vignettes/a03.1_server_functions_for_CIV.Rmd
a03.1_server_functions_for_CIV.RmdNOTE: Section headers on this file are duplicated. One set of headers exist for knitting this to an Rmarkdown (for documentation) The second set are hooks for RStudio’s document outline feature.
##
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~ Read in preloaded data ----
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Here we load in simulated data
contacts_list_sample <-
rio::import(here::here("data/liste_contacts_sample_CIV.xlsx"))
follow_up_list_sample <-
rio::import(here::here("data/suivi_contacts_sample_CIV.xlsx"))
tracing_data_sample <- list(
contacts_list = contacts_list_sample,
follow_up_list = follow_up_list_sample
)
# called by data_input UI element on page 1 of app
preloaded_data_options <-
list(`Sample tracing data` = tracing_data_sample)# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~ UI Outputs --------------------
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
country_specific_UI_for_loading_data <- function(input, output){
# ~~~~ data_to_use_picker ---------------------------Options for data source
output$data_to_use_picker <- renderUI({
radioButtons(inputId = "data_to_use",
label = "Input Data",
choices = c("Use preloaded data",
"Use uploaded data"))
})
# ~~~~ data_to_use_input ---------------------------Loads in the data.
output$data_to_use_input <- renderUI({
req(input$data_to_use)
if (input$data_to_use == "Use preloaded data") {
selectInput("preloaded_data_choice",
label = "Use preloaded data",
choices = c("Sample tracing data"
#,
#"Sample contacts list"
),
selected = NULL,
multiple = FALSE)
} else if (input$data_to_use == "Use uploaded data") {
tagList(fileInput(inputId = "uploaded_data_contacts_list",
label = "Upload the list of contacts",
multiple = FALSE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv",
".xlsx",
".xls")),
fileInput(inputId = "uploaded_data_follow_up_list",
label = "Upload the follow-up list",
multiple = FALSE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv",
".xlsx",
".xls")))
}
})
# ~~~~ analyze_action_bttn ---------------------------Renders when requisites elements have been loaded.
output$analyze_action_bttn <- renderUI({
req(input$data_to_use)
if(input$data_to_use == "Use uploaded data") {
req(input$uploaded_data_contacts_list)
req(input$uploaded_data_follow_up_list)
}
if(input$data_to_use == "Use preloaded data") {
req(input$preloaded_data_choice)
}
tagList(HTML("<p style='font-size:4px'> <br><br> </p>"),
actionBttn(inputId = "analyze_action_bttn", label = "Analyze",
style = "jelly", color = "primary"),
HTML("<br>
<span style='color: rgb(97, 189, 109);'>ℹ:</span>
<font size='1'>
After analyses have been triggered once,
the app must be reloaded before triggering again on a new dataset.
</font>")
)
})
# ~~~~ country_specific_data_to_use_section ---------------------------Combine different UI elements into single output
output$country_specific_data_to_use_section <-
renderUI({
tagList(column(width = 3,
uiOutput("data_to_use_picker")),
column(width = 6,
uiOutput("data_to_use_input")),
column(width = 3,
uiOutput("analyze_action_bttn"))
)
})
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~ Read file functions ----
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~The read_file_raw function does either of two things. - For countries using Go.Data, it takes in the input credentials, logs into a Go.Data session, and returns a list with the requisite dataframes. - For countries using KoboCollect, it takes in the two uploaded csv files, (contact list and follow-up list), and returns them as a list of a dataframes.
read_file_raw <- function(){
data_to_use <- input$data_to_use
preloaded_data_options <- preloaded_data_options ## defined above
preloaded_data_choice <- input$preloaded_data_choice
uploaded_data_contacts_list_path <- input$uploaded_data_contacts_list$datapath
uploaded_data_follow_up_list_path <- input$uploaded_data_follow_up_list$datapath
if (data_to_use == "Use preloaded data") {
contacts_list <-
preloaded_data_options[[preloaded_data_choice]]$contacts_list %>%
clean_names() %>%
type_convert() %>%
mutate(region_de_residence = str_to_sentence(region_de_residence)) # important for picking admin_1 in data subset
follow_up_list <-
preloaded_data_options[[preloaded_data_choice]]$follow_up_list %>%
clean_names() %>%
type_convert()
} else if (data_to_use == "Use uploaded data") {
contacts_list <-
uploaded_data_contacts_list_path %>%
rio::import() %>%
clean_names() %>%
type_convert() %>%
mutate(region_de_residence = str_to_sentence(region_de_residence)) # important for picking admin_1 in data subset
follow_up_list <-
