explicit call to setdiff -> base::setdiff

R/AnalysisConfiguration.R

@@ -228,7 +228,7 @@ setup_analysis <- function(data, configuration, cc = TRUE,  from_factors = FALSE
 #' bb <- prolfqua_data('data_ionstar')$filtered()
 #' bb$config <- old2new(bb$config)
 #' dt <- separate_hierarchy(bb$data, bb$config)
-#' setdiff(colnames(dt) ,colnames(bb$data))
+#' base::setdiff(colnames(dt) ,colnames(bb$data))
 #' stopifnot(ncol(dt) >= ncol(bb$data))
 #'
 separate_hierarchy <- function(data, config){
@@ -255,7 +255,7 @@ separate_hierarchy <- function(data, config){
 #' bb <- prolfqua_data('data_ionstar')$filtered()
 #' bb$config <- old2new(bb$config)
 #' dt <- separate_factors(bb$data, bb$config)
-#' setdiff(colnames(dt), colnames(bb$data))
+#' base::setdiff(colnames(dt), colnames(bb$data))
 #' stopifnot(ncol(bb$data) < ncol(dt))
 #'
 separate_factors <- function(data, config) {
@@ -486,7 +486,7 @@ summarize_hierarchy <- function(pdata,
 
   precursor <- pdata |> dplyr::select(factors, all_hierarchy) |> dplyr::distinct()
   x3 <- precursor |> dplyr::group_by_at(c(factors, hierarchy)) |>
-    dplyr::summarize_at( setdiff(all_hierarchy, hierarchy),
+    dplyr::summarize_at( base::setdiff(all_hierarchy, hierarchy),
                          list( n = dplyr::n_distinct))
   return(x3)
 }

R/tidyMS_R6_Modelling.R

@@ -383,7 +383,7 @@ plot_lmer_peptide_predictions <- function(m){
 plot_lmer_peptide_noRandom <- function(m,legend.position = "none"){
   data <- m@frame
   ran <- lme4::ranef(m)[[1]]
-  randeffect <- setdiff(all.vars( terms(formula(m)) ) , all.vars(terms(m)))
+  randeffect <- base::setdiff(all.vars( terms(formula(m)) ) , all.vars(terms(m)))
   ran <- tibble::as_tibble(ran,rownames = randeffect)
   colnames(ran) <- gsub("[()]","",colnames(ran))
   ran <- dplyr::inner_join(data, ran, by = randeffect)
@@ -579,7 +579,7 @@ linfct_matrix_contrasts <- function(linfct , contrasts, p.message = FALSE){
   make_contrasts <- function(data,
                              contrasts)
   {
-    cnams <- setdiff(colnames(data),"interaction")
+    cnams <- base::setdiff(colnames(data),"interaction")
     for (i in seq_along(contrasts)) {
       if (p.message) {message(names(contrasts)[i], "=", contrasts[i],"\n")}
       data <- dplyr::mutate(data, !!names(contrasts)[i] := !!rlang::parse_expr(contrasts[i]))

R/tidyMS_aggregation.R

@@ -267,7 +267,8 @@ medpolish_estimate <- function(x, name = FALSE, sampleName = "sampleName" ){
 #'  dplyr::group_by_at( configur$table$hierarchy_keys_depth() ) |>
 #'  tidyr::nest()
 #' x <- xnested$data[[1]]
-#' nn  <- x |> dplyr::select( setdiff(configur$table$hierarchy_keys() ,  configur$table$hierarchy_keys_depth()) ) |>
+#' nn  <- x |> dplyr::select( base::setdiff(configur$table$hierarchy_keys() ,
+#'   configur$table$hierarchy_keys_depth()) ) |>
 #'  dplyr::distinct() |> nrow()
 #'
 #' xx <- response_as_matrix(x,configur)
@@ -282,7 +283,8 @@ medpolish_estimate <- function(x, name = FALSE, sampleName = "sampleName" ){
 #'  tidyr::nest()
 #'
 #' x <- xnested$data[[1]]
-#' nn  <- x |> dplyr::select( setdiff(configur$table$hierarchy_keys(),  configur$table$hierarchy_keys_depth()) ) |>
+#' nn  <- x |> dplyr::select( base::setdiff(configur$table$hierarchy_keys(),
+#'   configur$table$hierarchy_keys_depth()) ) |>
 #'  dplyr::distinct() |> nrow()
 #'
 #' xx <- response_as_matrix(x,conf)
@@ -292,7 +294,7 @@ response_as_matrix <- function(pdata, config ){
   table <- config$table
   .extractInt(pdata,
               table$get_response(),
-              setdiff(table$hierarchy_keys(), table$hierarchy_keys_depth()),
+              base::setdiff(table$hierarchy_keys(), table$hierarchy_keys_depth()),
               table$sampleName)
 }
 
