Changing most drop_last to drop

Changes in summarize require changes in how groups are dropped.  Also fixing for issue #54 raised in SWMPrExtension GitHub comments.

R/annual_range.R

@@ -149,7 +149,7 @@ annual_range.swmpr <- function(swmpr_in
     summarise(mean = mean(!! parm, na.rm = TRUE)
               , min = min(!! parm, na.rm = TRUE)
               , max = max(!! parm, na.rm = TRUE)
-              , .groups = "drop_last")
+              , .groups = "drop")
 
   dat_month <- dat_day %>%
     group_by(!! seas) %>%
@@ -158,7 +158,7 @@ annual_range.swmpr <- function(swmpr_in
               , max_avg = mean(!! maxi, na.rm = TRUE)
               , min = min(!! mini, na.rm = TRUE)
               , max = max(!! maxi, na.rm = TRUE)
-              , .groups = "drop_last")
+              , .groups = "drop")
 
   # ensure all factor levels are accounted for, even if there is no data
   dat_month <- tidyr::complete(dat_month, !! seas)

R/historical_daily_range.R

@@ -154,7 +154,7 @@ historical_daily_range.swmpr <- function(swmpr_in
     dplyr::summarise(mean = mean(!! parm, na.rm = TRUE)
                      , min = min(!! parm, na.rm = TRUE)
                      , max = max(!! parm, na.rm = TRUE)
-                     , .groups = "drop_last")
+                     , .groups = "drop")
 
   dat_all$julian_day <- lubridate::yday(dat_all$date)
 
@@ -169,14 +169,14 @@ historical_daily_range.swmpr <- function(swmpr_in
     dplyr::summarise(mean = mean(!! avg, na.rm = TRUE)
                      , min = mean(!!  mini, na.rm = TRUE)
                      , max = mean(!! maxi, na.rm = TRUE)
-                     , .groups = "drop_last")
+                     , .groups = "drop")
 
   dat_hist_obs <- dat_all %>%
     dplyr::group_by(!! jd) %>%
     dplyr::summarise(mean = mean(!! avg, na.rm = TRUE)
                      , min = min(!!  mini, na.rm = TRUE)
                      , max = max(!! maxi, na.rm = TRUE)
-                     , .groups = "drop_last")
+                     , .groups = "drop")
 
   # account for missing julian days
   if(length(dat_yr[1, ] < 365)){

R/historical_range.R

@@ -161,7 +161,7 @@ historical_range.swmpr <- function(swmpr_in
     dplyr::summarise(mean = mean(!! parm, na.rm = TRUE)
                      , min = min(!! parm, na.rm = TRUE)
                      , max = max(!! parm, na.rm = TRUE)
-                     , .groups = "drop_last")
+                     , .groups = "drop")
 
   # Assign seasons
   dat_all$season <- assign_season(dat_all$date, ...)
@@ -179,7 +179,7 @@ historical_range.swmpr <- function(swmpr_in
     dplyr::summarise(mean = mean(!! avg, na.rm = TRUE)
                      , min = mean(!!  mini, na.rm = TRUE)
                      , max = mean(!! maxi, na.rm = TRUE)
-                     , .groups = "drop_last")
+                     , .groups = "drop")
 
   # Determine average min/max/mean for each month (for all years together)
   if(data_type != 'nut') {
@@ -188,7 +188,7 @@ historical_range.swmpr <- function(swmpr_in
       dplyr::summarise(mean = mean(!! avg, na.rm = TRUE)
                        , min = mean(!!  mini, na.rm = TRUE)
                        , max = mean(!! maxi, na.rm = TRUE)
-                       , .groups = "drop_last")
+                       , .groups = "drop")
 
     # Make some labels
     lab_hist_rng <- paste('Daily Avg Range \n(', rng[[1]], '-', rng[[2]], ')', sep = '')
@@ -202,7 +202,7 @@ historical_range.swmpr <- function(swmpr_in
       dplyr::summarise(mean = mean(!! avg, na.rm = TRUE)
                        , min = min(!!  mini, na.rm = TRUE)
                        , max = max(!! maxi, na.rm = TRUE)
-                       , .groups = "drop_last")
+                       , .groups = "drop")
 
     # Make some labels
     lab_hist_rng <- paste('Seasonal Range \n(', rng[[1]], '-', rng[[2]], ')', sep = '')
@@ -218,14 +218,14 @@ historical_range.swmpr <- function(swmpr_in
       dplyr::summarise(mean = mean(!! avg, na.rm = TRUE)
                        , min = mean(!!  mini, na.rm = TRUE)
                        , max = mean(!! maxi, na.rm = TRUE)
-                       , .groups = "drop_last")
+                       , .groups = "drop")
   } else {
     dat_yr <- dat_yr %>%
       dplyr::group_by(!! seas) %>%
       dplyr::summarise(mean = mean(!! avg, na.rm = TRUE)
                        , min = min(!!  mini, na.rm = TRUE)
                        , max = max(!! maxi, na.rm = TRUE)
-                       , .groups = "drop_last")
+                       , .groups = "drop")
   }
 
