Homework2
2025-05-12
library(foreign)
#install.packages("likert") # required to calculate Cronbach's alpha
library(ltm)## Loading required package: MASS## Loading required package: msm## Loading required package: polycorlibrary(likert) # create basic Likert tables and plots## Loading required package: ggplot2## Loading required package: xtablelibrary(kableExtra)
# for datasets, see release guide pp 22ff
# read data and assign to data frame
setwd("/Users/annarendez/Desktop/Master/1.Semester/Quantitavie Forschung/R Data")
df = read.spss("ESS11.sav", to.data.frame = T)## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25,
## 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 45, 46, 48, 50, 52, 55, 59,
## 60, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 74, 75, 76, 80, 82, 83, 85, 87,
## 88, 90, 91, 92, 93, 95, 96, 98, 99, 100, 102, 103, 105, 107, 108, 110, 115,
## 119, 120, 121, 122, 123, 125, 128, 129, 130, 131, 132, 133, 135, 139, 140, 145,
## 149, 150, 154, 155, 158, 160, 161, 165, 170, 175, 179, 180, 181, 182, 183, 185,
## 187, 188, 189, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 238, 240, 241,
## 243, 244, 250, 255, 260, 265, 270, 276, 277, 280, 285, 290, 300, 301, 305, 306,
## 310, 315, 320, 330, 350, 359, 360, 361, 366, 368, 370, 385, 390, 420, 435, 450,
## 480, 481, 484, 490, 493, 495, 505, 510, 515, 523, 528, 529, 530, 531, 533, 535,
## 537, 538, 539, 540, 541, 542, 543, 545, 546, 547, 548, 549, 550, 551, 553, 555,
## 556, 558, 559, 560, 561, 563, 565, 566, 567, 568, 570, 571, 572, 573, 575, 576,
## 578, 579, 580, 581, 582, 584, 585, 586, 589, 590, 592, 593, 594, 595, 596, 597,
## 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613,
## 614, 615, 616, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630,
## 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 645, 647, 649, 650, 653,
## 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 668, 669, 670,
## 672, 674, 675, 676, 680, 681, 684, 685, 686, 687, 688, 689, 690, 691, 692, 694,
## 695, 696, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 713, 715,
## 716, 717, 719, 720, 722, 724, 725, 727, 728, 729, 730, 731, 732, 733, 734, 735,
## 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 749, 750, 751, 752, 753,
## 754, 756, 757, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 772, 773,
## 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 789, 790, 791,
## 792, 793, 794, 795, 796, 797, 798, 800, 801, 802, 803, 804, 805, 806, 807, 809,
## 810, 811, 812, 813, 815, 816, 818, 819, 820, 821, 824, 825, 826, 827, 828, 829,
## 830, 831, 832, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846,
## 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 858, 859, 860, 861, 864, 865,
## 866, 867, 868, 869, 870, 871, 872, 873, 875, 876, 877, 878, 879, 880, 881, 882,
## 883, 884, 885, 886, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899,
## 900, 901, 902, 903, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916,
## 917, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 930, 931, 932, 933, 934,
## 935, 937, 938, 939, 940, 941, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953,
## 954, 955, 956, 957, 959, 960, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971,
## 972, 973, 974, 975, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989,
## 990, 992, 994, 995, 996, 997, 1000, 1002, 1003, 1004, 1005, 1006, 1007, 1008,
## 1010, 1011, 1014, 1015, 1016, 1017, 1018, 1020, 1021, 1022, 1023, 1024, 1025,
## 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038,
## 1039, 1040, 1041, 1042, 1043, 1045, 1046, 1048, 1049, 1050, 1052, 1054, 1055,
## 1056, 1058, 1059, 1060, 1061, 1063, 1064, 1065, 1068, 1069, 1070, 1073, 1075,
## 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1084, 1085, 1086, 1090, 1092, 1093,
## 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1103, 1104, 1105, 1106, 1107, 1108,
## 1109, 1110, 1112, 1113, 1114, 1115, 1116, 1117, 1119, 1120, 1122, 1124, 1125,
## 1126, 1127, 1128, 1130, 1135, 1137, 1138, 1140, 1142, 1143, 1144, 1145, 1147,
## 1148, 1150, 1153, 1154, 1157, 1160, 1162, 1163, 1165, 1168, 1170, 1172, 1176,
## 1179, 1185, 1189, 1190, 1196, 1200, 1420, 1439 added in variable: nwspol## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 2, 3, 4, 5, 7, 10, 14, 15, 20, 25, 30, 34, 35, 38, 39, 40, 42, 44, 45, 50, 55,
## 59, 60, 61, 62, 63, 65, 67, 68, 70, 74, 75, 79, 80, 85, 88, 90, 91, 95, 100,
## 102, 105, 108, 110, 115, 117, 119, 120, 121, 122, 123, 124, 125, 127, 128, 130,
## 134, 135, 136, 138, 140, 141, 145, 149, 150, 153, 154, 155, 158, 160, 165, 168,
## 170, 174, 175, 177, 179, 180, 181, 182, 183, 184, 185, 188, 190, 193, 195, 197,
## 198, 200, 205, 208, 209, 210, 211, 212, 213, 220, 225, 230, 238, 240, 241, 242,
## 243, 244, 245, 