传播与 dcast [英] Spread vs dcast
问题描述
我有一张这样的桌子，
> 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
我想要这样，
> 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?
But using tidyr::spread, How can I do that?
我正在使用，
> tidyr::spread(dt2, limit, value)
但得到错误，
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
推荐答案
假设您从看起来像这样的数据开始:
Let's say you were starting with data that looked like this:
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.
Notice the duplication in rows 5 and 6 of the grouping columns (1 to 5). This is essentially what "tidyr" is telling you. The first row and fifth are duplicates, as are the second and sixth.
tidyr::spread(mydf, limit, value)
# Error: Duplicate identifiers for rows (1, 5), (2, 6)
正如@Jaap 所建议的，解决方案是首先汇总"数据.由于tidyr"仅用于重塑数据(与聚合和重塑的reshape2"不同)，因此您需要在更改数据形式之前使用dplyr"执行聚合.在这里，我使用 summarise 为值"列完成了这项工作.
As suggested by @Jaap, the solution is to first "summarise" the data. Since "tidyr" is only for reshaping data (unlike "reshape2", which aggregated and reshaped), you need to perform the aggregation with "dplyr" before you change the data form. Here, I've done that with summarise for the "value" column.
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.
If you stopped the execution at the summarise step, you would find that our original 6-row dataset had "shrunk" to 4 rows. Now, spread would work as expected.
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
这将 dcast 的预期输出与 fun.aggregate = mean 匹配.
This matches the expected output from dcast with fun.aggregate = mean.
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
<小时>
示例数据:
Sample data:
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")
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