binding three dataframes with rbind - r

I have three sets of data with the same variables and different observations. The variables all share the same name, but when I try to bind them using the rbind function I see this:
names do not match previous names.
Does anyone know how to fix the problem? My desired goal is to have one dataset with numerous observations of the same variables.
What I have tried so far is this:
Daten1
> attach(Daten1)
rel.Var.1 <- data.frame(Q35, Q37, Q38, Q42, Q46, Q47, Q50, Q51, Q52, Q55, Q60, Q61,
Q91_1, Q92_1, Q93_1, Q94_1, Q95_1, Q96_1, Q97_1, Q301_1, Q300_1, Q98_1,
Q99_1, Q100_1, Q101_1, Q102_1, Q103_1, Q104_1, Q105_1, Q106_1,
Q107_1, Q108_1, Q109_1, Q110_1, Q111_1, Q112_1, Q113_1, Q114_1,
Q115_1, Q116_1, Q117_1, Q118_1, Q119_1, Q121_1, Q122_1,
Q123_1, Q124_1, Q125_1, Q126_1, Q127_1, Q128_1, Q129_1, Q130_1,
Q131_1, Q132_1, Q133_1, Q134_1, Q135_1, Q136_1, Q137_1, Q138_1,
Q139_1, Q140_1, Q141_1, Q142_1, Q143_1, Q144_1, Q145_1,
Q7, Q8, Q9, Q10, Q11, Q12, Q13, Q14, Q15, Q16, Q17, Q18, Q19, Q20,
Q21, Q22, Q23, Q24, Q25, Q26, Q27, Q28, Q29, Q30, Q31, Q32, Q33,
Q176, Q177, Q178, Q175, VPN)
>detach(Daten1)
>rel.Var.1 <- rel.Var.1 %>% rename(
neo_01 = Q35, neo_03 = Q37,neo_04 = Q38, neo_08 = Q42,
neo_12 = Q46,neo_13 = Q47, neo_16 = Q50, neo_17 = Q51, neo_18 = Q52,
neo_21 = Q55, neo_26 = Q60, neo_27 = Q61, TICS_1 = Q91_1, TICS_2 = Q92_1,
TICS_3 = Q93_1, TICS_4 = Q94_1, TICS_5 = Q95_1, TICS_6 = Q96_1,
TICS_7 = Q97_1, TICS_8 = Q301_1, TICS_9 = Q300_1, TICS_10 = Q98_1,
TICS_11 = Q99_1, TICS_12= Q100_1, TICS_13 = Q101_1, TICS_14 = Q102_1,
TICS_15 = Q103_1,
ICS_16 = Q104_1, TICS_17 = Q105_1, TICS_18 = Q106_1, TICS_19 = Q107_1,
TICS_20 = Q108_1, TICS_21 = Q109_1, TICS_22 = Q110_1, TICS_24 = Q111_1,
TICS_25 = Q112_1,
TICS_26 = Q113_1, TICS_27 = Q114_1, TICS_28 = Q115_1, TICS_29 = Q116_1,
TICS_30 = Q117_1, TICS_31 = Q118_1, TICS_32 = Q119_1, TICS_33 = Q121_1,
TICS_34 = Q122_1, TICS_35 = Q123_1, TICS_36 = Q124_1, TICS_37 = Q125_1,
TICS_38 = Q126_1, TICS_39 = Q127_1, TICS_40 = Q128_1, TICS_41 = Q129_1,
TICS_42 = Q130_1,
TICS_43 = Q131_1, TICS_44 = Q132_1, TICS_45 = Q133_1, TICS_46 = Q134_1,
TICS_47 = Q135_1, TICS_48 = Q136_1, TICS_49 = Q137_1,
TICS_50 = Q138_1, TICS_51 = Q139_1, TICS_52 = Q140_1, TICS_53 = Q141_1,
TICS_54 = Q142_1, TICS_55 = Q143_1, TICS_56 = Q144_1, TICS_57 = Q145_1,
HSPS_1 = Q7, HSPS_2 = Q8, HSPS_3 = Q9, HSPS_4 = Q10, HSPS_5 = Q11,
HSPS_6 = Q12, HSPS_7 = Q13, HSPS_8 = Q14,
HSPS_9 = Q15, HSPS_10 = Q16, HSPS_11 = Q17, HSPS_12 = Q18, HSPS_13 = Q19,
HSPS_14 = Q20, HSPS_15 = Q21, HSPS_16 = Q22,
HSPS_17 = Q23, HSPS_18 = Q24, HSPS_19 = Q25, HSPS_20 = Q26,
HSPS_21 = Q27, HSPS_22 = Q28, HSPS_23 = Q29,
HSPS_24 = Q30, HSPS_25 = Q31, HSPS_26 = Q32, HSPS_27 = Q33,
sex = Q176, Bildung = Q177, Tat = Q178, age= Q175)
>rel.Var.1 <- na.omit(rel.Var.1)
Daten 2
> attach(Daten2)
rel.Var.2 <- data.frame(Q182, Q186, Q188, Q196, Q204, Q206, Q212, Q214,
Q216, Q222, Q232, Q234,
Q221, Q222.1, Q223, Q224.1, Q225, Q226.1, Q227, Q174, Q175, Q228.1, Q229,
Q230.1,Q231, Q232.1, Q233, Q234.1, Q235, Q236.1, Q237, Q238.1,
Q239,Q240.1, Q241, Q242, Q243, Q244, Q245, Q246, Q247, Q248, Q249, Q251,
Q252, Q253, Q254, Q255, Q256, Q257, Q258, Q259, Q260, Q261, Q262, Q263,
Q264, Q265, Q266, Q267, Q268,
Q269, Q270, Q271, Q272, Q273, Q274, Q275,
Q207, Q209, Q211, Q213, Q215, Q217, Q219, Q221.1, Q223.1, Q225.1,
Q227.1, Q229.1, Q231.1, Q233.1, Q235.1, Q237.1, Q239.1, Q241.1, Q243.1,
