# Is working group_by function?

Hi everyone. I am really struggling to get done my analysis. I am just having problems with every line of code. Lately I have begun to mistrust the group_by function. A little piece of my code

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sample<-structure(list(paciente = structure(c(6363, 6052, 6519, 6371,
6555, 6185, 6002, 6155, 6287, 6217), format.spss = "F5.0"), sexo_s1 = structure(c(1L,
2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L), .Label = c("Hombre", "Mujer"
), label = "Sexo", class = "factor"), edad_s1 = structure(c(66,
63, 72, 64, 72, 64, 70, 73, 63, 65), label = "Edad", format.spss = "F3.0"),
peso1_v00 = structure(c(84.4, 76.2, 88.3, 122, 72, 86.4,
78, 79, 65.5, 98.8), label = "Peso: 1a determinación", format.spss = "F5.1"),
cintura1_v00 = structure(c(104.5, 101, 107.5, 128.5, 107,
109.5, 105.5, 109, 97, 118.5), label = "Cintura: 1a determinación", format.spss = "F5.1"),
tasis2_e_v00 = structure(c(155, 131, 148, 147, 136, 154,
130, 154, 147, 139), label = "TA: tensión arterial 2: sistólica", format.spss = "F4.0"),
tadias2_e_v00 = structure(c(98, 76, 83, 84, 73, 80, 64, 80,
82, 78), label = "TA: tensión arterial 2: diastólica", format.spss = "F4.0"),
p17_total_v00 = structure(c(10, 8, 10, 10, 10, 5, 11, 9,
8, 5), label = "Cuestionario de 17 puntos: Suma de puntuación de P17", format.spss = "F3.0"),
geaf_tot_v00 = structure(c(1048.95, 4195.8, 4615.38, 3356.64,
839.16, 1608.39, 2958.04, 10209.79, 4335.66, 157.34), label = "AF: Gasto energético en actividad física total (MET•min/sem)", format.spss = "F8.2"),
glucosa_v00 = structure(c(97, 122, 109, 201, 143, 139, 95,
110, 101, 181), label = "Analítica: Glucosa en mg/dL", format.spss = "F4.0"),
albumi_v00 = structure(c(4.61, 4.52, 4.44, 4.48, 4.75, 4.87,
4.66, 4.86, 4.75, 4.99), label = "Analítica: Albúmina en g/dL", format.spss = "F6.2"),
coltot_v00 = structure(c(221, 218, 130, 261, 190, 221, 199,
185, 233, 232), label = "Analítica: Colesterol total en mg/dL", format.spss = "F4.0"),
hdl_v00 = structure(c(52, 54, 43, 42, 50, 67, 90, 50, 43,
51), label = "Analítica: Colesterol HDL en mg/dL", format.spss = "F4.0"),
ldl_calc_v00 = structure(c(148, 135, 75, 173, 128, 133, 84,
109, 134, 144), label = "Analítica: LDL calculado en mg/dL si trigli<=300", format.spss = "F4.0"),
trigli_v00 = structure(c(103, 144, 58, 232, 60, 106, 126,
131, 282, 186), label = "Analítica: Triglicéridos en mg/dL", format.spss = "F5.0"),
hba1c_v00 = structure(c(5.87, NA, 5.76, 7.98, 6.38, 7.01,
NA, 6.51, 5.95, 9.01), label = "Analítica: Hemoglobina glicosilada (HbA1c %)", format.spss = "F5.2"),
peso1_v66 = structure(c(76.6, 74.2, 82.2, 115, 64, 73.5,
74.4, 75.5, 59.5, 100), label = "Peso: 1a determinación", format.spss = "F5.1"),
cintura1_v66 = structure(c(97.5, 99, 100, 122.5, 99, 101,
97, 104.5, 82.5, 119.5), label = "Cintura: 1a determinación", format.spss = "F5.1"),
tasis2_e_v66 = structure(c(133, 129, 144, 160, 122, 123,
130, 155, 139, 153), label = "TA: tensión arterial 2: sistólica", format.spss = "F4.0"),
tadias2_e_v66 = structure(c(87, 75, 77, 86, 68, 53, 64, 81,
73, 89), label = "TA: tensión arterial 2: diastólica", format.spss = "F4.0", display_width = 13L),
p17_total_v66 = structure(c(12, 12, 12, 13, 11, 15, 14, 9,
