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issue with individuals in efficacy #15

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issue with individuals in efficacy #15

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38 changes: 24 additions & 14 deletions scripts/metadata/generate_metadata.R
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Original file line number Diff line number Diff line change
Expand Up @@ -1405,7 +1405,7 @@ pryr::mem_used()


# <Efficacy> ##############################################################################

save.image('pre_efficacy.RData')
# One off request for community engagement team
if(FALSE){
pd <- efficacy[!is.na(efficacy$person_absent_reason),]
Expand Down Expand Up @@ -1461,11 +1461,17 @@ roster <- starting_roster %>%
mutate(dead = ifelse(is.na(dead), 0, dead),
migrated = ifelse(is.na(migrated), 0, migrated))
# Add more info to individuals
cluster_intervention <- v0demography_full %>%
dplyr::distinct(hhid, .keep_all = TRUE) %>%
dplyr::select(cluster, hhid) %>%
left_join(assignments %>% dplyr::select(cluster = cluster_number,
arm = assignment)) %>%
left_join(intervention_assignment) %>% dplyr::select(-arm)
individuals <- roster %>%
dplyr::mutate(fullname_dob = paste0(firstname, ' ', lastname, ' | ', dob)) %>%
dplyr::rename(fullname_id = roster_name) %>%
# get intervention, village, ward, cluster
left_join(households %>% dplyr::select(hhid, intervention, cluster)) %>%
left_join(cluster_intervention) %>%
left_join(v0demography %>% dplyr::select(hhid, village, ward))
# Get starting weight
# (generated in safety section)
Expand Down Expand Up @@ -1494,9 +1500,22 @@ efficacy_preselected_ids <- sort(unique(efficacy_selection$extid))
# arrange(cl, hhid)
# write_csv(mercy, '~/Desktop/efficacy_selections_with_cl.csv')


individuals$efficacy_preselected <- ifelse(individuals$extid %in% efficacy_preselected_ids, 1, 0)
efficacy_ids <- sort(unique(individuals$extid[individuals$efficacy_preselected == 1]))
#2816
# Remove 5 individuals manually
remove_these <-
c(
'02042-03',
'02042-02',
'26007-03',
'18039-03',
'74052-06'
)
individuals <- individuals %>%
filter(!extid %in% remove_these)


# Get some further efficacy status variables
# starting_efficacy_status
right <-
Expand All @@ -1512,6 +1531,8 @@ individuals <- left_join(individuals, right) %>%
mutate(starting_efficacy_status = ifelse(is.na(starting_efficacy_status) & extid %in% efficacy_ids,
'out',
starting_efficacy_status))
# Keep only those who are preselected
individuals <- individuals %>% filter(extid %in% efficacy_ids)
# efficacy_absent_most_recent_visit
right <-
efficacy %>% arrange(desc(start_time)) %>%
Expand Down Expand Up @@ -1565,17 +1586,6 @@ individuals <- individuals %>% left_join(starting_safety_statuses)

gc()

# Remove 5 individuals manually
remove_these <-
c(
'02042-03',
'02042-02',
'26007-03',
'18039-03',
'74052-06'
)
individuals <- individuals %>%
filter(!extid %in% remove_these)


# Create a household metadata per last minute request:
Expand Down