Merge pull request #250 from waldronlab/sdgamboa-bioc-review

Changes requested for inclusion in Bioconductor

.gitignore

@@ -46,3 +46,5 @@ jupyter_notebooks/updated_spore_shape_v2.xlsx
 protratis_data/
 
 misc
+
+*.Rproj

DESCRIPTION

@@ -59,7 +59,8 @@ Imports:
     tidyselect,
     BiocFileCache,
     httr2,
-    tools
+    tools,
+    S4Vectors
 Suggests: 
     DT,
     forcats,

R/bacdive.R

@@ -1,9 +1,9 @@
 
 ## Main function for importing BacDive
 .getBacDive <- function(verbose = FALSE ) {
-    bacdive_data <- .importBacDiveExcel(verbose = verbose)
-    colnames(bacdive_data) <- .changeBDColNames(colnames(bacdive_data))
-    .getTidyBD(bacdive_data)
+    bacdiveData <- .importBacDiveExcel(verbose = verbose)
+    colnames(bacdiveData) <- .changeBDColNames(colnames(bacdiveData))
+    .getTidyBD(bacdiveData)
 }
 
 ## Helper function for .getBacDive
@@ -34,8 +34,8 @@
 }
 
 ## Helper function for .getBacDive
-.getTidyBD <- function(bacdive_data) {
-    bacdive_data |>
+.getTidyBD <- function(bacdiveData) {
+    bacdiveData |>
         tidyr::pivot_longer(
             # Attributes start in the gram_stain column
             cols = gram_stain:tidyr::last_col(),
@@ -60,10 +60,10 @@
 .reshapeBacDive <- function(df) {
 
     df[['Attribute_source']] <- 'BacDive'
-    split_df <- split(df, factor(df[['Attribute']]))
+    splitDf <- split(df, factor(df[['Attribute']]))
 
     ## Attributes that must be changed from character to logical (simplest fix)
-    attr_names <- c(
+    attrNames <- c(
         'aerophilicity',
         'shape',
         'country',
@@ -72,20 +72,22 @@
         'isolation site'
         ## colony color (delete)
     )
-
-    for (i in seq_along(attr_names)) {
-        split_df[[attr_names[i]]] <- .catToLog(split_df[[attr_names[i]]])
-        if (attr_names[i] %in% c('aerophilicity', 'shape')) {
-            split_df[[attr_names[i]]]$Attribute_type <-
+
+    ## Modifying an already existing vector rather than creating a list
+    ## Keeping this for loop
+    for (i in seq_along(attrNames)) {
+        splitDf[[attrNames[i]]] <- .catToLog(splitDf[[attrNames[i]]])
+        if (attrNames[i] %in% c('aerophilicity', 'shape')) {
+            splitDf[[attrNames[i]]]$Attribute_type <-
                 'multistate-intersection'
         } else {
-            split_df[[attr_names[i]]]$Attribute_type <- 'multistate-union'
+            splitDf[[attrNames[i]]]$Attribute_type <- 'multistate-union'
         }
     }
 
     ## aerophilicity ####
     ## This is only to match the data in the bugphyzz spreadsheet
-    aer <- split_df[['aerophilicity']]
+    aer <- splitDf[['aerophilicity']]
     aer$Attribute <- dplyr::case_when(
         aer$Attribute == 'aerobe' ~ 'aerobic',
         aer$Attribute == 'anaerobe' ~ 'anaerobic',
@@ -95,51 +97,51 @@
         aer$Attribute == 'obligate aerobe' ~ 'obligately aerobic',
         TRUE ~ aer$Attribute
     )
-    split_df[['aerophilicity']] <- aer
+    splitDf[['aerophilicity']] <- aer
 
     ## animal pathogen ####
-    pos <- names(split_df) == 'animal pathongen'
-    names(split_df)[pos] <- 'animal pathogen'
-    x_ <- split_df[['animal pathogen']][['Attribute_value']]
+    pos <- names(splitDf) == 'animal pathongen'
+    names(splitDf)[pos] <- 'animal pathogen'
+    x_ <- splitDf[['animal pathogen']][['Attribute_value']]
     x_ <- ifelse(x_ == "yes, in single cases", "yes", x_)
     x_ <- dplyr::case_when(x_ == 'yes' ~ TRUE, x_ == 'no' ~ FALSE)
-    split_df[['animal pathogen']][['Attribute_value']] <- x_
-    split_df[['animal pathogen']][['Attribute_group']] <- 'animal pathogen'
-    split_df[['animal pathogen']][['Attribute']] <- 'animal pathogen'
-    split_df[['animal pathogen']][['Attribute_type']] <- 'binary'
+    splitDf[['animal pathogen']][['Attribute_value']] <- x_
+    splitDf[['animal pathogen']][['Attribute_group']] <- 'animal pathogen'
+    splitDf[['animal pathogen']][['Attribute']] <- 'animal pathogen'
+    splitDf[['animal pathogen']][['Attribute_type']] <- 'binary'
 
