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README.md
In forestry: Reshape Data Tree
forestry
forestry is an R package with a series of utility functions to help with
reshaping hierarchy of data tree, and reform the structure of data tree.
Installation
You can install the cran version of forestry:
install.packages("forestry")
Introduction
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 a Node with Assigned Attributes
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 C
Fill Missing Values Across a Level
The 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 NA
We 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 0
Create a Tree From a List
create_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$children (a list) as an example. We
can see a list of groupA, groupB and groupC.
data(test_df)
test_node <- data.tree::as.Node(test_df)
print(test_node$children)
#> $groupA
#> levelName
#> 1 groupA
#> 2 ¦--Male
#> 3 °--Female
#>
#> $groupB
#> levelName
#> 1 groupB
#> 2 ¦--Male
#> 3 °--Female
#>
#> $groupC
#> levelName
#> 1 groupC
#> 2 ¦--Male
#> 3 °--Female
Now we see that this list is reshaped into a list, new_tree, with
each item in test_node$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$children,"new_tree")
print(new_shape, "hc")
#> levelName hc
#> 1 new_tree NA
#> 2 ¦--1 NA
#> 3 ¦ °--groupA NA
#> 4 ¦ ¦--Male 80
#> 5 ¦ °--Female 97
#> 6 ¦--2 NA
#> 7 ¦ °--groupB NA
#> 8 ¦ ¦--Male 44
#> 9 ¦ °--Female 37
#> 10 °--3 NA
#> 11 °--groupC NA
#> 12 ¦--Male 81
#> 13 °--Female 46
Expand Children Nodes
fix_items() creates a tree with fixed children nodes from another
tree. It automatically copies fields to the tree and fills missing
values with NA. Similar to left joining to a tree with certian
children nodes.
This function is to make sure the tree has the desired children nodes.
See cell_node2, it has only B and C.
cell_node2 <- Node$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 C1
Now 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 °--D
Sort Chidren Nodes
children_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 °--Female
Cumulative Sum Across a Level
cumsum_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 3
In 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 0.29
exercise_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 3
Prepare for JSON array
The 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"
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forestry documentation built on Nov. 21, 2023, 1:07 a.m.
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forestry
forestry is an R package with a series of utility functions to help with reshaping hierarchy of data tree, and reform the structure of data tree.
Installation
You can install the cran version of forestry:
install.packages("forestry")
Introduction
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 a Node with Assigned Attributes
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 C
Fill Missing Values Across a Level
The 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 NA
We 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 0
Create a Tree From a List
create_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.
data(test_df)
test_node <- data.tree::as.Node(test_df)
print(test_node$children)
#> $groupA
#> levelName
#> 1 groupA
#> 2 ¦--Male
#> 3 °--Female
#>
#> $groupB
#> levelName
#> 1 groupB
#> 2 ¦--Male
#> 3 °--Female
#>
#> $groupC
#> levelName
#> 1 groupC
#> 2 ¦--Male
#> 3 °--Female
Now we see that this list is reshaped into a list, new_tree, with
each item in test_node$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$children,"new_tree")
print(new_shape, "hc")
#> levelName hc
#> 1 new_tree NA
#> 2 ¦--1 NA
#> 3 ¦ °--groupA NA
#> 4 ¦ ¦--Male 80
#> 5 ¦ °--Female 97
#> 6 ¦--2 NA
#> 7 ¦ °--groupB NA
#> 8 ¦ ¦--Male 44
#> 9 ¦ °--Female 37
#> 10 °--3 NA
#> 11 °--groupC NA
#> 12 ¦--Male 81
#> 13 °--Female 46
Expand Children Nodes
fix_items() creates a tree with fixed children nodes from another
tree. It automatically copies fields to the tree and fills missing
values with NA. Similar to left joining to a tree with certian
children nodes.
This function is to make sure the tree has the desired children nodes.
See cell_node2, it has only B and C.
cell_node2 <- Node$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 C1
Now 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 °--D
Sort Chidren Nodes
children_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 °--Female
Cumulative Sum Across a Level
cumsum_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 3
In 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 0.29
exercise_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 3
Prepare for JSON array
The 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"
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