R CMD check passing - no warnings or errors

waldronlab · Mar 22, 2024 · 4a898bb · 4a898bb
1 parent 551aa11
commit 4a898bb
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Showing 4 changed files with 101 additions and 95 deletions.
diff --git a/R/bacdive.R b/R/bacdive.R
@@ -37,18 +37,18 @@
 .getTidyBD <- function(bacdive_data) {
   bacdive_data |>
     tidyr::pivot_longer(
-      cols = .data$gram_stain:tidyr::last_col(), # Attributes start in the gram_stain column
+      cols = gram_stain:tidyr::last_col(), # Attributes start in the gram_stain column
       names_to = 'Attribute', values_to = 'Attribute_value'
     ) |>
-    dplyr::filter(.data$Attribute_value != '') |>
-    dplyr::mutate(Attribute = gsub('_', ' ', .data$Attribute)) |>
+    dplyr::filter(Attribute_value != '') |>
+    dplyr::mutate(Attribute = gsub('_', ' ', Attribute)) |>
     dplyr::mutate(
       Attribute = dplyr::case_when(
-        .data$Attribute == 'oxygen tolerance' ~ 'aerophilicity',
-        .data$Attribute == 'cell shape' ~ 'shape',
-        .data$Attribute == 'pathogenicity animal' ~ 'animal pathongen',
-        .data$Attribute == 'sample type' ~ 'isolation site',
-        TRUE ~ .data$Attribute
+        Attribute == 'oxygen tolerance' ~ 'aerophilicity',
+        Attribute == 'cell shape' ~ 'shape',
+        Attribute == 'pathogenicity animal' ~ 'animal pathongen',
+        Attribute == 'sample type' ~ 'isolation site',
+        TRUE ~ Attribute
       )
     ) |>
     dplyr::distinct()
@@ -163,42 +163,42 @@
   )
   regex <- paste0('(', paste0(valid_terms, collapse = '|'), ')')
   split_df[['halophily']] <- split_df[['halophily']] |>
-    dplyr::mutate(Attribute_value = strsplit(.data$Attribute_value, ';')) |>
+    dplyr::mutate(Attribute_value = strsplit(Attribute_value, ';')) |>
     tidyr::unnest(cols = 'Attribute_value') |>
-    dplyr::filter(!grepl('no growth', .data$Attribute_value)) |>
+    dplyr::filter(!grepl('no growth', Attribute_value)) |>
     dplyr::mutate(
-      Attribute_value = stringr::str_squish(.data$Attribute_value),
-      Attribute_value = sub('NaCL', 'NaCl', .data$Attribute_value),
-      Attribute_value = sub('Marine', 'Sea', .data$Attribute_value),
-      Attribute_value = sub('Salts', 'salts', .data$Attribute_value)
+      Attribute_value = stringr::str_squish(Attribute_value),
+      Attribute_value = sub('NaCL', 'NaCl', Attribute_value),
+      Attribute_value = sub('Marine', 'Sea', Attribute_value),
+      Attribute_value = sub('Salts', 'salts', Attribute_value)
     ) |>
-    dplyr::filter(grepl(regex, .data$Attribute_value)) |>
+    dplyr::filter(grepl(regex, Attribute_value)) |>
     dplyr::mutate(
-      Attribute = stringr::str_extract(.data$Attribute_value, regex),
-      Unit = .data$Attribute_value |>
+      Attribute = stringr::str_extract(Attribute_value, regex),
+      Unit = Attribute_value |>
         stringr::str_extract(' [<>]??[0-9]+\\.??[0-9]*.*') |>
         stringr::str_squish() |>
         stringr::str_remove('^.* '),
-      Attribute_value = .data$Attribute_value |>
+      Attribute_value = Attribute_value |>
         stringr::str_extract(' [<>]??[0-9]+\\.??[0-9]*.*') |>
         stringr::str_squish() |>
         stringr::str_remove(' .*$'),
       Attribute_group = 'halophily',
       Attribute_type = 'range'
     ) |>
-    dplyr::filter(!grepl('[0-9]', .data$Unit)) |>
+    dplyr::filter(!grepl('[0-9]', Unit)) |>
     dplyr::distinct()
 