248, 250, 251, 255, 257, 260, 265, 270, 278, 280, 285, 290, 292,
## 295, 298, 299, 300, 301, 302, 303, 305, 306, 308, 310, 315, 320, 325, 326, 330,
## 340, 345, 350, 360, 361, 362, 365, 366, 368, 369, 370, 375, 380, 385, 390, 395,
## 398, 400, 405, 410, 415, 420, 422, 425, 428, 430, 435, 440, 450, 454, 457, 459,
## 460, 465, 470, 474, 480, 481, 482, 485, 488, 489, 490, 495, 496, 500, 505, 507,
## 510, 518, 520, 525, 530, 535, 540, 541, 543, 550, 555, 560, 565, 570, 578, 585,
## 590, 595, 599, 600, 601, 602, 605, 606, 607, 610, 615, 620, 630, 639, 640, 650,
## 655, 659, 660, 661, 662, 668, 670, 690, 694, 695, 705, 710, 716, 720, 721, 730,
## 733, 735, 750, 765, 770, 778, 780, 785, 788, 801, 810, 840, 868, 870, 873, 900,
## 915, 930, 955, 960, 962, 965, 970, 990, 1020, 1080, 1109, 1200, 1215, 1230,
## 1260, 1320, 1322, 1380, 1439, 1440 added in variable: netustm## Warning in read.spss("ESS11.sav", to.data.frame = T): Duplicated levels in
## factor prtvtbrs: Ivica Dačić — Premijer Srbije, Dr Vojislav Šešelj - Srpska
## radikalna stranka, Other, Invalid ballot## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1934,
## 1936, 1938, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950,
## 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963,
## 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,
## 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989,
## 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 added in variable: livecnta## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 666
## added in variable: lnghom2## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 52000
## added in variable: fbrncntc## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 52000
## added in variable: mbrncntc## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 2, 3, 4, 5, 6, 7 added in variable: dosprt## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 4, 4.30000019073486, 5, 6, 7, 7.19999980926514, 8, 8.60000038146973, 9,
## 9.60000038146973, 10, 10.6000003814697, 11, 11.5, 12, 13, 13.1999998092651, 14,
## 14.3000001907349, 15, 15.6000003814697, 16, 16.7999992370605, 17, 18, 19,
## 19.2000007629395, 19.6000003814697, 19.7000007629395, 19.8999996185303, 20,
## 20.3999996185303, 21, 22, 22.7999992370605, 23, 23.2000007629395, 24, 25,
## 25.2000007629395, 25.6000003814697, 26, 26.3999996185303, 27, 27.2000007629395,
## 28, 28.7999992370605, 29, 29.2000007629395, 29.2999992370605, 29.6000003814697,
## 30, 31, 31.2000007629395, 32, 32.4000015258789, 32.7999992370605, 33, 34,
## 34.4000015258789, 35, 35.7999992370605, 36, 36.7000007629395, 36.7999992370605,
## 37, 37.4000015258789, 38, 38.4000015258789, 38.9000015258789, 39,
## 39.2000007629395, 39.4000015258789, 39.5999984741211, 40, 41, 42,
## 42.7999992370605, 43, 44, 44.4000015258789, 45, 45.2000007629395,
## 45.5999984741211, 46, 46.0999984741211, 47, 47.2000007629395, 48,
## 48.7999992370605, 49, 49.2000007629395, 49.5999984741211, 50, 51, 52,
## 52.2999992370605, 52.4000015258789, 52.7999992370605, 53, 54, 55, 56,
## 56.4000015258789, 56.5999984741211, 57, 57.5999984741211, 58, 58.4000015258789,
## 59, 59.2000007629395, 60, 61, 61.2000007629395, 62, 63, 64, 65,
## 65.5999984741211, 66, 67, 67.1999969482422, 68, 68.4000015258789,
## 68.8000030517578, 69, 69.1999969482422, 69.5999984741211, 70, 70.8000030517578,
## 71, 72, 73, 73.9000015258789, 74, 75, 76, 76.8000030517578, 77,
## 77.5999984741211, 78, 78.4000015258789, 78.8000030517578, 79, 79.5999984741211,
## 80, 81, 81.1999969482422, 81.8000030517578, 82, 82.0999984741211, 83, 84, 85,
## 86, 86.4000015258789, 87, 87.5999984741211, 87.8000030517578, 88, 89, 90, 91,
## 91.1999969482422, 92, 93, 94, 95, 96, 97, 97.1999969482422, 98, 98.5, 99,
## 99.5999984741211, 100, 101, 102, 103, 103.199996948242, 104, 105, 106, 107,
## 108, 109, 109.599998474121, 110, 111, 112, 112.800003051758, 113,
## 113.199996948242, 114, 115, 116, 117, 118, 118.400001525879, 119, 120, 120.5,
## 121, 122, 122.400001525879, 123, 124, 125, 126, 127, 128, 129, 130, 131,
## 131.199996948242, 132, 133, 134, 134.399993896484, 135, 136, 137,
## 137.300003051758, 138, 139, 140, 140.399993896484, 141, 142, 143, 144, 145,
## 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 156.800003051758, 157,
## 158, 159, 159.600006103516, 160, 161, 163, 164, 165, 166, 167, 168, 169, 170,
## 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 182.699996948242,
## 183, 184, 185, 186, 188, 189, 190, 192, 193, 194, 195, 198, 200, 201, 202, 203,
## 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 218, 219, 220,
## 221, 222, 223, 224, 225, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 240,
## 244, 245, 246, 248, 249, 250, 251, 252, 255, 256, 257, 258, 260, 261, 262, 263,
## 266, 267, 268, 272, 274, 276, 279, 