我有一张这样的表，
> head(dt2)
Weight Height Fitted interval limit value
1 65.6 174.0 71.91200 pred lwr 53.73165
2 80.7 193.5 91.63237 pred lwr 73.33198
3 72.6 186.5 84.55326 pred lwr 66.31751
4 78.8 187.2 85.26117 pred lwr 67.02004
5 74.8 181.5 79.49675 pred lwr 61.29244
6 86.4 184.0 82.02501 pred lwr 63.80652
我希望它有这样的，
> head(reshape2::dcast(dt2,
Weight + Height + Fitted + interval ~ limit,
fun.aggregate = mean))
Weight Height Fitted interval lwr upr
1 42.0 153.4 51.07920 conf 49.15463 53.00376
2 42.0 153.4 51.07920 pred 32.82122 69.33717
3 43.2 160.0 57.75378 conf 56.35240 59.15516
4 43.2 160.0 57.75378 pred 39.54352 75.96404
5 44.8 149.5 47.13512 conf 44.87642 49.39382
6 44.8 149.5 47.13512 pred 28.83891 65.43133
但是使用tidyr::spread，我该怎么做？
我在用，
> tidyr::spread(dt2, limit, value)
但是得到错误，
Error: Duplicate identifiers for rows (1052, 1056), (238, 242), (1209, 1218), (395, 404), (839, 1170), (25, 356), (1173, 1203, 1215), (359, 389, 401), (1001, 1200), (187, 386), (906, 907), (92, 93), (930, 1144), (116, 330), (958, 1171), (144, 357), (902, 1018), (88, 204), (960, 1008), (146, 194), (1459, 1463), (645, 649), (1616, 1625), (802, 811), (1246, 1577), (432, 763), (1580, 1610, 1622), (766, 796, 808), (1408, 1607), (594, 793), (1313, 1314), (499, 500), (1337, 1551), (523, 737), (1365, 1578), (551, 764), (1309, 1425), (495, 611), (1367, 1415), (553, 601)
随机10行::
> dt[sample(nrow(dt), 10), ]
Weight Height Fitted interval limit value
1253 52.2 162.5 60.28203 conf upr 61.51087
426 49.1 158.8 56.54022 pred upr 74.75756
1117 78.4 184.5 82.53066 conf lwr 80.98778
1171 85.9 166.4 64.22611 conf lwr 63.21254
948 61.4 177.8 75.75494 conf lwr 74.66393
384 90.9 172.7 70.59731 pred lwr 52.41828
289 75.9 172.7 70.59731 pred lwr 52.41828
3 44.8 149.5 47.13512 pred lwr 28.83891
774 87.3 182.9 80.91258 pred upr 99.12445
772 86.4 175.3 73.22669 pred upr 91.40919
答案 0 :(得分：11)
假设您开始使用如下所示的数据：
mydf
# Weight Height Fitted interval limit value
# 1 42 153.4 51.0792 conf lwr 49.15463
# 2 42 153.4 51.0792 pred lwr 32.82122
# 3 42 153.4 51.0792 conf upr 53.00376
# 4 42 153.4 51.0792 pred upr 69.33717
# 5 42 153.4 51.0792 conf lwr 60.00000
# 6 42 153.4 51.0792 pred lwr 90.00000
注意分组列（1到5）的第5行和第6行中的重复。这基本上就是“tidyr”告诉你的。第一行和第五行是重复的，第二行和第六行也是重复的。
tidyr::spread(mydf, limit, value)
# Error: Duplicate identifiers for rows (1, 5), (2, 6)
正如@Jaap所建议的，解决方案是首先“总结”数据。由于“tidyr”仅用于重塑数据（与“reshape2”不同，后者进行聚合和重新整形），因此在更改数据表单之前，需要使用“dplyr”执行聚合。在这里，我使用summarise作为“值”列。
如果您在summarise步骤停止执行，您会发现我们原来的6行数据集已“缩小”为4行。现在，spread将按预期工作。
mydf %>%
group_by(Weight, Height, Fitted, interval, limit) %>%
summarise(value = mean(value)) %>%
spread(limit, value)
# Source: local data frame [2 x 6]
#
# Weight Height Fitted interval lwr upr
# (dbl) (dbl) (dbl) (chr) (dbl) (dbl)
# 1 42 153.4 51.0792 conf 54.57731 53.00376
# 2 42 153.4 51.0792 pred 61.41061 69.33717
这符合dcast与fun.aggregate = mean的预期输出。
reshape2::dcast(mydf, Weight + Height + Fitted + interval ~ limit, fun.aggregate = mean)
# Weight Height Fitted interval lwr upr
# 1 42 153.4 51.0792 conf 54.57731 53.00376
# 2 42 153.4 51.0792 pred 61.41061 69.33717
示例数据：
mydf <- structure(list(Weight = c(42, 42, 42, 42, 42, 42), Height = c(153.4,
153.4, 153.4, 153.4, 153.4, 153.4), Fitted = c(51.0792, 51.0792,
51.0792, 51.0792, 51.0792, 51.0792), interval = c("conf", "pred",
"conf", "pred", "conf", "pred"), limit = structure(c(1L, 1L,
2L, 2L, 1L, 1L), .Label = c("lwr", "upr"), class = "factor"),
value = c(49.15463, 32.82122, 53.00376, 69.33717, 60,
90)), .Names = c("Weight", "Height", "Fitted", "interval",
"limit", "value"), row.names = c(NA, 6L), class = "data.frame")
答案 1 :(得分：1)
以下是data.table dplyr的替代方案。使用Ananda的答案中的mydf。
library(data.table)
library(magrittr)
library(tidyr)
DT <- data.table(mydf)
首先，您可以使用by按每个限制计算平均值。
DT[, .(lwr = mean(value[limit == "lwr"]),
upr = mean(value[limit == "upr"])),
by = .(Weight, Height, Fitted, interval)]
如果此limit == ...看起来太难编码，您可以先聚合成长格式，然后spread。这是有效的，因为一旦聚合，就没有重复。
DT[, .(value = mean(value)), by = .(Weight, Height, Fitted, interval, limit)] %>%
spread(key = "limit", value = "value")
两个都能帮到你
# Weight Height Fitted interval lwr upr
#1: 42 153.4 51.0792 conf 54.57731 53.00376
#2: 42 153.4 51.0792 pred 61.41061 69.33717