我正在尝试完成一个数据帧中的隐式缺失值，该数据帧描述了两个站点（WAI和HAN）的所有12个模块的所有3个方面（N，S和T）的3个不同藻类类别的覆盖率。某些封面数据丢失是因为“标签”（T，MA，CCA）特别是在“ WAI”站点的“标签”列中填充“ CCA”的隐式缺失值时遇到的问题。
我相信我遇到此问题的原因是，WAI网站上的大多数侧面和模块都缺少“ CCA”。但是，我不确定该如何解决。
此处的最终目标是使每个日期，站点，模块和侧面组合具有所有三个类别（T，MA，CCA）。如果缺少这三个类别中的任何一个，我希望n = 0并且percent_cover =0。这样，所有隐式丢失的值都将被显式显示。
如前所述，我在dplyr中使用了完整函数来填充隐式缺失的“标签”类别（T，MA，CCA）。但是，所有“日期”，“站点”，“模块”和“侧面”组合都不包括所有三个标签，尤其是对于WAI站点。
MA_cover_final <- structure(list(Date = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L), .Label = c("4/11/17", "4/23/17", "6/12/18", "6/7/18",
"8/26/17", "8/28/18", "9/1/18", "9/5/17"), class = "factor"),
Site = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("HAN", "WAI"), class = "factor"), Module = c(7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L,
6L, 6L, 6L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L,
6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L
), Side = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 1L,
1L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 3L, 1L, 1L, 1L, 2L,
2L, 3L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 1L, 2L, 3L, 3L, 1L, 1L,
2L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L,
1L, 2L, 2L, 3L, 3L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 1L,
1L, 2L, 2L, 3L, 3L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 1L, 1L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 3L, 1L, 1L, 2L,
2L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L,
1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L,
3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 1L, 1L,
2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 3L, 3L,
1L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 1L, 1L, 2L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 3L, 3L,
1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 2L, 2L,
3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 1L,
1L, 2L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 1L, 1L, 2L,
2L, 3L), .Label = c("N", "S", "T"), class = "factor"), nn = c(50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 49L, 49L, 49L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 49L,
49L, 49L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 46L, 46L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 49L, 49L, 51L, 51L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 47L, 47L, 50L, 51L, 51L, 50L, 50L, 50L,
50L, 41L, 41L, 48L, 48L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 49L,
49L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 49L, 50L, 50L, 50L, 50L, 50L, 50L, 49L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