15, 12), label = "Cuestionario de 17 puntos: Suma total de P17", format.spss = "F3.0"),
geaf_tot_v66 = structure(c(4615.38, 0, 3461.54, 3356.64,
2097.9, 5132.87, 5118.88, 8111.89, 6772.03, 209.79), label = "AF: Gasto energético en actividad física total (MET•min/sem)", format.spss = "F8.2"),
glucosa_v66 = structure(c(96, 107, 111, 173, 107, 98, 88,
118, 97, 185), label = "Analítica: Glucosa en mg/dL", format.spss = "F4.0"),
albumi_v66 = structure(c(4.5, 4.93, 4.41, 4.42, 4.82, 4.54,
4.8, 4.46, 4.64, 4.84), label = "Analítica: Albúmina en g/dL", format.spss = "F6.2"),
coltot_v66 = structure(c(215, 226, 156, 235, 154, 210, 283,
182, 225, 171), label = "Analítica: Colesterol total en mg/dL", format.spss = "F4.0"),
hdl_v66 = structure(c(54, 65, 54, 40, 51, 73, 88, 58, 46,
37), label = "Analítica: Colesterol HDL en mg/dL", format.spss = "F4.0"),
ldl_calc_v66 = structure(c(147, 133, 89, 156, 94, 123, 175,
106, 137, 102), label = "Analítica: LDL calculado en mg/dL si trigli<=300", format.spss = "F4.0"),
trigli_v66 = structure(c(72, 138, 63, 197, 47, 72, 99, 89,
209, 160), label = "Analítica: Triglicéridos en mg/dL", format.spss = "F5.0"),
hba1c_v66 = structure(c(5.67, NA, 5.54, 8.05, 5.5, 5.95,
NA, 6.17, 5.75, 8.98), label = "Analítica: Hemoglobina glicosilada (HbA1c %)", format.spss = "F5.2"),
peso1_v01 = structure(c(75.2, 72, 82.4, 116, 63, 73.5, 72.4,
79, 58.5, 100), label = "Peso: 1a determinación", format.spss = "F5.1"),
cintura1_v01 = structure(c(97.5, 98, 100, 122.5, 99, 101,
96, 106, 88.5, 119.5), label = "Cintura: 1a determinación", format.spss = "F5.1"),
tasis2_e_v01 = structure(c(130, 137, 137, 183, 122, 152,
138, 139, 147, 154), label = "TA: tensión arterial 2: sistólica", format.spss = "F4.0"),
tadias2_e_v01 = structure(c(84, 75, 72, 97, 71, 63, 72, 72,
60, 80), label = "TA: tensión arterial 2: diastólica", format.spss = "F4.0"),
p17_total_v01 = structure(c(14, 11, 12, 10, 14, 15, 14, 11,
13, 12), label = "Cuestionario de 17 puntos: Suma total de P17", format.spss = "F3.0"),
geaf_tot_v01 = structure(c(1678.32, 0, 4713.29, 559.44, 2769.23,
3212.12, 6853.15, 3776.22, 5841.49, 1048.95), label = "AF: Gasto energético en actividad física total (MET•min/sem)", format.spss = "F8.2"),
glucosa_v01 = structure(c(93, 116, 100, 200, 112, 109, 105,
118, 94, 242), label = "Analítica: Glucosa en mg/dL", format.spss = "F4.0"),
albumi_v01 = structure(c(4.57, 4.42, 4.68, 4.36, 4.98, 4.74,
4.87, 4.8, 4.81, 4.88), label = "Analítica: Albúmina en g/dL", format.spss = "F6.2"),
coltot_v01 = structure(c(198, 236, 158, 270, 181, 187, 213,
204, 226, 192), label = "Analítica: Colesterol total en mg/dL", format.spss = "F4.0"),
hdl_v01 = structure(c(58, 59, 48, 74, 60, 60, 87, 52, 49,
37), label = "Analítica: Colesterol HDL en mg/dL", format.spss = "F4.0"),
ldl_calc_v01 = structure(c(128, 160, 99, 168, 107, 109, 105,
130, 147, 125), label = "Analítica: LDL calculado en mg/dL si trigli<=300", format.spss = "F4.0"),
trigli_v01 = structure(c(62, 83, 53, 139, 71, 90, 105, 110,
148, 151), label = "Analítica: Triglicéridos en mg/dL", format.spss = "F5.0"),
hba1c_v01 = structure(c(5.61, 6.48, 5.53, 8.26, 5.86, 6.4,
5.26, 6.5, 5.7, 9.62), label = "Analítica: Hemoglobina glicosilada (HbA1c %)", format.spss = "F5.2"),
