我正在尝试使用pivot_wider整理数据集，但我遇到了一些我不知道如何解决的问题。在我的值列"OrigValueStr"中，我分配给"values_from"的值列中，我既有数字又有因子。由于存在一些重复项，我想从数值中获取平均值，但我想将因子保留为因子(也许通过将可能的重复项放在彼此之后，由";"或"_"分隔，或者只保留第一个 r 遇到并删除其他(。我的想法是将ifelse语句放入"values_fn"中，或者分配"names_from"中的哪些因素从中获取平均值并保留其余因素。但是，我不知道如何做到这一点。
我的另一个想法是将数据集一分为二，一个包含数值，另一个包含因子(来自"values_from"列(，做需要做的事情，然后再次将数据集放在一起。但我宁愿一次用pivot_wider做这一切。
由于我对R不是很熟练，我不知道如何编写我的代码以使其执行我想要的。 我没有找到任何其他人以我想象的方式使用values_fn的例子。
有没有人可以指出我正确的方向/帮助我如何整理这些数据？我想要的是每行一个物种("AccSpeciesName"(和每个唯一的"TraitName"作为一列。
这些是我在尝试新想法之前尝试过的事情，它们没有给我想要的东西：
df7<-Df_TR %>%
group_by(AccSpeciesName) %>%
mutate(row = row_number()) %>%
tidyr::pivot_wider(names_from = TraitName, values_from = OrigValueStr) %>%
select(-row)
levels(D_TRY$TraitName)
df8<-Df_TR %>%
mutate(OrigValueStr = as.numeric(OrigValueStr)) %>%
pivot_wider(., names_from = TraitName, values_from = OrigValueStr,values_fn = list(OrigValueStr = mean))
这是我的数据子集(原始数据有>2 000 000个观测值和27个变量，是从TRY植物性状数据库中收到的(，如果我选择了我感兴趣的3个变量：
structure(list(AccSpeciesName = structure(c(1L, 1L, 2L, 2L, 3L,
3L, 5L, 5L, 6L, 7L, 11L, 11L, 9L, 10L, 12L, 12L, 13L, 13L, 15L,
17L, 18L, 18L, 19L, 20L, 21L, 21L, 22L, 22L, 23L, 23L, 24L, 24L,
25L, 25L, 26L, 27L, 27L, 28L, 29L, 4L, 8L, 14L, 28L, 16L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L
), .Label = c("Achillea millefolium", "Angelica sylvestris",
"Anthriscus sylvestris", "Calluna vulgaris", "Caltha palustris",
"Carex rostrata", "Carex vaginata", "Clematis vitalba", "Deschampsia cespitosa",
"Elymus repens", "Epilobium angustifolium", "Filipendula ulmaria",
"Geranium sylvaticum", "Helianthemum nummularium", "Lathyrus pratensis",
"Ligustrum vulgare", "Luzula multiflora", "Melampyrum sylvaticum",
"Orthilia secunda", "Persicaria vivipara", "Rhinanthus minor",
"Rubus saxatilis", "Rumex obtusifolius", "Solidago virgaurea",
"Tanacetum vulgare", "Trifolium pratense", "Trollius europaeus",
"Vaccinium myrtillus", "Vicia cracca"), class = "factor"), TraitName = structure(c(4L,
5L, 4L, 5L, 4L, 5L, 4L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 4L, 5L, 4L,
5L, 5L, 5L, 4L, 5L, 5L, 5L, 4L, 5L, 4L, 5L, 4L, 5L, 4L, 5L, 4L,
5L, 5L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 3L, 2L, 1L, 3L,
2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L,
1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L,
2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L,
1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L,
3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 2L,
1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L,
3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L,
2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L,
3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 1L, 3L, 2L, 1L, 2L, 1L,
2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 1L,
3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 1L, 3L, 2L, 1L, 3L, 1L,
3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L,
2L, 1L, 3L, 2L, 1L, 2L, 1L, 3L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 2L,
1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L,
3L, 2L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L,
2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L,
1L, 3L, 2L, 1L, 3L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L,
1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L,
3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L,
2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 2L, 1L,
3L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 3L, 2L, 1L, 3L), .Label = c("Leaf area per leaf dry mass (specific leaf area, SLA or 1/LMA): petiole excluded",
