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# Example 1: Basics ====
x <- list(
group1 = list(
class1 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
),
class2 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
)
),
group2 = list(
class1 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
),
class2 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
)
)
)
# predict what dimensions `x` would have if casted as dimensional:
hier2dim(x)
x2 <- cast_hier2dim(x) # cast as dimensional
# since the original list uses the same names for all elements within the same depth,
# dimnames can be set easily:
dimnames(x2) <- list( # go from deep names to surface names
c("height", "weight", "sex"),
c("class1", "class2"),
c("group1", "group2")
)
print(x2) # very compact, maybe too compact...?
# print a small portion of the list, but less compact:
cast_dim2flat(x2[, 1:2, "group1", drop = FALSE])
# Example 2: Cast from outside to inside ====
x <- list(
group1 = list(
class1 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
),
class2 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
)
),
group2 = list(
class1 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
),
class2 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
)
)
)
# by default, `in2out = TRUE`;
# for this example, `in2out = FALSE` is used
# predict what dimensions `x` would have if casted as dimensional:
hier2dim(x, in2out = FALSE)
x2 <- cast_hier2dim(x, in2out = FALSE) # cast as dimensional
# since the original list uses the same names for all elements within the same depth,
# dimnames can be set easily:
# because in2out = FALSE, go from the shallow names to the deeper names:
dimnames(x2) <- list( # notice the order here is reversed, because in2out = FALSE
c("group1", "group2"),
c("class1", "class2"),
c("height", "weight", "sex")
)
print(x2) # very compact, maybe too compact...?
# print a small portion of the list, but less compact:
cast_dim2flat(x2["group1", 1:2, , drop = FALSE])
# Example 3: padding ====
# For Example 3, take the same list as before, but remove x$group1$class2:
x <- list(
group1 = list(
class1 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
)
),
group2 = list(
class1 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
),
class2 = list(
height = rnorm(10, 170),
weight = rnorm(10, 80),
sex = sample(c("M", "F", NA), 10, TRUE)
)
)
)
hier2dim(x) # as indicated here, dimension 2 (i.e. columns) will have padding
# casting this to a dimensional list will resulting in padding with `NULL`:
x2 <- cast_hier2dim(x)
print(x2)
# The `NULL` values are added for padding.
# This is because all slices of the same dimension need to have the same number of elements.
# For example, all rows need to have the same number of columns.
# one can also use custom padding:
x2 <- cast_hier2dim(x, padding = list(~ "this is padding"))
print(x2)
dimnames(x2) <- list(
c("height", "weight", "sex"),
c("class1", "class2"),
c("group1", "group2")
)
print(x2)
cast_dim2flat(x2[1:2, , , drop = FALSE])
# we can also use in2out = FALSE:
x2 <- cast_hier2dim(x, in2out = FALSE, padding = list(~ "this is padding"))
dimnames(x2) <- list( # notice the order here is reversed, because in2out = FALSE
c("group1", "group2"),
c("class1", "class2"),
c("height", "weight", "sex")
)
print(x2)
cast_dim2flat(x2[, , 1:2, drop = FALSE])
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