280, 281, 282, 283, 284, 285, 288, 289, 293,
## 299, 304, 306, 307, 308, 310, 311, 312, 316, 320, 322, 324, 326, 330, 333, 334,
## 340, 345, 350, 351, 358, 359, 360, 362, 365, 366, 380, 382, 386, 388, 390, 392,
## 394, 400, 402, 403, 407, 409, 410, 414, 418, 419, 420, 432, 436, 448, 450, 452,
## 463, 472, 480, 482, 497, 502, 504, 509, 528, 542, 550, 560, 568, 590, 600, 623,
## 630, 632, 640, 675, 680, 685, 742, 750, 780, 806, 866, 1000, 1024, 1079, 1797
## added in variable: alcwkdy## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 4,
## 4.30000019073486, 5, 6, 7, 7.19999980926514, 8, 8.60000038146973, 9,
## 9.60000038146973, 10, 10.6000003814697, 11, 11.5, 12, 12.8999996185303, 13,
## 13.1999998092651, 13.8999996185303, 14, 14.3000001907349, 14.3999996185303,
## 14.8999996185303, 15, 15.6000003814697, 16, 16.7999992370605, 17,
## 17.2000007629395, 18, 18.2000007629395, 19, 19.2000007629395, 19.6000003814697,
## 19.7000007629395, 20, 20.2000007629395, 20.3999996185303, 21, 21.6000003814697,
## 22, 22.7999992370605, 23, 23.2000007629395, 23.5, 24, 25, 25.2000007629395, 26,
## 26.2000007629395, 26.3999996185303, 27, 27.2000007629395, 28, 28.7999992370605,
## 29, 29.2000007629395, 29.2999992370605, 29.6000003814697, 30, 31,
## 31.2000007629395, 32, 32.4000015258789, 33, 33.5999984741211, 34,
## 34.7999992370605, 35, 35.2999992370605, 35.5999984741211, 36, 36.5, 37, 38,
## 38.4000015258789, 38.7999992370605, 38.9000015258789, 39, 39.2000007629395,
## 39.4000015258789, 39.5999984741211, 40, 41, 42, 42.4000015258789,
## 42.7999992370605, 43, 43.2000007629395, 44, 44.4000015258789, 44.7999992370605,
## 45, 45.2000007629395, 46, 47, 48, 48.5, 48.7999992370605, 49, 49.2000007629395,
## 49.5999984741211, 50, 50.4000015258789, 50.7999992370605, 51, 52,
## 52.5999984741211, 52.7999992370605, 53, 53.2000007629395, 54, 55,
## 55.2000007629395, 56, 56.5999984741211, 56.7000007629395, 57, 57.5999984741211,
## 58, 58.4000015258789, 58.7999992370605, 59, 59.0999984741211, 59.2000007629395,
## 60, 60.5999984741211, 61, 62, 62.4000015258789, 63, 63.5999984741211, 64,
## 64.8000030517578, 65, 65.5999984741211, 66, 66.1999969482422, 67,
## 67.1999969482422, 67.6999969482422, 68, 68.1999969482422, 68.8000030517578, 69,
## 69.5999984741211, 70, 71, 71.5999984741211, 72, 73, 74, 74.4000015258789, 75,
## 75.5999984741211, 76, 76.8000030517578, 77, 77.8000030517578, 78,
## 78.3000030517578, 78.8000030517578, 79, 79.1999969482422, 80, 80.6999969482422,
## 81, 82, 82.0999984741211, 83, 84, 85, 86, 86.4000015258789, 87,
## 87.4000015258789, 87.5999984741211, 88, 88.8000030517578, 89, 89.5999984741211,
## 90, 91, 92, 93, 94, 95, 96, 97, 98, 98.4000015258789, 98.8000030517578, 99,
## 99.0999984741211, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
## 111.599998474121, 112, 113, 114, 114.400001525879, 115, 115.199996948242, 116,
## 116.800003051758, 116.900001525879, 117, 118, 119, 120, 121, 122, 123, 124,
## 125, 126, 127, 128, 129, 130, 131, 132, 133, 133.199996948242, 134,
## 134.399993896484, 135, 136, 136.800003051758, 137, 138, 139, 140, 141,
## 141.199996948242, 142, 143, 144, 145, 146, 146.399993896484, 147, 148, 149,
## 150, 151, 152, 152.399993896484, 153, 153.600006103516, 154, 155, 156,
## 156.800003051758, 157, 158, 159, 160, 161, 162, 163, 164, 164.800003051758,
## 165, 165.600006103516, 166, 167, 168, 169, 170, 171, 172, 173, 175, 176, 177,
## 177.600006103516, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,
## 190, 191, 191.199996948242, 192, 193, 194, 195, 196, 197, 198,
## 198.399993896484, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,
## 208.800003051758, 209, 210, 211, 212, 213, 214, 214.399993896484, 215, 216,
## 217, 218, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
## 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,
## 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 263, 264, 265, 266, 268,
## 269.600006103516, 270, 271, 272, 274, 275, 276, 277, 279, 280, 283, 284, 285,
## 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 297.200012207031,
## 298, 300, 301, 303, 304, 305, 306, 307, 308, 310, 311, 312, 315, 316, 317, 319,
## 320, 322, 324, 326, 328, 329, 330, 332, 338, 340, 342, 345, 346, 347, 348, 349,
## 350, 355, 359, 360, 361, 362, 364, 365, 368, 371, 380, 381, 384, 385, 386, 390,
## 396, 398, 400, 404, 407, 408, 412, 413, 416, 417, 420, 424, 426, 427, 430, 432,
## 433, 434, 438, 439, 440, 441, 448, 456, 457, 460, 462, 466, 469,
## 473.600006103516, 474, 480, 481, 482, 491, 500, 503, 505, 508, 526, 528, 530,
## 534, 540, 552, 577, 580, 581, 595, 596, 600, 604, 624, 634, 635, 640, 641, 643,
## 660, 667, 676, 694, 695, 720, 722, 725, 730, 732, 733, 780, 800, 834, 842, 849,
## 851, 893, 948, 998, 1010, 1050, 1274, 1282, 1640, 1663, 2208, 2244 