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Built on the top of data.tree, a
Node (tree) is an R6 object that is especially useful when we are facing
hierarchical data. The forestry package helps
to reshape or create tree objects. This package is a series of utility
functions to help with nested data. Since data.tree has
the capability to convert a tree to JSON using toJSON()
after converting to a list using as.list(), the
forestry package is particularly useful when creating a
specific JSON object for building htmlwidgets. The
forestry package aims to reshape or create tree objects
with a specific format.
create_nodes() creates a Node object.
tree_name is to assign the name of this Node.
add_children_count is to assign the number of children to
this Node, it will be listed in numerical order. To assign values to
each node, simply put the appropriate variable as a parameter with a
vector containing the values. The name of the parameter will be the
variable name and the values in the vector will be assigned to each node
respectively.
library(data.tree)
library(forestry)
new_node <- create_nodes(tree_name = "tree1",
add_children_count = 3,
class = c("A", "B", "C") )
print(new_node, "class")#> levelName class
#> 1 tree1
#> 2 ¦--1 A
#> 3 ¦--2 B
#> 4 °--3 CThe fill_NA_level() function will fill missing values
across the desired level with desired value (default as 0). For example,
new_node is a tree with missing value in hc field.
new_node <- create_nodes(tree_name = "tree1",
add_children_count = 3,
hc = c(1, 2, NA))
print(new_node, "hc" )#> levelName hc
#> 1 tree1 NA
#> 2 ¦--1 1
#> 3 ¦--2 2
#> 4 °--3 NAWe apply fill_NA_level() to new_node,
simply put new_node as input_node, assign the
field_name with hc, and assign
by_level = 2, we will fill the NA in hc field
with 0 across level 2.
result <- fill_NA_level(input_node = new_node,
field_name = "hc",
by_level = 2,
fill_with = 0)
print(result, "hc")#> levelName hc
#> 1 tree1 NA
#> 2 ¦--1 1
#> 3 ¦--2 2
#> 4 °--3 0create_tree() creates a new tree from a list. It appends
each item of the input list as a numbered child in the new tree. This is
useful when we convert a Node to a JSON array.
For instance, let’s use test_node$children (a list) as
an example. We can see a list of groupA, groupB and groupC.
#> $groupA
#> levelName
#> 1 groupA
#> 2 ¦--Male
#> 3 °--Female
#>
#> $groupB
#> levelName
#> 1 groupB
#> 2 ¦--Male
#> 3 °--Female
#>
#> $groupC
#> levelName
#> 1 groupC
#> 2 ¦--Male
#> 3 °--FemaleNow we see that this list is reshaped into a list, new_tree,
with each item in test_node$children added as a child. The
index of each item in the list is assigned as the name of each
child.
library(data.tree)
test_node <- as.Node(test_df)
new_shape <- create_tree(test_node$children,"new_tree")
print(new_shape, "hc")#> levelName hc
#> 1 new_tree NA
#> 2 ¦--1 NA
#> 3 ¦ °--groupA NA
#> 4 ¦ ¦--Male 80
#> 5 ¦ °--Female 97
#> 6 ¦--2 NA
#> 7 ¦ °--groupB NA
#> 8 ¦ ¦--Male 44
#> 9 ¦ °--Female 37
#> 10 °--3 NA
#> 11 °--groupC NA
#> 12 ¦--Male 81
#> 13 °--Female 46fix_items() creates a tree with fixed children nodes
from another tree. It automatically copies fields to the tree and fills
missing values with NA. Similar to left joining to a tree
with certian children nodes.
This function is to make sure the tree has the desired children nodes.
See cell_node2, it has only B and C.
cell_node2 <- Node$new("cell2")
cell_node2$AddChild("B")
cell_node2$AddChild("C")
cell_node2$Set(class = c(NA, "B1", "C1"))
print(cell_node2, "class")#> levelName class
#> 1 cell2
#> 2 ¦--B B1
#> 3 °--C C1Now we put fix_vector = c("A", "B", "C", "D") and assign
to a new tree, cell_fixed_items. We can see that
cell_fixed_items has all of the nodes from
fix_vector and still inherits the fields from
cell_node2.
cell_fixed_items <- fix_items(fix_vector = c("A", "B", "C", "D"),
input_node = cell_node2)
print(cell_fixed_items, "class")#> levelName class
#> 1 cell2
#> 2 ¦--A
#> 3 ¦--B B1
#> 4 ¦--C C1
#> 5 °--Dchildren_sort() function sorts the children nodes into a
desired order. If there are children nodes not listed in the
input_order, we can set the mismatch_last
parameter (default is T) to put the mismatched children
nodes to the top or bottom.
data(test_df)
test_node <- data.tree::as.Node(test_df)
sorted_node <- children_sort(
input_node = test_node,
input_order = c("groupB", "groupA"),
mismatch_last = T)
print(sorted_node)#> levelName
#> 1 tree1
#> 2 ¦--groupB
#> 3 ¦ ¦--Male
#> 4 ¦ °--Female
#> 5 ¦--groupA
#> 6 ¦ ¦--Male
#> 7 ¦ °--Female
#> 8 °--groupC
#> 9 ¦--Male
#> 10 °--Femalecumsum_across_level() gets the cumulative value across a
level, the cumulative value will be added to the
cumsum_number field.