i_hucpeptide_v00 = structure(c(704.96, NA, 675.43, 913.16,
1325.4, 840.98, NA, 1932.23, 459.83, 422.15), label = "Hu C-peptide (72) IMIM S'han substituit en les següents var els codis de inf i sup a limit de detecció per el limit inf i sup de detecció", format.spss = "F9.2", display_width = 13L),
i_hucpeptide_v66 = structure(c(510.07, NA, 824.39, 926.34,
1199.51, 488.01, NA, 1461.11, 346.92, 679.09), label = "Hu C-peptide (72) IMIM", format.spss = "F9.2", display_width = 13L),
i_hucpeptide_v01 = structure(c(432.38, NA, 737.66, 707.55,
1057.83, 699.08, NA, 1512.69, 345.69, 356.67), label = "Hu C-peptide (72) IMIM", format.spss = "F9.2", display_width = 12L),
i_hughrelin_v00 = structure(c(1823.83, NA, 1050.11, 424.25,
198.06, 534.27, NA, 709.73, 117.69, 420.37), label = "Hu Ghrelin (26) IMIM", format.spss = "F7.2", display_width = 10L),
i_hughrelin_v66 = structure(c(1407.04, NA, 746.93, 423.13,
207.63, 464.17, NA, 728.57, 113.23, 463.65), label = "Hu Ghrelin (26) IMIM", format.spss = "F7.2", display_width = 13L),
i_hughrelin_v01 = structure(c(1133.96, NA, 670.23, 405.11,
260.24, 418.79, NA, 533.78, 122.07, 220.22), label = "Hu Ghrelin (26) IMIM", format.spss = "F7.2", display_width = 11L),
i_hugip_v00 = structure(c(2.67, NA, 2.67, 2.67, 2.67, 2.67,
NA, 2.67, 2.67, 2.67), label = "Hu GIP (14) IMIM", format.spss = "F9.2", display_width = 9L),
i_hugip_v66 = structure(c(2.67, NA, 2.67, 2.67, 256.21, 2.67,
NA, 2.67, 2.67, 2.67), label = "Hu GIP (14) IMIM", format.spss = "F9.2", display_width = 9L),
i_hugip_v01 = structure(c(2.67, NA, 2.67, 24.74, 1165.16,
2.67, NA, 2.67, 2.67, 2.67), label = "Hu GIP (14) IMIM", format.spss = "F9.2", display_width = 9L),
i_huglp1_v00 = structure(c(14.14, NA, 14.14, 216.2, 116.16,
228.14, NA, 359.48, 14.14, 219.02), label = "Hu GLP-1 (27) IMIM", format.spss = "F9.2", display_width = 9L),
i_huglp1_v66 = structure(c(14.14, NA, 14.14, 202.5, 92.49,
278.55, NA, 400.16, 14.14, 274.62), label = "Hu GLP-1 (27) IMIM", format.spss = "F9.2", display_width = 9L),
i_huglp1_v01 = structure(c(14.14, NA, 14.14, 202.5, 56.41,
303.77, NA, 451.91, 14.14, 58.55), label = "Hu GLP-1 (27) IMIM", format.spss = "F9.2", display_width = 9L),
i_huglucagon_v00 = structure(c(273.5, NA, 231.41, 491.4,
542.39, 489.35, NA, 525.39, 241.55, 362.72), label = "Hu Glucagon (15) IMIM", format.spss = "F9.2", display_width = 11L),
i_huglucagon_v66 = structure(c(94.97, NA, 276.26, 456.39,
306.42, 288.62, NA, 523.49, 143.14, 440.22), label = "Hu Glucagon (15) IMIM", format.spss = "F9.2", display_width = 11L),
i_huglucagon_v01 = structure(c(182.57, NA, 233.95, 522.65,
10.48, 409.99, NA, 511.95, 216.47, 326.51), label = "Hu Glucagon (15) IMIM", format.spss = "F9.2", display_width = 9L),
i_huinsulin_v00 = structure(c(97.94, NA, 171.33, 286.66,
390.29, 221.06, NA, 668.14, 125.36, 349.99), label = "Hu Insulin (12) IMIM", format.spss = "F7.2", display_width = 10L),
i_huinsulin_v66 = structure(c(64.73, NA, 236.01, 255.05,
284.71, 73.24, NA, 392.19, 64.75, 381.42), label = "Hu Insulin (12) IMIM", format.spss = "F7.2", display_width = 9L),
i_huinsulin_v01 = structure(c(52.52, NA, 213.4, 703.51, 247.48,
147.22, NA, 521.07, 98.66, 298.72), label = "Hu Insulin (12) IMIM", format.spss = "F7.2"),
i_huleptin_v00 = structure(c(3493.7, NA, 1965.55, 4767.39,