   # ensure all factor levels are accounted for, even if there is no data

R/seasonal_barplot.R

@@ -153,7 +153,7 @@ seasonal_barplot.swmpr <- function(swmpr_in
 
   dat_hist <- dat_hist %>%
     dplyr::group_by(!! yr, !! seas) %>%
-    dplyr::summarise(result = sum(!! parm, na.rm = TRUE), .groups = "drop_last")
+    dplyr::summarise(result = sum(!! parm, na.rm = TRUE), .groups = "drop")
 
   if(plot){
     seas_col <- cols
@@ -165,10 +165,10 @@ seasonal_barplot.swmpr <- function(swmpr_in
 
     if(season_facet) {
       yr_mx <- dat_hist %>% group_by(!! yr, !! seas) %>%
-        summarise(max_val = sum(!! res, na.rm = TRUE), .groups = "drop_last")
+        summarise(max_val = sum(!! res, na.rm = TRUE), .groups = "drop")
     } else {
       yr_mx <- dat_hist %>% group_by(!! yr) %>%
-        summarise(max_val = sum(!! res, na.rm = TRUE), .groups = "drop_last")
+        summarise(max_val = sum(!! res, na.rm = TRUE), .groups = "drop")
     }
 
     mx <- ceiling(max(yr_mx$max_val) / 10) * 10 * 1.1

R/seasonal_boxplot.R

@@ -176,7 +176,7 @@ seasonal_boxplot.swmpr <- function(swmpr_in
   # Calc summary stat defined by FUN by season and day
   dat_hist <- dat_hist %>%
     group_by(!! seas, !! dt) %>%
-    summarise(result = FUN(!! parm), .groups = "drop_last")
+    summarise(result = FUN(!! parm), .groups = "drop")
 
   # ensure all factor levels are accounted for, even if there is no data
   dat_hist <- tidyr::complete(dat_hist, !! seas)
@@ -226,9 +226,10 @@ seasonal_boxplot.swmpr <- function(swmpr_in
 
       dat_yr <- dat_yr %>%
         dplyr::group_by(!! seas, !! dt) %>%
-        dplyr::summarise(result = FUN(!! parm), .groups = "drop_last") %>%
+        dplyr::summarise(result = FUN(!! parm), .groups = "drop") %>%
         dplyr::group_by(!! seas) %>%
-        dplyr::summarise(med = stats::median(.data$result, na.rm = TRUE))
+        dplyr::summarise(med = stats::median(.data$result, na.rm = TRUE),
+                         .groups = "drop")
 
       pt_fill <- ifelse(data_type == 'nut', paste('Monthly Sample \n(', target_yr, ')', sep = '')
                         , paste('Median Daily ', stat_lab, ' \n(', target_yr, ')', sep = ''))

R/seasonal_dot.R

@@ -155,7 +155,7 @@ seasonal_dot.swmpr <- function(swmpr_in
     summarise(min = min(!! parm, na.rm = TRUE)
               , mean = mean(!! parm, na.rm = TRUE)
               , max = max(!! parm, na.rm = TRUE)
-              , .groups = "drop_last")
+              , .groups = "drop")
 
   # ensure all factor levels are accounted for, even if there is no data
   plt_data <- tidyr::complete(plt_data, !! seas)

R/sk_seasonal.R

@@ -95,7 +95,7 @@ sk_seasonal.swmpr <- function(swmpr_in
   # calc seasonal values
   sk_data <- dat %>%
     group_by(!! yr, !! seas) %>%
-    summarise(result = FUN(!! parm), .groups = "drop_last")
+    summarise(result = FUN(!! parm), .groups = "drop")
 
   data_check <- sk_data %>% group_by(!! seas) %>% summarise(count = n())
 

R/threshold_criteria_plot.R

@@ -303,7 +303,7 @@ threshold_criteria_plot.swmpr <- function(swmpr_in
 
     df_smooth <- dat %>%
       group_by(year = lubridate::year(!! dt), month = lubridate::month(!! dt)) %>%
-      summarise(mean = mean(!! parm, na.rm = TRUE), .groups = "drop_last") %>%
+      summarise(mean = mean(!! parm, na.rm = TRUE), .groups = "drop") %>%
       mutate(datetimestamp = paste(year, '-', month, '-', '01', ' ', '0:00', sep = ''))
 
     df_smooth$datetimestamp <- as.POSIXct(df_smooth$datetimestamp)