51L, 51L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 49L, 49L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 49L, 49L, 49L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L), Label = c("CCA", "MA", "T", "CCA", "MA",
"T", "CCA", "T", "MA", "T", "MA", "T", "MA", "T", "CCA",
"MA", "T", "CCA", "MA", "T", "MA", "T", "MA", "T", "CCA",
"MA", "T", "CCA", "T", "CCA", "MA", "T", "CCA", "T", "T",
"CCA", "MA", "T", "MA", "T", "T", "MA", "T", "CCA", "MA",
"T", "T", "MA", "T", "T", "MA", "T", "MA", "T", "T", "T",
"MA", "T", "MA", "T", "MA", "T", "MA", "T", "MA", "T", "MA",
"T", "MA", "T", "MA", "T", "MA", "T", "T", "T", "MA", "T",
"CCA", "MA", "T", "T", "CCA", "MA", "T", "MA", "T", "CCA",
"T", "T", "MA", "T", "MA", "T", "CCA", "MA", "T", "CCA",
"MA", "T", "T", "CCA", "T", "MA", "T", "T", "MA", "T", "MA",
"T", "T", "MA", "T", "MA", "T", "T", "MA", "T", "MA", "T",
"MA", "T", "MA", "T", "MA", "T", "MA", "T", "MA", "T", "MA",
"T", "MA", "T", "MA", "T", "MA", "T", "MA", "T", "MA", "T",
"MA", "T", "MA", "T", "MA", "T", "MA", "T", "MA", "T", "MA",
"T", "MA", "T", "T", "MA", "T", "MA", "T", "MA", "T", "MA",
"T", "MA", "T", "T", "MA", "T", "MA", "T", "MA", "T", "MA",
"T", "T", "T", "MA", "T", "T", "T", "CCA", "MA", "T", "CCA",
"MA", "T", "CCA", "T", "MA", "T", "MA", "T", "T", "CCA",
"MA", "T", "CCA", "MA", "T", "T", "CCA", "MA", "T", "CCA",
"MA", "T", "T", "T", "MA", "T", "T", "MA", "T", "MA", "T",
"MA", "T", "MA", "T", "MA", "T", "MA", "T", "MA", "T", "MA",
"T", "MA", "T", "T", "MA", "T", "MA", "T", "MA", "T", "MA",
"T", "MA", "T", "CCA", "MA", "T", "CCA", "MA", "T", "CCA",
"T", "MA", "T", "MA", "T", "MA", "T", "CCA", "MA", "T", "CCA",
"MA", "T", "T", "MA", "T", "MA", "T", "T", "CCA", "MA", "T",
"CCA", "MA", "T", "MA", "T", "MA", "T", "MA", "T", "T"),
n = c(1L, 5L, 34L, 3L, 2L, 39L, 1L, 6L, 5L, 37L, 4L, 38L,
3L, 9L, 1L, 3L, 26L, 2L, 6L, 28L, 1L, 9L, 3L, 29L, 1L, 6L,
34L, 1L, 7L, 3L, 1L, 28L, 1L, 16L, 5L, 1L, 6L, 39L, 5L, 37L,
4L, 1L, 48L, 1L, 2L, 42L, 39L, 3L, 43L, 45L, 1L, 37L, 3L,
39L, 38L, 47L, 5L, 34L, 2L, 40L, 6L, 40L, 6L, 42L, 3L, 46L,
1L, 45L, 4L, 40L, 3L, 42L, 3L, 39L, 46L, 48L, 3L, 31L, 1L,
1L, 36L, 10L, 2L, 1L, 43L, 1L, 42L, 1L, 1L, 36L, 1L, 33L,
1L, 9L, 1L, 1L, 45L, 3L, 5L, 36L, 6L, 1L, 41L, 1L, 40L, 7L,
3L, 43L, 3L, 41L, 34L, 4L, 45L, 2L, 44L, 29L, 8L, 39L, 6L,
40L, 2L, 34L, 8L, 31L, 2L, 40L, 1L, 35L, 3L, 46L, 5L, 42L,
1L, 41L, 2L, 43L, 3L, 44L, 1L, 35L, 3L, 44L, 7L, 43L, 1L,
48L, 7L, 38L, 2L, 40L, 3L, 40L, 6L, 37L, 9L, 38L, 44L, 1L,
39L, 4L, 27L, 4L, 44L, 5L, 42L, 9L, 38L, 48L, 5L, 36L, 8L,
33L, 3L, 38L, 1L, 47L, 50L, 12L, 2L, 31L, 33L, 30L, 1L, 3L,
