Merge pull request #209 from HopkinsIDD/time2date

change time to date in the R code

flepimop/R_packages/flepicommon/R/DataUtils.R

@@ -92,7 +92,7 @@ read_file_of_type <- function(extension,...){
     if(extension == 'csv'){
         return(function(x){suppressWarnings(readr::read_csv(x,,col_types = cols(
             .default = col_double(),
-            time=col_date(),
+            date=col_date(),
             uid=col_character(),
             comp=col_character(),
             subpop=col_character()
@@ -101,8 +101,8 @@ read_file_of_type <- function(extension,...){
     if(extension == 'parquet'){
         return(function(x){
             tmp <- arrow::read_parquet(x)
-            if("POSIXct" %in% class(tmp$time)){
-                tmp$time <- lubridate::as_date(tz="GMT",tmp$time)
+            if("POSIXct" %in% class(tmp$date)){
+                tmp$date <- lubridate::as_date(tz="GMT",tmp$date)
             }
             tmp
         })

flepimop/R_packages/flepiconfig/R/create_config_data.R

@@ -1146,7 +1146,7 @@ daily_mean_reduction <- function(dat,
         ) %>%
         dplyr::select(USPS, subpop, start_date, end_date, mean)
 
-    timeline <- tidyr::crossing(time = seq(from=min(dat$start_date), to=max(dat$end_date), by = 1),
+    timeline <- tidyr::crossing(date = seq(from=min(dat$start_date), to=max(dat$end_date), by = 1),
                                 subpop = unique(dat$subpop))
 
     if(any(stringr::str_detect(dat$subpop, '", "'))){
@@ -1175,12 +1175,12 @@ daily_mean_reduction <- function(dat,
         dplyr::select(subpop, start_date, end_date, mean) %>%
         dplyr::bind_rows(dat %>% dplyr::filter(subpop!="all") %>% dplyr::ungroup() %>% dplyr::select(-USPS)) %>%
         dplyr::left_join(timeline) %>%
-        dplyr::filter(time >= start_date & time <= end_date) %>%
-        dplyr::group_by(subpop, time) %>%
+        dplyr::filter(date >= start_date & date <= end_date) %>%
+        dplyr::group_by(subpop, date) %>%
         dplyr::summarize(mean = prod(1-mean))
 
     if(plot){
-        dat<- ggplot2::ggplot(data= dat, ggplot2::aes(x=time, y=mean))+
+        dat<- ggplot2::ggplot(data= dat, ggplot2::aes(x=date, y=mean))+
             ggplot2::geom_line()+
             ggplot2::facet_wrap(~subpop)+
             ggplot2::theme_bw()+

flepimop/R_packages/inference/R/functions.R

@@ -300,9 +300,9 @@ calc_prior_likadj  <- function(params,
 ##'
 compute_cumulative_counts <- function(sim_hosp) {
     res <- sim_hosp %>%
-        gather(var, value, -time, -subpop) %>%
+        gather(var, value, -date, -subpop) %>%
         group_by(subpop, var) %>%
-        arrange(time) %>%
+        arrange(date) %>%
         mutate(cumul = cumsum(value)) %>%
         ungroup() %>%
         pivot_wider(names_from = "var", values_from = c("value", "cumul")) %>%
@@ -324,7 +324,7 @@ compute_cumulative_counts <- function(sim_hosp) {
 ##'
 compute_totals <- function(sim_hosp) {
     sim_hosp %>%
-        group_by(time) %>%
+        group_by(date) %>%
         summarise_if(is.numeric, sum, na.rm = TRUE) %>%
         mutate(subpop = "all") %>%
         select(all_of(colnames(sim_hosp))) %>%

flepimop/R_packages/inference/R/inference_slot_runner_funcs.R

@@ -52,11 +52,11 @@ aggregate_and_calc_loc_likelihoods <- function(
             dplyr::filter(
                 modeled_outcome,
                 !!rlang::sym(obs_subpop) == location,
-                time %in% unique(obs$date[obs$subpop == location])
+                date %in% unique(obs$date[obs$subpop == location])
             ) %>%
             ## Reformat into form the algorithm is looking for
             inference::getStats(
-                "time",
+                "date",
                 "sim_var",
                 stat_list = targets_config,
                 start_date = start_date,