Q245.1, Q247.1, Q249.1, Q251.1, Q253.1, Q255.1, Q257.1, Q259.1,
Q6, Q7,Q8, Q5, VPN)
>detach(Daten2)
>rel.Var.2 <- rel.Var.2 %>% rename(
neo_01 = Q182, neo_03 = Q186, neo_04 = Q188, neo_08 = Q196, neo_12 =
Q204,
neo_13 = Q206, neo_16 = Q212, neo_17 = Q214, neo_18 = Q216, neo_21 =
Q222,
neo_26 = Q232, neo_27 = Q234, TICS_1 = Q221, TICS_2 = Q222.1, TICS_3 =
Q223,
TICS_4 = Q224.1, TICS_5 = Q225, TICS_6 = Q226.1, TICS_7 = Q227, TICS_8 =
Q174,
TICS_9 = Q175, TICS_10 = Q228.1, TICS_11 = Q229, TICS_12 = Q230.1,
TICS_13 = Q231,
TICS_14 = Q232.1, TICS_15 = Q233, TICS_16 = Q234.1, TICS_17 = Q235,
TICS_18 = Q236.1,
TICS_19 = Q237, TICS_20 = Q238.1, TICS_21 = Q239, TICS_22 = Q240.1,
TICS_24 = Q241,
TICS_25 = Q242, TICS_26 = Q243, TICS_27 = Q244, TICS_28 = Q245, TICS_29 =
Q246,
TICS_30 = Q247, TICS_31 = Q248, TICS_32 = Q249, TICS_33 = Q251, TICS_34 =
Q252,
TICS_35 = Q253, ICS_36 = Q254, TICS_37 = Q255, TICS_38 = Q256, TICS_39 =
Q257,
TICS_40 = Q258, TICS_41 = Q259, TICS_42 = Q260, TICS_43 = Q261, TICS_44 =
Q262,
TICS_45 = Q263, TICS_46 = Q264, TICS_47 = Q265, TICS_48 = Q266, TICS_49 =
Q267,
TICS_50 = Q268, TICS_51 = Q269, TICS_52 = Q270, TICS_53 = Q271, TICS_54 =
Q272,
TICS_55 = Q273, TICS_56 = Q274, TICS_57 = Q275, HSPS_1 = Q207, HSPS_2 =
Q209,
HSPS_3 = Q211, HSPS_4 = Q213, HSPS_5 = Q215, HSPS_6 = Q217, HSPS_7 =
Q219,
HSPS_8 = Q221.1, HSPS_9 = Q223.1, HSPS_10 = Q225.1, HSPS_11 = Q227.1,
HSPS_12 = Q229.1,
HSPS_13 = Q231.1, HSPS_14 = Q233.1, HSPS_15 = Q235.1, HSPS_16 = Q237.1,
HSPS_17 = Q239.1,
HSPS_18 = Q241, HSPS_19 = Q243, HSPS_20 = Q245, HSPS_21 = Q247, HSPS_22 =
Q249,
HSPS_23 = Q251.1, HSPS_24 = Q253.1, HSPS_25 = Q255.1, HSPS_26 = Q257.1,
HSPS_27 = Q259.1,
sex = Q6, Bildung = Q7, Tat = Q8, age= Q5)
>rel.Var.2 <- na.omit(rel.Var.2)
Daten3
> attach(Daten3)
rel.Var.3 <- data.frame(neo_03, neo_08, neo_12, neo_16, neo_21, neo_26,
neo_01, neo_04, neo_13,
neo_17, neo_18, neo_27, TICS_1, TICS_2, TICS_3, TICS_4, TICS_5, TICS_6,
TICS_7, TICS_8, TICS_9,
TICS_10, TICS_11, TICS_12, TICS_13, TICS_14, TICS_15, ICS_16, TICS_17,
TICS_18, TICS_19, TICS_20, TICS_21, TICS_22,
TICS_24, TICS_25, TICS_26, TICS_27, TICS_28, TICS_29, TICS_30, TICS_31,
TICS_32, TICS_33, TICS_34, TICS_35, TICS_36,
TICS_37, TICS_38, TICS_39, TICS_40, TICS_41, TICS_42, TICS_43, TICS_44,
TICS_45, TICS_46, TICS_47, TICS_48, TICS_49, TICS_50,
TICS_51, TICS_52, TICS_53, TICS_54, TICS_55, TICS_56, TICS_57,
HSPS_1, HSPS_2, HSPS_3, HSPS_4, HSPS_5, HSPS_6, HSPS_7, HSPS_8, HSPS_9,
HSPS_10, HSPS_11, HSPS_12, HSPS_13,
HSPS_14, HSPS_15, HSPS_16, HSPS_17, HSPS_18, HSPS_19, HSPS_20, HSPS_21,
HSPS_22, HSPS_23, HSPS_24, HSPS_25, HSPS_26, HSPS_27,
Geschlecht, Bildungsabschluss, derzeitige_Beschaeftigung, Alter, NR)
>detach(Daten3)
>rel.Var.3 <- rel.Var.3 %>% rename(
sex = Geschlecht, Bildung = Bildungsabschluss, Tat =
derzeitige_Beschaeftigung, age= Alter, VPN = NR)
>rel.Var.3 <- na.omit(rel.Var.3)
>View(rel.Var.1)
>View(rel.Var.2)
>View(rel.Var.3)
## Datensaetze zusammenfuegen ##bind data
data_gesamt <- rbind(rel.Var.1, rel.Var.2, rel.Var.3)
data_gesamt <- bind_rows(rel.Var.1, rel.Var.2, rel.Var.3)
With bind_rows I get this error:
Can't combine `..1$neo_01` <character> and `..3$neo_01` <integer>.
Backtrace:
1. dplyr::bind_rows(rel.Var.1, rel.Var.2, rel.Var.3)
2. vctrs::vec_rbind(!!!dots, .names_to = .id)
4. vctrs::vec_default_ptype2(...)
5. vctrs::stop_incompatible_type(...)
6. vctrs:::stop_incompatible(...)
7. vctrs:::stop_vctrs(...)