40L, 3L, 1L, 38L, 1L, 21L, 3L, 32L, 1L, 29L, 29L, 2L, 3L,
38L, 2L, 3L, 36L, 15L, 1L, 1L, 35L, 1L, 3L, 35L, 24L, 44L,
2L, 46L, 42L, 4L, 42L, 3L, 44L, 3L, 24L, 2L, 45L, 3L, 40L,
3L, 46L, 2L, 42L, 6L, 42L, 1L, 41L, 46L, 5L, 41L, 1L, 42L,
5L, 41L, 4L, 36L, 3L, 31L, 2L, 5L, 34L, 4L, 4L, 23L, 1L,
3L, 13L, 28L, 7L, 40L, 4L, 28L, 1L, 1L, 43L, 3L, 2L, 41L,
16L, 4L, 34L, 3L, 31L, 5L, 1L, 4L, 25L, 4L, 4L, 28L, 1L,
3L, 2L, 46L, 3L, 41L, 4L), percent_cover = c(0.02, 0.1, 0.68,
0.06, 0.04, 0.78, 0.02, 0.12, 0.1, 0.74, 0.08, 0.76, 0.06,
0.18, 0.0204081632653061, 0.0612244897959184, 0.530612244897959,
0.04, 0.12, 0.56, 0.02, 0.18, 0.06, 0.58, 0.0204081632653061,
0.122448979591837, 0.693877551020408, 0.02, 0.14, 0.06, 0.02,
0.56, 0.02, 0.32, 0.1, 0.02, 0.12, 0.78, 0.1, 0.74, 0.08,
0.02, 0.96, 0.02, 0.04, 0.84, 0.78, 0.06, 0.86, 0.9, 0.0217391304347826,
0.804347826086957, 0.06, 0.78, 0.76, 0.94, 0.1, 0.68, 0.04,
0.8, 0.12, 0.8, 0.12, 0.84, 0.06, 0.92, 0.02, 0.9, 0.0816326530612245,
0.816326530612245, 0.0588235294117647, 0.823529411764706,
0.06, 0.78, 0.92, 0.96, 0.06, 0.62, 0.02, 0.02, 0.72, 0.2,
0.04, 0.02, 0.86, 0.02, 0.84, 0.02, 0.02, 0.72, 0.02, 0.66,
0.02, 0.18, 0.02, 0.02, 0.9, 0.06, 0.1, 0.72, 0.12, 0.02,
0.82, 0.02, 0.8, 0.14, 0.06, 0.86, 0.06, 0.82, 0.68, 0.08,
0.9, 0.0425531914893617, 0.936170212765957, 0.58, 0.156862745098039,
0.764705882352941, 0.12, 0.8, 0.04, 0.68, 0.195121951219512,
0.75609756097561, 0.0416666666666667, 0.833333333333333,
0.02, 0.7, 0.06, 0.92, 0.1, 0.84, 0.02, 0.82, 0.04, 0.86,
0.06, 0.88, 0.02, 0.7, 0.06, 0.88, 0.14, 0.86, 0.0204081632653061,
0.979591836734694, 0.14, 0.76, 0.04, 0.8, 0.06, 0.8, 0.12,
0.74, 0.18, 0.76, 0.88, 0.02, 0.78, 0.08, 0.54, 0.08, 0.88,
0.1, 0.84, 0.18, 0.76, 0.96, 0.1, 0.72, 0.16, 0.66, 0.06,
0.76, 0.02, 0.94, 1, 0.24, 0.04, 0.62, 0.66, 0.6, 0.02, 0.06,
0.8, 0.06, 0.02, 0.76, 0.02, 0.42, 0.06, 0.64, 0.02, 0.58,
0.591836734693878, 0.04, 0.06, 0.76, 0.04, 0.06, 0.72, 0.306122448979592,
0.02, 0.02, 0.7, 0.02, 0.06, 0.7, 0.48, 0.88, 0.04, 0.92,
0.84, 0.08, 0.84, 0.06, 0.88, 0.0588235294117647, 0.470588235294118,
0.04, 0.9, 0.06, 0.8, 0.06, 0.92, 0.04, 0.84, 0.12, 0.84,
0.02, 0.82, 0.92, 0.1, 0.82, 0.02, 0.84, 0.102040816326531,
0.836734693877551, 0.08, 0.72, 0.06, 0.62, 0.04, 0.1, 0.68,
0.08, 0.08, 0.46, 0.02, 0.06, 0.26, 0.56, 0.14, 0.8, 0.08,
0.56, 0.0204081632653061, 0.0204081632653061, 0.877551020408163,
0.06, 0.04, 0.82, 0.32, 0.08, 0.68, 0.06, 0.62, 0.1, 0.02,
0.08, 0.5, 0.08, 0.08, 0.56, 0.02, 0.06, 0.04, 0.92, 0.06,
0.82, 0.08)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-281L))
MA_cover_final <- MA_cover_final %>% group_by(Date, Site) %>%
