change counts colname in conversion and filter by species funs, order…

… season in plot_record_by_season

R/compute_abundances.R

@@ -28,7 +28,8 @@ compute_abundances <- function(data, aggregate = TRUE) {
               .data$subsample_count_type,
               .data$subsample_all_shells_present_were_counted,
               .data$total_of_forams_counted_ind,
-              .data$sampling_device_type))
+              .data$sampling_device_type)) %>% 
+    rename('counts_raw_ab' = 'counts')
 
   conc_data_to_convert <- data %>%
     filter(.data$sample_volume_filtered > 0) %>% 
@@ -44,7 +45,7 @@ compute_abundances <- function(data, aggregate = TRUE) {
               .data$total_of_forams_counted_ind)) %>% 
     mutate(new_counts = floor(.data$counts * .data$sample_volume_filtered)) %>% 
     select(-c(.data$counts, .data$subsample_count_type)) %>% 
-    rename('counts' = 'new_counts') %>% 
+    rename('counts_raw_ab' = 'new_counts') %>% 
     distinct()
 
   rel_data_to_convert <- data %>%
@@ -65,7 +66,7 @@ compute_abundances <- function(data, aggregate = TRUE) {
               .data$subsample_all_shells_present_were_counted,
               .data$total_of_forams_counted_ind,
               .data$sampling_device_type)) %>% 
-    rename('counts' = 'new_counts') %>% 
+    rename('counts_raw_ab' = 'new_counts') %>% 
     distinct()
 
   excluded_samples_volume <- data %>%
@@ -98,13 +99,13 @@ compute_abundances <- function(data, aggregate = TRUE) {
 
     tot_dat <- tot_dat %>% 
       group_by(.data$sample_id, .data$taxa) %>% 
-      mutate(new_counts = sum(.data$counts, na.rm = TRUE)) %>% 
+      mutate(new_counts = sum(.data$counts_raw_ab, na.rm = TRUE)) %>% 
       ungroup() %>% 
-      select(-c(.data$counts, .data$subsample_id,
+      select(-c(.data$counts_raw_ab, .data$subsample_id,
                 .data$subsample_size_fraction_min,
                 .data$subsample_size_fraction_max)) %>%
       distinct() %>% 
-      rename('counts' = 'new_counts')
+      rename('counts_raw_ab' = 'new_counts')
   }
 
   tot_dat

R/compute_concentrations.R

@@ -64,7 +64,8 @@ compute_concentrations <- function(data, aggregate = TRUE) {
               .data$subsample_count_type,
               .data$subsample_all_shells_present_were_counted,
               .data$total_of_forams_counted_ind,
-              .data$sampling_device_type))
+              .data$sampling_device_type))%>% 
+    rename('counts_n_conc' = 'counts') 
 
   abs_data_to_convert <- data %>%
     filter(.data$sample_volume_filtered > 0) %>% 
@@ -81,7 +82,7 @@ compute_concentrations <- function(data, aggregate = TRUE) {
               .data$subsample_all_shells_present_were_counted,
               .data$total_of_forams_counted_ind,
               .data$sampling_device_type)) %>% 
-    rename('counts' = 'new_counts') %>% 
+    rename('counts_n_conc' = 'new_counts') %>% 
     distinct()
 
   rel_data_to_convert <- data %>%
@@ -104,7 +105,7 @@ compute_concentrations <- function(data, aggregate = TRUE) {
               .data$subsample_all_shells_present_were_counted,
               .data$total_of_forams_counted_ind,
               .data$sampling_device_type)) %>%
-    rename('counts' = 'new_counts') %>%
+    rename('counts_n_conc' = 'new_counts') %>%
     distinct()
 
   excluded_samples_volume <- data %>%
@@ -140,18 +141,18 @@ compute_concentrations <- function(data, aggregate = TRUE) {
     tot_dat <- tot_dat %>%
       filter(!is.na(.data$sample_volume_filtered)) %>%
       mutate(abs_sub_tot = floor(.data$sample_volume_filtered * 
-                                   .data$counts)) %>%
+                                   .data$counts_n_conc)) %>%
       group_by(.data$sample_id, .data$taxa) %>%
       mutate(new_counts = sum(.data$abs_sub_tot, na.rm = TRUE)) %>%
       ungroup() %>%
-      select(-c(.data$counts, .data$abs_sub_tot, .data$subsample_id,
+      select(-c(.data$counts_n_conc, .data$abs_sub_tot, .data$subsample_id,
                 .data$subsample_size_fraction_min,
                 .data$subsample_size_fraction_max)) %>%
       distinct() %>%
       mutate(conc_counts = .data$new_counts / .data$sample_volume_filtered) %>%
       select(-.data$new_counts) %>%
       distinct() %>%
-      rename('counts' = 'conc_counts')
+      rename('counts_n_conc' = 'conc_counts')
   }
 
   tot_dat

R/compute_frequencies.R

@@ -109,7 +109,8 @@ compute_frequencies <- function(data, aggregate = TRUE) {
     mutate(counts = (.data$counts / .data$tot_subsample) * 100) %>% 
     select(-c(.data$tot_subsample, .data$tot_sample))
 
-  tot_dat <- rbind(partial_data, ready_dat)
+  tot_dat <- rbind(partial_data, ready_dat)%>%
+    rename('counts_rel_ab' = 'counts')
 
 
   if (!aggregate) {
@@ -126,7 +127,8 @@ compute_frequencies <- function(data, aggregate = TRUE) {
       select(-c(.data$subsample_id,
                 .data$subsample_size_fraction_min,
                 .data$subsample_size_fraction_max)) %>% 
-      distinct()
+      distinct()%>%
+      rename('counts_rel_ab' = 'counts')
 
     return(aggregated_dat)
   }

R/filter_by_species.R

@@ -57,7 +57,10 @@ filter_by_species <- function(data, species, rm_na = FALSE) {
   ## Remove lines w/ NA counts (if required) ----
 
   if (rm_na) {
-    data <- data[!is.na(data$"counts"), ]
+    count_columns <- grepl("counts", names(data))
+    #data <- data[!is.na(data$"counts"), ]
+    data <- data[!is.na(data[, count_columns]), ]
+
   }
 
   data

R/plot_record_by_season.R

@@ -72,6 +72,8 @@ plot_record_by_season <- function(data) {
     select(.data$sample_id, .data$season) %>% 
     distinct()
 
+  data$season <-factor(data$season, levels = c("Fall", "Winter", "Spring", "Summer", "Unknown"))
+
 
   ## Plot ----