   ## hemolysis ####
   split_df[['hemolysis']] <- split_df[['hemolysis']] |>
     dplyr::mutate(
-      Attribute_value = strsplit(.data$Attribute_value, ';|/')
+      Attribute_value = strsplit(Attribute_value, ';|/')
     ) |>
-    tidyr::unnest(.data$Attribute_value) |>
-    dplyr::mutate(Attribute_value = stringr::str_squish(.data$Attribute_value)) |>
-    dplyr::filter(.data$Attribute_value != '') |>
+    tidyr::unnest(Attribute_value) |>
+    dplyr::mutate(Attribute_value = stringr::str_squish(Attribute_value)) |>
+    dplyr::filter(Attribute_value != '') |>
     dplyr::mutate(
-      Attribute = .data$Attribute_value,
+      Attribute = Attribute_value,
       Attribute_value = TRUE,
       Attribute_group = 'hemolysis',
       Attribute_type = 'multistate-intersection'
@@ -212,8 +212,8 @@
   split_df[['motility']] <- split_df[['motility']] |>
     dplyr::mutate(
       Attribute_value = dplyr::case_when(
-        .data[['Attribute_value']] == 'yes' ~ TRUE,
-        .data[['Attribute_value']] == 'no' ~ FALSE
+        Attribute_value == 'yes' ~ TRUE,
+        Attribute_value == 'no' ~ FALSE
       )
     )
   split_df[['motility']][['Attribute_group']] <- 'motility'
@@ -232,8 +232,8 @@
   ## metabolite production ####
   mp <- split_df[['metabolite production']]
   mp <- mp |>
-    dplyr::mutate(Attribute_value = strsplit(.data$Attribute_value, ';')) |>
-    tidyr::unnest(.data$Attribute_value)
+    dplyr::mutate(Attribute_value = strsplit(Attribute_value, ';')) |>
+    tidyr::unnest(Attribute_value)
   x <- stringr::str_extract(mp[['Attribute_value']], '(yes|no)$')
   mp <- mp[which(!is.na(x)),]
   y <- stringr::str_extract(mp[['Attribute_value']], '(yes|no)$')
@@ -249,9 +249,9 @@
   names(split_df)[pos] <- 'metabolite utilization'
   mu <- split_df[['metabolite utilization']]
   mu <- mu |>
-    dplyr::mutate(Attribute_value = strsplit(.data$Attribute_value, ';')) |>
-    tidyr::unnest(.data$Attribute_value) |>
-    dplyr::mutate(Attribute_value = stringr::str_squish(.data$Attribute_value))
+    dplyr::mutate(Attribute_value = strsplit(Attribute_value, ';')) |>
+    tidyr::unnest(Attribute_value) |>
+    dplyr::mutate(Attribute_value = stringr::str_squish(Attribute_value))
   x <- sub('^.* (\\+|-|\\+/-) *.*$', '\\1', mu[['Attribute_value']])
   y <- ifelse(!x %in% c('+', '-', '+/-'), NA, x)
   mu <- mu[which(!is.na(y)),]
@@ -265,9 +265,9 @@
     y == '+/-' ~ 'TRUE/FALSE'
   )
   mu <- mu |>
-    dplyr::mutate(Attribute_value = strsplit(.data$Attribute_value, '/')) |>
-    tidyr::unnest(.data$Attribute_value) |>
-    dplyr::mutate(Attribute_value = as.logical(.data$Attribute_value))
+    dplyr::mutate(Attribute_value = strsplit(Attribute_value, '/')) |>
+    tidyr::unnest(Attribute_value) |>
+    dplyr::mutate(Attribute_value = as.logical(Attribute_value))
   mu[['Attribute_group']] <- 'metabolite utilization'
   mu[['Attribute_type']] <- 'multistate-intersection'
   split_df[['metabolite utilization']] <- mu
@@ -277,13 +277,13 @@
   sf <- sf |>
     dplyr::mutate(
       Attribute_value = dplyr::case_when(
-        .data[['Attribute_value']] == 'yes' ~ TRUE,
-        .data[['Attribute_value']] == 'no' ~ FALSE
+        Attribute_value == 'yes' ~ TRUE,
+        Attribute_value == 'no' ~ FALSE
       ),
       Attribute_group = 'spore formation',
       Attribute_type = 'binary'
     ) |>
-    dplyr::filter(!is.na(.data$Attribute_value))
+    dplyr::filter(!is.na(Attribute_value))
   split_df[['spore formation']] <- sf
 