flepimop/R_packages/inference/R/inference_to_forecast.R

@@ -17,7 +17,7 @@ cum_death_forecast <- function (sim_data,
   require(dplyr)
 
   rc <- sim_data %>%
-    filter(time>start_date)%>%
+    filter(date>start_date)%>%
     inner_join(cum_dat)%>%
     group_by(sim_num, !!sym(loc_column))%>%
     mutate(cum_deaths_corr = cumsum(incidD)+cumDeaths)%>%
@@ -39,7 +39,7 @@ cum_death_forecast <- function (sim_data,
 ##' @param weights if not NA, the weights for the mean
 ##' @param loc_col the name of the  location column
 ##' 
-##' @return a forecast with columns time (end day), quantile steps_ahead and deaths
+##' @return a forecast with columns date (end day), quantile steps_ahead and deaths
 ##' 
 ##' @export
 create_cum_death_forecast <- function(sim_data, 
@@ -51,15 +51,15 @@ create_cum_death_forecast <- function(sim_data,
                                       loc_column="USPS") {
 
   ##Sanity checks
-  if(forecast_date>max(obs_data$time)+1) {stop("forecast date must be within one day after the range of observed times")}
-  if(forecast_date+1<min(sim_data$time)) {stop("no simulation support for first forecast date")}
+  if(forecast_date>max(obs_data$date)+1) {stop("forecast date must be within one day after the range of observed times")}
+  if(forecast_date+1<min(sim_data$date)) {stop("no simulation support for first forecast date")}
 
-  if(max(obs_data$time)==forecast_date){
+  if(max(obs_data$date)==forecast_date){
     ## USA Facts Data updates mid-day so forecasts run after noon will have a forecast date that overlaps with the obs_data
     ##convert data to a cumdeath forecast.
     print(glue::glue("Accumulate deaths through {forecast_date}, typically for USA Facts aggregation after noon."))
     start_deaths <- obs_data%>%
-      filter(time==forecast_date)%>%
+      filter(date==forecast_date)%>%
       select(!!sym(loc_column),cumDeaths)
 
     forecast_sims <- cum_death_forecast(sim_data,
@@ -70,7 +70,7 @@ create_cum_death_forecast <- function(sim_data,
     ## CSSE data updates at midnight so forecasts will not typically have a forecast date one day after the end of the obs_data       
     print(glue::glue("Accumulate deaths through {forecast_date-1}, typically for CSSE aggregation."))
     start_deaths <- obs_data%>%
-      filter(time==forecast_date-1)%>%
+      filter(date==forecast_date-1)%>%
       select(!!sym(loc_column),cumDeaths)
 
     forecast_sims <- cum_death_forecast(sim_data,
@@ -88,7 +88,7 @@ create_cum_death_forecast <- function(sim_data,
   }
 
   rc <- forecast_sims%>%
-    group_by(time, !!sym(loc_column))%>% 
+    group_by(date, !!sym(loc_column))%>% 
     summarize(x=list(enframe(c(quantile(cum_deaths_corr, probs=c(0.01, 0.025,
                                                                  seq(0.05, 0.95, by = 0.05), 0.975, 0.99)),
                                mean=mean(cum_deaths_corr)),
@@ -99,11 +99,11 @@ create_cum_death_forecast <- function(sim_data,
   ##Append on the the other deaths.
   rc<-dplyr::bind_rows(rc,
                        obs_data%>%
-                         select(time, !!sym(loc_column), cumDeaths)%>%
+                         select(date, !!sym(loc_column), cumDeaths)%>%
                          mutate(quantile="data"))
 
   rc<- rc%>%
-    mutate(steps_ahead=as.numeric(time-forecast_date))
+    mutate(steps_ahead=as.numeric(date-forecast_date))
 
   return(rc)