传播vs dcast

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
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

请注意分组列(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所建议的那样,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,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,我已经完成了对“值”列的总结.

如果您在汇总步骤中停止执行,您会发现我们原来的6行数据集已“缩小”为4行.现在,传播将按预期工作.

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

这与dcast的预期输出匹配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

样本数据:

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")
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