   split_df <- lapply(split_df, function(x) {

diff --git a/R/bugphyzz.R b/R/bugphyzz.R
@@ -1,5 +1,11 @@
-# utils::globalVariables(c("Rank"))
-Rank <- NULL
+utils::globalVariables(c(
+  "Rank",
+  "Attribute", "Attribute_group", "Attribute_new", "Attribute_range",
+  "Attribute_value", "Attribute_value_max", "Attribute_value_min", "Br-C10:1",
+  "Evidence", "Frequency", "NCBI_ID", "Oxo-C19:1", "Taxon_name", "Unit",
+  "attribute", "functionname", "gram_stain", "physiology", "unit", "value"
+))
+
 #' Import bugphyzz
 #'
 #' \code{importBugphyzz} imports bugphyzz annotations as a list of
@@ -68,7 +74,7 @@ importBugphyzz <- function(
         url = urls[i], verbose = TRUE, force = force_download
       )
       output[[i]] <- utils::read.csv(rpath, header = TRUE, skip = 1) |>
-        dplyr::mutate(Attribute = tolower(.data$Attribute))
+        dplyr::mutate(Attribute = tolower(Attribute))
     }
   }
   output <- lapply(output, function(x) split(x, x$Attribute))
@@ -82,43 +88,43 @@ importBugphyzz <- function(
     .x |>
       dplyr::mutate(
         Attribute = ifelse(
-          .data$Attribute == "plant pathogenity",
+          Attribute == "plant pathogenity",
           "plant pathogenicity",
-          .data$Attribute
+          Attribute
         )
       )
   })
 
   names(output) <- purrr::map_chr(output, ~ unique(.x$Attribute))
   val <- .validationData() |>
-    dplyr::filter(.data$rank == "all") |>
-    dplyr::select(.data$physiology, .data$attribute, .data$value) |>
-    dplyr::mutate(physiology = tolower(.data$physiology)) |>
-    dplyr::mutate(attribute = tolower(.data$attribute))
+    dplyr::filter(rank == "all") |>
+    dplyr::select(physiology, attribute, value) |>
+    dplyr::mutate(physiology = tolower(physiology)) |>
+    dplyr::mutate(attribute = tolower(attribute))
 
   output <- purrr::map(output, ~ {
     attr_type <- unique(.x$Attribute_type)
     if (attr_type == "binary") {
-      val <- dplyr::select(val, Attribute = .data$attribute, .data$value)
+      val <- dplyr::select(val, Attribute = attribute, value)
       o <- dplyr::left_join(.x, val, by = "Attribute" )
     } else if (attr_type == "multistate-intersection" || attr_type == "multistate-union") {
-      val <- dplyr::select(val, Attribute = .data$physiology, Attribute_value = .data$attribute, .data$value)
-      o <- dplyr::left_join(dplyr::mutate(.x, Attribute_value = tolower(.data$Attribute_value)) , val, by = c("Attribute", "Attribute_value"))
+      val <- dplyr::select(val, Attribute = physiology, Attribute_value = attribute, value)
+      o <- dplyr::left_join(dplyr::mutate(.x, Attribute_value = tolower(Attribute_value)) , val, by = c("Attribute", "Attribute_value"))
     } else if (attr_type == "numeric") {
-      val <- dplyr::select(val, Attribute = .data$attribute, .data$value)
+      val <- dplyr::select(val, Attribute = attribute, value)
       o <- dplyr::left_join(.x, val, by = "Attribute") |>
-        dplyr::rename(NSTI = .data$nsti)
+        dplyr::rename(NSTI = nsti)
     }
     o |>
       dplyr::filter(
-       !(.data$value < v & .data$Evidence == "asr")
+       !(value < v & Evidence == "asr")
       ) |>
-      dplyr::mutate(value = ifelse(.data$Evidence != "asr", NA, value)) |>
-      dplyr::rename(Validation = .data$value)
+      dplyr::mutate(value = ifelse(Evidence != "asr", NA, value)) |>
+      dplyr::rename(Validation = value)
   })
 
