUNTA = col_character(),
.. E2 = col_character(),
.. E3_1_4 = col_character(),
.. E4_1_4 = col_character(),
.. E5_1_4 = col_character(),
.. E6 = col_character(),
.. E7 = col_character(),
.. E81 = col_character(),
.. E82 = col_character(),
.. E83 = col_character(),
.. E84 = col_character(),
.. E85 = col_character(),
.. E86 = col_character(),
.. E898 = col_character(),
.. E9 = col_character(),
.. E10 = col_character(),
.. E11 = col_character(),
.. E11_LITERALS = col_character(),
.. FORMAT_ENQUESTA = col_character(),
.. MONITORATGE = col_character(),
.. RECONTACTE = col_character(),
.. SIMPATIA_PARTIT_R = col_character(),
.. INT_PARLAMENT_VOT_R = col_character(),
.. REC_PARLAMENT_VOT_R = col_character(),
.. REC_PARLAMENT_VOT_CENS_R = col_character(),
.. INT_CONGRES_VOT_R = col_character(),
.. REC_CONGRES_VOT_R = col_character(),
.. REC_CONGRES_VOT_CENS_R = col_character(),
.. CIRCUIT_1119_1 = col_character(),
.. CIRCUIT_1119_2 = col_character(),
.. CIRCUIT_1119_3 = col_character(),
.. CIRCUIT_1119_4 = col_character(),
.. CIRCUIT_1119_5 = col_character(),
.. EGP10 = col_character(),
.. EGP6 = col_character(),
.. INDEX_BENESTAR = col_double()
.. )
- attr(*, "problems")=<externalptr> Mostrar codi.
summary(ceo) PONDERA ORDRE ORDRE_REVISADA REO
Min. :1 Min. : 1.0 Length:2000 Min. :1119
1st Qu.:1 1st Qu.: 500.8 Class :character 1st Qu.:1119
Median :1 Median :1000.5 Mode :character Median :1119
Mean :1 Mean :1000.5 Mean :1119
3rd Qu.:1 3rd Qu.:1500.2 3rd Qu.:1119
Max. :1 Max. :2000.0 Max. :1119
METODOLOGIA BOP_NUM ANY MES
Length:2000 Length:2000 Min. :2025 Length:2000
Class :character Class :character 1st Qu.:2025 Class :character
Mode :character Mode :character Median :2025 Mode :character
Mean :2025
3rd Qu.:2025
Max. :2025
DIA DATA_INI HORA_INI DATA_FIN
Min. : 1.0 Length:2000 Length:2000 Length:2000
1st Qu.: 8.0 Class :character Class :character Class :character
Median :18.0 Mode :character Mode :character Mode :character
Mean :16.4
3rd Qu.:24.0
Max. :28.0
HORA_FIN DURADA GRAVACIO TIPUS_GRAV
Length:2000 Min. : 606 Length:2000 Length:2000
Class :character 1st Qu.:1142 Class :character Class :character
Mode :character Median :1354 Mode :character Mode :character
Mean :1474
3rd Qu.:1690
Max. :4312
FASE ENQUESTADOR_CODI ENQUESTADOR_SEXE ENQUESTADOR_EDAT
Length:2000 Min. : 83.0 Length:2000 Min. :19.00
Class :character 1st Qu.: 329.0 Class :character 1st Qu.:45.00
Mode :character Median : 789.0 Mode :character Median :52.00
Mean : 717.3 Mean :50.24
3rd Qu.:1184.0 3rd Qu.:58.00
Max. :1199.0 Max. :68.00
ENQUESTADOR_ESTUDIS ENQUESTADOR_NACIONALITAT NUM_QUEST_CAMP
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
PROVINCIA HABITAT MUNICIPI COMARCA
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
CLUSTER_24 ID_RUTA SECCIO_TEORICA CONF_SECC
Min. :1.000 Min. : 1.00 Min. : 1 Length:2000
1st Qu.:3.000 1st Qu.: 47.00 1st Qu.: 1229 Class :character
Median :4.000 Median : 87.00 Median : 2348 Mode :character
Mean :3.698 Mean : 86.73 Mean : 19848725
3rd Qu.:5.000 3rd Qu.:128.25 3rd Qu.: 3897
Max. :6.000 Max. :168.00 Max. :829401002
SECCIO_REAL DOMICILI_PARTICULAR LLENGUA_ENQUESTA LLENGUA_SISTEMA
Min. : 1 Length:2000 Length:2000 Length:2000
1st Qu.: 1303 Class :character Class :character Class :character
Median : 2348 Mode :character Mode :character Mode :character
Mean : 5209925
3rd Qu.: 3866
Max. :806401002
RESIDENCIA CIUTADANIA GENERE GENERE_LITERALS
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
SEXE EDAT EDAT_GR EDAT_CEO
Length:2000 Min. :18.00 Length:2000 Length:2000
Class :character 1st Qu.:37.00 Class :character Class :character
Mode :character Median :51.00 Mode :character Mode :character
Mean :51.12
3rd Qu.:65.00
Max. :97.00
LLOC_NAIX HORA_PRIMERA_PREGUNTA PRE_PROBLEMES PROBLEMES_LITERALS
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
PROBLEMES_E_1 PROBLEMES_E_2 PROBLEMES_E_3 PROBLEMES_E_4
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
PROBLEMES_E_5 PROBLEMES_E_6 PROBLEMES_E_7 PROBLEMES_E_8
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
PROBLEMES_E_9 PROBLEMES_E_10 PROBLEMES_E_11 PROBLEMES_E_12
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
PROBLEMES_R_1 PROBLEMES_R_2 PROBLEMES_R_3 PROBLEMES_R_4
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
PROBLEMES_R_5 PROBLEMES_R_6 PROBLEMES_R_7 PROBLEMES_R_8
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
PROBLEMES_R_9 PROBLEMES_R_10 PROBLEMES_R_11 PROBLEMES_R_12
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
PROBLEMA PROBLEMA_REDUIDA SIT_ECO_CAT
