replacing all instances of aes_string with aes

R/enhance_table.R

@@ -160,11 +160,11 @@ enhance_table <- function(res_enrich,
 
   if (plot_style == "point") {
     p <- ggplot(
-      gs_fulllist, aes_string(
-        x = "log2FoldChange",
-        y = "gs_desc",
-        fill = "gs_id",
-        text = "gene_name"
+      gs_fulllist, aes(
+        x = .data$log2FoldChange,
+        y = .data$gs_desc,
+        fill = .data$gs_id,
+        text = .data$gene_name
       )
     ) +
       scale_x_continuous(limits = c(-max_lfc, max_lfc)) +
@@ -188,16 +188,16 @@ enhance_table <- function(res_enrich,
     if (ridge_color == "gs_score") {
       gs_fulllist$gs_zscore <- res_enrich$z_score[match(gs_fulllist$gs_id, res_enrich$gs_id)]
       p <- ggplot(
-        gs_fulllist, aes_string(
-          x = "log2FoldChange",
-          y = "gs_desc",
-          fill = "gs_zscore"
+        gs_fulllist, aes(
+          x = .data$log2FoldChange,
+          y = .data$gs_desc,
+          fill = .data$gs_zscore
         )
       ) +
         scale_x_continuous(limits = c(-max_lfc, max_lfc)) + 
         scale_fill_gradient2(low = "#313695", mid = "#FFFFE5", high = "#A50026") + 
         ggridges::geom_density_ridges(
-          aes_string(group = "gs_id"),
+          aes(group = .data$gs_id),
           point_color = "#00000066",
           jittered_points = TRUE, scale = .95, rel_min_height = .01,
           point_shape = "|", point_size = 3, linewidth = 0.25,
@@ -212,15 +212,15 @@ enhance_table <- function(res_enrich,
     }
     else if (ridge_color == "gs_id") {
       p <- ggplot(
-        gs_fulllist, aes_string(
-          x = "log2FoldChange",
-          y = "gs_desc",
-          fill = "gs_id"
+        gs_fulllist, aes(
+          x = .data$log2FoldChange,
+          y = .data$gs_desc,
+          fill = .data$gs_id
         )
       ) +
         scale_x_continuous(limits = c(-max_lfc, max_lfc)) + 
         ggridges::geom_density_ridges(
-          aes_string(group = "gs_id"),
+          aes(group = .data$gs_id),
           point_color = "#00000066",
           jittered_points = TRUE, scale = .95, rel_min_height = .01,
           point_shape = "|", point_size = 3, linewidth = 0.25,

R/gene_plot.R

@@ -127,7 +127,9 @@ gene_plot <- function(dds,
     return(df)
   }
 
-  p <- ggplot(df, aes_string(x = "plotby", y = "exp_value", col = "plotby")) +
+  p <- ggplot(df, aes(x = .data$plotby, 
+                      y = .data$exp_value, 
+                      col = .data$plotby)) +
     scale_x_discrete(name = "") +
     scale_color_discrete(name = "Experimental\ngroup") +
     theme_bw()
@@ -139,18 +141,21 @@ gene_plot <- function(dds,
   # https://www.embopress.org/doi/full/10.15252/embj.201694659
   if (plot_type == "jitteronly" || (plot_type == "auto" & min_by_groups <= 3)) {
     p <- p +
-      geom_point(aes_string(x = "plotby", y = "exp_value"),
+      geom_point(aes(x = .data$plotby, 
+                     y = .data$exp_value),
         position = jit_pos
       )
     # do nothing - or add a line for the median?
   } else if (plot_type == "boxplot" || (plot_type == "auto" & (min_by_groups > 3 & min_by_groups < 10))) {
     p <- p +
       geom_boxplot(outlier.shape = NA) +
-      geom_point(aes_string(x = "plotby", y = "exp_value"), position = jit_pos)
+      geom_point(aes(x = .data$plotby, 
+                     y = .data$exp_value), position = jit_pos)
   } else if (plot_type == "violin" || (plot_type == "auto" & (min_by_groups >= 11 & min_by_groups < 40))) {
     p <- p +
       geom_violin() +
-      geom_point(aes_string(x = "plotby", y = "exp_value"), position = jit_pos) +
+      geom_point(aes(x = .data$plotby, 
+                     y = .data$exp_value), position = jit_pos) +
       stat_summary(
         fun = median, fun.min = median, fun.max = median,
         geom = "crossbar", width = 0.3
@@ -167,13 +172,13 @@ gene_plot <- function(dds,
   # handling the labels
   if (labels_display) {
     if (labels_repel) {
-      p <- p + ggrepel::geom_text_repel(aes_string(label = "sample_id"),
+      p <- p + ggrepel::geom_text_repel(aes(label = .data$sample_id),
         min.segment.length = 0,
         position = jit_pos
       )
     }
     else {
-      p <- p + geom_text(aes_string(label = "sample_id"),
+      p <- p + geom_text(aes(label = .data$sample_id),
         hjust = -0.1, vjust = 0.1,
         position = jit_pos
       )

