这次我真的很困惑,不知道该怎么办。 我有一个像这样的数据框
ID VALUE1 VALUE2 GROUP MODEL
# 1: id_1 0.5697779 17.6100597 Group_A Model B
# 2: id_10 246.7413330 6814.7109375 Group_A Model_B
# 3: id_100 0.0000000 26.5990372 Group_C Model_F
# 4: id_1000 0.0000000 0.0000000 Group_B Model_D
# 5: id_101 105.3097610 486.4001160 Group_C Model_C
# 6: id_102 0.0000000 108.9007416 Group_C Model_C
# 7: id_103 0.1306531 1.0070915 Group_C Model_F
# 8: id_104 0.0000000 0.5426522 Group_C Model_F
# 9: id_105 0.1352515 1.1137601 Group_C Model_C
# 10: id_106 0.0000000 0.2867465 Group_C Model_C
我需要计算VALUE1
和VALUE2
的汇总值。所以我做了
agg <- aggregate(cbind(dat[,"VALUE1"],dat[,"VALUE2"]), by = list(dat[,"GROUP"], dat[,"MODEL"]),FUN=sum)
但我得到
Summary.factor(structure(1L, .Label = "VALUE1", class = "factor"), :
‘sum’ not meaningful for factors
但如果我输入
apply(dat,2,is.factor)
给了我
ID VALUE1 VALUE2 GROUP MODEL
FALSE FALSE FALSE FALSE FALSE
为什么?我哪里错了?
编辑:dput(dat)
我得到了
structure(list(ID = c("id_1", "id_10", "id_100",
"id_1000", "id_101", "id_102", "id_103", "id_104",
"id_105", "id_106"), VALUE1 = c(0.56977790594101, 246.741333007812,
0, 0, 105.309761047363, 0, 0.13065305352211, 0, 0.135251507163048,
0), VALUE2 = c(17.6100597381592, 6814.7109375, 26.5990371704102,
0, 486.400115966797, 108.900741577148, 1.0070915222168, 0.542652249336243,
1.11376011371613, 0.28674653172493), GROUP = c("Group_A",
"Group_A", "Group_C", "Group_B", "Group_C", "Group_C",
"Group_C", "Group_C", "Group_C", "Group_C"),
MODEL = c("Model_B", "Model_B", "Model_F", "Model_D",
"Model_C", "Model_C", "Model_F", "Model_F", "Model_C", "Model_C"
)), .Names = c("ID", "VALUE1", "VALUE2", "GROUP", "MODEL"), sorted = "ID", class = c("data.table",
"data.frame"), row.names = c(NA, -10L), .internal.selfref = <pointer: 0x97b5290>)
ID VALUE1 VALUE2 GROUP MODEL
1: id_1 0.5697779 17.6100597 Group_A Model_B
2: id_10 246.7413330 6814.7109375 Group_A Model_B
3: id_100 0.0000000 26.5990372 Group_C Model_F
4: id_1000 0.0000000 0.0000000 Group_B Model_D
5: id_101 105.3097610 486.4001160 Group_C Model_C
6: id_102 0.0000000 108.9007416 Group_C Model_C
7: id_103 0.1306531 1.0070915 Group_C Model_F
8: id_104 0.0000000 0.5426522 Group_C Model_F
9: id_105 0.1352515 1.1137601 Group_C Model_C
10: id_106 0.0000000 0.2867465 Group_C Model_C