Description Usage Format See Also Examples
Provides lookup tables for use with Urgent care sensitive analyses.
1 |
data frame with 14 rows and 7 fields
1 2 3 4 5 6 7 8 9 | Observations: 14
Variables: 7
$ condition_description <chr> "COPD", "Acute mental health crisis", "Non-specific chest pain", "Falls", "Non-specific abdomin...
$ primary_diagnosis <chr> "J40; J41; J42; J43; J44", "F", "R072; R073; R074", "W0; W1-W19", "R10", "I80; I81; I82", "L03"...
$ age <chr> "All ages", "All ages", "All ages", "75+ yrs", "All ages", "All ages", "All ages", "0 - 5 yrs",...
$ primary_regex <chr> "J4[0-4]", "F", "R07[2-4]", "W[01][0-9]", "R10", "I8[0-2]", "L03", "R50", "T830", "E1[0-5];E16[...
$ cat1 <chr> "All", "All", "All", "All", "All", "All", "All", "All", "All", "All", "All", "All", "All", "All"
$ cat2 <chr> "All", "All", "All", "All", "All", "All", "All", "All", "All", "All", "All", "All", "All", "All"
$ condition_uid <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
|
Other urgent care sensitive datasets: lu_ucc_icd10
,
uc_attribution
, uc_versions
,
ucs_datasets
1 2 3 4 5 6 7 8 | if (isNamespaceLoaded("dplyr")) {
require("dplyr")
aafractions.ncc::uc_conditions %>%
mutate_if(is.character, as.factor) %>%
select(-starts_with("condition_"), -starts_with("primary")) %>%
summary(16)
}
|
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