5122.91, 12320.55, NA, 5367.22, 5217.35, 4682.33), label = "Hu Leptin (78) IMIM", format.spss = "F9.2", display_width = 9L),
i_huleptin_v66 = structure(c(1779.33, NA, 1410.07, 3977.58,
3645.73, 3608.76, NA, 4489.67, 3499.88, 5136.43), label = "Hu Leptin (78) IMIM", format.spss = "F9.2", display_width = 9L),
i_huleptin_v01 = structure(c(1865.4, NA, 1312.5, 8371.63,
2128.98, 6921.89, NA, 3754.42, 3092.53, 3921.64), label = "Hu Leptin (78) IMIM", format.spss = "F9.2", display_width = 9L),
i_hupai1_v00 = structure(c(1581.8, NA, 1442.88, 3209.36,
2349.08, 3202.1, NA, 3177.94, 1463.04, 1701.4), label = "Hu PAI-1 (61) IMIM", format.spss = "F7.2"),
i_hupai1_v66 = structure(c(1625.45, NA, 1093.24, 2152.24,
2083.31, 982.35, NA, 2245.81, 1611.27, 2645.45), label = "Hu PAI-1 (61) IMIM", format.spss = "F7.2"),
i_hupai1_v01 = structure(c(1726.35, NA, 1166.53, 2511.45,
2268.52, 1592.08, NA, 2560.71, 1936.33, 2500.51), label = "Hu PAI-1 (61) IMIM", format.spss = "F7.2"),
i_huresistin_v00 = structure(c(4292.62, NA, 3951.76, 4101.48,
6430.17, 5599.94, NA, 4855.32, 2144.19, 2421.1), label = "Hu Resistin (65) IMIM", format.spss = "F8.2", display_width = 9L),
i_huresistin_v66 = structure(c(3201.72, NA, 4774.83, 4500.78,
7574.37, 4403.32, NA, 3224.09, 2102.65, 2003.5), label = "Hu Resistin (65) IMIM", format.spss = "F8.2", display_width = 7L),
i_huresistin_v01 = structure(c(3872.84, NA, 4595.27, 3581.62,
9521.7, 4225.4, NA, 3150.62, 2093.1, 2048.76), label = "Hu Resistin (65) IMIM", format.spss = "F8.2", display_width = 6L),
i_huvisfatin_v00 = structure(c(8.64, NA, 2.06, 560.32, 1498.58,
1356.01, NA, 632.07, 315.62, 461.86), label = "Hu Visfatin (22) IMIM", format.spss = "F9.2", display_width = 6L),
i_huvisfatin_v66 = structure(c(8.64, NA, 8.64, 472.64, 683.91,
8.64, NA, 486.94, 8.64, 477.56), label = "Hu Visfatin (22) IMIM", format.spss = "F9.2", display_width = 6L),
i_huvisfatin_v01 = structure(c(8.64, NA, 8.64, 2113.05, 1415.55,
800.08, NA, 155.08, 8.64, 108), label = "Hu Visfatin (22) IMIM", format.spss = "F9.2", display_width = 10L),
col_rema_v00 = structure(c(21, 29, 12, 46, 12, 21, 25, 26,
56, 37), format.spss = "F8.2", display_width = 14L), col_rema_v66 = structure(c(14,
28, 13, 39, 9, 14, 20, 18, 42, 32), format.spss = "F8.2", display_width = 14L),
col_rema_v01 = structure(c(12, 17, 11, 28, 14, 18, 21, 22,
30, 30), format.spss = "F8.2", display_width = 14L), homa_v00 = structure(c(422.230222222222,
NA, 829.998666666667, 2560.82933333333, 2480.50977777778,
1365.65955555556, NA, 3266.46222222222, 562.727111111111,
2815.47511111111), format.spss = "F8.2", display_width = 10L),
homa_v66 = structure(c(276.181333333333, NA, 1164.316, 1961.05111111111,
1353.95422222222, 319.000888888889, NA, 2056.81866666667,
279.144444444444, 3136.12), format.spss = "F8.2", display_width = 10L),
homa_v01 = structure(c(217.082666666667, NA, 948.444444444444,
6253.42222222222, 1231.90044444444, 713.199111111111, NA,
2732.72266666667, 412.179555555556, 3212.89955555556), format.spss = "F8.2", display_width = 10L),
d_homa_v66 = structure(c(-146.048888888889, NA, 334.317333333333,
-599.778222222222, -1126.55555555556, -1046.65866666667,
NA, -1209.64355555556, -283.582666666667, 320.644888888889
), format.spss = "F8.2", display_width = 12L), d_homa_v01 = structure(c(-205.147555555556,