added in
## variable: alcwknd## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 106,
## 108, 120, 122, 130, 135, 137, 138, 140, 141, 142, 143, 144, 145, 146, 147, 148,
## 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
## 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,
## 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,
## 197, 198, 199, 200, 201, 202, 203, 204, 205, 207 added in variable: height## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 30,
## 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
## 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
## 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
## 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
## 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,
## 129, 130, 132, 133, 134, 135, 136, 138, 139, 140, 143, 145, 148 added in
## variable: weighta## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 added in variable: hhmmb## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1933,
## 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946,
## 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,
## 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972,
## 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
## 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009 added in
## variable: yrbrn## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 15,
## 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
## 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
## 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
## 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90 added in variable:
## agea## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1933,
## 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946,
## 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,
## 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972,
## 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
## 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024
## added in variable: yrbrn2## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1933,
## 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946,
## 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,
## 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972,
## 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
## 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024
## added in variable: yrbrn3## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1933,
## 1934, 1935, 1936, 1937, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947,
## 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960,
## 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,
## 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986,
## 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 added in
## variable: yrbrn4## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1933,
## 1934, 1936, 1937, 1938, 1939, 1940, 1943, 1944, 1945, 1946, 1947, 1948, 1949,
## 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1959, 1961, 1962, 1963, 1964,
## 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977,
## 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990,
## 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 added in variable: yrbrn5## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1933,
## 1934, 1940, 1943, 1945, 1946, 1947, 1949, 1951, 1956, 1957, 1958, 1959, 1961,
## 1962, 1963, 1965, 1966, 1968, 1969, 1971, 1972, 1974, 1975, 1976, 1977, 1978,
## 1979, 1980, 1981, 1983, 1984, 1985, 1986, 1987, 1989, 1990, 1991, 1992, 1993,
## 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 2021, 2022, 2023, 2024 added in variable: yrbrn6## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1933,
## 1941, 1945, 1946, 1950, 1952, 1961, 1964, 1968, 1972, 1975, 1976, 1977, 1978,
## 1982, 1985, 1986, 1989, 1990, 1993, 1995, 1996, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 added in variable: yrbrn7## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1939,
## 1947, 1953, 1965, 1981, 1991, 1992, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2005, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2016, 2017, 2018, 2019,
## 2020, 2021, 2022, 2023 added in variable: yrbrn8## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1947,
## 1951, 1995, 1997, 1998, 2000, 2005, 2006, 2009, 2010, 2012, 2013, 2014, 2015,
## 2016, 2018, 2019, 2020, 2021, 2023 added in variable: yrbrn9## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1953,
## 1998, 2002, 2012, 2016, 2019, 2020, 2021, 2022, 2023 added in variable: yrbrn10## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 2016,
## 2019, 2020, 2021 added in variable: yrbrn11## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 2011,
## 2015 added in variable: