perf:updated functions to pass R-CMD Check: no visible binding for gl…

…obal variable for underlisted functions(lineage.neighbors.plot, lineage_sunburst, make_accnums_unique,make_df_iprscan_domain

NAMESPACE

@@ -229,6 +229,7 @@ importFrom(readr,write_file)
 importFrom(readr,write_lines)
 importFrom(readr,write_tsv)
 importFrom(rentrez,entrez_fetch)
+importFrom(rlang,.data)
 importFrom(rlang,as_string)
 importFrom(rlang,sym)
 importFrom(sendmailR,mime_part)

R/cleanup.R

@@ -88,12 +88,12 @@ make_accnums_unique <- function(accnums) {
     # for the index of occurence for each accession number
     df_accnums <- tibble::tibble("accnum" = accnums)
     df_accnums <- df_accnums |>
-        dplyr::group_by(accnum) |>
+        dplyr::group_by(.data$accnum) |>
         dplyr::mutate(suffix = dplyr::row_number()) |>
         dplyr::ungroup() |>
-        dplyr::mutate(accnum_adjusted = paste0(accnum, "_", suffix)) |>
-        dplyr::arrange(accnum_adjusted)
-    accnums_adjusted <- df_accnums |> dplyr::pull(accnum_adjusted)
+        dplyr::mutate(accnum_adjusted = paste0(.data$accnum, "_", .data$suffix)) |>
+        dplyr::arrange(.data$accnum_adjusted)
+    accnums_adjusted <- df_accnums |> dplyr::pull(.data$accnum_adjusted)
 
     return(accnums_adjusted)
 }

R/fa2domain.R

@@ -138,10 +138,10 @@ make_df_iprscan_domains <- function(
     # filter for the accnum of interest (note: it's possible the accession
     # number is not in the table [i.e., it had no domains])
     df_iprscan_accnum <- df_iprscan |>
-        dplyr::filter(Analysis %in% analysis) |>
-        dplyr::filter(AccNum == accnum) |>
+        dplyr::filter(.data$Analysis %in% analysis) |>
+        dplyr::filter(.data$AccNum == accnum) |>
         dplyr::select(dplyr::all_of(c("AccNum", "DB.ID", "StartLoc", "StopLoc"))) |>
-        dplyr::arrange(StartLoc)
+        dplyr::arrange(.data$StartLoc)
     # handle the case of no records after filtering by "Analysis"; return the tibble
     # with 0 rows quickly
     if (nrow(df_iprscan_accnum) < 1) {
@@ -153,9 +153,9 @@ make_df_iprscan_domains <- function(
         dplyr::rowwise() |>
         dplyr::mutate(
             seq_domain = XVector::subseq(
-                fasta[[grep(pattern = AccNum, x = names(fasta), fixed = TRUE)]],
-                start = StartLoc,
-                end = StopLoc
+                fasta[[grep(pattern = .data$AccNum, x = names(fasta), fixed = TRUE)]],
+                start = .data$StartLoc,
+                end = .data$StopLoc
             ) |>
                 as.character()
         )
@@ -166,7 +166,7 @@ make_df_iprscan_domains <- function(
             id_domain = stringr::str_glue("{AccNum}-{DB.ID}-{StartLoc}_{StopLoc}")
         ) |>
         dplyr::ungroup() |>
-        dplyr::relocate(id_domain, .before = 1)
+        dplyr::relocate(.data$id_domain, .before = 1)
     return(df_iprscan_domains)
 }
 

R/ipr2viz.R

@@ -53,6 +53,7 @@ theme_genes2 <- function() {
 #' @importFrom shiny showNotification
 #' @importFrom stats na.omit
 #' @importFrom rlang sym
+#' @importFrom rlang .data
 #'
 #' @return
 #' @export
@@ -295,15 +296,15 @@ ipr2viz_web <- function(infile_ipr,
     ## @SAM, colnames, merges, everything neeeds to be done now based on the
     ## combined lookup table from "common_data"
     lookup_tbl_path <- "/data/research/jravilab/common_data/cln_lookup_tbl.tsv"
-    lookup_tbl <- read_tsv(lookup_tbl_path, col_names = T, col_types = lookup_table_cols)
+    lookup_tbl <- read_tsv(lookup_tbl_path, col_names = T, col_types = .data$lookup_table_cols)
 
     ## Read IPR file and subset by Accessions
     ipr_out <- read_tsv(infile_ipr, col_names = T)
     ipr_out <- ipr_out %>% filter(Name %in% accessions)
     ## Need to fix eventually based on 'real' gene orientation!
     ipr_out$Strand <- rep("forward", nrow(ipr_out))
 
-    ipr_out <- ipr_out %>% arrange(AccNum, StartLoc, StopLoc)
+    ipr_out <- ipr_out %>% arrange(.data$AccNum, .data$StartLoc, .data$StopLoc)
     ipr_out_sub <- filter(
         ipr_out,
         grepl(pattern = analysis, x = Analysis)

R/plotting.R

@@ -521,8 +521,8 @@ lineage.neighbors.plot <- function(query_data = "prot", query = "pspa",
         gather(key = TopNeighbors.DA, value = count, 19:ncol(query_data)) %>%
         select("Lineage", "TopNeighbors.DA", "count") %>% # "DomArch.norep","GenContext.norep",
         group_by(TopNeighbors.DA, Lineage) %>%
-        summarise(lincount = sum(count), bin = as.numeric(as.logical(lincount))) %>%
-        arrange(desc(lincount)) %>%
+        summarise(lincount =sum(count), bin = as.numeric(as.logical(.data$lincount))) %>%
+        arrange(desc(.data$lincount)) %>%
         within(TopNeighbors.DA <- factor(TopNeighbors.DA,
             levels = rev(names(sort(table(TopNeighbors.DA),
                 decreasing = TRUE
@@ -538,9 +538,9 @@ lineage.neighbors.plot <- function(query_data = "prot", query = "pspa",
         geom_tile(
             data = subset(
                 query.ggplot,
-                !is.na(lincount)
+                !is.na(.data$lincount)
             ), # bin
-            aes(fill = lincount), # bin
+            aes(fill = .data$lincount), # bin
             colour = "coral3", size = 0.3
         ) + # , width=0.7, height=0.7),
         scale_fill_gradient(low = "white", high = "darkred") +
@@ -1223,13 +1223,13 @@ lineage_sunburst <- function(prot, lineage_column = "Lineage",
         group_by_at(levels_vec) %>%
         summarise(size = n())
     protLevels <- protLevels %>% arrange()
-    tree <- d3_nest(protLevels, value_cols = "size")
+    tree <- .data$d3_nest(protLevels, value_cols = "size")
 
     # Plot sunburst
     if (type == "sunburst") {
-        result <- sunburst(tree, legend = list(w = 225, h = 15, r = 5, s = 5), colors = cpcols, legendOrder = legendOrder, width = "100%", height = "100%")
+        result <- sunburst(tree, legend = list(w = 225, h = 15, r = 5, s = 5), colors = .data$cpcols, legendOrder = legendOrder, width = "100%", height = "100%")
     } else if (type == "sund2b") {
-        result <- sund2b(tree)
+        result <- .data$sund2b(tree)
     }
 
     if (showLegend) {