我有一个包含情节，种类和事件的3列表格。我想总结数据，因为各个事件按图中的物种分组。我尝试了以下代码：
aggregate(occurrence ~ species, AbundTGLMSOn, FUN=sum)
但是这只按物种分组而不是地块内的物种，然后看起来像这样
species occurrence
1 albleb 1
2 albodo 17
3 antgha 1
4 apovil 65
5 artlak 3
6 briret 18
非常感谢任何有关如何做到这一点的建议。
这是一个数据：
> dput(AbundTGLMSOn)
structure(list(plot = 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, 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, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 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, 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, 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, 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, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 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, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L), .Label = c("H01",
"H02", "H03", "H04", "H05", "L01", "L02", "L03", "L04", "L05",
"O01", "O02", "O03", "O04", "O05"), class = "factor"), species = structure(c(30L,
14L, 32L, 34L, 32L, 34L, 34L, 32L, 32L, 30L, 32L, 32L, 34L, 41L,
30L, 30L, 32L, 34L, 34L, 29L, 30L, 30L, 30L, 32L, 32L, 30L, 30L,
34L, 35L, 30L, 30L, 31L, 27L, 30L, 32L, 30L, 30L, 34L, 34L, 34L,
32L, 41L, 41L, 41L, 41L, 32L, 32L, 32L, 41L, 40L, 41L, 10L, 41L,
1L, 39L, 1L, 20L, 32L, 32L, 35L, 14L, 29L, 32L, 39L, 39L, 40L,
40L, 1L, 40L, 40L, 41L, 22L, 40L, 32L, 32L, 37L, 10L, 10L, 32L,
10L, 32L, 32L, 34L, 32L, 32L, 10L, 41L, 41L, 41L, 41L, 41L, 32L,
32L, 40L, 32L, 32L, 10L, 35L, 7L, 32L, 32L, 32L, 32L, 3L, 32L,
1L, 21L, 30L, 32L, 18L, 32L, 32L, 32L, 32L, 32L, 34L, 34L, 32L,
34L, 34L, 30L, 30L, 32L, 32L, 32L, 32L, 34L, 24L, 30L, 34L, 18L,
30L, 32L, 34L, 18L, 34L, 30L, 32L, 34L, 32L, 32L, 34L, 32L, 32L,
14L, 32L, 32L, 32L, 32L, 34L, 32L, 32L, 32L, 41L, 41L, 32L, 32L,
4L, 32L, 34L, 34L, 39L, 39L, 41L, 34L, 41L, 32L, 6L, 30L, 34L,
34L, 39L, 30L, 34L, 30L, 34L, 34L, 30L, 30L, 32L, 34L, 30L, 34L,
32L, 34L, 32L, 34L, 32L, 32L, 32L, 34L, 34L, 34L, 34L, 1L, 32L,
32L, 32L, 34L, 34L, 32L, 32L, 34L, 34L, 18L, 32L, 34L, 34L, 24L,
27L, 31L, 32L, 34L, 34L, 34L, 34L, 35L, 39L, 10L, 25L, 32L, 32L,
34L, 18L, 30L, 32L, 34L, 34L, 34L, 34L, 34L, 34L, 32L, 30L, 34L,
34L, 34L, 34L, 41L, 3L, 32L, 30L, 30L, 31L, 30L, 30L, 1L, 30L,
30L, 32L, 32L, 32L, 32L, 32L, 34L, 1L, 32L, 34L, 32L, 1L, 24L,
4L, 32L, 32L, 34L, 1L, 32L, 32L, 32L, 34L, 34L, 34L, 41L, 41L,
34L, 13L, 17L, 32L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 41L,
12L, 34L, 18L, 34L, 34L, 34L, 34L, 34L, 30L, 30L, 9L, 32L, 32L,
14L, 1L, 14L, 32L, 32L, 32L, 32L, 1L, 26L, 32L, 32L, 11L, 13L,
32L, 32L, 32L, 32L, 41L, 41L, 23L, 32L, 32L, 34L, 34L, 1L, 4L,
32L, 41L, 4L, 13L, 32L, 34L, 24L, 41L, 32L, 33L, 30L, 32L, 34L,
34L, 25L, 24L, 28L, 2L, 7L, 36L, 19L, 7L, 30L, 7L, 12L, 24L,
