--- title: "Introduction to forestry" author: "Jiena Gu McLellan" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Introduction to forestry} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set( warning = FALSE, message = FALSE, echo = TRUE, comment = "#>" ) ``` Built on the top of [data.tree](https://cran.r-project.org/package=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](https://cran.r-project.org/package=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. ```{r create_node} 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. ```{r create_node11} 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. ```{r create_node12} 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. ```{r create_node2} 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. ```{r create_node3} library(data.tree) test_node <- as.Node(test_df) new_shape <- create_tree(test_node$children,"new_tree") print(new_shape, "hc") ``` ## Expand Children Nodes `fix_items()` creates a tree with fixed children nodes from another tree. It automatically copies fields to the tree and fills missing values with `NA`. Similar to left joining to a tree with certian children nodes. This function is to make sure the tree has the desired children nodes. See *cell_node2*, it has only B and C. ```{r create_node4_pre} cell_node2 <- Node$new("cell2") cell_node2$AddChild("B") cell_node2$AddChild("C") cell_node2$Set(class = c(NA, "B1", "C1")) print(cell_node2, "class") ``` Now we put `fix_vector = c("A", "B", "C", "D")` and assign to a new tree, `cell_fixed_items`. We can see that `cell_fixed_items` has all of the nodes from `fix_vector` and still inherits the fields from `cell_node2`. ```{r create_node5} cell_fixed_items <- fix_items(fix_vector = c("A", "B", "C", "D"), input_node = cell_node2) print(cell_fixed_items, "class") ``` ## Sort Chidren Nodes `children_sort()` function sorts the children nodes into a desired order. If there are children nodes not listed in the `input_order`, we can set the `mismatch_last` parameter (default is `T`) to put the mismatched children nodes to the top or bottom. ```{r create_node6} data(test_df) test_node <- data.tree::as.Node(test_df) sorted_node <- children_sort( input_node = test_node, input_order = c("groupB", "groupA"), mismatch_last = T) print(sorted_node) ``` ## Cumulative Sum Across a Level `cumsum_across_level()` gets the cumulative value across a level, the cumulative value will be added to the `cumsum_number` field. In this example, it calculates the cumulative `exercise_time` field across level 3. ```{r create_node7} data(exercise_df) exercise_node <- as.Node(exercise_df) test <- forestry::cumsum_across_level(input_node = exercise_node, attri_name = "exercise_time", level_num = 3) print(test, "cumsum_number", "exercise_time", "level") ``` In addition, `level_num = "All"` will get the cumulative value across all levels. Please note that there should be no missing values in the appropriate level when we apply `cumsum_across_level()`. ```{r create_node8} data(exercise_df) exercise_node <- as.Node(exercise_df) exercise_node$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. ```{r create_node9} 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()`. ```{r create_node10} new_node <- create_nodes(tree_name = "tree1", add_children_count = 3, class = c("A", "B", "C")) print(pre_get_array(as.list(new_node) ) ) ```