How can we add all-zero rows and columns in a table made by tbl_hierarchical?
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Adding All-Zero Rows and Columns to gtsummary's tbl_hierarchical Tables
Learn how to programmatically insert rows and columns containing only zeros into a tbl_hierarchical object generated by the gtsummary package in R, ensuring comprehensive data representation.
The gtsummary package in R is a powerful tool for creating publication-ready summary tables. Its tbl_hierarchical function is particularly useful for displaying nested or grouped data. However, a common challenge arises when certain categories or combinations have no observations, leading to their omission from the table. This article addresses how to programmatically add all-zero rows and columns to a tbl_hierarchical object, ensuring that all potential categories are represented, even if they have zero counts.
Understanding the Challenge with tbl_hierarchical
When tbl_hierarchical summarizes data, it typically only includes categories or combinations that have at least one observation. This behavior is efficient for displaying non-empty results but can be problematic when a complete representation of all possible categories is required, especially for comparative analysis or when certain categories are expected to exist but happen to have zero counts in the current dataset. Manually identifying and inserting these missing rows and columns can be tedious and error-prone, particularly with complex hierarchical structures.
flowchart TD
A[Input Data] --> B{tbl_hierarchical}
B --> C{Summarized Table (Missing Zeros)}
C --> D[Identify Missing Categories]
D --> E[Construct Zero-Filled Rows/Cols]
E --> F[Merge with Summarized Table]
F --> G[Final Table (with Zeros)]Workflow for adding zero rows/columns to a hierarchical table.
Preparing Data for Comprehensive Summarization
The key to ensuring all categories are present, even with zero counts, often lies in preparing the data before passing it to tbl_hierarchical. This involves explicitly defining all possible combinations of your grouping variables. The dplyr package, particularly functions like complete() and expand(), are invaluable for this task. By creating a 'complete' dataset that includes all combinations, even those with zero counts, gtsummary can then process them correctly.
library(gtsummary)
library(dplyr)
# Sample data with some missing combinations
data_raw <- tibble(
group_var = c("A", "A", "B", "C", "C"),
sub_group = c("X", "Y", "X", "Y", "Z"),
value = c(10, 15, 20, 25, 30)
)
# Define all possible combinations
all_combinations <- expand_grid(
group_var = c("A", "B", "C", "D"), # 'D' is a new group
sub_group = c("X", "Y", "Z")
)
# Join with original data and fill missing values with 0
data_complete <- all_combinations %>%
left_join(data_raw, by = c("group_var", "sub_group")) %>%
mutate(value = replace_na(value, 0))
# Now, summarize with tbl_hierarchical
tbl_complete <- data_complete %>%
group_by(group_var, sub_group) %>%
summarise(count = n(), total_value = sum(value)) %>%
ungroup() %>%
tbl_hierarchical(
label = group_var,
levels = c("group_var", "sub_group"),
statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{n}"),
include = c(count, total_value)
)
tbl_complete
Example of using expand_grid and left_join to create a complete dataset before tbl_hierarchical.
expand_grid() function is crucial here. It generates all unique combinations of the supplied vectors, ensuring that even categories not present in your original data are included. This is particularly useful for creating a 'template' for your summary.Post-Processing tbl_hierarchical Output
While pre-processing is often the most robust solution, there might be scenarios where you need to modify an existing tbl_hierarchical object. This is more complex as gtsummary objects are not simple data frames. You would typically need to extract the underlying data, manipulate it, and then potentially rebuild or merge it back. This approach requires a deeper understanding of the gtsummary object structure, specifically its table_body and table_header components.