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4.40751612903226, 4.25314444444444, 4.14589847009736), X382.5 = c(5.20306134969325,
4.64154722222222, 4.49053417015342, 4.31611079943899, 4.16125138888889,
4.05445757997218), X385 = c(5.07416871165644, 4.515375, 4.3646959553696,
4.19456802244039, 4.04227222222222, 3.93332127955494), X387.5 = c(4.96219325153374,
4.40868194444444, 4.25931520223152, 4.0922889200561, 3.9414,
3.83277607788595), X390 = c(4.83940797546012, 4.29276527777778,
4.14653974895398, 3.97920196353436, 3.82955277777778, 3.72600834492351
), X392.5 = c(4.71031288343558, 4.16622083333333, 4.02140446304045,
3.85572510518934, 3.70953194444445, 3.60538386648122), X395 = c(4.62059202453988,
4.07360138888889, 3.92935564853556, 3.76849228611501, 3.62277638888889,
3.51784283727399), X397.5 = c(4.49155521472393, 3.95385972222222,
3.81216178521618, 3.65344039270687, 3.50810833333333, 3.4116926286509
), X400 = c(4.37370858895706, 3.84129861111111, 3.70030125523013,
3.5432286115007, 3.39975277777778, 3.30254659248957), X402.5 = c(4.27044171779141,
3.738875, 3.59841562064156, 3.4434095371669, 3.30132638888889,
3.20369819193324), X405 = c(4.15562883435583, 3.6327375,
3.49318270571827, 3.33822159887798, 3.19675555555555, 3.10537273991655
), X407.5 = c(4.09877607361963, 3.57864305555556, 3.44027057182706,
3.28816129032258, 3.14875138888889, 3.05293602225313), X410 = c(4.01044785276074,
3.48679444444444, 3.35288842398884, 3.20463534361851, 3.06820833333333,
2.9671752433936), X412.5 = c(3.87490797546012, 3.35905694444444,
3.2244839609484, 3.07484291725105, 2.93775416666667, 2.8437983310153
), X415 = c(3.79108282208589, 3.28507361111111, 3.14952161785216,
3.00085273492286, 2.86421805555555, 2.77412378303199), X417.5 = c(3.68693558282209,
3.17824583333333, 3.04701394700139, 2.9043366058906, 2.76995972222222,
2.67318358831711), X420 = c(3.56437116564417, 3.05670972222222,
2.92747419804742, 2.786095371669, 2.65226666666667, 2.55702225312935
), X422.5 = c(3.51319018404908, 3.00990694444444, 2.87744769874477,
2.73367180925666, 2.60142361111111, 2.51227399165508), X425 = c(3.43132208588957,
2.92941805555555, 2.80041562064156, 2.65933099579243, 2.52816388888889,
2.43237969401947), X427.5 = c(3.31023926380368, 2.81629166666667,
2.68912831241283, 2.54957363253857, 2.41817777777778, 2.33177051460362
), X430 = c(3.26191717791411, 2.77505833333333, 2.64634030683403,
2.50681767180926, 2.37668055555556, 2.29549374130737), X432.5 = c(3.22984662576687,
2.74605555555556, 2.61894979079498, 2.48168022440393, 2.35282083333333,
2.26610987482615), X435 = c(3.13996932515337, 2.65807916666667,
2.5333179916318, 2.39782187938289, 2.26921388888889, 2.18432684283727
), X437.5 = c(3.08235582822086, 2.60515833333333, 2.4803249651325,
2.34626928471248, 2.21812083333333, 2.13608901251739), X440 = c(3.01686809815951,
2.54044583333333, 2.41671548117155, 2.2833842917251, 2.16062083333333,
2.07232267037552), X442.5 = c(2.93285889570552, 2.46135694444444,
2.33697489539749, 2.20531837307153, 2.08016805555556, 1.99902225312935
), X445 = c(2.8908773006135, 2.42607361111111, 2.30482426778243,
2.17530014025245, 2.04767916666667, 1.97140194714882), X447.5 = c(2.85198466257669,
2.38861111111111, 2.2694839609484, 2.14013884992987, 2.01676111111111,
1.93308901251738), X450 = c(2.8046472392638, 2.34394583333333,
2.22286750348675, 2.09689481065919, 1.97544444444445, 1.89651043115438
), X452.5 = c(2.77545705521472, 2.31733333333333, 2.20150627615063,
2.07733800841515, 1.95440277777778, 1.87571627260083), X455 = c(2.71198159509203,
2.26175277777778, 2.14609483960948, 2.02168022440393, 1.90238194444444,
1.82506536856745), X457.5 = c(2.69071779141104, 2.24759861111111,
2.1283709902371, 2.0067461430575, 1.88878055555556, 1.81217802503477
), X460 = c(2.63857055214724, 2.19480416666667, 2.07814365411437,
1.95862833099579, 1.84296111111111, 1.76875243393602), X462.5 = c(2.55233742331288,
2.10490416666667, 1.99411854951185, 1.87502664796634, 1.76141805555556,
1.68473435326843), X465 = c(2.54993251533742, 2.10804166666667,
1.99814644351464, 1.88111360448808, 1.76530277777778, 1.68772322670375
), X467.5 = c(2.54192944785276, 2.10937638888889, 1.99570990237099,
1.88144319775596, 1.76786666666667, 1.69657162726008), X470 = c(2.45424233128834,
2.02388472222222, 1.91476011157601, 1.80086255259467, 1.68827638888889,
1.61762865090403), X472.5 = c(2.38808588957055, 1.96169583333333,
1.85649232914923, 1.74286956521739, 1.63194444444444, 1.56048400556328
), X475 = c(2.40716871165644, 1.98333055555556, 1.8761589958159,
1.7647840112202, 1.65609861111111, 1.58707093184979), X477.5 = c(2.38796012269939,
1.96411527777778, 1.85740725244073, 1.74938990182328, 1.64029444444444,
1.57306258692629), X480 = c(2.32367484662577, 1.90594027777778,
1.80042677824268, 1.69331276297335, 1.58667777777778, 1.51872322670375
), X482.5 = c(2.26839877300614, 1.85602638888889, 1.75232914923291,
1.64626928471248, 1.542725, 1.47292350486787), X485 = c(2.24077914110429,
1.82308194444444, 1.72304323570432, 1.61980925666199, 1.5158,
1.44215159944367), X487.5 = c(2.19346625766871, 1.77742916666667,
1.67717712691771, 1.57596213183731, 1.46934861111111, 1.39640751043115
), X490 = c(2.1796226993865, 1.77528611111111, 1.67270432357043,
1.56773772791024, 1.46491111111111, 1.39967732962448), X492.5 = c(2.19796932515337,
1.79399861111111, 1.69254253835425, 1.58660028050491, 1.48578888888889,
1.42326842837274), X495 = c(2.16203067484663, 1.75784722222222,
1.66087866108787, 1.56017812061711, 1.45659583333333, 1.39502364394993
), X497.5 = c(2.12128220858896, 1.72433055555556, 1.62827615062762,
1.52875175315568, 1.42800833333333, 1.36483727399165), X500 = c(2.10455214723926,
1.71279027777778, 1.61463458856346, 1.51266058906031, 1.41571388888889,
1.35290403337969), X502.5 = c(2.09321779141104, 1.70464027777778,
1.60352022315202, 1.50263674614306, 1.40064722222222, 1.34636300417246
), X505 = c(2.06455828220859, 1.67302916666667, 1.57388145048815,
1.47552734922861, 1.37514166666667, 1.31759944367177), X507.5 = c(2.01607975460123,
1.62675138888889, 1.53272524407252, 1.43562692847125, 1.34284444444444,
1.27975660639777), X510 = c(1.97671165644172, 1.59260694444444,
1.50193444909344, 1.41013043478261, 1.31677916666667, 1.25430876216968
), X512.5 = c(1.96352147239264, 1.57798472222222, 1.48636262203626,
1.39764375876578, 1.30225555555556, 1.23669680111266), X515 = c(1.92503987730061,
1.54797083333333, 1.45469456066946, 1.36199298737728, 1.26766805555556,
1.21219749652295), X517.5 = c(1.90465030674847, 1.53300555555556,
1.44391352859135, 1.35238569424965, 1.2612375, 1.20831293463143
), X520 = c(1.87689570552147, 1.50217916666667, 1.4149330543933,
1.32729733520337, 1.23678194444445, 1.17577329624478), X522.5 = c(1.83759202453988,
1.46538888888889, 1.37917015341702, 1.29119775596073, 1.20174861111111,
1.14477607788595), X525 = c(1.83651533742331, 1.46525, 1.38037935843794,