In this example, it calculates the cumulative
exercise_time field across level 3.
data(exercise_df)
exercise_node <- as.Node(exercise_df)
test <- forestry::cumsum_across_level(input_node = exercise_node,
attri_name = "exercise_time",
level_num = 3)
print(test, "cumsum_number", "exercise_time", "level")#> levelName cumsum_number exercise_time level
#> 1 Year NA NA 1
#> 2 ¦--Q1 NA NA 2
#> 3 ¦ ¦--Jan 0.83 0.83 3
#> 4 ¦ ¦--Feb 1.14 0.31 3
#> 5 ¦ °--Mar 1.98 0.84 3
#> 6 ¦--Q2 NA NA 2
#> 7 ¦ ¦--Apr 2.17 0.19 3
#> 8 ¦ ¦--May 2.18 0.01 3
#> 9 ¦ °--Jun 2.45 0.27 3
#> 10 ¦--Q3 NA NA 2
#> 11 ¦ ¦--Jul 2.56 0.11 3
#> 12 ¦ ¦--Aug 3.54 0.98 3
#> 13 ¦ °--Sep 4.30 0.76 3
#> 14 °--Q4 NA NA 2
#> 15 ¦--Oct 4.49 0.19 3
#> 16 ¦--Nov 5.25 0.76 3
#> 17 °--Dec 5.54 0.29 3In addition, level_num = "All" will get the cumulative
value across all levels. Please note that there should be no missing
values in the appropriate level when we apply
cumsum_across_level().
data(exercise_df)
exercise_node <- as.Node(exercise_df)
exercise_node$Do(function(node) node$exercise_time <- Aggregate(node,
attribute = "exercise_time",
aggFun = sum),
traversal = "post-order")
print(exercise_node, "exercise_time")#> levelName exercise_time
#> 1 Year 5.54
#> 2 ¦--Q1 1.98
#> 3 ¦ ¦--Jan 0.83
#> 4 ¦ ¦--Feb 0.31
#> 5 ¦ °--Mar 0.84
#> 6 ¦--Q2 0.47
#> 7 ¦ ¦--Apr 0.19
#> 8 ¦ ¦--May 0.01
#> 9 ¦ °--Jun 0.27
#> 10 ¦--Q3 1.85
#> 11 ¦ ¦--Jul 0.11
#> 12 ¦ ¦--Aug 0.98
#> 13 ¦ °--Sep 0.76
#> 14 °--Q4 1.24
#> 15 ¦--Oct 0.19
#> 16 ¦--Nov 0.76
#> 17 °--Dec 0.29exercise_node_test <- cumsum_across_level(input_node = exercise_node,
attri_name = "exercise_time",
level_num = "All")
print(exercise_node_test,"exercise_time", "cumsum_number", "level")#> levelName exercise_time cumsum_number level
#> 1 Year 5.54 NA 1
#> 2 ¦--Q1 1.98 1.98 2
#> 3 ¦ ¦--Jan 0.83 0.83 3
#> 4 ¦ ¦--Feb 0.31 1.14 3
#> 5 ¦ °--Mar 0.84 1.98 3
#> 6 ¦--Q2 0.47 2.45 2
#> 7 ¦ ¦--Apr 0.19 2.17 3
#> 8 ¦ ¦--May 0.01 2.18 3
#> 9 ¦ °--Jun 0.27 2.45 3
#> 10 ¦--Q3 1.85 4.30 2
#> 11 ¦ ¦--Jul 0.11 2.56 3
#> 12 ¦ ¦--Aug 0.98 3.54 3
#> 13 ¦ °--Sep 0.76 4.30 3
#> 14 °--Q4 1.24 5.54 2
#> 15 ¦--Oct 0.19 4.49 3
#> 16 ¦--Nov 0.76 5.25 3
#> 17 °--Dec 0.29 5.54 3The pre_get_array() function changes the numeric item
name in a list into a format that is compatible with the JSON array
standard. As mentioned earlier, when converting a tree to JSON, we need
to save the tree as a list using as.list() then use
htmlwidgets:::toJSON() to convert the list to JSON
data.
For example, new_node is a tree with numeric children
nodes.
new_node <- create_nodes(tree_name = "tree1",
add_children_count = 3,
class = c("A", "B", "C"))
print(as.list(new_node) )#> $name
#> [1] "tree1"
#>
#> $`1`
#> $`1`$class
#> [1] "A"
#>
#>
#> $`2`
#> $`2`$class
#> [1] "B"
#>
#>
#> $`3`
#> $`3`$class
#> [1] "C"We can see the numeric children node names are listed. If we apply
pre_get_array() to this list, we can change all numeric
names so the nodes can be saved as a JSON array instead of JSON objects
after we use htmlwidgets:::toJSON().
new_node <- create_nodes(tree_name = "tree1",
add_children_count = 3,
class = c("A", "B", "C"))
print(pre_get_array(as.list(new_node) ) )#> [[1]]
#> [1] "tree1"
#>
#> [[2]]
#> [[2]]$class
#> [1] "A"
#>
#>
#> [[3]]
#> [[3]]$class
#> [1] "B"
#>
#>
#> [[4]]
#> [[4]]$class
#> [1] "C"These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.