Add grand summary rows using aggregation functionsSource:
Add grand summary rows to the gt table by using applying aggregation
functions to the table data. The summary rows incorporate all of the
available data, regardless of whether some of the data are part of row
groups. You choose how to format the values in the resulting summary cells by
use of a
formatter function (e.g,
fmt_number) and any relevant options.
grand_summary_rows( data, columns = everything(), fns, missing_text = "---", formatter = fmt_number, ... )
A table object that is created using the
The columns for which the summaries should be calculated.
Functions used for aggregations. This can include base functions like
sumor any other user-defined aggregation function. The function(s) should be supplied within a
list(). Within that list, we can specify the functions by use of function names in quotes (e.g.,
"sum"), as bare functions (e.g.,
sum), or as one-sided R formulas using a leading
~. In the formula representation, a
.serves as the data to be summarized (e.g.,
sum(., na.rm = TRUE)). The use of named arguments is recommended as the names will serve as summary row labels for the corresponding summary rows data (the labels can derived from the function names but only when not providing bare function names).
The text to be used in place of
NAvalues in summary cells with no data outputs.
A formatter function name. These can be any of the
fmt_*()functions available in the package (e.g.,
fmt_percent(), etc.), or a custom function using
fmt(). The default function is
fmt_number()and its options can be accessed through
Values passed to the
formatterfunction, where the provided values are to be in the form of named vectors. For example, when using the default
fmt_number(), options such as
localecan be used.
Should we need to obtain the summary data for external purposes, the
extract_summary() function can be used with a
gt_tbl object where grand
summary rows were added via
sp500 to create a gt table with row groups. Create the grand
avg for the table with the
sp500 %>% dplyr::filter(date >= "2015-01-05" & date <= "2015-01-16") %>% dplyr::arrange(date) %>% dplyr::mutate(week = paste0("W", strftime(date, format = "%V"))) %>% dplyr::select(-adj_close, -volume) %>% gt( rowname_col = "date", groupname_col = "week" ) %>% grand_summary_rows( columns = c(open, high, low, close), fns = list( min = ~min(.), max = ~max(.), avg = ~mean(.)), formatter = fmt_number, use_seps = FALSE )
Other row addition/modification functions: