时间戳

首页 > 解决方案 > 如何使用pivot_wider在R中的值列中整理具有重复和多个类的数据集

r - 如何使用pivot_wider在R中的值列中整理具有重复和多个类的数据集

问题描述

我正在尝试使用 pivot_wider 整理数据集，但遇到了一些我不知道如何解决的问题。在我分配给“values_from”的值列“OrigValueStr”中，我有数字和因子。由于有一些重复项，我想从数值中得到一个平均值，但我想将这些因素保留为因素（也许通过将可能的重复项彼此之间用“;”或“_”分隔，或者只保留第一个一个 r 撞到并丢下其他人）。我的想法是将 ifelse 语句放入“values_fn”或分配“names_from”中的哪些因素以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分成两部分，一个包含数值，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。但我更愿意使用 pivot_wider 一次性完成所有操作。

由于我对 R 不是很熟练，所以我不知道如何编写我的代码以便它执行我想要的。我没有找到任何其他使用 values_fn 的例子，我想像我想做的那样。

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”）（每个物种只有一行）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，但它们并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))

这是我的数据的一个子集（原始数据有 >2 000 000 个观察值和 27 个变量，并且是从 TRY 植物性状数据库收到的），我是否选择了我感兴趣的 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, 
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 
33L, 34L, 35L, 36L, 37L, 38L, 39L, 41L, 43L, 45L, 50L, 52L, 58L, 
59L, 60L, 67L, 68L, 69L, 76L, 77L, 78L, 85L, 86L, 87L, 94L, 95L, 
96L, 103L, 104L, 105L, 112L, 113L, 114L, 121L, 123L, 130L, 131L, 
132L, 139L, 140L, 141L, 148L, 149L, 150L, 157L, 158L, 159L, 166L, 
167L, 168L, 175L, 176L, 177L, 184L, 185L, 186L, 193L, 194L, 195L, 
202L, 203L, 204L, 211L, 212L, 213L, 220L, 221L, 222L, 229L, 230L, 
231L, 237L, 238L, 239L, 245L, 246L, 247L, 253L, 254L, 255L, 261L, 
262L, 263L, 269L, 270L, 271L, 277L, 278L, 279L, 285L, 286L, 287L, 
293L, 294L, 295L, 302L, 303L, 309L, 310L, 311L, 317L, 318L, 319L, 
325L, 326L, 327L, 333L, 334L, 335L, 341L, 342L, 343L, 349L, 350L, 
351L, 357L, 358L, 359L, 365L, 366L, 367L, 373L, 374L, 375L, 381L, 
382L, 383L, 389L, 390L, 391L, 397L, 398L, 399L, 405L, 406L, 407L, 
413L, 414L, 415L, 421L, 422L, 423L, 429L, 430L, 431L, 438L, 439L, 
445L, 446L, 447L, 453L, 454L, 455L, 461L, 462L, 469L, 470L, 471L, 
477L, 479L, 485L, 487L, 493L, 494L, 495L, 501L, 502L, 503L, 509L, 
510L, 511L, 517L, 518L, 519L, 525L, 526L, 533L, 534L, 535L, 541L, 
542L, 543L, 549L, 550L, 551L, 557L, 558L, 565L, 566L, 567L, 573L, 
574L, 581L, 582L, 583L, 589L, 590L, 591L, 597L, 598L, 599L, 603L, 
604L, 605L, 611L, 612L, 613L, 617L, 618L, 619L, 625L, 626L, 627L, 
631L, 633L, 639L, 640L, 641L, 646L, 647L, 653L, 655L, 659L, 660L, 
661L, 667L, 668L, 669L, 673L, 674L, 675L, 681L, 682L, 683L, 687L, 
688L, 689L, 695L, 696L, 697L, 701L, 702L, 703L, 709L, 711L, 715L, 
716L, 717L, 723L, 724L, 729L, 731L, 737L, 738L, 739L, 743L, 744L, 
745L, 751L, 752L, 753L, 757L, 758L, 759L, 765L, 766L, 767L, 771L, 
772L, 773L, 779L, 780L, 781L, 785L, 786L, 787L, 793L, 794L, 800L, 
801L, 807L, 808L, 809L, 815L, 816L, 817L, 823L, 824L, 825L, 831L, 
832L, 833L, 847L, 848L, 849L, 855L, 856L, 857L, 863L, 864L, 865L, 
871L, 872L, 873L, 879L, 880L, 881L, 887L, 888L, 889L, 895L, 896L, 
897L, 903L, 904L, 905L, 911L, 912L, 913L, 919L, 920L, 921L, 927L, 
928L, 929L, 935L, 936L, 937L, 943L, 944L, 945L, 951L, 952L, 953L, 
960L, 961L, 967L, 968L, 969L, 975L, 976L, 977L, 983L, 985L, 991L, 
992L, 993L, 999L, 1000L), class = "data.frame")

这是我的数据的负责人：

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

任何帮助将非常感激！

标签： r

解决方案

我认为您的第二个想法会更容易实现，即拆分数据集。

#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)

推荐阅读