     ## biosafety level ####
-    y <- split_df[['biosafety level comment']][
+    y <- splitDf[['biosafety level comment']][
         , c('BacDive_ID', 'Attribute_value')
     ]
     colnames(y)[2] <- 'Note'
-    x <- dplyr::left_join(split_df[['biosafety level']], y, by = 'BacDive_ID')
+    x <- dplyr::left_join(splitDf[['biosafety level']], y, by = 'BacDive_ID')
     x[['Attribute_value']] <- paste0('biosafety level ', x[['Attribute_value']])
     x[['Attribute']] <- x[['Attribute_value']]
     x[['Attribute_value']] <- TRUE
     x[['Attribute_group']] <- 'biosafety level'
     x[['Attribute_type']] <- 'multistate-intersection'
-    split_df[['biosafety level']] <- x
-    split_df[['biosafety level comment']] <- NULL
+    splitDf[['biosafety level']] <- x
+    splitDf[['biosafety level comment']] <- NULL
 
     ## colony color ####
     ## This one must be removed
-    split_df[['colony color']] <- NULL
+    splitDf[['colony color']] <- NULL
 
     ## cultivation medium used - growth medium ####
-    pos <- names(split_df) == 'cultivation medium used'
-    names(split_df)[pos] <- 'growth medium'
-    split_df[['growth medium']][['Attribute_group']] <- 'growth medium'
+    pos <- names(splitDf) == 'cultivation medium used'
+    names(splitDf)[pos] <- 'growth medium'
+    splitDf[['growth medium']][['Attribute_group']] <- 'growth medium'
 
     ## growth temperature ####
     ## culture temperature
     ## culture temperature growth
     ## culture temperature range (ignore)
     ## culture temperature type (ignore)
-    split_df[['culture temperature range']] <- NULL
-    split_df[['culture temperature type']] <- NULL
-    a <- split_df[['culture temperature']]
-    b <- split_df[['culture temperature growth']]
+    splitDf[['culture temperature range']] <- NULL
+    splitDf[['culture temperature type']] <- NULL
+    a <- splitDf[['culture temperature']]
+    b <- splitDf[['culture temperature growth']]
     b_ <- b[,c('BacDive_ID', 'Attribute_value')]
     colnames(b_)[2] <- 'growth'
     ab <- dplyr::left_join(a, b_, by = 'BacDive_ID')
@@ -148,25 +150,25 @@
     ab[['Attribute_group']] <- 'growth temperature'
     ab[['Attribute_type']] <- 'range'
     ab[['Attribute']] <- 'growth temperature'
-    split_df[['growth temperature']] <- ab
-    split_df[['culture temperature']] <- NULL
-    split_df[['culture temperature growth']] <- NULL
+    splitDf[['growth temperature']] <- ab
+    splitDf[['culture temperature']] <- NULL
+    splitDf[['culture temperature growth']] <- NULL
 
     ## gram stain ####
-    gs <- split_df[['gram stain']]
+    gs <- splitDf[['gram stain']]
     gs[['Attribute']] <- paste(gs[['Attribute']], gs[['Attribute_value']])
     gs[['Attribute_value']] <- TRUE
     gs[['Attribute_group']] <- 'gram stain'
     gs[['Attribute_type']] <- 'multistate-intersection'
-    split_df[['gram stain']] <- gs
+    splitDf[['gram stain']] <- gs
 
     ## halophily ####
-    valid_terms <- c(
+    validTerms <- c(
         'NaCl', 'KCl', 'MgCl2', 'MgCl2x6H2O', 'Na\\+', 'MgSO4x7H2O', 'Na2SO4',
         'Sea salts', 'Chromium \\(Cr6\\+\\)'
     )
-    regex <- paste0('(', paste0(valid_terms, collapse = '|'), ')')
-    split_df[['halophily']] <- split_df[['halophily']] |>
+    regex <- paste0('(', paste0(validTerms, collapse = '|'), ')')
+    splitDf[['halophily']] <- splitDf[['halophily']] |>
         dplyr::mutate(Attribute_value = strsplit(Attribute_value, ';')) |>
         tidyr::unnest(cols = 'Attribute_value') |>
         dplyr::filter(!grepl('no growth', Attribute_value)) |>
@@ -194,7 +196,7 @@
         dplyr::distinct()
 