1.29450490883591, 1.2079125, 1.15156189151599), X527.5 = c(1.82067791411043,
1.45758472222222, 1.36769874476987, 1.28326788218794, 1.19442916666667,
1.13923365785814), X530 = c(1.7854754601227, 1.42325833333333,
1.33740446304045, 1.25485553997195, 1.16439166666667, 1.11009457579972
), X532.5 = c(1.72706134969325, 1.36137083333333, 1.28353277545328,
1.20330014025245, 1.1164875, 1.05959248956885), X535 = c(1.73546012269939,
1.37703333333333, 1.2953570432357, 1.21220617110799, 1.12938333333333,
1.07361335187761), X537.5 = c(1.75189570552147, 1.39752083333333,
1.3135160390516, 1.23146283309958, 1.15030416666667, 1.10116133518776
), X540 = c(1.70230061349693, 1.35148888888889, 1.2665369595537,
1.18725666199158, 1.10636944444444, 1.06070792767733), X542.5 = c(1.71566564417178,
1.36359027777778, 1.28287029288703, 1.20369565217391, 1.1235375,
1.07357719054242), X545 = c(1.66589877300613, 1.31230416666667,
1.23633891213389, 1.15782889200561, 1.07775833333333, 1.01979972183588
), X547.5 = c(1.62098466257669, 1.27635555555556, 1.19730962343096,
1.11939270687237, 1.03757361111111, 0.990175243393602), X550 = c(1.64088650306749,
1.30173333333333, 1.22275592747559, 1.14718793828892, 1.06357916666667,
1.02470931849791), X552.5 = c(1.65776687116564, 1.31616944444444,
1.23976708507671, 1.16367180925666, 1.08286944444444, 1.03624200278164
), X555 = c(1.64775766871166, 1.30469861111111, 1.23003905160391,
1.15642496493689, 1.07983611111111, 1.02301529902643), X557.5 = c(1.57864417177914,
1.24234861111111, 1.16718131101813, 1.09518373071529, 1.01922638888889,
0.966821974965229), X560 = c(1.55608895705521, 1.22404166666667,
1.14994979079498, 1.07797896213184, 0.998729166666667, 0.953904033379694
), X562.5 = c(1.57327300613497, 1.24271111111111, 1.17039748953975,
1.10076437587658, 1.02429722222222, 0.982093184979138), X565 = c(1.54155828220859,
1.21247638888889, 1.14011994421199, 1.06987377279102, 0.995325,
0.950226703755216), X567.5 = c(1.5184754601227, 1.19163194444444,
1.11808926080893, 1.04673772791024, 0.969984722222222, 0.920390820584145
), X570 = c(1.52191104294479, 1.19953472222222, 1.12552022315202,
1.05369144460028, 0.979043055555555, 0.936059805285118),
X572.5 = c(1.49422699386503, 1.17113333333333, 1.09843793584379,
1.02884291725105, 0.956215277777778, 0.914119610570236),
X575 = c(1.48120552147239, 1.16091666666667, 1.08878382147838,
1.02371248246844, 0.950745833333333, 0.903524339360223),
X577.5 = c(1.49473312883436, 1.17405, 1.10468340306834, 1.04081346423562,
0.967102777777778, 0.918347705146036), X580 = c(1.45893558282209,
1.13822638888889, 1.07150488145049, 1.004904628331, 0.931488888888889,
0.888706536856745), X582.5 = c(1.40335889570552, 1.08981666666667,
1.02136262203626, 0.955345021037868, 0.882438888888889, 0.842457579972184
), X585 = c(1.41981901840491, 1.11120277777778, 1.04046164574617,
0.976112201963534, 0.902045833333333, 0.854774687065369),
X587.5 = c(1.44122085889571, 1.13389722222222, 1.06531938633194,
0.998193548387097, 0.927052777777778, 0.884666203059805),
X590 = c(1.43262883435583, 1.12188472222222, 1.05589539748954,
0.991642356241234, 0.923915277777778, 0.883214186369958),
X592.5 = c(1.39457668711656, 1.08495694444444, 1.01676150627615,
0.954368863955119, 0.886066666666667, 0.842401947148818),
X595 = c(1.39473006134969, 1.09030138888889, 1.02193584379358,
0.95813744740533, 0.889866666666667, 0.851438108484006),
X597.5 = c(1.39176380368098, 1.08874305555556, 1.02160251046025,
0.960784011220196, 0.891755555555556, 0.851367176634214),
X600 = c(1.32321472392638, 1.02342361111111, 0.958054393305439,
0.896713884992987, 0.828456944444444, 0.789112656467316),
X602.5 = c(1.3699754601227, 1.06539861111111, 1.00389818688982,
0.94194950911641, 0.875038888888889, 0.832920723226704),
X605 = c(1.39004294478528, 1.0856125, 1.02410460251046, 0.964893408134642,
0.897881944444444, 0.851397774687065), X607.5 = c(1.33887423312883,
1.04344305555556, 0.978672245467225, 0.918176718092567, 0.853443055555556,
0.810876216968011), X610 = c(1.34292944785276, 1.04911111111111,
0.98465690376569, 0.922030855539972, 0.858780555555556, 0.820618915159944
), X612.5 = c(1.32123006134969, 1.02857083333333, 0.962319386331939,
0.899249649368864, 0.834901388888889, 0.797037552155772),
X615 = c(1.30576380368098, 1.01544166666667, 0.949560669456067,
0.889232819074334, 0.824005555555556, 0.787675938803894),
X617.5 = c(1.30801840490798, 1.01950138888889, 0.959458856345886,
0.902255259467041, 0.839116666666667, 0.80459388038943),
X620 = c(1.25273619631902, 0.963565277777778, 0.903634588563459,
0.845955119214586, 0.781859722222222, 0.751739916550765),
X622.5 = c(1.24233742331288, 0.951993055555556, 0.889652719665272,
0.832720897615708, 0.765165277777778, 0.728485396383866),
X625 = c(1.3115736196319, 1.020975, 0.960577405857741, 0.902300140252454,
0.835329166666667, 0.789098748261474), X627.5 = c(1.32251840490798,
1.03869861111111, 0.977846582984658, 0.916507713884993, 0.852429166666667,
0.812655076495132), X630 = c(1.26833435582822, 0.9928125,
0.93086750348675, 0.87539270687237, 0.812751388888889, 0.775739916550765
), X632.5 = c(1.24898773006135, 0.965934722222222, 0.908059972105997,
0.85682889200561, 0.794722222222222, 0.751435326842837),
X635 = c(1.28299386503067, 0.995876388888889, 0.936375174337517,
0.881263674614306, 0.819331944444444, 0.78326842837274),
X637.5 = c(1.2784018404908, 1.00060833333333, 0.939645746164575,
0.880981767180926, 0.821463888888889, 0.792333796940195),
X640 = c(1.24037116564417, 0.960616666666667, 0.905357043235704,
0.846816269284712, 0.787144444444444, 0.744055632823366),
X642.5 = c(1.19373619631902, 0.907856944444444, 0.852075313807531,
0.797594670406732, 0.732779166666667, 0.684244784422809),
X645 = c(1.17663190184049, 0.898422222222222, 0.835518828451883,
0.782708274894811, 0.717233333333333, 0.684310152990264),
X647.5 = c(1.21073619631902, 0.943138888888889, 0.879875871687587,
0.826015427769986, 0.7630125, 0.737840055632823), X650 = c(1.20088036809816,
0.934829166666667, 0.877008368200837, 0.821608695652174,
0.760254166666667, 0.727004172461752), X652.5 = c(1.18600920245399,
0.915881944444444, 0.860887029288703, 0.80358064516129, 0.744027777777778,
0.705867872044506), X655 = c(1.22061349693252, 0.951930555555556,
0.896224546722455, 0.840730715287518, 0.784806944444444,
0.750878998609179), X657.5 = c(1.19243865030675, 0.924801388888889,
0.865479776847978, 0.81258064516129, 0.757759722222222, 0.721283727399165
), X660 = c(1.10550920245399, 0.841702777777778, 0.780730822873082,
0.731366058906031, 0.673393055555556, 0.638547983310153)), row.names = c(NA,
6L), class = "data.frame")
My code:
Data <- data.frame(Data)
library(rgl)
library(pls)
x <- as.POSIXct(Data$Date, format = "2013-01-10", tz = "Australia/Adelaide")
y <- Data$Wavelength
z <- as.matrix(Data[,3:167])
open3d()
plot3d(x, y, z,col="purple", size=3, xlab = "Date", ylab = "Wavelength", zlab = "Absorbance (/cm)")
I get a not very nice plot as shown in the second graph.