NA, 118.445777777778, 3692.59288888889, -1248.60933333333,
-652.460444444444, NA, -533.739555555555, -150.547555555556,
397.424444444444), format.spss = "F8.2", display_width = 12L),
d_hughrelin_v66 = structure(c(-416.79, NA, -303.18, -1.12,
9.56999999999999, -70.1, NA, 18.84, -4.45999999999999, 43.28
), format.spss = "F8.2", display_width = 18L), d_hughrelin_v01 = structure(c(-689.87,
NA, -379.88, -19.14, 62.18, -115.48, NA, -175.95, 4.38, -200.15
), format.spss = "F8.2", display_width = 18L), d_huinsulin_v66 = structure(c(-33.21,
NA, 64.68, -31.61, -105.58, -147.82, NA, -275.95, -60.61,
31.43), format.spss = "F8.2", display_width = 17L), d_huinsulin_v01 = structure(c(-45.42,
NA, 42.07, 416.85, -142.81, -73.84, NA, -147.07, -26.7, -51.27
), format.spss = "F8.2", display_width = 17L), d_hucpeptide_v66 = structure(c(-194.89,
NA, 148.96, 13.1800000000001, -125.89, -352.97, NA, -471.12,
-112.91, 256.94), format.spss = "F8.2", display_width = 18L),
d_hucpeptide_v01 = structure(c(-272.58, NA, 62.23, -205.61,
-267.57, -141.9, NA, -419.54, -114.14, -65.48), format.spss = "F8.2", display_width = 18L),
d_huglucagon_v66 = structure(c(-178.53, NA, 44.85, -35.01,
-235.97, -200.73, NA, -1.89999999999998, -98.41, 77.5), format.spss = "F8.2", display_width = 18L),
d_huglucagon_v01 = structure(c(-90.93, NA, 2.53999999999999,
31.25, -531.91, -79.36, NA, -13.44, -25.08, -36.21), format.spss = "F8.2", display_width = 18L),
d_huleptin_v66 = structure(c(-1714.37, NA, -555.48, -789.81,
-1477.18, -8711.79, NA, -877.55, -1717.47, 454.1), format.spss = "F8.2", display_width = 16L),
d_huleptin_v01 = structure(c(-1628.3, NA, -653.05, 3604.24,
-2993.93, -5398.66, NA, -1612.8, -2124.82, -760.69), format.spss = "F8.2", display_width = 16L),
d_huresistin_v66 = structure(c(-1090.9, NA, 823.07, 399.3,
1144.2, -1196.62, NA, -1631.23, -41.54, -417.6), format.spss = "F8.2", display_width = 18L),
d_huresistin_v01 = structure(c(-419.78, NA, 643.51, -519.86,
3091.53, -1374.54, NA, -1704.7, -51.0900000000001, -372.34
), format.spss = "F8.2", display_width = 18L), d_huvisfatin_v66 = structure(c(0,
NA, 6.58, -87.6800000000001, -814.67, -1347.37, NA, -145.13,
-306.98, 15.7), format.spss = "F8.2", display_width = 18L),
d_huvisfatin_v01 = structure(c(0, NA, 6.58, 1552.73, -83.03,
-555.93, NA, -476.99, -306.98, -353.86), format.spss = "F8.2", display_width = 18L),
d_glucosa_v66 = structure(c(-1, -15, 2, -28, -36, -41, -7,
8, -4, 4), format.spss = "F8.2", display_width = 15L), d_glucosa_v01 = structure(c(-4,
-6, -9, -1, -31, -30, 10, 8, -7, 61), format.spss = "F8.2", display_width = 15L),
d_coltot_v66 = structure(c(-6, 8, 26, -26, -36, -11, 84,
-3, -8, -61), format.spss = "F8.2", display_width = 14L),
d_coltot_v01 = structure(c(-23, 18, 28, 9, -9, -34, 14, 19,
-7, -40), format.spss = "F8.2", display_width = 14L), d_hdl_v66 = structure(c(2,
11, 11, -2, 1, 6, -2, 8, 3, -14), format.spss = "F8.2", display_width = 11L),
d_hdl_v01 = structure(c(6, 5, 5, 32, 10, -7, -3, 2, 6, -14
), format.spss = "F8.2", display_width = 11L), d_ldl_calc_v66 = structure(c(-1,
-2, 14, -17, -34, -10, 91, -3, 3, -42), format.spss = "F8.2", display_width = 16L),
d_ldl_calc_v01 = structure(c(-20, 25, 24, -5, -21, -24, 21,
21, 13, -19), format.spss = "F8.2", display_width = 16L),