yrbrn12## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 10,
## 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
## 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 49, 54, 69 added in variable:
## edagegb## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
## 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 42, 44, 45, 47, 50,
## 67, 69 added in variable: eduyrs## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 1933,
## 1944, 1949, 1951, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962,
## 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975,
## 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988,
## 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 added in variable:
## pdjobyr## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
## 24, 25, 26, 27, 28, 30, 31, 33, 35, 37, 38, 40, 42, 43, 44, 45, 50, 52, 60, 70,
## 75, 80, 100, 110, 124, 150, 178, 180, 200, 300, 400 added in variable: emplno## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
## 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,
## 44, 45, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 59, 60, 61, 62, 63, 65, 68, 70,
## 72, 73, 75, 76, 77, 78, 80, 82, 84, 85, 87, 88, 89, 90, 92, 95, 97, 98, 99,
## 100, 103, 104, 106, 107, 108, 109, 110, 116, 120, 121, 124, 127, 130, 135, 140,
## 150, 157, 159, 160, 170, 180, 190, 200, 219, 220, 230, 240, 250, 300, 320, 350,
## 375, 400, 450, 480, 500 added in variable: njbspv## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
## 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,
## 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 63, 64, 65, 66, 68,
## 70, 72, 75, 76, 77, 78, 80, 82, 84, 85, 86, 89, 90, 91, 96, 97, 98, 100, 105,
## 108, 110, 112, 114, 116, 120, 123, 126, 130, 140, 144, 150, 151, 154, 156, 160,
## 164, 165, 167, 168 added in variable: wkhct## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
## 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,
## 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64,
## 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 82, 84, 85, 86, 88,
## 89, 90, 91, 92, 94, 95, 96, 98, 100, 102, 105, 108, 110, 112, 114, 120, 126,
## 130, 132, 140, 144, 150, 151, 156, 158, 160, 162, 164, 165, 168 added in
## variable: wkhtot## Warning in read.spss("ESS11.sav", to.data.frame = T): Duplicated levels in
## factor isco08: Commissioned armed forces officers, Non-commissioned armed
## forces officers, Armed forces occupations, other ranks, Police inspectors and
## detectives, Other clerical support workers, Protective services workers,
## Assemblers, Agricultural, forestry and fishery labourers, Food preparation
## assistants## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 11,
## 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32,
## 33, 35, 38, 40, 44, 45, 48, 60 added in variable: edagepgb## Warning in read.spss("ESS11.sav", to.data.frame = T): Duplicated levels in
## factor isco08p: Commissioned armed forces officers, Non-commissioned armed
## forces officers, Armed forces occupations, other ranks, Police inspectors and
## detectives, Other clerical support workers, Protective services workers,
## Assemblers, Agricultural, forestry and fishery labourers, Food preparation
## assistants## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 0, 1,
## 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
## 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
## 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65,
## 66, 68, 69, 70, 72, 75, 77, 78, 80, 84, 85, 88, 90, 95, 96, 98, 100, 105, 111,
## 120, 133, 140, 150, 152, 160, 164, 168 added in variable: wkhtotp## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 10,
## 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
## 32, 33, 34, 35, 38, 40, 45, 50 added in variable: edagefgb## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) 10,
## 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
## 32, 33, 35, 36, 37, 40, 45, 48, 50, 51, 52 added in variable: edagemgb## Warning in read.spss("ESS11.sav", to.data.frame = T): Undeclared level(s) IS01,
## IS02 added in variable: region## Warning in read.spss("ESS11.sav", to.data.frame = T): Duplicated levels in
## factor region: Wien, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS
## 3, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Extra-Regio NUTS
## 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Zürich, Ticino, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, Κύπρος, Κύπρος, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, Extra-Regio NUTS 1, Extra-Regio NUTS 2,
## Extra-Regio NUTS 3, Berlin, Berlin, Brandenburg, Bremen, Hamburg, Hamburg,
## Mecklenburg-Vorpommern, Saarland, Leipzig, Sachsen-Anhalt, Schleswig-Holstein,