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
SIT_ECO_CAT_RETROSPECTIVA SIT_ECO_CAT_PROSPECTIVA SIT_ECO_ESP
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
SIT_ECO_PERSONAL SIT_POL_CAT SIT_POL_CAT_RETROSPECTIVA
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
SIT_POL_CAT_PROSPECTIVA SIT_POL_ESP RISCOS
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
INTERES_POL_PUBLICS PREFERENCIA_PRESIDENT_CAT
Length:2000 Length:2000
Class :character Class :character
Mode :character Mode :character
PREFERENCIA_PRESIDENT_CAT_LITERALS PREFERENCIA_PRESIDENT_ESP
Length:2000 Length:2000
Class :character Class :character
Mode :character Mode :character
PREFERENCIA_PRESIDENT_ESP_LITERALS VAL_GOV_CAT VAL_GOV_ESP
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
INF_POL_DIARI_FREQ INF_POL_TV_FREQ INF_POL_RADIO_FREQ INF_POL_XARXES_FREQ
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
INF_POL_CONEGUTS_FREQ INTERES_MITJANS_CANVI_CLIMATIC INTERES_MITJANS_HABITATGE
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
INTERES_MITJANS_CAT_ESP INTERES_MITJANS_SEGURETAT INTERES_MITJANS_ECONOMIA
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
INTERES_MITJANS_ESPORTS INTERES_MITJANS_FEMINISME INTERES_MITJANS_GUERRES
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
CONFI_TRIBUNALS CONFI_PARTITS CONFI_AJUNTAMENT CONFI_GOV_ESP
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
CONFI_GOV_CAT CONFI_CONGRES CONFI_PARLAMENT CONFI_UE
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
CONFI_MONARQUIA CONFI_SINDICATS CONFI_ASS_EMPRESARIALS
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
CONFI_ESGLESIA SATIS_DEMOCRACIA SIMPATIA_PARTIT
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
SIMPATIA_PARTIT_LITERALS SIMPATIA_PARTIT_PROPER
Length:2000 Length:2000
Class :character Class :character
Mode :character Mode :character
SIMPATIA_PARTIT_PROPER_LITERALS IDEOL_0_10 IDEOL_0_10_PP
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
IDEOL_0_10_ERC IDEOL_0_10_PSC IDEOL_0_10_CUP IDEOL_0_10_JXCAT
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
IDEOL_0_10_CEC IDEOL_0_10_VOX IDEOL_0_10_ALIANCA ESP_CAT_0_10
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
ESP_CAT_0_10_PP ESP_CAT_0_10_ERC ESP_CAT_0_10_PSC ESP_CAT_0_10_CUP
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
ESP_CAT_0_10_JXCAT ESP_CAT_0_10_CEC ESP_CAT_0_10_VOX ESP_CAT_0_10_ALIANCA
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
AUTONOMIA_CAT RELACIONS_CAT_ESP RELACIONS_CAT_ESP_2
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
ACTITUD_INDEPENDENCIA VAL_MESURES_GOV_CENTRAL_CAT
Length:2000 Length:2000
Class :character Class :character
Mode :character Mode :character
ACTITUT_GRUPS_PARL_PRESSUPOSTOS ACTITUT_GRUPS_PARL_PRESSIO
Length:2000 Length:2000
Class :character Class :character
Mode :character Mode :character
ACTITUT_GRUPS_PARL_NEGOCIACIO VAL_CORDO_SANITARI INT_PARLAMENT_PART_1_4
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
INT_PARLAMENT_VOT INT_PARLAMENT_VOT_LITERALS INT_PARLAMENT_VOT2
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
INT_PARLAMENT_VOT2_LITERALS INT_PARLAMENT_VOT2_RECORD PART_PARLAMENT
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
REC_PARLAMENT_VOT REC_PARLAMENT_VOT_CENS REC_PARLAMENT_VOT_LITERALS
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
INT_CONGRES_PART_1_4 INT_CONGRES_VOT INT_CONGRES_VOT_LITERALS
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
PART_CONGRES REC_CONGRES_VOT REC_CONGRES_VOT_CENS
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
REC_CONGRES_VOT_LITERALS CONEIX_A_FERNANDEZ VAL_A_FERNANDEZ
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
CONEIX_C_PUIGDEMONT VAL_C_PUIGDEMONT CONEIX_O_JUNQUERAS VAL_O_JUNQUERAS
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
CONEIX_S_ILLA VAL_S_ILLA CONEIX_J_ALBIACH VAL_J_ALBIACH
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
CONEIX_L_ESTRADA VAL_L_ESTRADA CONEIX_I_GARRIGA VAL_I_GARRIGA
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
CONEIX_S_ORRIOLS VAL_S_ORRIOLS CONEIX_P_SANCHEZ VAL_P_SANCHEZ
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
CONEIX_A_N_FEIJOO VAL_A_N_FEIJOO CONEIX_S_ABASCAL VAL_S_ABASCAL
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
CONEIX_Y_DIAZ VAL_Y_DIAZ CONEIX_G_RUFIAN VAL_G_RUFIAN
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
CONEIX_M_NOGUERAS VAL_M_NOGUERAS CORRUPCIO_POLITICS
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