d_col_rema_v66 = structure(c(-7, -1, 1, -7, -3, -7, -5, -8,
-14, -5), format.spss = "F8.2", display_width = 16L), d_col_rema_v01 = structure(c(-9,
-12, -1, -18, 2, -3, -4, -4, -26, -7), format.spss = "F8.2", display_width = 16L),
d_trigli_v66 = structure(c(-31, -6, 5, -35, -13, -34, -27,
-42, -73, -26), format.spss = "F8.2", display_width = 14L),
d_trigli_v01 = structure(c(-41, -61, -5, -93, 11, -16, -21,
-21, -134, -35), format.spss = "F8.2", display_width = 14L),
d_hba1c_v66 = structure(c(-0.2, NA, -0.22, 0.0700000000000003,
-0.88, -1.06, NA, -0.34, -0.2, -0.0299999999999994), format.spss = "F8.2", display_width = 13L),
d_hba1c_v01 = structure(c(-0.26, NA, -0.23, 0.279999999999999,
-0.52, -0.609999999999999, NA, -0.00999999999999979, -0.25,
0.609999999999999), format.spss = "F8.2", display_width = 13L),
d_tasis2_e_v66 = structure(c(-22, -2, -4, 13, -14, -31, 0,
1, -8, 14), format.spss = "F8.2", display_width = 16L), d_tasis2_e_v01 = structure(c(-25,
6, -11, 36, -14, -2, 8, -15, 0, 15), format.spss = "F8.2", display_width = 16L),
d_tadias2_e_v66 = structure(c(-11, -1, -6, 2, -5, -27, 0,
1, -9, 11), format.spss = "F8.2", display_width = 17L), d_tadias2_e_v01 = structure(c(-14,
-1, -11, 13, -2, -17, 8, -8, -22, 2), format.spss = "F8.2", display_width = 17L),
d_peso1_v66 = structure(c(-7.80000000000001, -2, -6.09999999999999,
-7, -8, -12.9, -3.59999999999999, -3.5, -6, 1.2), format.spss = "F8.2", display_width = 13L),
d_peso1_v01 = structure(c(-9.2, -4.2, -5.89999999999999,
-6, -9, -12.9, -5.59999999999999, 0, -7, 1.2), format.spss = "F8.2", display_width = 13L),
d_cintura1_v66 = structure(c(-7, -2, -7.5, -6, -8, -8.5,
-8.5, -4.5, -14.5, 1), format.spss = "F8.2", display_width = 16L),
d_cintura1_v01 = structure(c(-7, -3, -7.5, -6, -8, -8.5,
-9.5, -3, -8.5, 1), format.spss = "F8.2", display_width = 16L),
d_geaf_tot_v66 = structure(c(3566.43, -4195.8, -1153.84,
0, 1258.74, 3524.48, 2160.84, -2097.9, 2436.37, 52.45), format.spss = "F8.2", display_width = 16L),
d_geaf_tot_v01 = structure(c(629.37, -4195.8, 97.9099999999999,
-2797.2, 1930.07, 1603.73, 3895.11, -6433.57, 1505.83, 891.61
), format.spss = "F8.2", display_width = 16L), d_p17_total_v66 = structure(c(2,
4, 2, 3, 1, 10, 3, 0, 7, 7), format.spss = "F8.2", display_width = 11L),
d_p17_total_v01 = structure(c(4, 3, 2, 0, 4, 10, 3, 2, 5,
7), format.spss = "F8.2"), d_hupai1_v66 = structure(c(43.6500000000001,
NA, -349.64, -1057.12, -265.77, -2219.75, NA, -932.13, 148.23,
944.05), format.spss = "F8.2", display_width = 13L), d_hupai1_v01 = structure(c(144.55,
NA, -276.35, -697.91, -80.5599999999999, -1610.02, NA, -617.23,
473.29, 799.11), format.spss = "F8.2", display_width = 13L),
d_hugip_v66 = structure(c(0, NA, 0, 0, 253.54, 0, NA, 0,
0, 0), format.spss = "F8.2", display_width = 13L), d_hugip_v01 = structure(c(0,
NA, 0, 22.07, 1162.49, 0, NA, 0, 0, 0), format.spss = "F8.2", display_width = 13L),
d_huglp1_v66 = structure(c(0, NA, 0, -13.7, -23.67, 50.41,
NA, 40.68, 0, 55.6), format.spss = "F8.2", display_width = 13L),
d_huglp1_v01 = structure(c(0, NA, 0, -13.7, -59.75, 75.63,
NA, 92.43, 0, -160.47), format.spss = "F8.2", display_width = 13L),
ln_trigli_v00 = structure(c(4.63472898822964, 4.969813299576,