## Thüringen, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3,
## Nordjylland, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Eesti,
## Kesk-Eesti, Kirde-Eesti, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio
## NUTS 3, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Cantabria,
## La Rioja, Comunidad de Madrid, Canarias, Extra-Regio NUTS 1, Extra-Regio NUTS
## 2, Extra-Regio NUTS 3, Helsinki-Uusimaa, Varsinais-Suomi, Kanta-Häme,
## Päijät-Häme, Kymenlaakso, Etelä-Karjala, Etelä-Savo, Pohjois-Savo,
## Pohjois-Karjala, Kainuu, Keski-Pohjanmaa, Pohjois-Pohjanmaa, Lappi, Kainuu,
## Pohjois-Pohjanmaa, Åland, Åland, Extra-Regio NUTS 1, Extra-Regio NUTS 2,
## Extra-Regio NUTS 3, Ile-de-France, Centre — Val de Loire, Bourgogne,
## Franche-Comté, Jura, Picardie, Alsace, Champagne-Ardenne, Lorraine, Pays de la
## Loire, Pays de la Loire, Bretagne, Bretagne, Aquitaine, Limousin,
## Poitou-Charentes, Languedoc-Roussillon, Midi-Pyrénées, Auvergne, Rhône-Alpes,
## Provence-Alpes-Côte d’Azur, Corse, Guadeloupe, Martinique , Guyane, Extra-Regio
## NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Georgia, Extra-region,
## Extra-region, Bjelovarsko-bilogorska županija, Virovitičko-podravska županija,
## Požeško-slavonska županija, Brodsko-posavska županija, Osječko-baranjska
## županija, Vukovarsko-srijemska županija, Karlovačka županija,
## Sisačko-moslavačka županija, Grad Zagreb, Grad Zagreb, Međimurska županija,
## Varaždinska županija, Koprivničko-križevačka županija, Krapinsko-zagorska
## županija, Zagrebačka županija, Extra-Regio NUTS 1, Extra-Regio NUTS 2,
## Extra-Regio NUTS 3, Budapest, Budapest, Pest, Pest, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, Ireland, Border, West, Dublin,
## Mid-East, Midland, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3,
## Ísland, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Valle
## d’Aosta/Vallée d’Aoste, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio
## NUTS 3, Liechtenstein, Liechtenstein, Liechtenstein, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, Vilniaus apskritis, Alytaus apskritis,
## Kauno apskritis, Utenos apskritis, Extra-Regio NUTS 1, Extra-Regio NUTS 2,
## Extra-Regio NUTS 3, Luxembourg, Luxembourg, Luxembourg, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, Latvija, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, Црна Гора, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, Северна Македонија, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, Malta, Malta, Malta, Extra-Regio NUTS
## 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Flevoland, Utrecht, Extra-Regio NUTS
## 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Innlandet, Trøndelag, Extra-Regio
## NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Lubelskie, Podkarpackie,
## Podlaskie, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Algarve,
## Área Metropolitana de Lisboa, Região Autónoma dos Açores, Região Autónoma dos
## Açores, Região Autónoma da Madeira, Região Autónoma da Madeira, Extra-Regio
## NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Nord-Est, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, Extra-Regio NUTS 1, Extra-Regio NUTS 2,
## Extra-Regio NUTS 3, Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3,
## Pomurska, Podravska, Koroška, Savinjska, Zasavska, Jugovzhodna Slovenija,
## Osrednjeslovenska, Gorenjska, Goriška, Obalno-kraška, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, Bratislavský kraj, Extra-Regio NUTS 1,
## Extra-Regio NUTS 2, Extra-Regio NUTS 3, İstanbul, İstanbul, İzmir, Ankara,
## Extra-Regio NUTS 1, Extra-Regio NUTS 2, Extra-Regio NUTS 3, Lincolnshire,
## Cornwall and Isles of Scilly, Northern Ireland, Extra-Regio NUTS 1, Extra-Regio
## NUTS 2, Extra-Regio NUTS 3, Kosovo, Kosovonames(df)## [1] "name" "essround" "edition" "proddate" "idno" "cntry"
## [7] "dweight" "pspwght" "pweight" "anweight" "nwspol" "netusoft"
## [13] "netustm" "ppltrst" "pplfair" "pplhlp" "polintr" "psppsgva"
## [19] "actrolga" "psppipla" "cptppola" "trstprl" "trstlgl" "trstplc"
## [25] "trstplt" "trstprt" "trstep" "trstun" "vote" "prtvtdat"
## [31] "prtvtebe" "prtvtchr" "prtvtccy" "prtvtffi" "prtvtffr" "prtvgde1"
## [37] "prtvgde2" "prtvtegr" "prtvthhu" "prtvteis" "prtvteie" "prtvteit"
## [43] "prtvclt1" "prtvclt2" "prtvclt3" "prtvtinl" "prtvtcno" "prtvtfpl"
## [49] "prtvtept" "prtvtbrs" "prtvtesk" "prtvtgsi" "prtvtges" "prtvtdse"
## [55] "prtvthch" "prtvtdgb" "contplt" "donprty" "badge" "sgnptit"
## [61] "pbldmna" "bctprd" "pstplonl" "volunfp" "clsprty" "prtcleat"
## [67] "prtclebe" "prtclbhr" "prtclccy" "prtclgfi" "prtclgfr" "prtclgde"
## [73] "prtclegr" "prtclihu" "prtcleis" "prtclfie" "prtclfit" "prtclclt"
## [79] "prtclhnl" "prtclcno" "prtcljpl" "prtclgpt" "prtclbrs" "prtclesk"