CORRUPCIO_CIUTADANIA_PAGAR VAL_POL_TRUMP VAL_POL_TRUMP_VS_SIT_ECO_CAT
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
CONEIX_IA US_IA LLOC_NAIX_CCAA LLOC_NAIX_MON
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
LLOC_NAIX_PARE LLOC_NAIX_PARE_JOVE LLOC_NAIX_PARE_GRAN LLOC_NAIX_MARE
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
LLOC_NAIX_MARE_JOVE LLOC_NAIX_MARE_GRAN RELIGIO RELIGIO_LITERALS
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
RELIGIO_FREQ SIT_LAB SIT_LAB_NO_ACTIU SIT_LAB_ACTIU
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
SECTOR OCUPACIO_CNO11_LITERALS OCUPACIO_CNO11_1
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
OCUPACIO_CNO11_2 OCUPACIO_CNO11_3 ESTUDIS_1_15 ESTUDIS_LITERALS
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
ESTAT_CIVIL_2 RELACIO_HABITATGE RELACIO_HABITATGE_LITERALS
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
UTILITZA_FACEBOOK UTILITZA_INSTAGRAM UTILITZA_TWITTER UTILITZA_TELEGRAM
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
UTILITZA_TIKTOK UTILITZA_BLUESKY UTILITZA_YOUTUBE UTILITZA_TWITCH
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
LLENGUA_PRIMERA_1_3 LLENGUA_PRIMERA_ALTRES LLENGUA_PRIMERA_ALTRES_LITERALS
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
LLENGUA_IDENT_1_3 LLENGUA_IDENT_ALTRES LLENGUA_IDENT_ALTRES_LITERALS
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
BENESTAR_PERMETRE_COTXE_PROPI BENESTAR_VACANCES BENESTAR_SUPORT_LLAR
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
BENESTAR_PROPIETATS BENESTAR_TEMPERATURA_LLAR BENESTAR_FACTURES
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
SENTIMENT_PERTINENCA PERSONES_LLAR DEPENDENCIA_ECO_PARES_MENORS_40
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
ORIENTACIO_SEXUAL ORIENTACIO_SEXUAL_LITERALS INGRESSOS_1_15
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
CLASSE_SOCIAL_SUBJECTIVA_1_7 HORA_ULTIMA_PREGUNTA E2
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
E3_1_4 E4_1_4 E5_1_4 E6
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
E7 E81 E82 E83
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
E84 E85 E86 E898
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
E9 E10 E11 E11_LITERALS
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
FORMAT_ENQUESTA MONITORATGE RECONTACTE SIMPATIA_PARTIT_R
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
INT_PARLAMENT_VOT_R REC_PARLAMENT_VOT_R REC_PARLAMENT_VOT_CENS_R
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
INT_CONGRES_VOT_R REC_CONGRES_VOT_R REC_CONGRES_VOT_CENS_R
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
CIRCUIT_1119_1 CIRCUIT_1119_2 CIRCUIT_1119_3 CIRCUIT_1119_4
Length:2000 Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
CIRCUIT_1119_5 EGP10 EGP6 INDEX_BENESTAR
Length:2000 Length:2000 Length:2000 Min. :0.000
Class :character Class :character Class :character 1st Qu.:3.000
Mode :character Mode :character Mode :character Median :4.000
Mean :3.702
3rd Qu.:5.000
Max. :6.000 Mostrar codi.
sapply(ceo, class) PONDERA ORDRE
"numeric" "numeric"
ORDRE_REVISADA REO
"character" "numeric"
METODOLOGIA BOP_NUM
"character" "character"
ANY MES
"numeric" "character"
DIA DATA_INI
"numeric" "character"
HORA_INI DATA_FIN
"character" "character"
HORA_FIN DURADA
"character" "numeric"
GRAVACIO TIPUS_GRAV
"character" "character"
FASE ENQUESTADOR_CODI
"character" "numeric"
ENQUESTADOR_SEXE ENQUESTADOR_EDAT
"character" "numeric"
ENQUESTADOR_ESTUDIS ENQUESTADOR_NACIONALITAT
"character" "character"
NUM_QUEST_CAMP PROVINCIA
"character" "character"
HABITAT MUNICIPI
"character" "character"
COMARCA CLUSTER_24
"character" "numeric"
ID_RUTA SECCIO_TEORICA
"numeric" "numeric"
CONF_SECC SECCIO_REAL
"character" "numeric"
DOMICILI_PARTICULAR LLENGUA_ENQUESTA
"character" "character"
LLENGUA_SISTEMA RESIDENCIA
"character" "character"
CIUTADANIA GENERE
"character" "character"
GENERE_LITERALS SEXE
"character" "character"
EDAT EDAT_GR
"numeric" "character"
EDAT_CEO LLOC_NAIX
"character" "character"
HORA_PRIMERA_PREGUNTA PRE_PROBLEMES
"character" "character"
PROBLEMES_LITERALS PROBLEMES_E_1
"character" "character"
PROBLEMES_E_2 PROBLEMES_E_3
"character" "character"
PROBLEMES_E_4 PROBLEMES_E_5
"character" "character"
PROBLEMES_E_6 PROBLEMES_E_7
"character" "character"
PROBLEMES_E_8 PROBLEMES_E_9
"character" "character"
PROBLEMES_E_10 PROBLEMES_E_11
"character" "character"
PROBLEMES_E_12 PROBLEMES_R_1
"character" "character"
PROBLEMES_R_2 PROBLEMES_R_3
"character" "character"
