In jienagu/forestry: Reshape Data Tree

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

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")

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" )

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")

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)

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")

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")

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")

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)

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")

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")

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")

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) )

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) ) )