The x-axis has labels in numerical dates, but I want the actual dates in the format like "1 June 2013" or "1/6/2013".
I also don't know how to make a nice rainbow colour looking 3D graph.
Any help is welcome :)
a typical UV-Vis spectrum looks like graph 3 below
using the codes provided by Marco Sandri,
library(tidyr)
library(ggplot2)
library(plotly)
Data %>%
gather(Series, y, X250:X660, factor_key=TRUE) %>%
plot_ly(x = ~Date, y = ~Wavelength, z = ~y,
type = 'scatter3d', mode = 'lines', color = ~Series)
The graph 4 is the plot I get.

A solution based on the plotly package:
library(tidyr)
library(plotly)
Data %>%
gather(Series, y, X250:X660, factor_key=TRUE) %>%
plot_ly(x = ~Date, y = ~Wavelength, z = ~y, type = 'scatter3d', mode = 'lines', color = ~Series)

You may omit and redraw the axes using bbox3d. In bbox3d just xat= and xlab= specifications are needed, where we use x.
open3d()
plot3d(x, y, z, col="purple", size=3, xlab="Date", ylab="Wavelength",
zlab="Absorbance (/cm)", axes=FALSE, top=TRUE)
bbox3d(xat=x, xlab=x, col="black", front="line", back="line", lit=FALSE)

Remove all rows tha contain values below 0.10 (in all row) in R

This is my matrix:
x<-structure(list(Sample_250 = list(`ITUB4~time+ITSA4` = 0.0189772705000679,
`ITSA4~time+ITUB4` = 0.0172247829378391, `KROT3~time+ESTC3` = 0.362976295896543,
`ESTC3~time+KROT3` = 0.919654541750147, `ELET6~time+ELET3` = 0.563149047013394,
`ELET3~time+ELET6` = 0.938978962441099, `VALE5~time+BRAP4` = 0.00879735041567956,
`BRAP4~time+VALE5` = 0.00327639807633581, `RSID3~time+PDGR3` = 0.537991430220927,
`PDGR3~time+RSID3` = 0.246554103682342, `PDGR3~time+BISA3` = 0.559254391144534,
`BISA3~time+PDGR3` = 0.61031816244403, `VALE5~time+VALE3` = 0.180842743583616,
`VALE3~time+VALE5` = 0.66647273985911, `BRPR3~time+BRML3` = 0.338499489464644,
`BRML3~time+BRPR3` = 0.319063657443075, `PETR4~time+PETR3` = 0.125540460125629,
`PETR3~time+PETR4` = 0.124801328997536, `DTEX3~time+CSAN3` = 0.93868928574058,
`CSAN3~time+DTEX3` = 0.237699406950144, `RSID3~time+BISA3` = 0.449718913669525,
`BISA3~time+RSID3` = 0.7561632200477, `ELPL4~time+ELET3` = 0.174294574975377,
`ELET3~time+ELPL4` = 0.300066723578605, `EVEN3~time+CSAN3` = 0.734452997271797,
`CSAN3~time+EVEN3` = 0.104402290451259, `KROT3~time+CIEL3` = 0.93683315998679,
`CIEL3~time+KROT3` = 0.936544198858508, `MRFG3~time+BISA3` = 0.588077047082012,
`BISA3~time+MRFG3` = 0.241408284405396), Sample_220 = list(
`ITUB4~time+ITSA4` = 0.0173697888550166, `ITSA4~time+ITUB4` = 0.0149942952128483,
`KROT3~time+ESTC3` = 0.482794731209648, `ESTC3~time+KROT3` = 0.890472799194387,
`ELET6~time+ELET3` = 0.289262231792853, `ELET3~time+ELET6` = 0.583772170805346,
`VALE5~time+BRAP4` = 0.0115132699560557, `BRAP4~time+VALE5` = 0.00454387128721931,
`RSID3~time+PDGR3` = 0.701361295124465, `PDGR3~time+RSID3` = 0.276392398580336,
`PDGR3~time+BISA3` = 0.459917895151059, `BISA3~time+PDGR3` = 0.932334809205404,
`VALE5~time+VALE3` = 0.228621489426817, `VALE3~time+VALE5` = 0.599616896543261,
`BRPR3~time+BRML3` = 0.423214373690621, `BRML3~time+BRPR3` = 0.43367402957197,
`PETR4~time+PETR3` = 0.0726218638061883, `PETR3~time+PETR4` = 0.0684556705423691,
`DTEX3~time+CSAN3` = 0.957213428702438, `CSAN3~time+DTEX3` = 0.643249328242026,
`RSID3~time+BISA3` = 0.140702283930701, `BISA3~time+RSID3` = 0.438759561659429,
`ELPL4~time+ELET3` = 0.108415504373493, `ELET3~time+ELPL4` = 0.259235741006097,
`EVEN3~time+CSAN3` = 0.995097190780355, `CSAN3~time+EVEN3` = 0.35833286961364,
`KROT3~time+CIEL3` = 0.883381800410008, `CIEL3~time+KROT3` = 0.58096328992918,
`MRFG3~time+BISA3` = 0.811273794794714, `BISA3~time+MRFG3` = 0.162511686203042),
Sample_200 = list(`ITUB4~time+ITSA4` = 0.0269410475431228,
`ITSA4~time+ITUB4` = 0.0268281043283851, `KROT3~time+ESTC3` = 0.648973944293657,
`ESTC3~time+KROT3` = 0.843925839073412, `ELET6~time+ELET3` = 0.85074648265282,
`ELET3~time+ELET6` = 0.926090646237098, `VALE5~time+BRAP4` = 0.0298988391464108,
`BRAP4~time+VALE5` = 0.0210534678726486, `RSID3~time+PDGR3` = 0.913261323047721,
`PDGR3~time+RSID3` = 0.460744060168818, `PDGR3~time+BISA3` = 0.681848278084124,
`BISA3~time+PDGR3` = 0.700508228924671, `VALE5~time+VALE3` = 0.404824931817606,
`VALE3~time+VALE5` = 0.858492744479535, `BRPR3~time+BRML3` = 0.282313695830455,
`BRML3~time+BRPR3` = 0.421361074266136, `PETR4~time+PETR3` = 0.0389941410401918,
`PETR3~time+PETR4` = 0.0366363568643157, `DTEX3~time+CSAN3` = 0.593381022274927,
`CSAN3~time+DTEX3` = 0.296186622367649, `RSID3~time+BISA3` = 0.136337062156413,
`BISA3~time+RSID3` = 0.253647313739565, `ELPL4~time+ELET3` = 0.0404140463603602,
`ELET3~time+ELPL4` = 0.0584026420525388, `EVEN3~time+CSAN3` = 0.992224496682121,
`CSAN3~time+EVEN3` = 0.364016491282029, `KROT3~time+CIEL3` = 0.923443434909376,
`CIEL3~time+KROT3` = 0.492267643047159, `MRFG3~time+BISA3` = 0.505439622239642,
`BISA3~time+MRFG3` = 0.433741779126583), Sample_180 = list(