30L, 6L, 24L, 30L, 12L, 35L, 7L, 35L, 14L, 28L, 14L, 33L, 28L,
30L, 30L, 30L, 34L, 34L, 35L, 30L, 32L, 32L, 34L, 30L, 30L, 34L,
34L, 34L, 32L, 32L, 32L, 34L, 28L, 32L, 32L, 32L, 32L, 32L, 32L,
32L, 32L, 34L, 32L, 32L, 32L, 32L, 32L, 32L, 34L, 34L, 3L, 32L,
32L, 34L, 34L, 34L, 16L, 32L, 32L, 32L, 34L, 34L, 34L, 32L, 32L,
32L, 34L, 32L, 32L, 34L, 34L, 34L, 31L, 34L, 34L, 34L, 29L, 34L,
34L, 34L, 34L, 34L, 41L, 8L, 34L, 3L, 30L, 34L, 34L, 34L, 34L,
34L, 34L, 10L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 32L,
32L, 32L, 24L, 32L, 34L, 32L, 32L, 32L, 32L, 11L, 32L, 32L, 32L,
32L, 32L, 5L, 32L, 32L, 32L, 38L, 3L, 32L, 32L, 34L, 3L, 31L,
32L, 34L, 34L, 6L, 13L, 31L, 32L, 32L, 32L, 32L, 32L, 34L, 34L,
13L, 15L, 32L, 32L, 32L, 32L, 34L, 34L, 34L, 34L, 34L, 34L, 34L,
32L, 34L, 34L, 34L, 34L, 34L, 1L, 32L, 7L, 11L, 32L, 32L, 32L,
24L, 32L, 32L, 32L, 30L, 32L, 32L, 32L, 32L, 15L, 32L, 34L, 18L,
32L, 32L, 32L, 34L, 7L, 7L, 34L, 32L, 32L, 34L, 7L, 35L, 7L,
12L, 34L, 30L, 34L), .Label = c("apovil", "briret", "cansub",
"carsph", "catspa", "dalcul", "dallan", "dalnig", "dilobo", "diptub",
"elltom", "flesoo", "garcow", "garobt", "garsoo", "greeri", "halcor",
"irvoli", "lancor", "lopdup", "mancal", "memscu", "milleu", "mitrot",
"morcor", "ochint", "parama", "pavtom", "phyemb", "ptemac", "rotwit",
"shoobt", "shorox", "shosia", "sinsia", "stegut", "strnux", "symrac",
"syz001", "terche", "xylxyl"), class = "factor"), occurrence = 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, 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, 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, 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, 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, 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, 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, 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, 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,
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, 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, 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L)), .Names = c("plot", "species", "occurrence"
), class = "data.frame", row.names = c(NA, -551L))
答案 0 :(得分：0)
您只按species进行聚合，如果您想按图中的物种分组，则必须在公式的rhs中使用这两个变量。请注意，下面的两个命令给出相同的结果，唯一的区别是结果的显示方式。
sp1 <- aggregate(occurrence ~ species + plot, AbundTGLMSOn, FUN=sum)
head(sp1)
# species plot occurrence
#1 garobt H01 1
#2 parama H01 1
#3 phyemb H01 1
#4 ptemac H01 14
#5 rotwit H01 1
#6 shoobt H01 10
sp2 <- aggregate(occurrence ~ plot + species, AbundTGLMSOn, FUN=sum)
head(sp2)
# plot species occurrence
#1 H03 apovil 3
#2 L01 apovil 1
#3 L03 apovil 2
#4 L04 apovil 3
#5 L05 apovil 3
#6 O05 apovil 1