complete(Side, Label, fill = list(n = 0, percent_cover = 0)) %>%
ungroup()
结果数据框应具有432行[12个模块（1-12）x 3面（N，S，T）x 3标签（“ T”，“ MA”，“ CCA”）x 4日期]
答案 0 :(得分：1)
我想我已经确定了您要寻找的东西，但是您必须确认：
我们要完成每个因子列，但Date例外，它在Site之间是离散的，因此，我们将其包装在nesting()中以仅使用Site和数据中存在的日期。
final <- MA_cover_final %>% group_by(Site) %>%
complete(Label, Side, Module, nesting(Date), fill = list(n= 0, percent_cover =0))
# A tibble: 432 x 8
# Groups: Site [2]
Site Label Side Module Date nn n percent_cover
<fct> <chr> <fct> <int> <fct> <int> <dbl> <dbl>
1 HAN CCA N 7 4/11/17 50 1 0.02
2 HAN CCA N 7 6/12/18 NA 0 0
3 HAN CCA N 7 8/28/18 NA 0 0
4 HAN CCA N 7 9/5/17 50 2 0.04
5 HAN CCA N 8 4/11/17 NA 0 0
6 HAN CCA N 8 6/12/18 NA 0 0
7 HAN CCA N 8 8/28/18 NA 0 0
8 HAN CCA N 8 9/5/17 NA 0 0
9 HAN CCA N 9 4/11/17 49 1 0.0204
10 HAN CCA N 9 6/12/18 50 2 0.04
# ... with 422 more rows
答案 1 :(得分：0)
如果要在分组数据集上使用complete()，则不会扩展该组中不存在的因子水平。相反，我们必须提供要添加到每个组的级别。这会在您的数据集中出现Label。
此外，我认为您需要Module成为您采用的任何方法的一部分。
似乎模块嵌套在站点中，而站点嵌套在日期中，所以我认为您不希望为整个数据集填充这些模块的组合。您可以将它们用作分组变量，然后在Side中使用另外两个变量Label和complete()。由于这些值仅在组内使用，因此我们需要为Label定义值。 （对于您的示例，Side可能还可以，但也应谨慎提供Side的值。）
test <- MA_cover_final %>%
group_by(Site, Date, Module) %>%
complete(Side, Label = unique(test$Label), fill = list(n = 0, percent_cover = 0)) %>%
ungroup()
str(test)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 432 obs. of 8 variables:
$ Date : Factor w/ 8 levels "4/11/17","4/23/17",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Site : Factor w/ 2 levels "HAN","WAI": 1 1 1 1 1 1 1 1 1 1 ...
$ Module : int 7 7 7 7 7 7 7 7 7 8 ...
$ Side : Factor w/ 3 levels "N","S","T": 1 1 1 2 2 2 3 3 3 1 ...
$ Label : chr "CCA" "MA" "T" "CCA" ...
$ nn : int 50 50 50 50 50 50 50 NA 50 NA ...
$ n : num 1 5 34 3 2 39 1 0 6 0 ...
$ percent_cover: num 0.02 0.1 0.68 0.06 0.04 0.78 0.02 0 0.12 0 ...
如果获得正确的嵌套和交叉组合，则无需分组即可完成所有操作。您可能会追求以下类似的东西。这仅保留嵌套变量的组内组合，但填充缺失的Side和Label值：
test2 <- MA_cover_final %>%
complete(nesting(Site, Date, Module), Side, Label, fill = list(n = 0, percent_cover = 0))
str(test)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 432 obs. of 8 variables:
$ Site : Factor w/ 2 levels "HAN","WAI": 1 1 1 1 1 1 1 1 1 1 ...
$ Date : Factor w/ 8 levels "4/11/17","4/23/17",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Module : int 7 7 7 7 7 7 7 7 7 8 ...
$ Side : Factor w/ 3 levels "N","S","T": 1 1 1 2 2 2 3 3 3 1 ...
$ Label : chr "CCA" "MA" "T" "CCA" ...
$ nn : int 50 50 50 50 50 50 50 NA 50 NA ...
$ n : num 1 5 34 3 2 39 1 0 6 0 ...
$ percent_cover: num 0.02 0.1 0.68 0.06 0.04 0.78 0.02 0 0.12 0 ...
identical(test, test2)
[1] TRUE