## [85] "prtclgsi" "prtclhes" "prtcldse" "prtclhch" "prtcldgb" "prtdgcl"
## [91] "lrscale" "stflife" "stfeco" "stfgov" "stfdem" "stfedu"
## [97] "stfhlth" "gincdif" "freehms" "hmsfmlsh" "hmsacld" "euftf"
## [103] "lrnobed" "loylead" "imsmetn" "imdfetn" "impcntr" "imbgeco"
## [109] "imueclt" "imwbcnt" "happy" "sclmeet" "inprdsc" "sclact"
## [115] "crmvct" "aesfdrk" "health" "hlthhmp" "atchctr" "atcherp"
## [121] "rlgblg" "rlgdnm" "rlgdnbat" "rlgdnacy" "rlgdnafi" "rlgdnade"
## [127] "rlgdnagr" "rlgdnhu" "rlgdnais" "rlgdnie" "rlgdnlt" "rlgdnanl"
## [133] "rlgdnno" "rlgdnapl" "rlgdnapt" "rlgdnrs" "rlgdnask" "rlgdnase"
## [139] "rlgdnach" "rlgdngb" "rlgblge" "rlgdnme" "rlgdebat" "rlgdeacy"
## [145] "rlgdeafi" "rlgdeade" "rlgdeagr" "rlgdehu" "rlgdeais" "rlgdeie"
## [151] "rlgdelt" "rlgdeanl" "rlgdeno" "rlgdeapl" "rlgdeapt" "rlgders"
## [157] "rlgdeask" "rlgdease" "rlgdeach" "rlgdegb" "rlgdgr" "rlgatnd"
## [163] "pray" "dscrgrp" "dscrrce" "dscrntn" "dscrrlg" "dscrlng"
## [169] "dscretn" "dscrage" "dscrgnd" "dscrsex" "dscrdsb" "dscroth"
## [175] "dscrdk" "dscrref" "dscrnap" "dscrna" "ctzcntr" "brncntr"
## [181] "cntbrthd" "livecnta" "lnghom1" "lnghom2" "feethngr" "facntr"
## [187] "fbrncntc" "mocntr" "mbrncntc" "ccnthum" "ccrdprs" "wrclmch"
## [193] "admrclc" "testjc34" "testjc35" "testjc36" "testjc37" "testjc38"
## [199] "testjc39" "testjc40" "testjc41" "testjc42" "vteurmmb" "vteubcmb"
## [205] "ctrlife" "etfruit" "eatveg" "dosprt" "cgtsmok" "alcfreq"
## [211] "alcwkdy" "alcwknd" "icgndra" "alcbnge" "height" "weighta"
## [217] "dshltgp" "dshltms" "dshltnt" "dshltref" "dshltdk" "dshltna"
## [223] "medtrun" "medtrnp" "medtrnt" "medtroc" "medtrnl" "medtrwl"
## [229] "medtrnaa" "medtroth" "medtrnap" "medtrref" "medtrdk" "medtrna"
## [235] "medtrnu" "hlpfmly" "hlpfmhr" "trhltacu" "trhltacp" "trhltcm"
## [241] "trhltch" "trhltos" "trhltho" "trhltht" "trhlthy" "trhltmt"
## [247] "trhltpt" "trhltre" "trhltsh" "trhltnt" "trhltref" "trhltdk"
## [253] "trhltna" "fltdpr" "flteeff" "slprl" "wrhpp" "fltlnl"
## [259] "enjlf" "fltsd" "cldgng" "hltprhc" "hltprhb" "hltprbp"
## [265] "hltpral" "hltprbn" "hltprpa" "hltprpf" "hltprsd" "hltprsc"
## [271] "hltprsh" "hltprdi" "hltprnt" "hltprref" "hltprdk" "hltprna"
## [277] "hltphhc" "hltphhb" "hltphbp" "hltphal" "hltphbn" "hltphpa"
## [283] "hltphpf" "hltphsd" "hltphsc" "hltphsh" "hltphdi" "hltphnt"
## [289] "hltphnap" "hltphref" "hltphdk" "hltphna" "hltprca" "cancfre"
## [295] "cnfpplh" "fnsdfml" "jbexpvi" "jbexpti" "jbexpml" "jbexpmc"
## [301] "jbexpnt" "jbexpnap" "jbexpref" "jbexpdk" "jbexpna" "jbexevl"
## [307] "jbexevh" "jbexevc" "jbexera" "jbexecp" "jbexebs" "jbexent"
## [313] "jbexenap" "jbexeref" "jbexedk" "jbexena" "nobingnd" "likrisk"
## [319] "liklead" "sothnds" "actcomp" "mascfel" "femifel" "impbemw"
## [325] "trmedmw" "trwrkmw" "trplcmw" "trmdcnt" "trwkcnt" "trplcnt"
## [331] "eqwrkbg" "eqpolbg" "eqmgmbg" "eqpaybg" "eqparep" "eqparlv"
## [337] "freinsw" "fineqpy" "wsekpwr" "weasoff" "wlespdm" "wexashr"
## [343] "wprtbym" "wbrgwrm" "hhmmb" "gndr" "gndr2" "gndr3"
## [349] "gndr4" "gndr5" "gndr6" "gndr7" "gndr8" "gndr9"
## [355] "gndr10" "gndr11" "gndr12" "yrbrn" "agea" "yrbrn2"
## [361] "yrbrn3" "yrbrn4" "yrbrn5" "yrbrn6" "yrbrn7" "yrbrn8"
## [367] "yrbrn9" "yrbrn10" "yrbrn11" "yrbrn12" "rshipa2" "rshipa3"
## [373] "rshipa4" "rshipa5" "rshipa6" "rshipa7" "rshipa8" "rshipa9"
## [379] "rshipa10" "rshipa11" "rshipa12" "rshpsts" "rshpsgb" "lvgptnea"
## [385] "dvrcdeva" "marsts" "marstgb" "maritalb" "chldhhe" "domicil"
## [391] "paccmoro" "paccdwlr" "pacclift" "paccnbsh" "paccocrw" "paccxhoc"
## [397] "paccnois" "paccinro" "paccnt" "paccref" "paccdk" "paccna"
## [403] "edulvlb" "eisced" "edlveat" "edlvebe" "edlvehr" "edlvgcy"
## [409] "edlvdfi" "edlvdfr" "edudde1" "educde2" "edlvegr" "edlvdahu"
## [415] "edlvdis" "edlvdie" "edlvfit" "edlvdlt" "edlvenl" "edlveno"
## [421] "edlvipl" "edlvept" "edlvdrs" "edlvdsk" "edlvesi" "edlvies"
## [427] "edlvdse" "edlvdch" "educgb1" "edubgb2" "edagegb" "eduyrs"
## [433] "pdwrk" "edctn" "uempla" "uempli" "dsbld" "rtrd"
## [439] "cmsrv" "hswrk" "dngoth" "dngref" "dngdk" "dngna"
## [445] "mainact" "mnactic" "crpdwk" "pdjobev" "pdjobyr" "emplrel"
## [451] "emplno" "wrkctra" "estsz" "jbspv" "njbspv" "wkdcorga"
## [457] "iorgact" "wkhct" "wkhtot" "nacer2" "tporgwk" "isco08"
## [463] "wrkac6m" "uemp3m" "uemp12m" "uemp5yr" "mbtru" "hincsrca"
## [469] "hinctnta" "hincfel" "edulvlpb" "eiscedp" "edlvpfat" "edlvpebe"
## [475] "edlvpehr" "edlvpgcy" "edlvpdfi" "edlvpdfr" "edupdde1" "edupcde2"
## [481] "edlvpegr" "edlvpdahu" "edlvpdis" "edlvpdie" "edlvpfit" "edlvpdlt"
## [487] "edlvpenl" "edlvpeno" "edlvphpl" "edlvpept" "edlvpdrs" "edlvpdsk"
## [493] "edlvpesi" "edlvphes" "edlvpdse" "edlvpdch" "edupcgb1" "edupbgb2"
