Merge pull request #236 from jpquast/Fix-typos-and-style

Fix typos and style

NEWS.md

@@ -128,6 +128,7 @@
 * The default batchsize of `fetch_pdb()` was changed to 100 (from 200). This was done since more information is retrieved now, which slows to function down and is slightly improved when batch sizes are smaller.
 * `try_query()` now only retries to retrieve information once if the returned message was "Timeout was reached". In addition, a `timeout` and `accept` argument have been added.
 * The UniProt database has changed its API, therefore column names have changed as well as the format of data. We adjusted the `fetch_uniprot()` and `fetch_uniprot_proteome()` function accordingly. Please be aware that some columns names might have changed and your code might throw error messages if you did not adjust it accordingly. 
+* Some typo fixes. Thank you Steffi!
 
 # protti 0.3.1
 

R/barcode_plot.R

@@ -123,7 +123,7 @@ barcode_plot <- function(data,
       size = 0.7
     ) +
     {
-      if (is.numeric(dplyr::pull(data, {{ colouring }}))){
+      if (is.numeric(dplyr::pull(data, {{ colouring }}))) {
         ggplot2::scale_fill_gradientn(colours = fill_colour_gradient)
       } else {
         ggplot2::scale_fill_manual(values = c(

R/calculate_go_enrichment.R

@@ -235,7 +235,7 @@ calculate_go_enrichment <- function(data,
 
   if (!(plot_style %in% c("barplot", "heatmap"))) stop("Invalid plot_style. Available styles: barplot, heatmap")
 
-  if(length(barplot_fill_colour) < 2) stop('Please provide at least two colours to "barplot_fill_colour"!')
+  if (length(barplot_fill_colour) < 2) stop('Please provide at least two colours to "barplot_fill_colour"!')
 
   if (!stringr::str_detect(plot_cutoff, pattern = "^(pval|adj_pval) (top\\d+|\\d+(\\.\\d+)?)$")) {
     stop("Invalid format for plot_cutoff. Please provide the type (pval or adj_pval) followed by
@@ -520,7 +520,7 @@ if you used the right organism ID.", prefix = "\n", initial = ""))
       {
         if (!missing(group)) {
           if (y_axis_free) {
-            if(facet_n_col == 1){
+            if (facet_n_col == 1) {
               ggforce::facet_col(rlang::new_formula(NULL, rlang::enquo(group)), scales = "free_y", space = "free")
             } else {
               ggplot2::facet_wrap(rlang::new_formula(NULL, rlang::enquo(group)), scales = "free_y", ncol = facet_n_col)

R/drc_4p_plot.R

@@ -173,7 +173,7 @@ drc_4p_plot <- function(data,
       ) +
         ggplot2::geom_point(size = 2, col = "#5680C1") +
         {
-          if(nrow(input_curve_plot) != 1) {
+          if (nrow(input_curve_plot) != 1) {
             ggplot2::geom_ribbon(
               data = input_curve_plot,
               ggplot2::aes(
@@ -188,7 +188,7 @@ drc_4p_plot <- function(data,
           }
         } +
         {
-          if(nrow(input_curve_plot) != 1) {
+          if (nrow(input_curve_plot) != 1) {
             ggplot2::geom_line(
               data = input_curve_plot,
               ggplot2::aes(
@@ -286,7 +286,7 @@ drc_4p_plot <- function(data,
       ggplot2::ggplot(data = x, ggplot2::aes(x = {{ dose }}, y = {{ response }})) +
         ggplot2::geom_point(size = 2, col = "#5680C1") +
         {
-          if(nrow(y) != 1) {
+          if (nrow(y) != 1) {
             ggplot2::geom_ribbon(
               data = y,
               ggplot2::aes(
@@ -301,7 +301,7 @@ drc_4p_plot <- function(data,
           }
         } +
         {
-          if(nrow(y) != 1) {
+          if (nrow(y) != 1) {
             ggplot2::geom_line(
               data = y,
               ggplot2::aes(