"Leaf area per leaf fresh mass (specific leaf area (SLA or 1/LMA) based on leaf fresh mass)",
"Leaf dry mass per leaf fresh mass (leaf dry matter content, LDMC)",
"Plant lifespan (longevity)", "Plant nitrogen(N) fixation capacity",
"Seed dry mass"), class = "factor"), OrigValueStr = structure(c(346L,
345L, 346L, 345L, 346L, 345L, 346L, 345L, 345L, 345L, 346L, 345L,
345L, 345L, 346L, 345L, 346L, 345L, 344L, 345L, 343L, 345L, 345L,
345L, 343L, 345L, 346L, 345L, 346L, 345L, 346L, 345L, 346L, 345L,
344L, 346L, 345L, 345L, 344L, 1L, 3L, 4L, 2L, 100L, 170L, 204L,
325L, 89L, 120L, 318L, 31L, 7L, 311L, 81L, 124L, 310L, 84L, 111L,
320L, 42L, 5L, 324L, 163L, 196L, 307L, 92L, 326L, 70L, 127L,
296L, 93L, 172L, 301L, 74L, 103L, 323L, 17L, 6L, 299L, 167L,
210L, 297L, 85L, 142L, 303L, 55L, 102L, 312L, 8L, 134L, 239L,
341L, 110L, 256L, 37L, 105L, 289L, 14L, 104L, 279L, 331L, 130L,
201L, 46L, 211L, 215L, 39L, 248L, 183L, 49L, 178L, 272L, 56L,
222L, 220L, 11L, 203L, 175L, 50L, 180L, 270L, 44L, 207L, 219L,
27L, 231L, 181L, 174L, 275L, 28L, 205L, 199L, 61L, 202L, 260L,
19L, 147L, 252L, 53L, 193L, 264L, 77L, 274L, 228L, 36L, 151L,
276L, 47L, 190L, 254L, 69L, 227L, 246L, 12L, 138L, 245L, 62L,
198L, 269L, 75L, 251L, 250L, 18L, 152L, 240L, 33L, 195L, 223L,
60L, 208L, 253L, 22L, 154L, 243L, 30L, 192L, 217L, 186L, 263L,
40L, 160L, 267L, 20L, 188L, 206L, 67L, 216L, 10L, 146L, 232L,
72L, 257L, 65L, 249L, 34L, 159L, 259L, 78L, 236L, 268L, 90L,
265L, 261L, 26L, 156L, 255L, 83L, 238L, 57L, 200L, 258L, 35L,
185L, 235L, 86L, 229L, 277L, 71L, 214L, 38L, 155L, 273L, 73L,
262L, 59L, 213L, 242L, 24L, 158L, 241L, 332L, 106L, 226L, 29L,
115L, 281L, 342L, 133L, 234L, 54L, 135L, 288L, 334L, 113L, 224L,
51L, 292L, 333L, 123L, 209L, 148L, 287L, 338L, 230L, 52L, 149L,
285L, 16L, 145L, 247L, 48L, 141L, 284L, 339L, 136L, 225L, 64L,
161L, 286L, 335L, 122L, 218L, 76L, 182L, 290L, 21L, 221L, 41L,
132L, 283L, 337L, 128L, 43L, 282L, 32L, 177L, 244L, 45L, 109L,
291L, 336L, 139L, 212L, 15L, 119L, 271L, 25L, 173L, 233L, 23L,
118L, 278L, 9L, 140L, 237L, 13L, 121L, 266L, 340L, 143L, 114L,
280L, 168L, 157L, 330L, 94L, 131L, 327L, 165L, 171L, 321L, 80L,
126L, 309L, 66L, 107L, 304L, 96L, 191L, 298L, 68L, 108L, 302L,
164L, 179L, 317L, 79L, 125L, 308L, 169L, 189L, 328L, 87L, 129L,
313L, 166L, 153L, 329L, 58L, 112L, 293L, 101L, 176L, 315L, 88L,
144L, 306L, 98L, 194L, 300L, 82L, 116L, 314L, 99L, 184L, 305L,
150L, 322L, 97L, 197L, 295L, 91L, 137L, 319L, 162L, 316L, 63L,
117L, 294L, 95L, 187L), .Label = c("0.028", "0.277", "1.18",
"1.228", "1.80326086956522", "1.82538461538462", "1.87352941176471",
"10.0730769230769", "10.2839116719243", "10.2857142857143", "10.3172978505629",
"10.4545454545455", "10.5833333333333", "10.6786516853933", "10.743670886076",
"10.7611940298507", "10.7630769230769", "10.8724832214765", "10.8888888888889",
"10.9649122807018", "10.9861591695502", "11.0655737704918", "11.3061002178649",
"11.319587628866", "11.4805194805195", "11.4963503649635", "11.5434782608696",
"11.6022099447514", "11.6552356020942", "11.6666666666667", "11.8470588235294",
"11.90036900369", "11.9148936170213", "11.9601328903654", "12",
"12.0670391061453", "12.1090909090909", "12.2093023255814", "12.3068893528184",
"12.3287671232877", "12.413436123348", "12.4434782608696", "12.5626326963907",
"12.664907651715", "12.789817232376", "12.8070175438596", "12.8735632183908",
"12.9442567567568", "12.9661016949153", "13.0057803468208", "13.0934984520124",
