我想从dat2
添加变量:
concreteness familiarity typicality
amoeba 3.60 1.30 1.71
bacterium 3.82 3.48 2.13
leech 5.71 1.83 4.50
到
dat1
: ID variable value
1 1 amoeba 0
2 2 amoeba 0
3 3 amoeba NA
251 1 bacterium 0
252 2 bacterium 0
253 3 bacterium 0
501 1 leech 1
502 2 leech 1
503 3 leech 0
提供以下输出:
X ID variable value concreteness familiarity typicality
1 1 1 amoeba 0 3.60 1.30 1.71
2 2 2 amoeba 0 3.60 1.30 1.71
3 3 3 amoeba NA 3.60 1.30 1.71
4 251 1 bacterium 0 3.82 3.48 2.13
5 252 2 bacterium 0 3.82 3.48 2.13
6 253 3 bacterium 0 3.82 3.48 2.13
7 501 1 leech 1 5.71 1.83 4.50
8 502 2 leech 1 5.71 1.83 4.50
9 503 3 leech 0 5.71 1.83 4.50
如您所见,必须将
dat1
中的信息复制到dat2
中的多行中。这是我失败的尝试:
dat3 <- merge(dat1, dat2, by=intersect(dat1$variable(dat1), dat2$row.names(dat2)))
出现以下错误:
Error in as.vector(y) : attempt to apply non-function
请在此处找到重复的示例:
dat1:
structure(list(ID = c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), variable = structure(c(1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("amoeba", "bacterium",
"leech", "centipede", "lizard", "tapeworm", "head lice", "maggot",
"ant", "moth", "mosquito", "earthworm", "caterpillar", "scorpion",
"snail", "spider", "grasshopper", "dust mite", "tarantula", "termite",
"bat", "wasp", "silkworm"), class = "factor"), value = c(0L,
0L, NA, 0L, 0L, 0L, 1L, 1L, 0L)), .Names = c("ID", "variable",
"value"), row.names = c(1L, 2L, 3L, 251L, 252L, 253L, 501L, 502L,
503L), class = "data.frame")
dat2:
structure(list(concreteness = c(3.6, 3.82, 5.71), familiarity = c(1.3,
3.48, 1.83), typicality = c(1.71, 2.13, 4.5)), .Names = c("concreteness",
"familiarity", "typicality"), row.names = c("amoeba", "bacterium",
"leech"), class = "data.frame")
最佳答案
您可以将连接变量添加到dat2,然后使用merge:
dat2$variable <- rownames(dat2)
merge(dat1, dat2)
variable ID value concreteness familiarity typicality
1 amoeba 1 0 3.60 1.30 1.71
2 amoeba 2 0 3.60 1.30 1.71
3 amoeba 3 NA 3.60 1.30 1.71
4 bacterium 1 0 3.82 3.48 2.13
5 bacterium 2 0 3.82 3.48 2.13
6 bacterium 3 0 3.82 3.48 2.13
7 leech 1 1 5.71 1.83 4.50
8 leech 2 1 5.71 1.83 4.50
9 leech 3 0 5.71 1.83 4.50