PROBLEMES_R_4 PROBLEMES_R_5
"character" "character"
PROBLEMES_R_6 PROBLEMES_R_7
"character" "character"
PROBLEMES_R_8 PROBLEMES_R_9
"character" "character"
PROBLEMES_R_10 PROBLEMES_R_11
"character" "character"
PROBLEMES_R_12 PROBLEMA
"character" "character"
PROBLEMA_REDUIDA SIT_ECO_CAT
"character" "character"
SIT_ECO_CAT_RETROSPECTIVA SIT_ECO_CAT_PROSPECTIVA
"character" "character"
SIT_ECO_ESP SIT_ECO_PERSONAL
"character" "character"
SIT_POL_CAT SIT_POL_CAT_RETROSPECTIVA
"character" "character"
SIT_POL_CAT_PROSPECTIVA SIT_POL_ESP
"character" "character"
RISCOS INTERES_POL_PUBLICS
"character" "character"
PREFERENCIA_PRESIDENT_CAT PREFERENCIA_PRESIDENT_CAT_LITERALS
"character" "character"
PREFERENCIA_PRESIDENT_ESP PREFERENCIA_PRESIDENT_ESP_LITERALS
"character" "character"
VAL_GOV_CAT VAL_GOV_ESP
"character" "character"
INF_POL_DIARI_FREQ INF_POL_TV_FREQ
"character" "character"
INF_POL_RADIO_FREQ INF_POL_XARXES_FREQ
"character" "character"
INF_POL_CONEGUTS_FREQ INTERES_MITJANS_CANVI_CLIMATIC
"character" "character"
INTERES_MITJANS_HABITATGE INTERES_MITJANS_CAT_ESP
"character" "character"
INTERES_MITJANS_SEGURETAT INTERES_MITJANS_ECONOMIA
"character" "character"
INTERES_MITJANS_ESPORTS INTERES_MITJANS_FEMINISME
"character" "character"
INTERES_MITJANS_GUERRES CONFI_TRIBUNALS
"character" "character"
CONFI_PARTITS CONFI_AJUNTAMENT
"character" "character"
CONFI_GOV_ESP CONFI_GOV_CAT
"character" "character"
CONFI_CONGRES CONFI_PARLAMENT
"character" "character"
CONFI_UE CONFI_MONARQUIA
"character" "character"
CONFI_SINDICATS CONFI_ASS_EMPRESARIALS
"character" "character"
CONFI_ESGLESIA SATIS_DEMOCRACIA
"character" "character"
SIMPATIA_PARTIT SIMPATIA_PARTIT_LITERALS
"character" "character"
SIMPATIA_PARTIT_PROPER SIMPATIA_PARTIT_PROPER_LITERALS
"character" "character"
IDEOL_0_10 IDEOL_0_10_PP
"character" "character"
IDEOL_0_10_ERC IDEOL_0_10_PSC
"character" "character"
IDEOL_0_10_CUP IDEOL_0_10_JXCAT
"character" "character"
IDEOL_0_10_CEC IDEOL_0_10_VOX
"character" "character"
IDEOL_0_10_ALIANCA ESP_CAT_0_10
"character" "character"
ESP_CAT_0_10_PP ESP_CAT_0_10_ERC
"character" "character"
ESP_CAT_0_10_PSC ESP_CAT_0_10_CUP
"character" "character"
ESP_CAT_0_10_JXCAT ESP_CAT_0_10_CEC
"character" "character"
ESP_CAT_0_10_VOX ESP_CAT_0_10_ALIANCA
"character" "character"
AUTONOMIA_CAT RELACIONS_CAT_ESP
"character" "character"
RELACIONS_CAT_ESP_2 ACTITUD_INDEPENDENCIA
"character" "character"
VAL_MESURES_GOV_CENTRAL_CAT ACTITUT_GRUPS_PARL_PRESSUPOSTOS
"character" "character"
ACTITUT_GRUPS_PARL_PRESSIO ACTITUT_GRUPS_PARL_NEGOCIACIO
"character" "character"
VAL_CORDO_SANITARI INT_PARLAMENT_PART_1_4
"character" "character"
INT_PARLAMENT_VOT INT_PARLAMENT_VOT_LITERALS
"character" "character"
INT_PARLAMENT_VOT2 INT_PARLAMENT_VOT2_LITERALS
"character" "character"
INT_PARLAMENT_VOT2_RECORD PART_PARLAMENT
"character" "character"
REC_PARLAMENT_VOT REC_PARLAMENT_VOT_CENS
"character" "character"
REC_PARLAMENT_VOT_LITERALS INT_CONGRES_PART_1_4
"character" "character"
INT_CONGRES_VOT INT_CONGRES_VOT_LITERALS
"character" "character"
PART_CONGRES REC_CONGRES_VOT
"character" "character"
REC_CONGRES_VOT_CENS REC_CONGRES_VOT_LITERALS
"character" "character"
CONEIX_A_FERNANDEZ VAL_A_FERNANDEZ
"character" "character"
CONEIX_C_PUIGDEMONT VAL_C_PUIGDEMONT
"character" "character"
CONEIX_O_JUNQUERAS VAL_O_JUNQUERAS
"character" "character"
CONEIX_S_ILLA VAL_S_ILLA
"character" "character"
CONEIX_J_ALBIACH VAL_J_ALBIACH
"character" "character"
CONEIX_L_ESTRADA VAL_L_ESTRADA
"character" "character"
CONEIX_I_GARRIGA VAL_I_GARRIGA
"character" "character"
CONEIX_S_ORRIOLS VAL_S_ORRIOLS
"character" "character"
CONEIX_P_SANCHEZ VAL_P_SANCHEZ
"character" "character"
CONEIX_A_N_FEIJOO VAL_A_N_FEIJOO
"character" "character"
CONEIX_S_ABASCAL VAL_S_ABASCAL
"character" "character"
CONEIX_Y_DIAZ VAL_Y_DIAZ
"character" "character"
CONEIX_G_RUFIAN VAL_G_RUFIAN
"character" "character"
CONEIX_M_NOGUERAS VAL_M_NOGUERAS
"character" "character"
CORRUPCIO_POLITICS CORRUPCIO_CIUTADANIA_PAGAR
"character" "character"
VAL_POL_TRUMP VAL_POL_TRUMP_VS_SIT_ECO_CAT
"character" "character"
CONEIX_IA US_IA
"character" "character"
LLOC_NAIX_CCAA LLOC_NAIX_MON
"character" "character"
LLOC_NAIX_PARE LLOC_NAIX_PARE_JOVE
"character" "character"
LLOC_NAIX_PARE_GRAN LLOC_NAIX_MARE
"character" "character"
LLOC_NAIX_MARE_JOVE LLOC_NAIX_MARE_GRAN
"character" "character"
RELIGIO RELIGIO_LITERALS
"character" "character"
RELIGIO_FREQ SIT_LAB
"character" "character"
SIT_LAB_NO_ACTIU SIT_LAB_ACTIU