`ITUB4~time+ITSA4` = 0.0709729806619366, `ITSA4~time+ITUB4` = 0.0703318148854131,
`KROT3~time+ESTC3` = 0.714222637099451, `ESTC3~time+KROT3` = 0.983192555139107,
`ELET6~time+ELET3` = 0.651446390753224, `ELET3~time+ELET6` = 0.504251519490735,
`VALE5~time+BRAP4` = 0.0655201102796135, `BRAP4~time+VALE5` = 0.064459649024225,
`RSID3~time+PDGR3` = 0.966515813873172, `PDGR3~time+RSID3` = 0.353225059948276,
`PDGR3~time+BISA3` = 0.819582167704402, `BISA3~time+PDGR3` = 0.457403474593761,
`VALE5~time+VALE3` = 0.834891076683459, `VALE3~time+VALE5` = 0.624305154223115,
`BRPR3~time+BRML3` = 0.338684631277372, `BRML3~time+BRPR3` = 0.645983354906404,
`PETR4~time+PETR3` = 0.016615774081754, `PETR3~time+PETR4` = 0.0165629129043023,
`DTEX3~time+CSAN3` = 0.642061011299162, `CSAN3~time+DTEX3` = 0.424690135396935,
`RSID3~time+BISA3` = 0.101897354576195, `BISA3~time+RSID3` = 0.204241392846169,
`ELPL4~time+ELET3` = 0.0729734425567139, `ELET3~time+ELPL4` = 0.128996393897499,
`EVEN3~time+CSAN3` = 0.899884399768484, `CSAN3~time+EVEN3` = 0.146722568327017,
`KROT3~time+CIEL3` = 0.830125914939971, `CIEL3~time+KROT3` = 0.567087012782755,
`MRFG3~time+BISA3` = 0.122725171728208, `BISA3~time+MRFG3` = 0.459448430490008)), row.names = c("ITUB4~time+ITSA4",
"ITSA4~time+ITUB4", "KROT3~time+ESTC3", "ESTC3~time+KROT3", "ELET6~time+ELET3",
"ELET3~time+ELET6", "VALE5~time+BRAP4", "BRAP4~time+VALE5", "RSID3~time+PDGR3",
"PDGR3~time+RSID3", "PDGR3~time+BISA3", "BISA3~time+PDGR3", "VALE5~time+VALE3",
"VALE3~time+VALE5", "BRPR3~time+BRML3", "BRML3~time+BRPR3", "PETR4~time+PETR3",
"PETR3~time+PETR4", "DTEX3~time+CSAN3", "CSAN3~time+DTEX3", "RSID3~time+BISA3",
"BISA3~time+RSID3", "ELPL4~time+ELET3", "ELET3~time+ELPL4", "EVEN3~time+CSAN3",
"CSAN3~time+EVEN3", "KROT3~time+CIEL3", "CIEL3~time+KROT3", "MRFG3~time+BISA3",
"BISA3~time+MRFG3"), class = "data.frame")
1º Question) I want to remove all rows that contain values bellow 0.10. It is necessary that values bellow 0.10 belongs for the 4 columns
2º Question) I want to remove all rows that contain values bellow 0.10 on the first 3 columns.
I tried this:
x[x[1:nrow(x),]<.10,]
Is it possible to do this with a basic function in R?
Any help ?
Thanks

Try for question 1 x[!apply(x, 1, function(x) any(x < .10)), ]
Sample_250 Sample_220 Sample_200 Sample_180
KROT3~time+ESTC3 0.3629763 0.4827947 0.6489739 0.7142226
ESTC3~time+KROT3 0.9196545 0.8904728 0.8439258 0.9831926
ELET6~time+ELET3 0.563149 0.2892622 0.8507465 0.6514464
ELET3~time+ELET6 0.938979 0.5837722 0.9260906 0.5042515
RSID3~time+PDGR3 0.5379914 0.7013613 0.9132613 0.9665158
PDGR3~time+RSID3 0.2465541 0.2763924 0.4607441 0.3532251
PDGR3~time+BISA3 0.5592544 0.4599179 0.6818483 0.8195822
BISA3~time+PDGR3 0.6103182 0.9323348 0.7005082 0.4574035
VALE5~time+VALE3 0.1808427 0.2286215 0.4048249 0.8348911
VALE3~time+VALE5 0.6664727 0.5996169 0.8584927 0.6243052
BRPR3~time+BRML3 0.3384995 0.4232144 0.2823137 0.3386846
BRML3~time+BRPR3 0.3190637 0.433674 0.4213611 0.6459834
DTEX3~time+CSAN3 0.9386893 0.9572134 0.593381 0.642061
CSAN3~time+DTEX3 0.2376994 0.6432493 0.2961866 0.4246901
RSID3~time+BISA3 0.4497189 0.1407023 0.1363371 0.1018974
BISA3~time+RSID3 0.7561632 0.4387596 0.2536473 0.2042414
EVEN3~time+CSAN3 0.734453 0.9950972 0.9922245 0.8998844
CSAN3~time+EVEN3 0.1044023 0.3583329 0.3640165 0.1467226
KROT3~time+CIEL3 0.9368332 0.8833818 0.9234434 0.8301259
CIEL3~time+KROT3 0.9365442 0.5809633 0.4922676 0.567087
MRFG3~time+BISA3 0.588077 0.8112738 0.5054396 0.1227252
BISA3~time+MRFG3 0.2414083 0.1625117 0.4337418 0.4594484
For question 2: x[!apply(x[, 1:3], 1, function(x) any(x < .10)), ]
Sample_250 Sample_220 Sample_200 Sample_180
KROT3~time+ESTC3 0.3629763 0.4827947 0.6489739 0.7142226
ESTC3~time+KROT3 0.9196545 0.8904728 0.8439258 0.9831926
ELET6~time+ELET3 0.563149 0.2892622 0.8507465 0.6514464
ELET3~time+ELET6 0.938979 0.5837722 0.9260906 0.5042515
RSID3~time+PDGR3 0.5379914 0.7013613 0.9132613 0.9665158
PDGR3~time+RSID3 0.2465541 0.2763924 0.4607441 0.3532251
PDGR3~time+BISA3 0.5592544 0.4599179 0.6818483 0.8195822
BISA3~time+PDGR3 0.6103182 0.9323348 0.7005082 0.4574035
VALE5~time+VALE3 0.1808427 0.2286215 0.4048249 0.8348911
VALE3~time+VALE5 0.6664727 0.5996169 0.8584927 0.6243052
BRPR3~time+BRML3 0.3384995 0.4232144 0.2823137 0.3386846
BRML3~time+BRPR3 0.3190637 0.433674 0.4213611 0.6459834
DTEX3~time+CSAN3 0.9386893 0.9572134 0.593381 0.642061
CSAN3~time+DTEX3 0.2376994 0.6432493 0.2961866 0.4246901
RSID3~time+BISA3 0.4497189 0.1407023 0.1363371 0.1018974
BISA3~time+RSID3 0.7561632 0.4387596 0.2536473 0.2042414
EVEN3~time+CSAN3 0.734453 0.9950972 0.9922245 0.8998844
CSAN3~time+EVEN3 0.1044023 0.3583329 0.3640165 0.1467226
KROT3~time+CIEL3 0.9368332 0.8833818 0.9234434 0.8301259
CIEL3~time+KROT3 0.9365442 0.5809633 0.4922676 0.567087
MRFG3~time+BISA3 0.588077 0.8112738 0.5054396 0.1227252
BISA3~time+MRFG3 0.2414083 0.1625117 0.4337418 0.4594484

Does this do what you want?