R/fetch_chebi.R

@@ -207,8 +207,10 @@ fetch_chebi <- function(relation = FALSE, stars = c(3), timeout = 60) {
 
   chebi_names_clean <- chebi_names %>%
     dplyr::distinct(.data$COMPOUND_ID, .data$NAME, .data$TYPE) %>%
-    dplyr::rename(ID = "COMPOUND_ID",
-                  TYPE_NAME = "TYPE") %>%
+    dplyr::rename(
+      ID = "COMPOUND_ID",
+      TYPE_NAME = "TYPE"
+    ) %>%
     dplyr::bind_rows(chebi_compounds_names_clean)
 
   chebi <- chebi_compounds_clean %>%

R/fetch_pdb.R

@@ -897,7 +897,7 @@ fetch_pdb <- function(pdb_ids, batchsize = 100, show_progress = TRUE) {
 
   combined <- polymer_entities %>%
     dplyr::full_join(nonpolymer_entities, by = c("pdb_ids", "auth_asym_ids"), relationship = "many-to-many") %>%
-    dplyr::left_join(rcsb_binding_affinity, by = "pdb_ids", relationship = "many-to-many" ) %>%
+    dplyr::left_join(rcsb_binding_affinity, by = "pdb_ids", relationship = "many-to-many") %>%
     dplyr::left_join(additional_info, by = "pdb_ids") %>%
     dplyr::left_join(crystal_growth_info, by = "pdb_ids") %>%
     dplyr::left_join(nmr_info, by = "pdb_ids") %>%

R/fit_drc_4p.R

@@ -321,19 +321,23 @@ fit_drc_4p <- function(data,
         list(unique(.data$n_vector_add_rev))[[1]][2]
       )$pval <= 0.1) %>%
       dplyr::ungroup() %>%
-      tidyr::replace_na(list("pval_vector" = TRUE,
-                             "pval_vector_rev" = TRUE,
-                             "pval_vector_add" = TRUE,
-                             "pval_vector_add_rev" = TRUE,
-                             "mean_vector" = 0,
-                             "mean_vector_rev" = 0,
-                             "mean_vector_add" = 0,
-                             "mean_vector_add_rev" = 0)) %>%
+      tidyr::replace_na(list(
+        "pval_vector" = TRUE,
+        "pval_vector_rev" = TRUE,
+        "pval_vector_add" = TRUE,
+        "pval_vector_add_rev" = TRUE,
+        "mean_vector" = 0,
+        "mean_vector_rev" = 0,
+        "mean_vector_add" = 0,
+        "mean_vector_add_rev" = 0
+      )) %>%
       dplyr::group_by({{ grouping }}) %>%
-      dplyr::mutate(mean_vector = min(.data$mean_vector) == .data$mean_vector,
-                    mean_vector_rev = min(.data$mean_vector_rev) == .data$mean_vector_rev,
-                    mean_vector_add = min(.data$mean_vector_add) == .data$mean_vector_add,
-                    mean_vector_add_rev = min(.data$mean_vector_add_rev) == .data$mean_vector_add_rev) %>%
+      dplyr::mutate(
+        mean_vector = min(.data$mean_vector) == .data$mean_vector,
+        mean_vector_rev = min(.data$mean_vector_rev) == .data$mean_vector_rev,
+        mean_vector_add = min(.data$mean_vector_add) == .data$mean_vector_add,
+        mean_vector_add_rev = min(.data$mean_vector_add_rev) == .data$mean_vector_add_rev
+      ) %>%
       dplyr::mutate(dose_MNAR = ifelse((all((.data$lower_vector & .data$enough_replicates == FALSE & .data$mean_vector) |
         (!.data$lower_vector & .data$enough_replicates == TRUE & !.data$mean_vector)) &
         .data$pval_vector) |

R/normalise.R

@@ -60,7 +60,7 @@ normalise <-
     if (!(method %in% c("median"))) {
       stop("Invalid method. Available methods: median")
     }
-    
+
     if (method == "median") {
       median_normalised <- data %>%
         dplyr::distinct() %>%

R/qc_charge_states.R

@@ -130,9 +130,11 @@ qc_charge_states <-
             if (interactive == FALSE) {
               ggplot2::geom_text(
                 data = label_positions,
-                aes(y = label_y,
-                    label = round(.data$charge_per, digits = 1)),
-                    vjust = 1.5
+                aes(
+                  y = label_y,
+                  label = round(.data$charge_per, digits = 1)
+                ),
+                vjust = 1.5
               )
             }
           } +
@@ -192,9 +194,11 @@ qc_charge_states <-
             if (interactive == FALSE) {
               ggplot2::geom_text(
                 data = label_positions,
-                aes(y = label_y,
-                    label = round(.data$charge_per, digits = 1)),
-                    vjust = 1.5
+                aes(
+                  y = label_y,
+                  label = round(.data$charge_per, digits = 1)
+                ),
+                vjust = 1.5
               )
             }
           } +

R/qc_data_completeness.R

@@ -78,7 +78,7 @@ qc_data_completeness <- function(data,
           dplyr::group_by({{ sample }}) %>%
           dplyr::summarise(completeness = sum(!is.na({{ intensity }})) / dplyr::n() * 100, .groups = "drop") %>%
           dplyr::mutate({{ digestion }} := .y)
-      ) %>% 
+      ) %>%
       filter(.data$completeness > 0)
   } else {
     result <- data %>%

R/qc_missed_cleavages.R

@@ -136,9 +136,11 @@ intensities or set remove_na_intensities to FALSE",
             if (interactive == FALSE) {
               ggplot2::geom_text(
                 data = label_positions,
-                aes(y = .data$label_y,
-                  label = round(.data$mc_percent, digits = 1)),
-                  vjust = 1.5
+                aes(
+                  y = .data$label_y,
+                  label = round(.data$mc_percent, digits = 1)
+                ),
+                vjust = 1.5
               )
             }
           } +
@@ -206,9 +208,11 @@ intensities or set remove_na_intensities to FALSE",
             if (interactive == FALSE) {
               ggplot2::geom_text(
                 data = label_positions,
-                aes(y = .data$label_y,
-                    label = round(.data$mc_percent, digits = 1)),
-                    vjust = 1.5
+                aes(
+                  y = .data$label_y,
+                  label = round(.data$mc_percent, digits = 1)
+                ),
+                vjust = 1.5
               )
             }
           } +

R/qc_peptide_type.R

@@ -126,9 +126,11 @@ qc_peptide_type <- function(data,
         ggplot2::geom_col(col = "black", size = 1) +
         ggplot2::geom_text(
           data = label_positions,
-          aes(y = label_y,
-              label = round(.data$peptide_type_percent, digits = 1)),
-              vjust = 1.5
+          aes(
+            y = label_y,
+            label = round(.data$peptide_type_percent, digits = 1)
+          ),
+          vjust = 1.5
         ) +
         ggplot2::labs(
           title = "Peptide types per .raw file",
@@ -221,9 +223,11 @@ qc_peptide_type <- function(data,
         ggplot2::geom_col(col = "black", size = 1) +
         ggplot2::geom_text(
           data = label_positions,
-          aes(y = label_y,
-              label = round(.data$peptide_type_percent, digits = 1)),
-              vjust = 1.5
+          aes(
+            y = label_y,
+            label = round(.data$peptide_type_percent, digits = 1)
+          ),
+          vjust = 1.5
         ) +
         ggplot2::labs(
           title = "Peptide type intensity per .raw file",

R/qc_proteome_coverage.R

@@ -66,7 +66,7 @@ qc_proteome_coverage <- function(data,
 
   proteome <- fetch_uniprot_proteome(organism_id, reviewed = reviewed)
 
-  if(is(proteome, "character")){
+  if (is(proteome, "character")) {
     # UniProt information could not be fetched.
     message("UniProt information could not be fetched")
     return(NULL)

R/qc_sequence_coverage.R

@@ -47,7 +47,6 @@ qc_sequence_coverage <- function(data,
                                  coverage,
                                  sample = NULL,
                                  interactive = FALSE) {
-
   # Validate inputs
   if (!all(c(rlang::as_name(rlang::enquo(protein_identifier)), rlang::as_name(rlang::enquo(coverage))) %in% colnames(data))) {
     stop("Column names for protein_identifier and coverage must exist in the dataset.")
@@ -76,10 +75,12 @@ qc_sequence_coverage <- function(data,
       boundary = 0,
       size = 1
     ) +
-    ggplot2::geom_vline(data = result %>% dplyr::distinct(.data$median_coverage, {{ sample }}),
-                        mapping = aes(xintercept = .data$median_coverage),
-                        linewidth = 1,
-                        linetype = "dashed") +
+    ggplot2::geom_vline(
+      data = result %>% dplyr::distinct(.data$median_coverage, {{ sample }}),
+      mapping = aes(xintercept = .data$median_coverage),
+      linewidth = 1,
+      linetype = "dashed"
+    ) +
     ggplot2::labs(
       title = "Protein coverage distribution",
       x = "Coverage [%]",