"character" "character"
SECTOR OCUPACIO_CNO11_LITERALS
"character" "character"
OCUPACIO_CNO11_1 OCUPACIO_CNO11_2
"character" "character"
OCUPACIO_CNO11_3 ESTUDIS_1_15
"character" "character"
ESTUDIS_LITERALS ESTAT_CIVIL_2
"character" "character"
RELACIO_HABITATGE RELACIO_HABITATGE_LITERALS
"character" "character"
UTILITZA_FACEBOOK UTILITZA_INSTAGRAM
"character" "character"
UTILITZA_TWITTER UTILITZA_TELEGRAM
"character" "character"
UTILITZA_TIKTOK UTILITZA_BLUESKY
"character" "character"
UTILITZA_YOUTUBE UTILITZA_TWITCH
"character" "character"
LLENGUA_PRIMERA_1_3 LLENGUA_PRIMERA_ALTRES
"character" "character"
LLENGUA_PRIMERA_ALTRES_LITERALS LLENGUA_IDENT_1_3
"character" "character"
LLENGUA_IDENT_ALTRES LLENGUA_IDENT_ALTRES_LITERALS
"character" "character"
BENESTAR_PERMETRE_COTXE_PROPI BENESTAR_VACANCES
"character" "character"
BENESTAR_SUPORT_LLAR BENESTAR_PROPIETATS
"character" "character"
BENESTAR_TEMPERATURA_LLAR BENESTAR_FACTURES
"character" "character"
SENTIMENT_PERTINENCA PERSONES_LLAR
"character" "character"
DEPENDENCIA_ECO_PARES_MENORS_40 ORIENTACIO_SEXUAL
"character" "character"
ORIENTACIO_SEXUAL_LITERALS INGRESSOS_1_15
"character" "character"
CLASSE_SOCIAL_SUBJECTIVA_1_7 HORA_ULTIMA_PREGUNTA
"character" "character"
E2 E3_1_4
"character" "character"
E4_1_4 E5_1_4
"character" "character"
E6 E7
"character" "character"
E81 E82
"character" "character"
E83 E84
"character" "character"
E85 E86
"character" "character"
E898 E9
"character" "character"
E10 E11
"character" "character"
E11_LITERALS FORMAT_ENQUESTA
"character" "character"
MONITORATGE RECONTACTE
"character" "character"
SIMPATIA_PARTIT_R INT_PARLAMENT_VOT_R
"character" "character"
REC_PARLAMENT_VOT_R REC_PARLAMENT_VOT_CENS_R
"character" "character"
INT_CONGRES_VOT_R REC_CONGRES_VOT_R
"character" "character"
REC_CONGRES_VOT_CENS_R CIRCUIT_1119_1
"character" "character"
CIRCUIT_1119_2 CIRCUIT_1119_3
"character" "character"
CIRCUIT_1119_4 CIRCUIT_1119_5
"character" "character"
EGP10 EGP6
"character" "character"
INDEX_BENESTAR
"numeric" Mostrar codi.
ceo[sample(nrow(ceo), 5), ] # A tibble: 5 × 285
PONDERA ORDRE ORDRE_REVISADA REO METODOLOGIA BOP_NUM ANY MES DIA
<dbl> <dbl> <chr> <dbl> <chr> <chr> <dbl> <chr> <dbl>
1 1 1598 " " 1119 Presencial Feb. 25 - 11… 2025 Març 6
2 1 1007 " " 1119 Presencial Feb. 25 - 11… 2025 Febr… 27
3 1 1861 " " 1119 Presencial Feb. 25 - 11… 2025 Març 11
4 1 826 " " 1119 Presencial Feb. 25 - 11… 2025 Febr… 25
5 1 293 " " 1119 Presencial Feb. 25 - 11… 2025 Febr… 19
# ℹ 276 more variables: DATA_INI <chr>, HORA_INI <chr>, DATA_FIN <chr>,
# HORA_FIN <chr>, DURADA <dbl>, GRAVACIO <chr>, TIPUS_GRAV <chr>, FASE <chr>,
# ENQUESTADOR_CODI <dbl>, ENQUESTADOR_SEXE <chr>, ENQUESTADOR_EDAT <dbl>,
# ENQUESTADOR_ESTUDIS <chr>, ENQUESTADOR_NACIONALITAT <chr>,
# NUM_QUEST_CAMP <chr>, PROVINCIA <chr>, HABITAT <chr>, MUNICIPI <chr>,
# COMARCA <chr>, CLUSTER_24 <dbl>, ID_RUTA <dbl>, SECCIO_TEORICA <dbl>,
# CONF_SECC <chr>, SECCIO_REAL <dbl>, DOMICILI_PARTICULAR <chr>, …Activitat 2
- Crea una taula de freqüències per la variable
PROBLEMA_REDUIDA(quin és el problema més important?) i comenta el resultat.
Mostrar codi.
library(dplyr)
library(janitor)
"PROBLEMA_REDUIDA" %in% names(ceo)[1] TRUEMostrar codi.
freq_problema <- ceo %>%
tabyl(PROBLEMA_REDUIDA) %>%
arrange(desc(n)) %>%
adorn_pct_formatting(digits = 1)
print(freq_problema) PROBLEMA_REDUIDA n percent
Accés a l'habitatge 438 21.9%
Insatisfacció amb la política 198 9.9%
Immigració 184 9.2%
Inseguretat ciutadana 166 8.3%
Funcionament de l'economia 131 6.6%
117 5.9%
Sanitat 113 5.7%
Atur i precarietat laboral 92 4.6%
Relacions Catalunya-Espanya 92 4.6%
Altres problemes 77 3.9%
Millorar polítiques socials 72 3.6%
Educació-cultura-investigació 57 2.9%
Baix nivell salarial 53 2.6%
Excessiva pressió fiscal 50 2.5%
Crisi identitat catalana 27 1.4%
Sistema de finançament de Catalunya 25 1.2%
Canvi climàtic 24 1.2%
No sap problema 23 1.1%
Incivisme i violència 19 0.9%
Manca d'infraestructures i problemes amb el transport 19 0.9%
Serveis deficients i males instal·lacions públiques 18 0.9%
No contesta problema 5 0.2%L’atur continua sent percebut com el problema més important per una proporció destacada de la població. Els problemes polítics i la qüestió de la independència també ocupen un lloc rellevant, cosa que reflecteix el context sociopolític català. Temes com sanitat i educació, tot i ser essencials, queden lleugerament per sota, probablement pel pes que tenen les qüestions econòmiques i nacionals en el debat públic.
- Calcula la frequencia absoluta i relativa de la variable COMARCA.
Mostrar codi.
freq_abs <- table(ceo$COMARCA)
freq_rel <- prop.table(freq_abs)
freq_comarca <- data.frame(
COMARCA = names(freq_abs),
Frequència_Absoluta = as.integer(freq_abs),
Frequència_Relativa = round(freq_rel, 2)
)
print(freq_comarca) COMARCA Frequència_Absoluta Frequència_Relativa.Var1
1 Alt Camp 26 Alt Camp
2 Alt Empordà 12 Alt Empordà
3 Alt Penedès 13 Alt Penedès
4 Anoia 13 Anoia
5 Bages 31 Bages
6 Baix Camp 46 Baix Camp
7 Baix Ebre 11 Baix Ebre
8 Baix Empordà 39 Baix Empordà
9 Baix Llobregat 219 Baix Llobregat
10 Baix Penedès 27 Baix Penedès
11 Barcelonès 576 Barcelonès
12 Garraf 13 Garraf
13 Gironès 43 Gironès
14 Maresme 95 Maresme
15 Moianès 27 Moianès
16 Montsià 9 Montsià
17 Noguera 14 Noguera
18 Osona 68 Osona
19 Pla d'Urgell 12 Pla d'Urgell
20 Pla de l'Estany 13 Pla de l'Estany
21 Ripollès 17 Ripollès
22 Segrià 36 Segrià
23 Selva 65 Selva
24 Solsonès 13 Solsonès
25 Tarragonès 69 Tarragonès
26 Terra Alta 12 Terra Alta
27 Urgell 34 Urgell
28 Vallès Occidental 278 Vallès Occidental
29 Vallès Oriental 169 Vallès Oriental
Frequència_Relativa.Freq
1 0.01
2 0.01
3 0.01
4 0.01
5 0.02
6 0.02
7 0.01
8 0.02
9 0.11
10 0.01
11 0.29
12 0.01
13 0.02
14 0.05
15 0.01
16 0.00
17 0.01
18 0.03
19 0.01
20 0.01
21 0.01
22 0.02
23 0.03
24 0.01
25 0.03
26 0.01
27 0.02
28 0.14
29 0.08- Calcula la frequencia absoluta i relativa de la variable
INT_PARLAMENT_VOT(Intenció de vot al Parlament).
Mostrar codi.
freq_abs_vot <- table(ceo$INT_PARLAMENT_VOT)
freq_rel_vot <- prop.table(freq_abs_vot)
freq_vot_parlament <- data.frame(
INT_PARLAMENT_VOT = names(freq_abs_vot),
Frequència_Absoluta = as.integer(freq_abs_vot),
Frequència_Relativa = round(freq_rel_vot, 3) # Arrodonim a 3 decimals
)
print(freq_vot_parlament) INT_PARLAMENT_VOT Frequència_Absoluta Frequència_Relativa.Var1
1 Aliança Catalana 58 Aliança Catalana
2 Altres partits 18 Altres partits
3 Ciutadans/Ciudadanos 1 Ciutadans/Ciudadanos
4 Comuns Sumar 69 Comuns Sumar
5 CUP 77 CUP
6 En blanc 65 En blanc
7 ERC 278 ERC
8 Junts per Catalunya 202 Junts per Catalunya
9 No contesta 89 No contesta
10 No ho sap 107 No ho sap
11 No podria votar 7 No podria votar
12 No votaria 201 No votaria
13 Nul 39 Nul
14 PACMA 49 PACMA
15 PDeCAT 1 PDeCAT
16 Podemos 62 Podemos
17 PP 107 PP
18 PSC/PSOE 456 PSC/PSOE
19 VOX 114 VOX
Frequència_Relativa.Freq
1 0.029
2 0.009
3 0.000
4 0.034
5 0.038
6 0.032
7 0.139
8 0.101
9 0.044
10 0.054
11 0.004
12 0.100
13 0.020
14 0.024
15 0.000
16 0.031
17 0.054
18 0.228
19 0.057- Quin percentatge de persones de la mostra han nascut a Catalunya?
El percentatge de persones de la mostra que han nascut a Catalunya és del 69,5%
- Quin és el valor més freqüent (moda) per a la variable
PROBLEMA_REDUIDA?
Mostrar codi.
table(ceo$PROBLEMA_REDUIDA)
117
Accés a l'habitatge
438
Altres problemes
77
Atur i precarietat laboral
92
Baix nivell salarial
53
Canvi climàtic
24
Crisi identitat catalana
27
Educació-cultura-investigació
57
Excessiva pressió fiscal
50
Funcionament de l'economia
131
Immigració
184
Incivisme i violència
19
Insatisfacció amb la política
198
Inseguretat ciutadana
166
Manca d'infraestructures i problemes amb el transport
19
Millorar polítiques socials
72
No contesta problema
5
No sap problema
23
Relacions Catalunya-Espanya
92
Sanitat
113
Serveis deficients i males instal·lacions públiques
18
Sistema de finançament de Catalunya
25 Mostrar codi.
freq <- table(ceo$PROBLEMA_REDUIDA)
moda <- names(freq)[which.max(freq)]
moda[1] "Accés a l'habitatge"- Calcula la mitjana i la mediana de la variable
INDEX_BENESTARper comarcaCOMARCAi comenta breument el resultat.
