To apply styling or formatting, you can use the
columns and rows arguments. The syntax should be very familiar for dplyr
users as you can use the tidyselect specification.
Targeting of values is done through columns and additionally by rows (if
nothing is provided for rows then entire columns are selected). The
columns argument allows us to target a subset of cells contained in the
resolved columns. We say resolved because aside from declaring column names
in c() (with bare column names or names in quotes) we can use
tidyselect-style expressions. This can be as basic as supplying a select
helper like starts_with(), or, providing a more complex incantation like
where(~ is.numeric(.x) & max(.x, na.rm = TRUE) > 1E6)
which targets numeric columns that have a maximum value greater than
1,000,000 (excluding any NAs from consideration).
By default all columns and rows are selected (with the everything()
defaults). Cell values that are incompatible with a given formatting function
will be skipped over, like character values and numeric fmt_*()
functions. So it's safe to select all columns with a particular formatting
function (only those values that can be formatted will be formatted), but,
you may not want that. One strategy is to format the bulk of cell values with
one formatting function and then constrain the columns for later passes with
other types of formatting (the last formatting done to a cell is what you get
in the final output).
Once the columns are targeted, we may also target the rows within those
columns. This can be done in a variety of ways. If a stub is present, then we
potentially have row identifiers. Those can be used much like column names in
the columns-targeting scenario. We can use simpler tidyselect-style
expressions (the select helpers should work well here) and we can use quoted
row identifiers in c(). It's also possible to use row indices (e.g.,
c(3, 5, 6)) though these index values must correspond to the row numbers of
the input data (the indices won't necessarily match those of rearranged rows
if row groups are present). One more type of expression is possible, an
expression that takes column values (can involve any of the available columns
in the table) and returns a logical vector. This is nice if you want to base
formatting on values in the column or another column, or, you'd like to use a
more complex predicate expression.
Examples
gt_tbl <- gt(exibble)
gt_tbl %>%
fmt_time(
columns = contains("time") & !starts_with("date"),
rows = num > 100 & group == "grp_b"
)
num
char
fctr
date
time
datetime
currency
row
group
# Styling numeric columns based on range
gt_tbl %>% tab_style(
style = cell_text(weight = "bold"),
locations = cells_body(
columns = where(is.factor)
)
)
num
char
fctr
date
time
datetime
currency
row
group
# Naming rows
gt_tbl_rows <- gt(exibble, rowname_col = "row")
gt_tbl_rows %>%
fmt_datetime(
columns = datetime,
rows = c("row_1", "row_8")
)
num
char
fctr
date
time
datetime
currency
group
row_1
row_2
row_3
row_4
row_5
row_6
row_7
row_8