Returns a named list ready to pass to render_onepager() as data:
every token template_tokens() lists for template, each already set
to a real value, not a blank you have to guess the shape of – an
optional token's own shipped default, or (for a required token, which
has no default) the matching value from that template's
example_data(). Starting from this instead of an empty list means
every value you haven't changed yet is already correctly formatted (a
percentage as "84%", a color as a real theme value, and so on),
which is the actual point: reading template_tokens()'s token names
alone tells you what to set, not what a working value for it looks
like.
Usage
template_data(template, tokens = c("both", "required", "optional"))Arguments
- template
Character. A built-in template name (see
list_templates()). Unliketemplate_tokens(), this does not take a path to a hand-editedexport_template()copy, since there is no known example value for a token that copy might add.- tokens
Character. Which tokens to include:
"both"(default, everythingrender_onepager()would read),"required"(only the values you must actually supply), or"optional"(only the reworded-text and type/spacing tokens, each left at its default).
Value
Named list of character values, with a "required" attribute
naming the entries that came from a required token.
Details
Which entries came from a required token, as opposed to an optional
one, is recorded in the result's "required" attribute
(attr(x, "required")) rather than in the list itself, so the result
stays a plain data list: pass it straight to render_onepager(),
overwrite fields directly, or merge your own values in with
utils::modifyList().
Examples
data <- template_data("cohort_summary")
attr(data, "required")
#> [1] "contact_email" "logo_primary_path"
#> [3] "logo_primary_alt" "org_full"
#> [5] "contact_url" "doc_title"
#> [7] "doc_subtitle" "strip_data"
#> [9] "strip_period" "strip_design"
#> [11] "strip_geography" "n_decedents"
#> [13] "n_ems_total" "pct_linked"
#> [15] "n_prior_ems" "n_prior_od_ems"
#> [17] "pct_linked_male_width" "n_male"
#> [19] "pct_linked_female_width" "n_female"
#> [21] "pct_linked_appalachian_width" "n_appalachian"
#> [23] "pct_linked_nonappalachian_width" "n_nonappalachian"
#> [25] "pct_linked_white_width" "n_white"
#> [27] "pct_linked_black_width" "n_black"
#> [29] "pct_linked_other_width" "n_other"
#> [31] "pct_linked_age_lt25_width" "n_age_lt25"
#> [33] "pct_linked_age_25_34_width" "n_age_25_34"
#> [35] "pct_linked_age_35_44_width" "n_age_35_44"
#> [37] "pct_linked_age_45_54_width" "n_age_45_54"
#> [39] "pct_linked_age_55_64_width" "n_age_55_64"
#> [41] "pct_linked_age_65plus_width" "n_age_65plus"
#> [43] "tl1_yr" "tl1_label"
#> [45] "tl2_yr" "tl2_label"
#> [47] "tl3_yr" "tl3_label"
#> [49] "pct_any_prior_enc" "pct_od_prior_enc"
#> [51] "pct_naloxone_width" "pct_no_naloxone_width"
#> [53] "pct_naloxone" "n_naloxone_enc"
#> [55] "pct_no_naloxone" "n_no_naloxone_enc"
#> [57] "domain_diff_scene" "domain_diff_history"
#> [59] "domain_diff_drug" "domain_diff_medication"
#> [61] "domain_diff_mental" "median_days"
#> [63] "mean_days" "pct_30d_width"
#> [65] "n_30d" "pct_90d_width"
#> [67] "n_90d" "pct_365d_width"
#> [69] "n_365d" "pct_gt365d_width"
#> [71] "n_gt365d" "lessons_learned_text"
#> [73] "disclaimer_text" "footnote_sources"
#> [75] "n_od_ems_denom" "timing_denom"
#> [77] "n_eligible_decedents" "n_unlinked_decedents"
#> [79] "wc_linked_median" "wc_unlinked_median"
#> [81] "mean_prior_enc" "median_prior_enc"
#> [83] "pct_decedent_nax" "timing_iqr"
# Change just the numbers you have; every heading and label keeps its
# default wording.
data$n_decedents <- "6,000"
if (FALSE) { # \dontrun{
render_onepager(
data, "cohort_summary", output = file.path(tempdir(), "report.pdf")
)
} # }