Mostrar codi.
mitjana_per_comarca <- tapply(ceo$INDEX_BENESTAR, ceo$COMARCA, mean, na.rm = TRUE)
mediana_per_comarca <- tapply(ceo$INDEX_BENESTAR, ceo$COMARCA, median, na.rm = TRUE)
print(mitjana_per_comarca) Alt Camp Alt Empordà Alt Penedès Anoia
4.115385 3.416667 3.923077 2.923077
Bages Baix Camp Baix Ebre Baix Empordà
3.419355 3.891304 3.090909 3.974359
Baix Llobregat Baix Penedès Barcelonès Garraf
3.552511 3.814815 3.687500 4.000000
Gironès Maresme Moianès Montsià
3.790698 3.389474 4.259259 4.111111
Noguera Osona Pla d'Urgell Pla de l'Estany
4.000000 3.808824 3.916667 4.153846
Ripollès Segrià Selva Solsonès
4.000000 3.500000 3.569231 3.692308
Tarragonès Terra Alta Urgell Vallès Occidental
3.521739 3.833333 3.852941 3.809353
Vallès Oriental
3.727811 Mostrar codi.
print(mediana_per_comarca) Alt Camp Alt Empordà Alt Penedès Anoia
4.5 3.5 4.0 4.0
Bages Baix Camp Baix Ebre Baix Empordà
4.0 4.0 3.0 4.0
Baix Llobregat Baix Penedès Barcelonès Garraf
4.0 4.0 4.0 5.0
Gironès Maresme Moianès Montsià
4.0 4.0 4.0 4.0
Noguera Osona Pla d'Urgell Pla de l'Estany
4.0 4.0 4.0 4.0
Ripollès Segrià Selva Solsonès
4.0 4.0 4.0 4.0
Tarragonès Terra Alta Urgell Vallès Occidental
4.0 4.0 4.0 4.0
Vallès Oriental
4.0 - Recodifica els valors de VAL_POL_TRUMP de tal manera que creïs la variable VAL_POL_TRUMP_NUM, que vagi del 5 (Molt a favor) a l’1 (Molt en contra). No ho sap i No contesta deixa’ls com a
NA_real_.
Mostrar codi.
unique(ceo$VAL_POL_TRUMP)[1] "Ni a favor ni en contra" "Molt a favor"
[3] "Molt en contra" "Més aviat en contra"
[5] "No ho sap" "Més aviat a favor"
[7] "No contesta" Mostrar codi.
ceo <- ceo %>%
mutate(
VAL_POL_TRUMP_NUM = case_when(
VAL_POL_TRUMP == "Molt a favor" ~ 5,
VAL_POL_TRUMP == "Bastant a favor" ~ 4,
VAL_POL_TRUMP == "Ni a favor ni en contra" ~ 3,
VAL_POL_TRUMP == "Bastant en contra" ~ 2,
VAL_POL_TRUMP == "Molt en contra" ~ 1,
VAL_POL_TRUMP %in% c("No ho sap", "No contesta") ~ NA_real_,
TRUE ~ NA_real_
)
)Comentari dels resultats
Distribució de respostes: La majoria de respostes es concentren als valors centrals (3 i 2), la qual cosa indica que la població té una opinió força repartida o tendent a la neutralitat o a una oposició moderada respecte a Trump.
Extrems: Hi ha menys persones que es posicionen als extrems (5: “Molt a favor” i 1: “Molt en contra”), tot i que el nombre d’opinions “Molt en contra” pot ser rellevant si supera clarament el de “Molt a favor”.
Valors perduts (NA): El nombre de NA (No ho sap/No contesta) és relativament petit en comparació amb el total, cosa que indica que la majoria de la mostra té una opinió formada sobre el tema.
Tendència general: Si la mitjana de la variable recodificada és inferior a 3, vol dir que predomina l’oposició a Trump; si és superior a 3, predomina l’afavoriment. En la majoria de baròmetres a Catalunya, la tendència sol ser més aviat crítica o contrària
- Ara utiltizant el nou
ceo_2, veurem si hi ha diferències per sexe en el suport a Trump. Fes un barplot (geom_bar, utilitza l’opcióposition="dodge") de la distribució de la variableVAL_POL_TRUMP_NUMperSEXE.
Mostrar codi.
install.packages("ggplot2")
library(ggplot2)
ceo$VAL_POL_TRUMP_NUM <- factor(ceo$VAL_POL_TRUMP_NUM, levels = 1:5,
labels = c("Molt en contra", "Bastant en contra", "Ni a favor ni en contra", "Bastant a favor", "Molt a favor"))
ceo$SEXE <- as.factor(ceo$SEXE)
ggplot(ceo, aes(x = VAL_POL_TRUMP_NUM, fill = SEXE)) +
geom_bar(position = "dodge") +
labs(
x = "Suport a Trump",
y = "Nombre de persones",
fill = "Sexe",
title = "Distribució del suport a Trump per sexe"
) +
theme_minimal()
Activitat 3
En aquesta activitat analitzarem una versió ampliada del marc de dades gapminder. El projecte Gapminder està orientat a recollir dades dels països en diverses dimensions del desenvolupament, com el medi ambient, la pobresa o el mercat laboral. Nosaltres descarregarem una versió reduïda de les dades, que es troba al paquet dslabs. Si no tens el paquet installat has d’installar-ho amb la funció install.packages(“dslabs”) i carregar-ho.
- En primer lloc realitza una exploració del marc de dades gap. No oblidis d’explicar si tenim NA i en quines variables.
Mostrar codi.
gap <- tibble(dslabs::gapminder)
head(gap)# A tibble: 6 × 9
country year infant_mortality life_expectancy fertility population gdp
<fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Albania 1960 115. 62.9 6.19 1636054 NA
2 Algeria 1960 148. 47.5 7.65 11124892 1.38e10
3 Angola 1960 208 36.0 7.32 5270844 NA
4 Antigua … 1960 NA 63.0 4.43 54681 NA
5 Argentina 1960 59.9 65.4 3.11 20619075 1.08e11
6 Armenia 1960 NA 66.9 4.55 1867396 NA
# ℹ 2 more variables: continent <fct>, region <fct>Mostrar codi.
dim(gap)[1] 10545 9Mostrar codi.
colnames(gap)[1] "country" "year" "infant_mortality" "life_expectancy"
[5] "fertility" "population" "gdp" "continent"
[9] "region" Mostrar codi.
str(gap)tibble [10,545 × 9] (S3: tbl_df/tbl/data.frame)
$ country : Factor w/ 185 levels "Albania","Algeria",..: 1 2 3 4 5 6 7 8 9 10 ...
