To more easily insert graphics into body cells, we can use the fmt_image()
function. This allows for one or more images to be placed in the targeted
cells. The cells need to contain some reference to an image file, either: (1)
complete http/https or local paths to the files; (2) the file names, where a
common path can be provided via path
; or (3) a fragment of the file name,
where the file_pattern
helps to compose the entire file name and path
provides the path information. This should be expressly used on columns that
contain only references to image files (i.e., no image references as part
of a larger block of text). Multiple images can be included per cell by
separating image references by commas. The sep
argument allows for a common
separator to be applied between images.
Usage
fmt_image(
data,
columns = everything(),
rows = everything(),
height = "2em",
sep = " ",
path = NULL,
file_pattern = "{x}",
encode = TRUE
)
Arguments
- data
The gt table data object
obj:<gt_tbl>
--- requiredThis is the gt table object that is commonly created through use of the
gt()
function.- columns
Columns to target
<column-targeting expression>
--- default:everything()
Can either be a series of column names provided in
c()
, a vector of column indices, or a select helper function. Examples of select helper functions includestarts_with()
,ends_with()
,contains()
,matches()
,one_of()
,num_range()
, andeverything()
.- rows
Rows to target
<row-targeting expression>
--- default:everything()
In conjunction with
columns
, we can specify which of their rows should undergo formatting. The defaulteverything()
results in all rows incolumns
being formatted. Alternatively, we can supply a vector of row captions withinc()
, a vector of row indices, or a select helper function. Examples of select helper functions includestarts_with()
,ends_with()
,contains()
,matches()
,one_of()
,num_range()
, andeverything()
. We can also use expressions to filter down to the rows we need (e.g.,[colname_1] > 100 & [colname_2] < 50
).- height
Height of image
scalar<character>
--- default:"1em"
The absolute height of the image in the table cell. By default, this is set to
"1em"
.- sep
Separator between images
scalar<character>
--- default:" "
In the output of images within a body cell,
sep
provides the separator between each image.- path
Path to image files
scalar<character>
--- default:NULL
(optional
)An optional path to local image files (this is combined with all filenames).
- file_pattern
File pattern specification
scalar<character>
--- default:"{x}"
The pattern to use for mapping input values in the body cells to the names of the graphics files. The string supplied should use
"{x}"
in the pattern to map filename fragments to input strings.- encode
Use Base64 encoding
scalar<logical>
--- default:TRUE
The option to always use Base64 encoding for image paths that are determined to be local. By default, this is
TRUE
.
Targeting cells with columns
and rows
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 NA
s 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
Using a small portion of metro
dataset, let's create a gt table. We
will only include a few columns and rows from that table. The lines
and
connect_rer
columns have comma-separated listings of numbers/letters
(corresponding to lines served at each station). We have a directory SVG
graphics for all of these lines in the package (the path for the image
directory can be accessed via system.file("metro_svg", package = "gt")
),
and the filenames roughly correspond to the data in those two columns. The
fmt_image()
function can be used with these inputs since the path
and
file_pattern
arguments allow us to compose complete and valid file
locations. What you get from this are sequences of images in the table cells,
taken from the referenced graphics files on disk.
metro |>
dplyr::select(name, caption, lines, connect_rer) |>
dplyr::slice_head(n = 10) |>
gt() |>
cols_merge(
columns = c(name, caption),
pattern = "{1}<< ({2})>>"
) |>
text_replace(
locations = cells_body(columns = name),
pattern = "\\((.*?)\\)",
replacement = "<br>(<em>\\1</em>)"
) |>
sub_missing(columns = connect_rer, missing_text = "") |>
fmt_image(
columns = lines,
path = system.file("metro_svg", package = "gt"),
file_pattern = "metro_{x}.svg"
) |>
fmt_image(
columns = connect_rer,
path = system.file("metro_svg", package = "gt"),
file_pattern = "rer_{x}.svg"
) |>
cols_label(
name = "Station",
lines = "Lines",
connect_rer = "RER"
) |>
cols_align(align = "left") |>
tab_style(
style = cell_borders(
sides = c("left", "right"),
weight = px(1),
color = "gray85"
),
locations = cells_body(columns = lines)
) |>
opt_stylize(style = 6, color = "blue") |>
opt_all_caps() |>
opt_horizontal_padding(scale = 1.75)
See also
Other data formatting functions:
data_color()
,
fmt_auto()
,
fmt_bins()
,
fmt_bytes()
,
fmt_currency()
,
fmt_datetime()
,
fmt_date()
,
fmt_duration()
,
fmt_engineering()
,
fmt_flag()
,
fmt_fraction()
,
fmt_index()
,
fmt_integer()
,
fmt_markdown()
,
fmt_number()
,
fmt_partsper()
,
fmt_passthrough()
,
fmt_percent()
,
fmt_roman()
,
fmt_scientific()
,
fmt_spelled_num()
,
fmt_time()
,
fmt_url()
,
fmt()
,
sub_large_vals()
,
sub_missing()
,
sub_small_vals()
,
sub_values()
,
sub_zero()