4.06044301054642, 5.44673737166631, 4.0943445622221, 4.66343909411207,
4.83628190695148, 4.87519732320115, 5.64190707093811, 5.2257466737132
), label = "Analítica: Triglicéridos en mg/dL", format.spss = "F5.0"),
ln_trigli_v66 = structure(c(4.27666611901606, 4.92725368515721,
4.14313472639153, 5.28320372873799, 3.85014760171006, 4.27666611901606,
4.59511985013459, 4.48863636973214, 5.34233425196481, 5.07517381523383
), label = "Analítica: Triglicéridos en mg/dL", format.spss = "F5.0"),
ln_trigli_v01 = structure(c(4.12713438504509, 4.4188406077966,
3.97029191355212, 4.93447393313069, 4.26267987704132, 4.49980967033027,
4.65396035015752, 4.70048036579242, 4.99721227376411, 5.01727983681492
), label = "Analítica: Triglicéridos en mg/dL", format.spss = "F5.0"),
ln_homa_v00 = structure(c(6.04555071556718, NA, 6.72142409436365,
7.84808644334377, 7.81621937359094, 7.21939278180313, NA,
8.09146278899802, 6.33279480567865, 7.94288630470217), format.spss = "F8.2", display_width = 10L),
ln_homa_v66 = structure(c(5.62105765481488, NA, 7.05988906911122,
7.58123588965634, 7.21078464361132, 5.76519388926651, NA,
7.62891573149717, 5.63172936987266, 8.05074164604432), format.spss = "F8.2", display_width = 10L),
ln_homa_v01 = structure(c(5.38027823337748, NA, 6.85482321564537,
8.74088414843216, 7.11631333274751, 6.56976063964933, NA,
7.91305370498932, 6.02145906886522, 8.07492909673972), format.spss = "F8.2", display_width = 10L),
ln_hba1c_v00 = structure(c(1.76985463384001, NA, 1.7509374747078,
2.07693841146172, 1.8531680973567, 1.9473377010465, NA, 1.87333945622048,
1.78339121955754, 2.19833507162025), label = "Analítica: Hemoglobina glicosilada (HbA1c %)", format.spss = "F5.2"),
ln_hba1c_v66 = structure(c(1.73518911773966, NA, 1.71199450075919,
2.08567209143047, 1.70474809223843, 1.78339121955754, NA,
1.8196988379173, 1.74919985480926, 2.19499988231411), label = "Analítica: Hemoglobina glicosilada (HbA1c %)", format.spss = "F5.2"),
ln_hba1c_v01 = structure(c(1.72455071953461, 1.86872051036418,
1.71018781553424, 2.11142458753289, 1.76814960358892, 1.85629799036563,
1.66013102674962, 1.87180217690159, 1.7404661748405, 2.26384426467762
), label = "Analítica: Hemoglobina glicosilada (HbA1c %)", format.spss = "F5.2"),
ln_geaf_tot_v00 = structure(c(6.95554494281799, 8.34183930393788,
8.4371494837422, 8.11869575262367, 6.73240139150378, 7.38298895764493,
7.99228216582975, 9.23110234287667, 8.37462912676087, 5.0584090689011
), label = "AF: Gasto energético en actividad física total (MET•min/sem)", format.spss = "F8.2"),
ln_geaf_tot_v66 = structure(c(8.4371494837422, -Inf, 8.14946885573527,
8.11869575262367, 7.64869212337793, 8.5434202359197, 8.54069094410428,
9.00108616558123, 8.82055617324912, 5.34610703038388), label = "AF: Gasto energético en actividad física total (MET•min/sem)", format.spss = "F8.2"),
ln_geaf_tot_v01 = structure(c(7.42554857206372, -Inf, 8.45814145696367,
6.32693628339561, 7.92632458219889, 8.07468643426916, 8.83246367957041,
8.23647878828005, 8.67274118026667, 6.95554494281799), label = "AF: Gasto energético en actividad física total (MET•min/sem)", format.spss = "F8.2")), row.names = c(NA,
-10L), class = c("tbl_df", "tbl", "data.frame"))
``````

Then it comes a little bit of data wrangling before the t.test operations. I have taken 2 pathways to perform the analysis and confirm if it is working the group_by function. Confirm my method is what I want. The long way to confirm my operations. I subset the database a previous step to filter individually the variables

``````s_sample <-s_sample<-sample %>%
dplyr::select(paciente, matches(c("_v00", "_v01", "_v66")))  %>%
mutate(terciles_d_coltot_v66 = ntile(.\$d_coltot_v66,3)) %>%
dplyr::select(paciente, matches("_v66")) %>%
pivot_longer(!c(paciente, terciles_d_coltot_v66)) %>%
dplyr::filter(terciles_d_coltot_v66 == "1" | terciles_d_coltot_v66 == "2") %>%
mutate(terciles_d_coltot_v66 = factor(terciles_d_coltot_v66)) %>%
pivot_wider(names_from= terciles_d_coltot_v66, values_from=value) %>%
select(-paciente) %>%
group_by(name)
``````

After this I am picking variables individually

``````s_sample %>% filter(name=="tadias2_e_v66") %>% do(tidy(t.test(.\$`2`, .\$`1`, na.rm = TRUE)))
``````

name estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high method alternative

1 tadias2_e_v66 6.33 80.3 74 0.679 0.532 4.22 -19.0 31.7 Welch Two Sample t-test two.sided

Ok, this is the long-way code to confirm what I have done before. This is:

``````sample %>%
dplyr::select(paciente, matches(c("_v00", "_v01", "_v66")))  %>%
mutate(terciles_d_coltot_v66 = ntile(.\$d_coltot_v66,3)) %>%
dplyr::select(paciente, matches("_v66")) %>%
pivot_longer(!c(paciente, terciles_d_coltot_v66)) %>%
dplyr::filter(terciles_d_coltot_v66 == "1" | terciles_d_coltot_v66 == "2") %>%
mutate(terciles_d_coltot_v66 = factor(terciles_d_coltot_v66)) %>%
pivot_wider(names_from= terciles_d_coltot_v66, values_from=value) %>%
select(-paciente) %>%
group_by(name) %>%
do(tidy(t.test(.\$"1", .\$"2", na.rm = TRUE)))
``````

If you compare both results and repeat it with more variables you will see that something is happening there.
I am interested in pvalues but the thing is I don't get why the results don't match. Is the group_by function not grouping variables by name?? This function is supposed to do that?

Clarification: The 1 and 2 groups are terciles of one specific variable

Your results don't match between the two codes, because you have swapped the variable positions.

`````` t.test(.\$`2`, .\$`1`, na.rm = TRUE)
t.test(.\$"1", .\$"2", na.rm = TRUE)``````

This doesn't alter the analysis. I've tried your option, but this is not the explanation because it changes the low conf and high conf order but not the p.value. However the way I put it the p.value is different

You only wrote that you saw p.value of 0.532 , what other p value did you see for tadias2_e_v66 ?

``````  name     estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high method       alternative
<chr>       <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>        <chr>
1 tadias2~     6.33      80.3        74     0.679  **0.532**      4.22    -19.0      31.7 Welch Two S~ two.sided
# A tibble: 1 x 11
# Groups:   name [1]
name     estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high method       alternative
<chr>       <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>        <chr>
1 tadias2~    -6.33        74      80.3    -0.679  **0.532**      4.22    -31.7      19.0 Welch Two S~ two.sided``````
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