R/gs_heatmap.R

@@ -524,8 +524,8 @@ gs_scoresheat <- function(mat,
   score_df <- cbind(score_df, scores)
   colnames(score_df) <- c("GeneSet", "Sample", "Value")
 
-  p <- ggplot(score_df, aes_string(x = "Sample", y = "GeneSet")) +
-    geom_tile(aes_string(fill = "Value"), color = "white") +
+  p <- ggplot(score_df, aes(x = .data$Sample, y = .data$GeneSet)) +
+    geom_tile(aes(fill = .data$Value), color = "white") +
     # from brewer.pal(n = 11, name = 'RdYlBu') and brewer.pal(n = 11, name = 'YlGn') - or Spectal, 11?
     scale_fill_gradient2(low = "#313695", mid = "#FFFFE5", high = "#A50026") +
     theme(

R/gs_mds.R

@@ -162,14 +162,14 @@ gs_mds <- function(res_enrich,
 
   this_contrast <- (sub(".*p-value: (.*)", "\\1", mcols(res_de, use.names = TRUE)["pvalue", "description"]))
 
-  p <- ggplot(mds_gs_df, aes_string(
-    x = "dim1",
-    y = "dim2",
-    text = "gs_text"
+  p <- ggplot(mds_gs_df, aes(
+    x = .data$dim1,
+    y = .data$dim2,
+    text = .data$gs_text
   )) +
-    geom_point(aes_string(
-      color = "gs_colby",
-      size = "gs_DEcount"
+    geom_point(aes(
+      color = .data$gs_colby,
+      size = .data$gs_DEcount
     )) +
     scale_color_gradient2(
       limit = limit,
@@ -203,7 +203,7 @@ gs_mds <- function(res_enrich,
   df_gs_labels <- mds_gs_df[mds_gs_df$gs_id %in% unique(c(label_these, label_those)), ]
 
   p <- p + geom_label_repel(
-    aes_string(label = "gs_name"),
+    aes(label = .data$gs_name),
     data = df_gs_labels,
     size = 3,
     min.segment.length = 0

R/gs_summaries.R

@@ -99,7 +99,7 @@ gs_summary_overview <- function(res_enrich,
     mutate(gs_description = factor(.data$gs_description, .data$gs_description))
 
   if (return_barchart) {
-    p <- ggplot(re_sorted, (aes_string(x = "gs_description", y = "logp10")))
+    p <- ggplot(re_sorted, (aes(x = .data$gs_description, y = .data$logp10)))
 
     if (is.null(color_by)) {
       p <- p + geom_col()
@@ -117,12 +117,12 @@ gs_summary_overview <- function(res_enrich,
       ) +
       theme_minimal()
   } else {
-    p <- ggplot(re_sorted, (aes_string(x = "gs_description", y = "logp10"))) +
-      geom_segment(aes_string(
-        x = "gs_description",
-        xend = "gs_description",
+    p <- ggplot(re_sorted, (aes(x = .data$gs_description, y = .data$logp10))) +
+      geom_segment(aes(
+        x = .data$gs_description,
+        xend = .data$gs_description,
         y = 0,
-        yend = "logp10"
+        yend = .data$logp10
       ), color = "grey")
 
     if (is.null(color_by)) {
@@ -265,10 +265,13 @@ gs_summary_overview_pair <- function(res_enrich,
     arrange(.data$logp10) %>%
     mutate(gs_description = factor(.data$gs_description, .data$gs_description))
 
-  p <- ggplot(re_both_sorted, aes_string(x = "gs_description", y = "logp10")) +
-    geom_segment(aes_string(x = "gs_description", xend = "gs_description", y = "logp10_2", yend = "logp10"), color = "grey") +
+  p <- ggplot(re_both_sorted, aes(x = .data$gs_description, y = .data$logp10)) +
+    geom_segment(aes(x = .data$gs_description, 
+                     xend = .data$gs_description, 
+                     y = .data$logp10_2, 
+                     yend = .data$logp10), color = "grey") +
     geom_point(aes(fill = .data[[color_by]]), size = 4, pch = 21) +
-    geom_point(aes_string(y = "logp10_2", col = paste0(color_by, "_2")),
+    geom_point(aes(y = .data$logp10_2, col = .data[[paste0(color_by, "_2")]]),
       size = 4, alpha = alpha_set2
     ) +
     scale_color_gradient2(low = "#313695", mid = "#FFFFE5", high = "#A50026", name = paste0(color_by, " set 2")) +
@@ -518,9 +521,9 @@ gs_horizon <- function(res_enrich,
     p <- merged_res_enh %>%
       mutate(gs_description = factor(.data$gs_description, rev(unique(.data$gs_description)))) %>%
       arrange((.data$logp10)) %>%
-      ggplot(aes_string(x = "gs_description", y = "logp10")) +
-      geom_line(aes_string(group = "scenario", col = "scenario"), linewidth = 3, alpha = 0.7) +
-      geom_point(aes_string(fill = "z_score"), size = 4, pch = 21) +
+      ggplot(aes(x = .data$gs_description, y = .data$logp10)) +
+      geom_line(aes(group = .data$scenario, col = .data$scenario), linewidth = 3, alpha = 0.7) +
+      geom_point(aes(fill = .data$z_score), size = 4, pch = 21) +
       scale_color_brewer(palette = "Set2") +
       scale_fill_gradient2(low = "#313695", mid = "#FFFFE5", high = "#A50026") +
       ylim(c(0, NA)) +
@@ -541,10 +544,10 @@ gs_horizon <- function(res_enrich,
       # mutate(gs_description=factor(gs_description, unique(gs_description))) %>%
       mutate(gs_description = factor(.data$gs_description, rev(unique(nicerorder_terms)))) %>%
       arrange(desc(.data$logp10)) %>%
-      ggplot(aes_string(x = "gs_description", y = "logp10")) +
-      geom_line(aes_string(group = "scenario", col = "scenario"), linewidth = 3, alpha = 0.7) +
+      ggplot(aes(x = .data$gs_description, y = .data$logp10)) +
+      geom_line(aes(group = .data$scenario, col = .data$scenario), linewidth = 3, alpha = 0.7) +
       scale_color_brewer(palette = "Set2") +
-      geom_point(aes_string(fill = "z_score"), size = 4, pch = 21) +
+      geom_point(aes(fill = .data$z_score), size = 4, pch = 21) +
       scale_fill_gradient2(low = "#313695", mid = "#FFFFE5", high = "#A50026") +
       ylim(c(0, NA)) +
       coord_flip() +
@@ -653,9 +656,9 @@ gs_summary_heat <- function(res_enrich,
 