library(gtsummary)
library(dplyr)
# Create a simple tbl_hierarchical table
tbl_example <-
trial %>%
select(trt, grade) %>%
tbl_hierarchical(
label = trt,
levels = c("trt", "grade"),
statistic = all_categorical() ~ "{n} ({p}%)"
)
# Extract the table body
tbl_body <- tbl_example$table_body
# Identify all unique combinations of levels that *should* exist
# This is a simplified example; real-world might need more complex logic
all_trt <- c("Drug A", "Drug B")
all_grade <- c("I", "II", "III")
# Create a template for missing rows
missing_rows_template <- expand_grid(
variable = c("trt", "grade"), # Assuming these are the variable names in table_body
variable_level = c(all_trt, all_grade)
) %>%
filter(
(variable == "trt" & variable_level %in% all_trt) |
(variable == "grade" & variable_level %in% all_grade)
) %>%
distinct(variable, variable_level)
# This part is highly dependent on the exact structure of your tbl_hierarchical
# and is generally more complex than pre-processing.
# For demonstration, let's just show how to identify missing levels.
# Example: Find missing 'grade' levels for 'Drug A'
existing_grades_for_A <- tbl_body %>%
filter(variable == "grade", parent_id == "trt_Drug A") %>%
pull(variable_level)
missing_grades_for_A <- setdiff(all_grade, existing_grades_for_A)
# To actually insert these, you'd need to construct new rows for tbl_body
# with appropriate 'row_type', 'stat_0', 'stat_1', etc., and then bind them.
# This is non-trivial and often requires custom functions or direct manipulation
# of the gtsummary object's internal structure, which is not officially supported
# for direct modification in this way.
# A more practical approach for post-processing might involve converting to a data frame,
# adding rows/cols, and then re-formatting (e.g., with flextable or kableExtra)
# if gtsummary's formatting is not strictly required after the modification.
# For example, converting to a tibble and then manipulating:
# tbl_df <- as_tibble(tbl_example)
# # Now manipulate tbl_df and then format using other packages if needed.
print("Direct post-processing of tbl_hierarchical for zero rows/columns is complex.")
print("Pre-processing the data is generally the recommended and more robust approach.")
Illustrating the complexity of post-processing tbl_hierarchical for missing zero rows/columns.
gtsummary object (like $table_body or $table_header) to insert rows or columns is generally discouraged. It can lead to unexpected behavior or break future gtsummary functions. Pre-processing your data is almost always the safer and more maintainable approach.Adding All-Zero Columns for Missing Variables
Similar to rows, if you need to ensure certain columns (e.g., specific statistics or variables) are present even if they are all zeros, the strategy remains similar: ensure your underlying data or the tbl_hierarchical call explicitly accounts for them. If a column represents a statistic that is always zero for a given group, it might not appear. You can sometimes force its inclusion by ensuring the variable exists in your data frame with zero values, or by carefully constructing your statistic argument to tbl_hierarchical.
library(gtsummary)
library(dplyr)
# Sample data where 'event_count' might be zero for some groups
data_events <- tibble(
group = c("A", "A", "B", "C"),
outcome = c("X", "Y", "X", "Y"),
event_occurred = c(1, 0, 1, 0)
)
# Create a complete dataset, ensuring all combinations and a 'zero' event_occurred column
all_combinations_events <- expand_grid(
group = c("A", "B", "C", "D"), # 'D' has no events
outcome = c("X", "Y", "Z")
)
data_complete_events <- all_combinations_events %>%
left_join(data_events, by = c("group", "outcome")) %>%
mutate(event_occurred = replace_na(event_occurred, 0))
# Now summarize. The 'event_occurred' column will be present for all groups,
# even if its sum is zero.
tbl_events <- data_complete_events %>%
group_by(group, outcome) %>%
summarise(total_events = sum(event_occurred), .groups = 'drop') %>%
tbl_hierarchical(
label = group,
levels = c("group", "outcome"),
statistic = all_continuous() ~ "{sum}",
include = total_events
)
tbl_events
Ensuring a 'total_events' column appears for all groups, even if all events are zero.
tbl_hierarchical, think of it as summarizing the data you provide. If you want a category or a statistic to appear, it must be represented in the input data, even if its value is zero. This is generally more straightforward than trying to inject missing elements into the gtsummary object after creation.