## [499] "edagepgb" "pdwrkp" "edctnp" "uemplap" "uemplip" "dsbldp"
## [505] "rtrdp" "cmsrvp" "hswrkp" "dngothp" "dngdkp" "dngnapp"
## [511] "dngrefp" "dngnap" "mnactp" "crpdwkp" "isco08p" "emprelp"
## [517] "wkhtotp" "edulvlfb" "eiscedf" "edlvfeat" "edlvfebe" "edlvfehr"
## [523] "edlvfgcy" "edlvfdfi" "edlvfdfr" "edufcde1" "edufbde2" "edlvfegr"
## [529] "edlvfdahu" "edlvfdis" "edlvfdie" "edlvffit" "edlvfdlt" "edlvfenl"
## [535] "edlvfeno" "edlvfgpl" "edlvfept" "edlvfdrs" "edlvfdsk" "edlvfesi"
## [541] "edlvfges" "edlvfdse" "edlvfdch" "edufcgb1" "edufbgb2" "edagefgb"
## [547] "emprf14" "occf14b" "edulvlmb" "eiscedm" "edlvmeat" "edlvmebe"
## [553] "edlvmehr" "edlvmgcy" "edlvmdfi" "edlvmdfr" "edumcde1" "edumbde2"
## [559] "edlvmegr" "edlvmdahu" "edlvmdis" "edlvmdie" "edlvmfit" "edlvmdlt"
## [565] "edlvmenl" "edlvmeno" "edlvmgpl" "edlvmept" "edlvmdrs" "edlvmdsk"
## [571] "edlvmesi" "edlvmges" "edlvmdse" "edlvmdch" "edumcgb1" "edumbgb2"
## [577] "edagemgb" "emprm14" "occm14b" "atncrse" "anctrya1" "anctrya2"
## [583] "regunit" "region" "ipcrtiva" "impricha" "ipeqopta" "ipshabta"
## [589] "impsafea" "impdiffa" "ipfrulea" "ipudrsta" "ipmodsta" "ipgdtima"
## [595] "impfreea" "iphlppla" "ipsucesa" "ipstrgva" "ipadvnta" "ipbhprpa"
## [601] "iprspota" "iplylfra" "impenva" "imptrada" "impfuna" "testji1"
## [607] "testji2" "testji3" "testji4" "testji5" "testji6" "testji7"
## [613] "testji8" "testji9" "respc19a" "symtc19" "symtnc19" "vacc19"
## [619] "recon" "inwds" "ainws" "ainwe" "binwe" "cinwe"
## [625] "dinwe" "einwe" "finwe" "hinwe" "iinwe" "kinwe"
## [631] "rinwe" "inwde" "jinws" "jinwe" "inwtm" "mode"
## [637] "domain" "prob" "stratum" "psu"vnames = c("fltdpr", "flteeff", "slprl", "wrhpp", "fltlnl", "enjlf", "fltsd", "cldgng")
likert_df = df[,vnames]
#Nur UK Daten
#df_uk = df[df$cntry == "United Kingdom", ]
# check
#df_uk$depression = rowSums(df_uk[, c("d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27")]) / 8
#Likert Scale
likert_table = likert(likert_df)$results
likert_numeric_df = as.data.frame(lapply((df[,vnames]), as.numeric))
likert_table$Mean = unlist(lapply((likert_numeric_df[,vnames]), mean, na.rm=T))
# ... and append new columns to the data frame
likert_table$Count = unlist(lapply((likert_numeric_df[,vnames]), function (x) sum(!is.na(x))))
likert_table$Item = c(
d20="how much of the time during the past week you felt depressed?",
d21="…you felt that everything you did was an effort?",
d22="…your sleep was restless?",
d23="…you were happy?",
d24="…you felt lonely?",
d25="…you enjoyed life?",
d26="…you felt sad?",
d27="…you could not get going?")
likert_table## Item
## 1 how much of the time during the past week you felt depressed?
## 2 …you felt that everything you did was an effort?
## 3 …your sleep was restless?
## 4 …you were happy?
## 5 …you felt lonely?
## 6 …you enjoyed life?
## 7 …you felt sad?
## 8 …you could not get going?
## None or almost none of the time Some of the time Most of the time
## 1 64.915835 29.06631 4.557165
## 2 48.395568 38.42383 9.814171
## 3 43.873854 39.87056 11.625059
## 4 4.003510 23.53973 48.886939
## 5 68.136458 24.27532 5.302253
## 6 5.338783 24.82572 44.804153
## 7 52.489933 41.07451 4.859808
## 8 55.673484 36.10353 6.217928
## All or almost all of the time Mean Count
## 1 1.460694 1.425627 39981
## 2 3.366431 1.681515 39983
## 3 4.630532 1.770123 40017
## 4 23.569817 2.920231 39890
## 5 2.285972 1.417377 39983
## 6 25.031346 2.895281 39878
## 7 1.575748 1.555214 39981
## 8 2.005056 1.545546 39949# round all percentage values to 1 decimal digit
likert_table[,2:6] = round(likert_table[,2:6],1)
# round means to 3 decimal digits
likert_table[,7] = round(likert_table[,7],3)
# create formatted table
kable_styling(kable(likert_table,
format="html",
caption = "Distribution of answers regarding same sex partnerships (ESS round 11, all countries)"
)
)| Item | None or almost none of the time | Some of the time | Most of the time | All or almost all of the time | Mean | Count |
|---|---|---|---|---|---|---|
| how much of the time during the past week you felt depressed? | 64.9 | 29.1 | 4.6 | 1.5 | 1.4 | 39981 |
| …you felt that everything you did was an effort? | 48.4 | 38.4 | 9.8 | 3.4 | 1.7 | 39983 |
| …your sleep was restless? | 43.9 | 39.9 | 11.6 | 4.6 | 1.8 | 40017 |
| …you were happy? | 4.0 | 23.5 | 48.9 | 23.6 | 2.9 | 39890 |
| …you felt lonely? | 68.1 | 24.3 | 5.3 | 2.3 | 1.4 | 39983 |
| …you enjoyed life? | 5.3 | 24.8 | 44.8 | 25.0 | 2.9 | 39878 |
| …you felt sad? | 52.5 | 41.1 | 4.9 | 1.6 | 1.6 | 39981 |
| …you could not get going? | 55.7 | 36.1 | 6.2 | 2.0 | 1.5 | 39949 |
# create basic plot (code also valid)
plot(likert(summary=likert_table[,1:6])) # limit to columns 1:6 to skip mean and count
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