     ## hemolysis ####
-    split_df[['hemolysis']] <- split_df[['hemolysis']] |>
+    splitDf[['hemolysis']] <- splitDf[['hemolysis']] |>
         dplyr::mutate(
             Attribute_value = strsplit(Attribute_value, ';|/')
         ) |>
@@ -210,21 +212,21 @@
 
     ## incubation period
     ## This one must be removed
-    split_df[['incubation period']] <- NULL
+    splitDf[['incubation period']] <- NULL
 
     ## motility ####
-    split_df[['motility']] <- split_df[['motility']] |>
+    splitDf[['motility']] <- splitDf[['motility']] |>
         dplyr::mutate(
             Attribute_value = dplyr::case_when(
                 Attribute_value == 'yes' ~ TRUE,
                 Attribute_value == 'no' ~ FALSE
             )
         )
-    split_df[['motility']][['Attribute_group']] <- 'motility'
-    split_df[['motility']][['Attribute_type']] <- 'binary'
+    splitDf[['motility']][['Attribute_group']] <- 'motility'
+    splitDf[['motility']][['Attribute_type']] <- 'binary'
 
     ## pathogenicity human ####
-    pat <- split_df[['pathogenicity human']]
+    pat <- splitDf[['pathogenicity human']]
     pat[['Note']] <- stringr::str_extract(
         pat[['Attribute_value']], 'in single cases'
     )
@@ -235,10 +237,10 @@
     pat <- pat[!is.na(pat[['Attribute_value']]),]
     pat[['Attribute_group']] <- 'pathogenicity human'
     pat[['Attribute_type']] <- 'binary'
-    split_df[['pathogenicity human']] <- pat
+    splitDf[['pathogenicity human']] <- pat
 
     ## metabolite production ####
-    mp <- split_df[['metabolite production']]
+    mp <- splitDf[['metabolite production']]
     mp <- mp |>
         dplyr::mutate(Attribute_value = strsplit(Attribute_value, ';')) |>
         tidyr::unnest(Attribute_value)
@@ -250,12 +252,12 @@
     mp[['Attribute']] <- sub(' (yes|no)$', '', mp[['Attribute']])
     mp[['Attribute_group']] <- 'metabolite utilization'
     mp[['Attribute_type']] <- 'multistate-intersection'
-    split_df[['metabolite production']] <- mp
+    splitDf[['metabolite production']] <- mp
 
     ## metabolite utilization ####
-    pos <- names(split_df) == 'metabolite utiilization'
-    names(split_df)[pos] <- 'metabolite utilization'
-    mu <- split_df[['metabolite utilization']]
+    pos <- names(splitDf) == 'metabolite utiilization'
+    names(splitDf)[pos] <- 'metabolite utilization'
+    mu <- splitDf[['metabolite utilization']]
     mu <- mu |>
         dplyr::mutate(Attribute_value = strsplit(Attribute_value, ';')) |>
         tidyr::unnest(Attribute_value) |>
@@ -280,10 +282,10 @@
         dplyr::mutate(Attribute_value = as.logical(Attribute_value))
     mu[['Attribute_group']] <- 'metabolite utilization'
     mu[['Attribute_type']] <- 'multistate-intersection'
-    split_df[['metabolite utilization']] <- mu
+    splitDf[['metabolite utilization']] <- mu
 
     ## spore formation ####
-    sf <- split_df[['spore formation']]
+    sf <- splitDf[['spore formation']]
     sf <- sf |>
         dplyr::mutate(
             Attribute_value = dplyr::case_when(
@@ -294,9 +296,9 @@
             Attribute_type = 'binary'
         ) |>
         dplyr::filter(!is.na(Attribute_value))
-    split_df[['spore formation']] <- sf
+    splitDf[['spore formation']] <- sf
 
-    split_df <- lapply(split_df, function(x) {
+    splitDf <- lapply(splitDf, function(x) {
         x <- as.data.frame(x)
         x[['NCBI_ID']] <- as.character(x[['NCBI_ID']])
         x[['Parent_NCBI_ID']] <- as.character(x[['Parent_NCBI_ID']])
@@ -312,7 +314,7 @@
         as.data.frame(x)
     })
 
-    return(split_df)
+    return(splitDf)
 }
 
 ## Helper function for .reshapeBacDive