rio::import(uploaded_data_follow_up_list_path) %>%
clean_names() %>%
type_convert() %>%
rename(code_unique_du_contact = quel_est_le_code_du_contact) ## need to rename now to permit join
}
tracing_data_raw <- list(contacts_list = contacts_list,
follow_up_list = follow_up_list)
return(tracing_data_raw)
}The ‘read_file_transformed’ function takes in data from read_file_raw_reactive, and ‘transforms’ it into a single, ‘long’ dataframe, with one row per contact-follow-up-day
read_file_transformed <- function(tracing_data_raw){
contacts_df_long_transformed <-
tracing_data_raw %>%
.$contacts_list %>%
## for speeding up testing
{if (PARAMS$testing_mode) slice_sample(., n = 10) else .} %>%
mutate(counter = 1) %>%
# row numbers to match Excel spreadsheet
mutate(row_id = row_number() + 1) %>%
# clean admin levels
mutate(across(c(region_de_residence, district_de_residence), # EDIT 2021-03-04 I changed it. don't change region spelling as we used the raw spellings to populate the dropdown select on the admin_1 tab
~ .x %>%
str_to_lower() %>%
str_to_title() %>%
replace_na("NA") %>%
str_trim() %>%
str_replace_all(" ", " "))) %>%
left_join(tracing_data_raw$follow_up_list,
by = "code_unique_du_contact") %>%
## rename to match columns for which scripts were originally written
rename_with(~
case_when(.x == "code_unique_du_contact" ~ "contact_id",
.x == 'code_du_cas_index' ~ "linked_case_id",
.x == "sexe" ~ "sex",
.x == 'quel_est_le_nom_du_contact' ~ "last_name",
.x == 'quel_est_le_prenom_du_contact' ~ 'first_name',
.x == 'quel_est_l_age_du_contact' ~ 'age',
.x == 'quelle_est_l_unite_de_l_age' ~ 'age_unit',
.x == 'region_de_residence' ~ 'admin_1',
.x == 'district_de_residence' ~ 'admin_2',
.x == 'quel_est_le_lien_du_contact_avec_le_cas' ~ 'link_with_the_case',
.x == 'quel_type_de_contact' ~ 'type_of_contact' ,
.x == 'date_du_dernier_contact_avec_le_cas' ~ 'date_of_last_contact',
.x == 'date_du_suivi' ~ 'follow_up_date',
.x == 'date_de_suivi' ~ 'follow_up_date',
.x == 'jour_du_suivi' ~ 'follow_up_day',
.x == 'jour_de_suivi' ~ 'follow_up_day',
.x == 'issue_du_suivi' ~ 'follow_up_status',
.x == 'issue_de_suivi' ~ 'follow_up_status',
.x == 'etat_du_suivi' ~ 'follow_up_status_simple',
.x == 'etat_de_suivi' ~ 'follow_up_status_simple',
TRUE ~ .x)) %>%
mutate(across(.cols = any_of(c("admin_1", "admin_2",
"linked_case_id","link_with_the_case",
"follow_up_status","follow_up_status_simple")),
.fns = ~ replace_na(.x, "Manquant")
)) %>%
## possibly temporary. replace all nas
# mutate(across(.cols = where(~ is.character(.x) | is.factor(.x)),
# .fns = ~ replace_na(.x, "Manquant")
# )) %>%
# if follow-up lasted the full 21 days, change last follow_up state to "Fin de suivi"
mutate(follow_up_status = if_else(follow_up_day == 10 & follow_up_status == "vu ou contacte",
"Fin de suivi",
follow_up_status)) %>%
mutate(follow_up_status = str_to_sentence(follow_up_status)) %>%
## shorten
mutate(follow_up_status = recode(follow_up_status,
"Devenu symptomatique et resultats tests attendus" = "Symptomatique, resultats attendus"
)) %>%
## shorten
mutate(follow_up_status_simple = recode(follow_up_status_simple,
"vu ou contacte" = "Seen",
"non vu ou contacte" = "Not seen"
)) %>%
## what exactly does poursuite du suivi mean?
## I am not sure. But in the meantime we replace it where possible
mutate(follow_up_status = ifelse(follow_up_status_simple == "Not seen",
"Manquant",
follow_up_status)) %>%
# convert dates to dates
mutate(across(.cols = matches("date|Date"),
.fns =
~ .x %>%
str_replace_all(" UTC", "") %>%
as.Date())) %>%
## start and end date
mutate(follow_up_start_date = if_else(follow_up_day == 1, follow_up_date, NA_Date_)) %>%
mutate(follow_up_end_date = if_else(follow_up_day == 14, follow_up_date, NA_Date_)) %>%
## complete data
group_by(row_id) %>%
complete(row_id, follow_up_date = seq.Date(unique(date_of_last_contact) + days(1),
unique(date_of_last_contact) + days(10),
by = "1 days")) %>%
## what does this do? I can't remember (I guess it removes NAs. Why?)
mutate(across(.cols = -tidyr::one_of("follow_up_date", "follow_up_day",
"follow_up_status", "follow_up_status_simple"),
.fns = ~ first(na.omit(.x))) ) %>%
ungroup() %>%
# for the simple version of follow up state, assume that manquant means no follow-up
mutate(follow_up_status_simple = ifelse(follow_up_status_simple == "Manquant",
"Not seen",
follow_up_status_simple)) %>%
## row number for easy tracking
mutate(row_number = row_number()) %>%
mutate(follow_up_day = as.numeric(follow_up_date - date_of_last_contact)) %>%
distinct(row_id, follow_up_date, .keep_all = TRUE) # not sure why there are duplicates but there are
return(contacts_df_long_transformed)
}