"13.2892966360856", "13.3333333333333", "13.3995348837209", "13.4366197183099",
"13.4615384615385", "13.7055837563452", "13.8461538461538", "13.8709677419355",
"13.9285714285714", "13.9490445859873", "14.1333333333333", "14.3333333333333",
"14.5278538812785", "14.5398773006135", "14.58", "14.6092184368737",
"14.6428571428571", "14.7368421052632", "14.8109589041096", "15.1048951048951",
"15.2719665271967", "15.3846153846154", "15.6489795918367", "15.8029978586724",
"15.8326446280992", "16.0336134453782", "16.1094224924012", "16.1407491486947",
"16.258064516129", "16.2920547945206", "16.3157894736842", "16.4265129682997",
"16.530612244898", "16.56", "16.6204986149585", "16.7095588235294",
"16.8352941176471", "16.9363636363636", "16.9465648854962", "17.7372262773723",
"17.82", "18.0495652173913", "18.7354085603113", "18.7873831775701",
"18.9743276283619", "19.070821529745", "19.2223463687151", "19.3025059665871",
"19.46", "19.8472392638037", "2.1", "2.25857142857143", "2.35112359550562",
"2.35318181818182", "2.36631016042781", "2.3808", "2.39880952380952",
"2.45383812010444", "2.45785714285714", "2.46734693877551", "2.46807692307692",
"2.49122807017544", "2.49721627408994", "2.50130890052356", "2.50175438596491",
"2.50583333333333", "2.51938997821351", "2.52236286919831", "2.53636363636364",
"2.5426116838488", "2.54666666666667", "2.55700325732899", "2.5641095890411",
"2.56515323496027", "2.57", "2.57465753424658", "2.57911392405063",
"2.58143382352941", "2.58333333333333", "2.58735408560311", "2.59030837004405",
"2.6", "2.61115384615385", "2.62093023255814", "2.64516129032258",
"2.64744525547445", "2.65378787878788", "2.66086956521739", "2.66561514195584",
"2.68506756756757", "2.70733333333333", "2.71074380165289", "2.71341176470588",
"2.71641791044776", "2.72357142857143", "2.73259259259259", "2.75022222222222",
"2.75418960244648", "2.75885167464115", "2.77960893854749", "2.80872483221477",
"2.81130099228225", "2.82049180327869", "2.84767441860465", "2.85109489051095",
"2.8855376344086", "2.91463917525773", "2.93521594684385", "2.96198630136986",
"2.99369863013699", "20.146408839779", "20.196", "20.2125", "20.5180555555556",
"20.6174200661522", "20.91", "22.1182795698925", "23.2993527508091",
"24.14", "3.00648148148148", "3.01032608695652", "3.01298701298701",
"3.01412742382271", "3.01777777777778", "3.02319018404908", "3.02583025830258",
"3.04745762711864", "3.04757352941176", "3.06416184971098", "3.0686327077748",
"3.0695867768595", "3.10684596577017", "3.11115751789976", "3.14428571428571",
"3.15802139037433", "3.16845794392523", "3.17982456140351", "3.18333333333333",
"3.19540229885058", "3.19889975550122", "3.20833333333333", "3.22222222222222",
"3.22245810055866", "3.23404255319149", "3.247", "3.32762039660057",
"3.33333333333333", "3.36538461538462", "3.3753807106599", "3.38618181818182",
"3.41592356687898", "3.42681678607984", "3.44378787878788", "3.4475138121547",
"3.44827586206897", "3.46965699208443", "3.47642857142857", "3.53503184713376",
"3.53784090909091", "3.54385964912281", "3.54901960784314", "3.56666666666667",
"3.57692307692308", "3.61386138613861", "3.63426853707415", "3.63636363636364",
"3.63874345549738", "3.65019011406844", "3.66666666666667", "3.67052023121387",
"3.67132867132867", "3.68421052631579", "3.69014084507042", "3.70121951219512",
"3.72881355932203", "3.73450292397661", "3.73890909090909", "3.73961218836565",
"3.76966292134831", "3.77054347826087", "3.78536585365854", "3.81034482758621",
"3.82051282051282", "3.82535211267606", "3.82978723404255", "3.85798816568047",