In regards to question 1:
cond1 <- apply(x[,1:3] < 0.1, 1, any)
y <- x[!cond1, ]
head(x)
# Sample_250 Sample_220 Sample_200 Sample_180
#ITUB4~time+ITSA4 0.01897727 0.01736979 0.02694105 0.07097298
#ITSA4~time+ITUB4 0.01722478 0.0149943 0.0268281 0.07033181
#KROT3~time+ESTC3 0.3629763 0.4827947 0.6489739 0.7142226
#ESTC3~time+KROT3 0.9196545 0.8904728 0.8439258 0.9831926
#ELET6~time+ELET3 0.563149 0.2892622 0.8507465 0.6514464
#ELET3~time+ELET6 0.938979 0.5837722 0.9260906 0.5042515
In regards to question 2:
cond2 <- apply(x < 0.1, 1, all)
z <- x[!cond2, ]
head(y)
# Sample_250 Sample_220 Sample_200 Sample_180
#ITUB4~time+ITSA4 0.01897727 0.01736979 0.02694105 0.07097298
#ITSA4~time+ITUB4 0.01722478 0.0149943 0.0268281 0.07033181
#KROT3~time+ESTC3 0.3629763 0.4827947 0.6489739 0.7142226
#ESTC3~time+KROT3 0.9196545 0.8904728 0.8439258 0.9831926
#ELET6~time+ELET3 0.563149 0.2892622 0.8507465 0.6514464
#ELET3~time+ELET6 0.938979 0.5837722 0.9260906 0.5042515

For the first question:
subset(x, apply(x, 1, function(x) all(x > 0.1)) == TRUE)
For the second one:
subset(x, apply(x[, 1:3], 1, function(x) all(x > 0.1)) == TRUE)

Retrieve the position (column name) of the maximum value of the derivative of an interval

To calculate the Red Edge Position Index, I need to find the wavelength value (column name) corresponding to the maximum derivative of reflectance in the red edge region from 690nm to 740nm. I have included a subset of my dataframe below, it contains the correct interval...
I have 640 rows (Sample) of 2151 measurements (values) plus a few catagoricals in the first columns (e.g. plantType and plantCondition). I need to find the column of the value corresponding to the maximum of the derivative of the values in the interval specified and return the wavelength value to the REPI column.
I am trying something like this but I do not know how to calculate the maximum of the derivative in the specified interval
# find the maximum of the derivative of the values in columns x690:x740
# attempt to find for single sample first
> which( colnames(spec.data)=="X690")
[1] 352
> which( colnames(spec.data)=="X740")
[1] 402
# I want to return the values of the differential but this doesn't work
> foo.vector <- diff(spec.data[1,352:402])
>> Error in r[i1] - r[-length(r):-(length(r) - lag + 1L)] : non-numeric argument to binary operator
This makes sense because I don't have the dt in dx/dt but I am not sure how to retrieve the position of the maximum value of the derivative of this interval. once I did I think I would
> spec.data$REPI <- which( colnames(spec.data) == max(foo.vector))
Then I think I would lapply this for each row?
Can anyone point me towards a solution for this?
Thank you...
subset of data from dput
> dput(spec.data[1:2, c(1:3, 7, 300:450)])
structure(list(Sample = c("JUMO_G1 P1T9 Leaf Clip00000.asd",
"JUMO_G1 P1T9 Leaf Clip00001.asd"), plantType = c("JUMO", "JUMO"
), plantCondition = c("G", "G"), REPI = c(NA_real_, NA_real_),
X638 = c(0.0611, 0.06114), X639 = c(0.0606, 0.06064), X640 = c(0.0601,
0.06012), X641 = c(0.0595, 0.05953), X642 = c(0.0589, 0.05893
), X643 = c(0.0584, 0.05834), X644 = c(0.0577, 0.05775),
X645 = c(0.05717, 0.05717), X646 = c(0.0566, 0.05664), X647 = c(0.0562,
0.05618), X648 = c(0.0557, 0.05573), X649 = c(0.0554, 0.05536
), X650 = c(0.0551, 0.05505), X651 = c(0.0547, 0.05475),
X652 = c(0.05448, 0.05447), X653 = c(0.0542, 0.05421), X654 = c(0.054,
0.05395), X655 = c(0.0536, 0.05357), X656 = c(0.0532, 0.05319
), X657 = c(0.0528, 0.05277), X658 = c(0.0523, 0.05229),
X659 = c(0.0518, 0.05176), X660 = c(0.05128, 0.05126), X661 = c(0.0508,
0.05077), X662 = c(0.0503, 0.05024), X663 = c(0.0498, 0.04978
), X664 = c(0.0494, 0.04936), X665 = c(0.049, 0.04897), X666 = c(0.04869,
0.04866), X667 = c(0.0484, 0.04838), X668 = c(0.0482, 0.04815
), X669 = c(0.048, 0.04797), X670 = c(0.0479, 0.04782), X671 = c(0.0478,
0.04775), X672 = c(0.0478, 0.04773), X673 = c(0.0478, 0.04773
), X674 = c(0.0478, 0.04776), X675 = c(0.0479, 0.04786),
X676 = c(0.0481, 0.04802), X677 = c(0.0483, 0.0482), X678 = c(0.0486,
0.04843), X679 = c(0.0489, 0.04873), X680 = c(0.04925, 0.04911
), X681 = c(0.0498, 0.04962), X682 = c(0.0504, 0.05026),
X683 = c(0.05122, 0.05103), X684 = c(0.0522, 0.052), X685 = c(0.0533,
0.05317), X686 = c(0.0548, 0.05458), X687 = c(0.05647, 0.05627
), X688 = c(0.0584, 0.05824), X689 = c(0.0608, 0.06057),
X690 = c(0.0634, 0.06326), X691 = c(0.0664, 0.06626), X692 = c(0.0698,
0.06958), X693 = c(0.0734, 0.07317), X694 = c(0.0773, 0.07701
), X695 = c(0.0814, 0.08109), X696 = c(0.0856, 0.0854), X697 = c(0.0901,
0.08989), X698 = c(0.0947, 0.09449), X699 = c(0.0994, 0.09917
), X700 = c(0.10417, 0.10395), X701 = c(0.10899, 0.10881),
X702 = c(0.11385, 0.11366), X703 = c(0.11871, 0.11854), X704 = c(0.12356,
0.12342), X705 = c(0.1284, 0.12829), X706 = c(0.13324, 0.13312
), X707 = c(0.13803, 0.13792), X708 = c(0.14281, 0.14273),
X709 = c(0.14763, 0.14755), X710 = c(0.15243, 0.15235), X711 = c(0.15718,
0.15713), X712 = c(0.16192, 0.16189), X713 = c(0.1667, 0.16663
), X714 = c(0.17143, 0.17137), X715 = c(0.17609, 0.17605),
X716 = c(0.18069, 0.18062), X717 = c(0.18528, 0.1852), X718 = c(0.18977,
0.18968), X719 = c(0.19417, 0.19406), X720 = c(0.19851, 0.19838
), X721 = c(0.20276, 0.20263), X722 = c(0.20686, 0.20671),
X723 = c(0.2108, 0.21063), X724 = c(0.21465, 0.21449), X725 = c(0.21837,
0.21819), X726 = c(0.22194, 0.22174), X727 = c(0.22534, 0.22515
), X728 = c(0.2286, 0.22838), X729 = c(0.23164, 0.23142),
X730 = c(0.23447, 0.23427), X731 = c(0.23719, 0.23696), X732 = c(0.23984,
0.23959), X733 = c(0.24229, 0.24203), X734 = c(0.24452, 0.24426
), X735 = c(0.24668, 0.24638), X736 = c(0.24867, 0.24839),
X737 = c(0.25053, 0.25028), X738 = c(0.25229, 0.25203), X739 = c(0.25382,
0.25359), X740 = c(0.25531, 0.25508), X741 = c(0.25672, 0.25646
), X742 = c(0.25791, 0.25766), X743 = c(0.25907, 0.25884),
X744 = c(0.26014, 0.25993), X745 = c(0.2611, 0.26089), X746 = c(0.26201,
0.26178), X747 = c(0.26278, 0.26257), X748 = c(0.26347, 0.26329
), X749 = c(0.26414, 0.26397), X750 = c(0.26475, 0.26459),
X751 = c(0.26525, 0.2651), X752 = c(0.26568, 0.26554), X753 = c(0.26614,
0.266), X754 = c(0.26652, 0.26639), X755 = c(0.26682, 0.26671
), X756 = c(0.2671, 0.26701), X757 = c(0.26743, 0.26734),
X758 = c(0.26767, 0.26758), X759 = c(0.26789, 0.26781), X760 = c(0.26814,
0.26808), X761 = c(0.2682, 0.26817), X762 = c(0.26835, 0.26831
), X763 = c(0.26856, 0.26851), X764 = c(0.26872, 0.26869),
X765 = c(0.26884, 0.26881), X766 = c(0.26892, 0.2689), X767 = c(0.26896,
0.26894), X768 = c(0.26898, 0.26896), X769 = c(0.2691, 0.26909
), X770 = c(0.2692, 0.2692), X771 = c(0.26921, 0.26921),
X772 = c(0.26923, 0.26926), X773 = c(0.26927, 0.26931), X774 = c(0.26935,
0.26939), X775 = c(0.26945, 0.26947), X776 = c(0.26946, 0.26949
), X777 = c(0.26948, 0.26952), X778 = c(0.26953, 0.26958),
X779 = c(0.26958, 0.26963), X780 = c(0.26965, 0.2697), X781 = c(0.2697,
0.26975), X782 = c(0.2697, 0.26977), X783 = c(0.26972, 0.26978
), X784 = c(0.26979, 0.26982), X785 = c(0.26987, 0.2699),
X786 = c(0.26991, 0.26998), X787 = c(0.26989, 0.26997), X788 = c(0.26991,
0.26998)), .Names = c("Sample", "plantType", "plantCondition",
"REPI", "X638", "X639", "X640", "X641", "X642", "X643", "X644",
"X645", "X646", "X647", "X648", "X649", "X650", "X651", "X652",
"X653", "X654", "X655", "X656", "X657", "X658", "X659", "X660",
"X661", "X662", "X663", "X664", "X665", "X666", "X667", "X668",
"X669", "X670", "X671", "X672", "X673", "X674", "X675", "X676",
"X677", "X678", "X679", "X680", "X681", "X682", "X683", "X684",
"X685", "X686", "X687", "X688", "X689", "X690", "X691", "X692",
"X693", "X694", "X695", "X696", "X697", "X698", "X699", "X700",
"X701", "X702", "X703", "X704", "X705", "X706", "X707", "X708",
"X709", "X710", "X711", "X712", "X713", "X714", "X715", "X716",
"X717", "X718", "X719", "X720", "X721", "X722", "X723", "X724",
"X725", "X726", "X727", "X728", "X729", "X730", "X731", "X732",
"X733", "X734", "X735", "X736", "X737", "X738", "X739", "X740",
"X741", "X742", "X743", "X744", "X745", "X746", "X747", "X748",
"X749", "X750", "X751", "X752", "X753", "X754", "X755", "X756",
"X757", "X758", "X759", "X760", "X761", "X762", "X763", "X764",
"X765", "X766", "X767", "X768", "X769", "X770", "X771", "X772",
"X773", "X774", "X775", "X776", "X777", "X778", "X779", "X780",
"X781", "X782", "X783", "X784", "X785", "X786", "X787", "X788"
), row.names = 1:2, class = "data.frame")

You can try this
spec.data$REPI <- apply(spec.data[,-(1:4)], 1, function(x) which.max(diff(x)))
Or you can try using dplyr and tidyr:
library(dplyr)
library(tidyr)
spec.data %>%
gather(key, value, -Sample, -plantType, - plantCondition, -REPI) %>%
group_by(Sample) %>%
summarise(which.max(diff(value)))
They both seem to give same results.

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