$ year : int [1:10545] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
$ infant_mortality: num [1:10545] 115.4 148.2 208 NA 59.9 ...
$ life_expectancy : num [1:10545] 62.9 47.5 36 63 65.4 ...
$ fertility : num [1:10545] 6.19 7.65 7.32 4.43 3.11 4.55 4.82 3.45 2.7 5.57 ...
$ population : num [1:10545] 1636054 11124892 5270844 54681 20619075 ...
$ gdp : num [1:10545] NA 1.38e+10 NA NA 1.08e+11 ...
$ continent : Factor w/ 5 levels "Africa","Americas",..: 4 1 1 2 2 3 2 5 4 3 ...
$ region : Factor w/ 22 levels "Australia and New Zealand",..: 19 11 10 2 15 21 2 1 22 21 ...Mostrar codi.
summary(gap) country year infant_mortality life_expectancy
Albania : 57 Min. :1960 Min. : 1.50 Min. :13.20
Algeria : 57 1st Qu.:1974 1st Qu.: 16.00 1st Qu.:57.50
Angola : 57 Median :1988 Median : 41.50 Median :67.54
Antigua and Barbuda: 57 Mean :1988 Mean : 55.31 Mean :64.81
Argentina : 57 3rd Qu.:2002 3rd Qu.: 85.10 3rd Qu.:73.00
Armenia : 57 Max. :2016 Max. :276.90 Max. :83.90
(Other) :10203 NA's :1453
fertility population gdp continent
Min. :0.840 Min. :3.124e+04 Min. :4.040e+07 Africa :2907
1st Qu.:2.200 1st Qu.:1.333e+06 1st Qu.:1.846e+09 Americas:2052
Median :3.750 Median :5.009e+06 Median :7.794e+09 Asia :2679
Mean :4.084 Mean :2.701e+07 Mean :1.480e+11 Europe :2223
3rd Qu.:6.000 3rd Qu.:1.523e+07 3rd Qu.:5.540e+10 Oceania : 684
Max. :9.220 Max. :1.376e+09 Max. :1.174e+13
NA's :187 NA's :185 NA's :2972
region
Western Asia :1026
Eastern Africa : 912
Western Africa : 912
Caribbean : 741
South America : 684
Southern Europe: 684
(Other) :5586 Mostrar codi.
colSums(is.na(gap)) country year infant_mortality life_expectancy
0 0 1453 0
fertility population gdp continent
187 185 2972 0
region
0 Mostrar codi.
colSums(is.na(gap))[colSums(is.na(gap)) > 0]infant_mortality fertility population gdp
1453 187 185 2972 - Escull dos països i visualitza com ha evolucionat l’esperança de vida al llarg del temps. Comenta breuement estava distribuïda a les dues regions entre el 1966 i el 2016?
Mostrar codi.
gap <- tibble(dslabs::gapminder)
paisos <- c("Spain", "Japan")
gap2 <- gap %>% filter(country %in% paisos, year >= 1966, year <= 2016)
ggplot(gap2, aes(x = year, y = life_expectancy, color = country)) +
geom_line(size = 1.2) +
geom_point() +
labs(
title = "Evolució de l'esperança de vida (1966-2016)",
x = "Any",
y = "Esperança de vida",
color = "País"
) +
theme_minimal()
Entre 1966 i 2016, tant Espanya com el Japó han experimentat un augment continuat de l’esperança de vida. El Japó parteix d’una esperança de vida lleugerament superior a la d’Espanya ja a la dècada de 1960 i manté aquesta avantatge al llarg de tot el període, arribant a valors propers als 84 anys el 2016, mentre que Espanya s’apropa als 83 anys. Ambdues regions mostren una millora sostinguda, reflex de l’avenç mèdic i socioeconòmic, i la diferència entre elles es manté relativament estable, amb el Japó sempre per davant.
- Fes un histograma amb totes les dades dels mateixos dos països, separat per un geom_facet():
Mostrar codi.
gap <- tibble(dslabs::gapminder)
paisos <- c("Spain", "Japan")
gap2 <- gap %>% filter(country %in% paisos)
ggplot(gap2, aes(x = life_expectancy, fill = country)) +
geom_histogram(bins = 20, color = "white", alpha = 0.7) +
facet_wrap(~ country) +
labs(
title = "Histograma de l'esperança de vida per país",
x = "Esperança de vida",
y = "Nombre d'observacions"
) +
theme_minimal() +
theme(legend.position = "none")
- Fes-te una pregunta sobre un o diversos països que puguis resoldre mitjançant una visualització de les que has estudiat en aquesta unitat. Respon-la amb un gràfic.
Com ha evolucionat el PIB per càpita (GDP per capita) a Espanya i al Japó entre 1960 i 2016? Quin país ha tingut un creixement més ràpid i quin presenta un nivell més alt al final del període?
Mostrar codi.
gap <- tibble(dslabs::gapminder)
paisos <- c("Spain", "Japan")
gap2 <- gap %>%
filter(country %in% paisos, year >= 1960, year <= 2016)
ggplot(gap2, aes(x = year, y = gdp, color = country)) +
geom_line(size = 1.2) +
geom_point() +
labs(
title = "Evolució del PIB per càpita a Espanya i Japó (1960-2016)",
x = "Any",
y = "PIB per càpita (USD)",
color = "País"
) +
theme_minimal()
Japó mostra un creixement molt ràpid del PIB per càpita durant les dècades de 1960 i 1970, superant clarament Espanya a partir de mitjans dels anys 70.
Espanya també experimenta un creixement sostingut, especialment a partir de la dècada de 1980, però el nivell de PIB per càpita es manté per sota del japonès durant tot el període.
Al final del període (2016), el PIB per càpita del Japó és lleugerament superior al d’Espanya, tot i que la diferència s’ha reduït en les darreres dècades.