View source: R/text_transform.R
text_transform | R Documentation |
Text transforming in gt is the act of modifying formatted strings in
targeted cells. text_transform()
provides the most flexibility
of all the text_*()
functions in their family of functions. With it, you
target the cells to undergo modification in the locations
argument while
also supplying a function to the fn
argument. The function given to fn
should ideally at the very least take x
as an input (it stands for the
character vector that is essentially the targeted cells) and return a
character vector of the same length as the input. Using the construction
function(x) { .. }
for the function is recommended.
text_transform(data, fn, locations = cells_body())
data |
The gt table data object
This is the gt table object that is commonly created through use of the
|
fn |
Function for text transformation
The function to use for text transformation. It should include |
locations |
Locations to target
The cell or set of cells to be associated with the text transformation.
Only |
An object of class gt_tbl
.
Use a subset of the sp500
dataset to create a gt table. Transform the
text in the date
column using a function supplied to text_transform()
(via the fn
argument). Note that the x
in the fn = function (x)
part
consists entirely of ISO 8601 date strings (which are acceptable as input to
vec_fmt_date()
and vec_fmt_datetime()
).
sp500 |> dplyr::slice_head(n = 10) |> dplyr::select(date, open, close) |> dplyr::arrange(-dplyr::row_number()) |> gt() |> fmt_currency() |> text_transform( fn = function(x) { paste0( "<strong>", vec_fmt_date(x, date_style = "m_day_year"), "</strong>", "—W", vec_fmt_datetime(x, format = "w") ) }, locations = cells_body(columns = date) ) |> cols_label( date = "Date and Week", open = "Opening Price", close = "Closing Price" )
Let's use a summarized version of the gtcars
dataset to create a gt
table. First, the numeric values in the n
column are formatted as
spelled-out numbers with fmt_spelled_num()
. The output values are indeed
spelled out but exclusively with lowercase letters. We actually want these
words to begin with a capital letter and end with a period. To make this
possible, text_transform()
will be used since it can modify
already-formatted text. Through the fn
argument, we provide a custom
function that uses R's toTitleCase()
operating on x
(the numbers-as-text
strings) within paste0()
so that a period can be properly placed.
gtcars |> dplyr::filter(ctry_origin %in% c("Germany", "Italy", "Japan")) |> dplyr::count(mfr, ctry_origin, sort = TRUE) |> dplyr::arrange(ctry_origin) |> gt(rowname_col = "mfr", groupname_col = "ctry_origin") |> cols_label(n = "No. of Entries") |> tab_stub_indent(rows = everything(), indent = 2) |> cols_align(align = "center", columns = n) |> fmt_spelled_num() |> text_transform( fn = function(x) { paste0(tools::toTitleCase(x), ".") }, locations = cells_body(columns = n) )
There may be occasions where you'd want to remove all text. Here in this
example based on the pizzaplace
dataset, we generate a gt table that
summarizes an entire year of data by colorizing the daily sales revenue.
Individual cell values are not needed here (since the encoding by color
suffices), so, text_transform()
is used to turn every value to an empty
string: ""
.
pizzaplace |> dplyr::group_by(date) |> dplyr::summarize(rev = sum(price)) |> dplyr::ungroup() |> dplyr::mutate( month = lubridate::month(date, label = TRUE), day_num = lubridate::mday(date) ) |> dplyr::select(-date) |> tidyr::pivot_wider(names_from = month, values_from = rev) |> gt(rowname_col = "day_num") |> data_color( method = "numeric", palette = "wesanderson::Zissou1", na_color = "white" ) |> text_transform( fn = function(x) "", locations = cells_body() ) |> opt_table_lines(extent = "none") |> opt_all_caps() |> cols_width(everything() ~ px(35)) |> cols_align(align = "center")
4-4
v0.2.0.5
(March 31, 2020)
Other text transforming functions:
text_case_match()
,
text_case_when()
,
text_replace()
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