#' Getting Invalid Examples and Summary for Facility_Type
#'
#' This function will generate two data frames: first, a frame to be used later to extract invalid examples from;
#' second, a frame that contains facility-level summaries for counts and percentages of invalid Facility_Type.
#'
#' The valid values were taken from the `PHVS_FacilityVisitType_SyndromicSurveillance_V3.xls` file from
#' Public Health Information Network Vocabulary Access and Distribution System value sets
#' (https://phinvads.cdc.gov/vads/ViewView.action?name=Syndromic%20Surveillance). The package will
#' be updated as the CDC provides new or different codes that are considered valid or invalid.
#'
#' You can view the concept codes that are considered valid by calling `data("facility_type")`.
#'
#' @param data The raw data from BioSense on which you will do the invalid facility type checks.
#' @return A list of two data frames: examples and summary for invalid Facility_Type.
#' @import dplyr
#' @export
facility_type_invalid <- function(data) {
# generate valid values
data("facility_type", envir=environment())
valid_factype_values <- facility_type %>% # get file
select(Concept.Code) %>% # the variable we want is called concept code
filter(!is.na(Concept.Code)) %>% # get rid of any nas
c() %>% # turn this into a vector
unlist() %>% # unlist them from the concept name object
unname()
# generate examples
facility_type_examples <- data %>% # take data
select(c(C_Biosense_Facility_ID, C_BioSense_ID, Facility_Type_Code)) %>% # taking just the variables we need
mutate(Facility_Type_Code=toupper(as.character(Facility_Type_Code)), # make as character and uppercase
Invalid_Facility_Type_Code=case_when(
is.na(Facility_Type_Code) ~ NA, # if field is na, then invalid is na
Facility_Type_Code %in% valid_factype_values ~ FALSE, # if code is in valid values, false
!Facility_Type_Code %in% valid_factype_values ~ TRUE # if not, true
))
# generate summary
facility_type_summary <- facility_type_examples %>% # take examples
group_by(C_BioSense_ID) %>% # group by patient visit
mutate(Any_Invalid_Facility_Type_Code=case_when(
all(is.na(Invalid_Facility_Type_Code)) ~ NA, # if all na, keep na
sum(Invalid_Facility_Type_Code, na.rm=TRUE) == 0 ~ FALSE, # if all false, then false invalid
TRUE ~ TRUE # otherwise, true
)) %>%
slice(1) %>% # get one row per patient visit
ungroup() %>% # explicitly ungroup
group_by(C_Biosense_Facility_ID) %>% # group by facility
summarise(Facility_Type_Code.Percent=round(mean(Any_Invalid_Facility_Type_Code, na.rm=TRUE)*100,2), # percent
Facility_Type_Code.Count=sum(Any_Invalid_Facility_Type_Code, na.rm=TRUE)) # count
return(
list(facility_type_examples=facility_type_examples,
facility_type_summary=facility_type_summary)
)
}
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