   if (exclude_rarely) {
-    output <- purrr::map(output, ~ dplyr::filter(.x, .data$Frequency != "rarely"))
+    output <- purrr::map(output, ~ dplyr::filter(.x, Frequency != "rarely"))
   }
   return(output)
 }
@@ -170,8 +176,8 @@ makeSignatures <- function(
   }
   dat <- dat |>
     dplyr::filter(Rank %in% tax_level) |>
-    dplyr::filter(.data$Evidence %in% evidence) |>
-    dplyr::filter(.data$Frequency %in% frequency)
+    dplyr::filter(Evidence %in% evidence) |>
+    dplyr::filter(Frequency %in% frequency)
   if (!nrow(dat)) {
     warning(
       "Not enough data for creating signatures. Try different filtering options",
@@ -229,7 +235,7 @@ getTaxonSignatures <- function(tax, bp, ...) {
 .makeSignaturesDiscrete <- function(dat, tax_id_type = "NCBI_ID") {
   dat |>
     dplyr::mutate(
-      Attribute = paste0("bugphyzz:", .data$Attribute, "|", .data$Attribute_value)
+      Attribute = paste0("bugphyzz:", Attribute, "|", Attribute_value)
       ) |>
     {\(y) split(y, y$Attribute)}() |>
     lapply(function(x) unique(x[[tax_id_type]]))
@@ -249,14 +255,14 @@ getTaxonSignatures <- function(tax, bp, ...) {
     }
     dat <- dat |>
       dplyr::filter(
-        .data$Attribute_value >= min & .data$Attribute_value <= max
+        Attribute_value >= min & Attribute_value <= max
       ) |>
       dplyr::mutate(
-        Attribute = paste0("bugphyzz:", .data$Attribute, "| >=", min, " & <=", max)
+        Attribute = paste0("bugphyzz:", Attribute, "| >=", min, " & <=", max)
       )
   } else {
     thr <- .thresholds() |>
-      dplyr::filter(.data$Attribute_group == unique(dat$Attribute))
+      dplyr::filter(Attribute_group == unique(dat$Attribute))
     attr_name <- thr$Attribute
     min_values <- thr$lower
     max_values <- thr$upper
@@ -282,15 +288,15 @@ getTaxonSignatures <- function(tax, bp, ...) {
   utils::read.table(fname, header = TRUE, sep = '\t') |>
     dplyr::mutate(
       range = dplyr::case_when(
-        is.na(.data$lower) ~ paste0('<=', .data$upper),
-        is.na(.data$upper) ~ paste0('>=', .data$lower),
-        TRUE ~ paste0(.data$lower, '-', .data$upper)
+        is.na(lower) ~ paste0('<=', upper),
+        is.na(upper) ~ paste0('>=', lower),
+        TRUE ~ paste0(lower, '-', upper)
       ),
-      unit = ifelse(is.na(.data$unit), '', .data$unit)
+      unit = ifelse(is.na(unit), '', unit)
     ) |>
     dplyr::mutate(Attribute_range = paste0(range, unit)) |>
     dplyr::relocate(
-      .data$Attribute_group, .data$Attribute, .data$Attribute_range
+      Attribute_group, Attribute, Attribute_range
     )
 }
 

diff --git a/R/fattyAcidComposition.R b/R/fattyAcidComposition.R
@@ -5,15 +5,15 @@
 ## TODO Maybe a threshold should be decided to consider a FA as present or not.
 .fattyAcidComposition <- function(){
   link <- .customLinks() |>
-    dplyr::filter(.data[["functionname"]] == "fattyAcidComposition") |>
-    dplyr::pull(.data[["link"]])
+    dplyr::filter(functionname == "fattyAcidComposition") |>
+    dplyr::pull(link)
   fac_wide <- utils::read.csv(link, check.names = FALSE)
   fac_long <- fac_wide |>
     tidyr::pivot_longer(
-      cols = .data[["Br-C10:1"]]:.data[["Oxo-C19:1"]],
+      cols = `Br-C10:1`:`Oxo-C19:1`,
       names_to = "Attribute_new", values_to = "Attribute_value"
   ) |>
-    dplyr::mutate(NCBI_ID = as.character(.data[["NCBI_ID"]]))
+    dplyr::mutate(NCBI_ID = as.character(NCBI_ID))
   dplyr::left_join(fac_long, ranks_parents, by = "NCBI_ID") |>
     as.data.frame() |>
     .addSourceInfo() |>