Most of the behaviour of dtrackr
can be specified at the
individual call level using the .headline
and
.messages
glue specifications to define a format. Sometimes
however this is annoying to do for all the stages in a flow chart and a
global configuration of behaviour is desirable.
One of the areas where default behaviour may be undesirable is the
naming of groups. The default setting combines the group name
{.group}
and the group value {.value}
into a
concatenated colon separated string as demonstrated below:
# these are the defaults
old = options(
dtrackr.strata_glue="{.group}:{.value}",
dtrackr.strata_sep="; "
)
survival::cgd %>%
track() %>%
group_by(treat) %>%
comment() %>%
group_by(sex,.add = TRUE) %>%
comment(
.messages = c(
"{.count} patients",
"{sprintf('%1.0f',.count/.total*100)}% of the total")) %>%
ungroup() %>%
flowchart()
In particular in situations like this where you are faceting on
factors or strings, disposing of the group name may make this clearer.
In the following example we only include the group value, force it to
lower case, and use a comma to separate multiple facets. We have used
manual override of the messages in the grouping stages, by providing a
.messages
parameter, to specify what we are faceting by in
a more natural way:
# only include the group value in the description of the group
old = options(
dtrackr.strata_glue="{tolower(.value)}",
dtrackr.strata_sep=", "
)
survival::cgd %>%
track() %>%
group_by(treat, .messages = "case or control") %>%
comment() %>%
group_by(sex,
.add = TRUE,
.messages = "by {tolower(.cols)}" #.cols contains a csv string of the grouping variables
) %>%
comment(
.messages = c(
"{.count} patients",
"{sprintf('%1.0f',.count/.total*100)}% of the total")) %>%
ungroup() %>%
flowchart()
N.B. this setting affects the “strata” label of the group, which in turn affects the flowchart branching. If this is not unique from one group to another strange behaviours will be observed.
With the group strata label defined you can set other defaults. In the flowchart above the “583 items” labels are generated by the default message setting, and the headings for the groups by the default headline setting. In this example we change these to alter the default text.
old = options(
dtrackr.strata_glue="{tolower(.value)}",
dtrackr.strata_sep=", ",
dtrackr.default_message = "containing {.count} patients",
dtrackr.default_headline = "subgroup: {.strata}"
)
survival::cgd %>%
track() %>%
group_by(
treat,
.messages = "case or control"
) %>%
comment() %>%
group_by(
sex,
.add = TRUE,
.messages = "by gender"
) %>%
comment(
.messages = c(
"{.count} patients",
"{sprintf('%1.0f',.count/.total*100)}% of the total")) %>%
ungroup() %>%
flowchart()
# N.b. this setting includes some unwanted headlines in the ungrouped stages of
# the flow chart. If a headline evaluates to "" then the headline is suppressed
# and we can get rid of unwanted headlines. An example of doing this is as
# follows:
# options(dtrackr.default_headline = "{ifelse(.strata != '', glue::glue('subgroup: {.strata}'), '')}")
# reset options
options(old)
Subgroup counts are a slightly neater way of doing this. Their
default layout can be modified using
dtrackr.default_count_subgroup
.
Elsewhere we discuss the possibility of capturing excluded items for
debugging. This behaviour can be added to any pipeline with the
capture_exclusions()
function. Alternatively it can be
globally enabled with the following option. Usual caveats about
performance apply.
Sometimes in a pipeline we have a exclusion criteria which is not triggered, or is not triggered for a particular subgroup. In this case the default is not to show the zero items that were excluded. However sometimes it is reassuring to know that an filter was applied even if it results in nothing:
In count_subgroup()
and group_by()
statements there can be a large number of items generated if a
particular grouping variable has a lot of possible values. This can
cause performance issues and legibility issues for the resulting graph
and is usually a result of an interim stage of the data pipeline where
grouping is used to do fine scale summarisation operation (e.g. a
dataset %>% group_by(nearly_unique_id) %>% filter(row_number()==1)
or a timeseries where things need to be aggregated by date, and the data
is quickly ungrouped (e.g.
timeseries %>% group_by(date) %>% summarise(count = n())
).
The most number of groups that dtrackr
will attempt to keep
track of is configurable but defaults to 16: