This code is conceptually similar to as.data.frame.table
# S3 method for class 'array'
melt(
data,
varnames = names(dimnames(data)),
...,
na.rm = FALSE,
as.is = FALSE,
value.name = "value"
)
# S3 method for class 'table'
melt(
data,
varnames = names(dimnames(data)),
...,
na.rm = FALSE,
as.is = FALSE,
value.name = "value"
)
# S3 method for class 'matrix'
melt(
data,
varnames = names(dimnames(data)),
...,
na.rm = FALSE,
as.is = FALSE,
value.name = "value"
)array to melt
variable names to use in molten data.frame
further arguments passed to or from other methods.
Should NA values be removed from the data set? This will convert explicit missings to implicit missings.
if FALSE, the default, dimnames will be converted
using type.convert. If TRUE, they will be left
as strings.
name of variable used to store values
Other melt methods:
melt.data.frame(),
melt.default(),
melt.list()
a <- array(c(1:23, NA), c(2,3,4))
melt(a)
#> Var1 Var2 Var3 value
#> 1 1 1 1 1
#> 2 2 1 1 2
#> 3 1 2 1 3
#> 4 2 2 1 4
#> 5 1 3 1 5
#> 6 2 3 1 6
#> 7 1 1 2 7
#> 8 2 1 2 8
#> 9 1 2 2 9
#> 10 2 2 2 10
#> 11 1 3 2 11
#> 12 2 3 2 12
#> 13 1 1 3 13
#> 14 2 1 3 14
#> 15 1 2 3 15
#> 16 2 2 3 16
#> 17 1 3 3 17
#> 18 2 3 3 18
#> 19 1 1 4 19
#> 20 2 1 4 20
#> 21 1 2 4 21
#> 22 2 2 4 22
#> 23 1 3 4 23
#> 24 2 3 4 NA
melt(a, na.rm = TRUE)
#> Var1 Var2 Var3 value
#> 1 1 1 1 1
#> 2 2 1 1 2
#> 3 1 2 1 3
#> 4 2 2 1 4
#> 5 1 3 1 5
#> 6 2 3 1 6
#> 7 1 1 2 7
#> 8 2 1 2 8
#> 9 1 2 2 9
#> 10 2 2 2 10
#> 11 1 3 2 11
#> 12 2 3 2 12
#> 13 1 1 3 13
#> 14 2 1 3 14
#> 15 1 2 3 15
#> 16 2 2 3 16
#> 17 1 3 3 17
#> 18 2 3 3 18
#> 19 1 1 4 19
#> 20 2 1 4 20
#> 21 1 2 4 21
#> 22 2 2 4 22
#> 23 1 3 4 23
melt(a, varnames=c("X","Y","Z"))
#> X Y Z value
#> 1 1 1 1 1
#> 2 2 1 1 2
#> 3 1 2 1 3
#> 4 2 2 1 4
#> 5 1 3 1 5
#> 6 2 3 1 6
#> 7 1 1 2 7
#> 8 2 1 2 8
#> 9 1 2 2 9
#> 10 2 2 2 10
#> 11 1 3 2 11
#> 12 2 3 2 12
#> 13 1 1 3 13
#> 14 2 1 3 14
#> 15 1 2 3 15
#> 16 2 2 3 16
#> 17 1 3 3 17
#> 18 2 3 3 18
#> 19 1 1 4 19
#> 20 2 1 4 20
#> 21 1 2 4 21
#> 22 2 2 4 22
#> 23 1 3 4 23
#> 24 2 3 4 NA
dimnames(a) <- lapply(dim(a), function(x) LETTERS[1:x])
melt(a)
#> Var1 Var2 Var3 value
#> 1 A A A 1
#> 2 B A A 2
#> 3 A B A 3
#> 4 B B A 4
#> 5 A C A 5
#> 6 B C A 6
#> 7 A A B 7
#> 8 B A B 8
#> 9 A B B 9
#> 10 B B B 10
#> 11 A C B 11
#> 12 B C B 12
#> 13 A A C 13
#> 14 B A C 14
#> 15 A B C 15
#> 16 B B C 16
#> 17 A C C 17
#> 18 B C C 18
#> 19 A A D 19
#> 20 B A D 20
#> 21 A B D 21
#> 22 B B D 22
#> 23 A C D 23
#> 24 B C D NA
melt(a, varnames=c("X","Y","Z"))
#> X Y Z value
#> 1 A A A 1
#> 2 B A A 2
#> 3 A B A 3
#> 4 B B A 4
#> 5 A C A 5
#> 6 B C A 6
#> 7 A A B 7
#> 8 B A B 8
#> 9 A B B 9
#> 10 B B B 10
#> 11 A C B 11
#> 12 B C B 12
#> 13 A A C 13
#> 14 B A C 14
#> 15 A B C 15
#> 16 B B C 16
#> 17 A C C 17
#> 18 B C C 18
#> 19 A A D 19
#> 20 B A D 20
#> 21 A B D 21
#> 22 B B D 22
#> 23 A C D 23
#> 24 B C D NA
dimnames(a)[1] <- list(NULL)
melt(a)
#> Var1 Var2 Var3 value
#> 1 1 A A 1
#> 2 2 A A 2
#> 3 1 B A 3
#> 4 2 B A 4
#> 5 1 C A 5
#> 6 2 C A 6
#> 7 1 A B 7
#> 8 2 A B 8
#> 9 1 B B 9
#> 10 2 B B 10
#> 11 1 C B 11
#> 12 2 C B 12
#> 13 1 A C 13
#> 14 2 A C 14
#> 15 1 B C 15
#> 16 2 B C 16
#> 17 1 C C 17
#> 18 2 C C 18
#> 19 1 A D 19
#> 20 2 A D 20
#> 21 1 B D 21
#> 22 2 B D 22
#> 23 1 C D 23
#> 24 2 C D NA