"3.86167146974063", "3.86671232876712", "3.88", "3.89276315789474",
"3.8981308411215", "3.93243243243243", "3.93292682926829", "3.94867256637168",
"3.95533980582524", "3.96153846153846", "3.97045929018789", "3.97046632124352",
"3.977", "3.98286937901499", "3.99411764705882", "4.01592356687898",
"4.02877697841727", "4.04166666666667", "4.04478764478765", "4.05555555555556",
"4.06993006993007", "4.08421052631579", "4.09306358381503", "4.10171428571429",
"4.11642411642412", "4.1244094488189", "4.13793103448276", "4.1412213740458",
"4.16324503311258", "4.17204301075269", "4.20634920634921", "4.24",
"4.25438596491228", "4.26024590163934", "4.26465517241379", "4.29746835443038",
"4.29831932773109", "4.31111111111111", "4.35140186915888", "4.44444444444444",
"4.4885593220339", "4.55253333333333", "4.60058823529412", "4.66061538461538",
"4.68139534883721", "4.79325", "4.82182490752158", "4.82611534276387",
"4.85381165919282", "4.87160633484163", "5.1135652173913", "5.15784431137725",
"5.1589709762533", "5.21324296141814", "5.26390243902439", "5.6231884057971",
"5.73333333333333", "5.73658536585366", "5.8122905027933", "5.9160736196319",
"5.93722627737226", "5.95752212389381", "5.97086092715232", "6.00165517241379",
"6.11846153846154", "6.13099236641221", "6.13828125", "6.21025641025641",
"6.21895161290323", "6.22588235294118", "6.30699588477366", "6.34085603112841",
"6.36", "6.38901098901099", "6.46478873239437", "6.48807947019868",
"6.53695652173913", "6.57131782945736", "6.61245674740484", "6.63870967741935",
"6.693", "6.71538461538461", "6.71538461538462", "6.83117647058824",
"6.88387096774194", "6.94487394957983", "6.97215189873418", "7.01647058823529",
"7.04807339449541", "7.25804195804196", "7.32621359223301", "7.34082397003745",
"7.67259786476868", "8.72727272727273", "8.82352941176471", "9.03908794788274",
"9.19298245614035", "9.26666666666667", "9.44347826086956", "9.4620253164557",
"9.64080459770115", "9.79032258064516", "9.86776859504132", "9.91836734693878",
"9.93333333333333", "annual", "N-FIXER", "NO-N-fixer", "perennial"
), class = "factor")), row.names = c(1L, 2L, 3L, 4L, 5L, 6L,
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992L, 993L, 999L, 1000L), class = "data.frame")
这是我的数据负责人：
head(Df_TR)
AccSpeciesName TraitName OrigValueStr
1 Achillea millefolium Plant lifespan (longevity) perennial
2 Achillea millefolium Plant nitrogen(N) fixation capacity NO-N-fixer
3 Angelica sylvestris Plant lifespan (longevity) perennial
4 Angelica sylvestris Plant nitrogen(N) fixation capacity NO-N-fixer
5 Anthriscus sylvestris Plant lifespan (longevity) perennial
6 Anthriscus sylvestris Plant nitrogen(N) fixation capacity NO-N-fixer
任何帮助将不胜感激！
我认为你的第二个想法会更容易实现，即。e 拆分数据集。
#My example data
Df_TR <- data.frame("AccSpeciesName" = c("A","A","B","B"),
"TraitName" = c("Fixer","Height","Fixer","Height"),
"OrigValueStr" = c("Yes",12,"No", 15))
##Replace c("Height") with c("NumericName1, NumericName2)
Df_TR_NumericTraits <- Df_TR %>% filter(TraitName %in% c("Height")) %>%
mutate(OrigValueStr = as.numeric(as.character(OrigValueStr)))%>%
group_by(AccSpeciesName, TraitName)%>%
summarise(., "MeanNumericTraitValue"= mean(OrigValueStr))%>%
pivot_wider(names_from = TraitName, values_from = MeanNumericTraitValue)
#Pivot your factors
#Replace c("Fixer") with c("FactorName1, FactorName2)
Df_TR_FactorTraits <- Df_TR %>% filter(TraitName %in% c("Fixer"))%>%
pivot_wider(names_from = TraitName, values_from = OrigValueStr)
#Combine the two data sets
Df_Recombined <- full_join(Df_TR_FactorTraits,Df_TR_NumericTraits)