fixed dplyr masking

Author: Paul Huettl

R/cell_proportions.R

@@ -80,7 +80,7 @@ cell_proportions_singlemodel <- function(
     mutate(wsum=sum(weight)) %>% 
     # discard redundant entries
     dplyr::select(dplyr::all_of(c(bulk_id, by, "wsum"))) %>% unique() %>%
-    rename(weight = wsum)
+    dplyr::rename(weight = wsum)
   return(props)
 }
 
@@ -161,7 +161,7 @@ cell_proportions <- function(
                             by,
                             weight,
                             bulk_id, 
-                            cell_id) %>% rename (tmpweight_boot = weight)
+                            cell_id) %>% dplyr::rename (tmpweight_boot = weight)
     for( irun in 2:tissuemodel$bootstrap_nruns ){
       message(paste0("Computing cell proportions for bootstrap sample ", irun, " / ", tissuemodel$bootstrap_nruns))
       # fetch the tissuemodel of the irun-th bootstrap run
@@ -175,7 +175,7 @@ cell_proportions <- function(
                       by,
                       weight,
                       bulk_id,
-                      cell_id) %>% rename(current_weight = weight)
+                      cell_id) %>% dplyr::rename(current_weight = weight)
       # join the bootstrapped cell population weights as list to the props dataframe
       # within a column "tmpweight_boot"
       props <-  props %>%
@@ -190,11 +190,11 @@ cell_proportions <- function(
 
     props <-  props %>% 
               mutate(tmpweight = mean(tmpweight_boot)) %>%
-              # rename tmpweight with weight and tmp_boot with weight_boot
-              rename(!!weight := tmpweight, !!paste0(weight, "_boot") := tmpweight_boot)
+              # dplyr::rename tmpweight with weight and tmp_boot with weight_boot
+              dplyr::rename(!!weight := tmpweight, !!paste0(weight, "_boot") := tmpweight_boot)
               # quoname is deprecated
-              # rename(!!quo_name(weight) := tmpweight) %>%
-              # rename(!!quo_name(paste0(weight, "_boot")) := tmpweight_boot)
+              # dplyr::rename(!!quo_name(weight) := tmpweight) %>%
+              # dplyr::rename(!!quo_name(paste0(weight, "_boot")) := tmpweight_boot)
     return(props)
   } else {
     return(cell_proportions_singlemodel(tissuemodel, mapping, by, weight, bulk_id, cell_id))

R/cse_visualization.R

@@ -146,15 +146,15 @@ plot_csre <- function(
   diff <- groupavg %>%
     # join group avarage and total explained avarage 
     # and add the total_expr column giving the total expression across cells
-    inner_join(total_explained %>% rename(total_expr = avgexpr), by=c("group", "gene")) %>%
+    inner_join(total_explained %>% dplyr::rename(total_expr = avgexpr), by=c("group", "gene")) %>%
     # give avgexpr, avgreg and total_expr separate columns for both bulkgroups A and B
     pivot_wider(names_from = group, values_from=c(avgexpr, avgregulation, total_expr), names_prefix="group_", values_fill = 0.0) %>%
-    rename(avgexpr_A = paste0("avgexpr_group_", groupA)) %>%
-    rename(avgexpr_B = paste0("avgexpr_group_", groupB)) %>%
-    rename(avgregulation_A = paste0("avgregulation_group_", groupA)) %>%
-    rename(avgregulation_B = paste0("avgregulation_group_", groupB)) %>%
-    rename(total_expr_A = paste0("total_expr_group_", groupA)) %>%
-    rename(total_expr_B = paste0("total_expr_group_", groupB)) %>%
+    dplyr::rename(avgexpr_A = paste0("avgexpr_group_", groupA)) %>%
+    dplyr::rename(avgexpr_B = paste0("avgexpr_group_", groupB)) %>%
+    dplyr::rename(avgregulation_A = paste0("avgregulation_group_", groupA)) %>%
+    dplyr::rename(avgregulation_B = paste0("avgregulation_group_", groupB)) %>%
+    dplyr::rename(total_expr_A = paste0("total_expr_group_", groupA)) %>%
+    dplyr::rename(total_expr_B = paste0("total_expr_group_", groupB)) %>%
     # for each row (comprising gene,celltype) give logp1 difference of avgexpr, avgreg and total_expr
     mutate(logdiffexpr = log2((avgexpr_A+1.0) / (avgexpr_B+1.0))) %>%
     mutate(logdiffreg = log2((avgregulation_A+1.0) / (avgregulation_B+1.0))) %>%
@@ -237,7 +237,7 @@ plot_csre <- function(
       mutate(logexpr = log2(avgexpr+1.0)) %>%
       dplyr::select(-avgexpr) %>%
       pivot_wider(names_from=group, values_from=logexpr) %>%
-      rename(group_A = groupA, group_B = groupB) %>%
+      dplyr::rename(group_A = groupA, group_B = groupB) %>%
       mutate(logdiff=group_A-group_B) %>%
       ggplot(aes(x=logdiff, y=factor(gene, rev(ord_genes)))) +
       geom_bar(stat="identity", position="stack") +
@@ -258,7 +258,7 @@ plot_csre <- function(
       filter(celltype != "total_explained") %>%
       # give both groups their own column
       pivot_wider(names_from = group, values_from =  avgexpr, values_fill=0) %>%
-      rename(group_A = all_of(groupA), group_B = all_of(groupB)) %>%
+      dplyr::rename(group_A = all_of(groupA), group_B = all_of(groupB)) %>%
       group_by(gene) %>%
         # cumulate the avgexpr for each gene in the relgroup across celltypes
         mutate(normsum = sum(.data[[relgroup]])) %>%

R/specific_expression.R

@@ -236,9 +236,9 @@ specific_expression_regulation <- function(
         dplyr::select(-expression.x, -expression.y, -regulation.x, -regulation.y)
     }
     # add column storing mean over all bootstrapped expressions and regulations
-    csre <- csre %>% rename(expression_boot = expression) %>%
+    csre <- csre %>% dplyr::rename(expression_boot = expression) %>%
       mutate(expression = mean(expression_boot)) %>%
-      rename(regulation_boot = regulation) %>%
+      dplyr::rename(regulation_boot = regulation) %>%
       mutate(regulation = mean(regulation_boot))
     return(csre)
   } else {

R/weight_visualization.R

@@ -488,8 +488,8 @@ plot_element_differential_density <- function(
     # give each group a separate column
     pivot_wider(names_from=all_of(group), values_from=all_of(z), names_prefix="group_") %>%
     # give both groups the names specified by the input groups
-    rename(groupA = all_of(paste0("group_", groupA))) %>%
-    rename(groupB = all_of(paste0("group_", groupB))) %>%
+    dplyr::rename(groupA = all_of(paste0("group_", groupA))) %>%
+    dplyr::rename(groupB = all_of(paste0("group_", groupB))) %>%
     # store the differential density between groupA and groupB in the column diff
     mutate(diff = groupA - groupB) %>%
     # delete the seperate densities and keep only the differential density