R/02_QPR_SPDATs.R

In COHHIO/Rm_data: Data munging dependencies for Rminor & Rminor_elevated Shiny apps

# this script uses the HMIS data to compare the avg SPDAT score of
# clients served in a county during the reporting period to the avg
# SPDAt score of those who enrolled into a PSH or RRH project during the
# reporting period.
qpr_spdats <- function(Enrollment_extra_Client_Exit_HH_CL_AaE,
                       Project,
                       clarity_api = get_clarity_api(e = rlang::caller_env()),
                       app_env = get_app_env(e = rlang::caller_env())

) {
if (is_app_env(app_env))
  app_env$set_parent(missing_fmls())

# more paring down, only taking what variables I need from Enrollment
enrollment_small <- Enrollment_extra_Client_Exit_HH_CL_AaE %>%
  dplyr::left_join(Project, by = c("ProjectType", "ProjectID", "ProjectName")) %>%
  dplyr::select(
    EnrollmentID,
    UniqueID,
    PersonalID,
    ProjectID,
    ProjectType,
    ProjectName,
    OperatingStartDate,
    OperatingEndDate,
    EntryDate,
    ExitDate,
    RelationshipToHoH,
    CountyServed
  ) |>
  dplyr::left_join(Regions, by = c("CountyServed" = "County"))
# Entries will give us all the times a hh has an Entry into a PH project
Entries <- enrollment_small %>%
  dplyr::filter(ProjectType %in% data_types$Project$ProjectType$ph)


# this object is used in the app to create the plot. it has date variables
# included so the numbers can be filtered by date range in the app. it takes
# long to run.

qpr_spdats_county <-
  dplyr::left_join(enrollment_small, Scores |> dplyr::mutate(PersonalID = as.character(PersonalID)),
                   by = UU::common_names(enrollment_small, Scores)) %>%
  dplyr::filter(
    ProjectType %in% c(0, 1, 2, 4, 8) &
      RelationshipToHoH == 1 &
      ScoreDate <= EntryDate &
      !is.na(Region)
  ) %>%
  dplyr::select(
    EnrollmentID,
    UniqueID,
    PersonalID,
    ProjectName,
    EntryDate,
    ExitDate,
    CountyServed,
    Region,
    ScoreDate,
    Score
  ) %>%
  dplyr::group_by(PersonalID) %>%
  dplyr::slice_max(EntryDate) %>% # most recent EE
  dplyr::slice_max(ScoreDate) %>% # most recent score
  dplyr::slice_max(Score) %>% # highest score
  dplyr::ungroup() %>%
  dplyr::select(PersonalID,
                UniqueID,
                Region,
                ProjectName,
                CountyServed,
                Score,
                EntryDate,
                ExitDate)

# notes moved to Rminor notes.R






# this pulls all entries into PSH or RRH
entry_scores <- dplyr::left_join(Entries, Scores |> dplyr::mutate(PersonalID = as.character(PersonalID)),
                                                                  by = UU::common_names(Entries, Scores))

qpr_spdats_project <- entry_scores %>%
  dplyr::select(-ProjectType,
         -OperatingStartDate,
         -OperatingEndDate) %>%
  dplyr::mutate(ScoreDate = as.Date(ScoreDate)) |>
  dplyr::filter(
    RelationshipToHoH == 1 &
      (ScoreDate <= EntryDate | is.na(ScoreDate)) &
      !is.na(Region)
  ) %>%
  dplyr::mutate(
    ScoreAdjusted = dplyr::if_else(is.na(Score), 0, Score),
    ScoreDateAdjusted = dplyr::if_else(is.na(ScoreDate), lubridate::today(), ScoreDate)
  ) %>%
  dplyr::group_by(EnrollmentID) %>%
  dplyr::slice_max(ScoreDateAdjusted) %>%
  dplyr::slice_max(ScoreAdjusted) %>%
  dplyr::distinct() %>%
  dplyr::ungroup() %>%
  dplyr::select(-ScoreDateAdjusted, -RegionName)

# If you have clients here, you should either verify the scores saved here are
# valid or the correct client is marked as the Head of Household.

# SPDATsOnNonHoHs <- entry_scores %>%
#   HMIS::served_between(rm_dates$calc$data_goes_back_to, rm_dates$meta_HUDCSV$Export_End) |>
#   dplyr::filter(RelationshipToHoH != 1 &
#            !is.na(Score)) %>%
#   dplyr::select(ProjectName, PersonalID, UniqueID, EntryDate, ExitDate, Score) %>%
#   dplyr::arrange(ProjectName)


# WARNING save.image does not save the environment properly, save must be used.
app_env$gather_deps(ls(pattern = "(?:^qpr)"))

}