   p <- ggplot(
     gs_expanded,
-    aes_string(x = "gs_genes", y = "gs_description")
+    aes(x = .data$gs_genes, y = .data$gs_description)
   ) +
-    geom_tile(aes_string(fill = "log2FoldChange"),
+    geom_tile(aes(fill = .data$log2FoldChange),
       color = "white"
     ) +
     scale_fill_gradient2(

R/gs_upset.R

@@ -193,7 +193,7 @@ gs_upset <- function(res_enrich,
     param_upset_baseanno <- list(
       "Intersection size" = intersection_size(
         counts = FALSE,
-        mapping = aes_string(fill = "logFCsign")
+        mapping = aes(fill = .data$logFCsign)
       ) +
         scale_fill_manual(
           values = c("FALSE" = col_downDE, "TRUE" = col_upDE),
@@ -217,8 +217,8 @@ gs_upset <- function(res_enrich,
 
     param_upset_anno <- list(
       "logFC" = (
-        ggplot(mapping = aes_string(x = "intersection", y = "logFC")) +
-          geom_jitter(aes_string(color = "logFC"), na.rm = TRUE) +
+        ggplot(mapping = aes(x = .data$intersection, y = .data$logFC)) +
+          geom_jitter(aes(color = .data$logFC), na.rm = TRUE) +
           # geom_violin(alpha = 0.5, na.rm = TRUE) +
           theme(legend.position = "none") +
           scale_colour_gradient2(low = col_downDE, high = col_upDE)

R/gs_volcano.R

@@ -121,9 +121,9 @@ gs_volcano <- function(res_enrich,
 
   p <- ggplot(
     volcano_df,
-    aes_string(x = "z_score", y = "logpval", size = "`set members`", text = "gs_name")
+    aes(x = .data$z_score, y = .data$logpval, size = .data$`set members`, text = .data$gs_name)
   ) +
-    geom_point(aes_string(col = color_by), shape = 20, alpha = 1) +
+    geom_point(aes(col = .data[[color_by]]), shape = 20, alpha = 1) +
     labs(
       x = "geneset Z score",
       y = "-log10 p-value",
@@ -141,7 +141,7 @@ gs_volcano <- function(res_enrich,
     df_gs_labels <- volcano_df[volcano_df$gs_id %in% gs_to_use, ]
 
     p <- p + geom_label_repel(
-      aes_string(label = "gs_name"),
+      aes(label = .data$gs_name),
       data = df_gs_labels,
       size = 4,
       min.segment.length = 0

R/signature_volcano.R

@@ -174,11 +174,11 @@ signature_volcano <- function(res_de,
   # Plot data
   p <- ggplot(
     volcano_df_complete,
-    aes_string(x = "log2FoldChange", y = "logTransformedpvalue")
+    aes(x = .data$log2FoldChange, y = .data$logTransformedpvalue)
   ) +
-    geom_point(aes_string(
-      color = "significant",
-      alpha = "belonging"
+    geom_point(aes(
+      color = .data$significant,
+      alpha = .data$belonging
     )) +
     labs(
       x = "log2 Fold Change",
@@ -205,7 +205,7 @@ signature_volcano <- function(res_de,
   # adding labels to the significant points of the geneset
   p <- p + geom_text_repel(
     data = subset(volcano_df_complete, filter_info_complete),
-    aes_string(label = "genes_name"),
+    aes(label = .data$genes_name),
     size = 4,
     max.overlaps = volcano_labels
   )