@@ -320,7 +322,8 @@ response_as_matrix <- function(pdata, config ){
 #' xnested <- data |>
 #'   dplyr::group_by_at(conf$table$hierarchy_keys_depth()) |> tidyr::nest()
 #'
-#' feature <- setdiff(conf$table$hierarchy_keys(),  conf$table$hierarchy_keys_depth())
+#' feature <- base::setdiff(conf$table$hierarchy_keys(),
+#'   conf$table$hierarchy_keys_depth())
 #' x <- xnested$data[[1]]
 #' bb <- medpolish_estimate_df(x,
 #'  response = conf$table$get_response(),
@@ -356,7 +359,7 @@ medpolish_estimate_df <- function(pdata, response, feature, sampleName  ){
 #' xnested <- data |>
 #'   dplyr::group_by_at(conf$table$hierarchy_keys_depth()) |> tidyr::nest()
 #'
-#' feature <- setdiff(conf$table$hierarchy_keys(),  conf$table$hierarchy_keys_depth())
+#' feature <- base::setdiff(conf$table$hierarchy_keys(),  conf$table$hierarchy_keys_depth())
 #' x <- xnested$data[[1]]
 #' bb <- medpolish_estimate_dfconfig(x,conf)
 #' prolfqua:::.reestablish_condition(x,bb, conf)
@@ -366,7 +369,7 @@ medpolish_estimate_dfconfig <- function(pdata, config, name=FALSE){
     return("medpolish")
   }
 
-  feature <- setdiff(config$table$hierarchy_keys(),  config$table$hierarchy_keys_depth())
+  feature <- base::setdiff(config$table$hierarchy_keys(),  config$table$hierarchy_keys_depth())
   res <- medpolish_estimate_df(pdata,
                                response = config$table$get_response(),
                                feature = feature,
@@ -490,7 +493,7 @@ medpolish_estimate_dfconfig <- function(pdata, config, name=FALSE){
 #' xnested <- data |>
 #'   dplyr::group_by_at(conf$table$hierarchy_keys_depth()) |> tidyr::nest()
 #'
-#' feature <- setdiff(conf$table$hierarchy_keys(),  conf$table$hierarchy_keys_depth())
+#' feature <- base::setdiff(conf$table$hierarchy_keys(),  conf$table$hierarchy_keys_depth())
 #' x <- xnested$data[[1]]
 #' bb <- rlm_estimate(x,
 #'  response = conf$table$get_response(),
@@ -524,7 +527,7 @@ rlm_estimate <- function(pdata, response, feature , samples, maxIt = 20) {
 #' xnested <- data |>
 #'   dplyr::group_by_at(conf$table$hierarchy_keys_depth()) |> tidyr::nest()
 #'
-#' feature <- setdiff(conf$table$hierarchy_keys(),  conf$table$hierarchy_keys_depth())
+#' feature <- base::setdiff(conf$table$hierarchy_keys(),  conf$table$hierarchy_keys_depth())
 #' x <- xnested$data[[1]]
 #' bb <- rlm_estimate_dfconfig(x, conf)
 #'
@@ -533,7 +536,7 @@ rlm_estimate <- function(pdata, response, feature , samples, maxIt = 20) {
 rlm_estimate_dfconfig <- function(pdata, config, name= FALSE){
   if (name) {return("lmrob")}
 
-  feature <- setdiff(config$table$hierarchy_keys(),  config$table$hierarchy_keys_depth())
+  feature <- base::setdiff(config$table$hierarchy_keys(),  config$table$hierarchy_keys_depth())
   rlm_estimate(pdata, response = config$table$get_response(),
                feature = feature,
                samples = config$table$sampleName

R/tidyMS_missigness.R

@@ -144,19 +144,19 @@ interaction_missing_stats <- function(pdata,
 
       pid <- config$table$hierarchy_keys_depth()
       nrReplicates <- mstats |>
-        dplyr::select( -one_of(c(setdiff(x_summaries,"nrReplicates"),"imputed") )) |>
+        dplyr::select( -one_of(c(base::setdiff(x_summaries,"nrReplicates"),"imputed") )) |>
         tidyr::spread(interaction, nrReplicates, sep = ".nrReplicates.") |>
         arrange(!!!syms(pid)) |>
         dplyr::ungroup()
-      nrMeasured <- mstats |> dplyr::select(-one_of(c(setdiff(x_summaries,"nrMeasured"),"imputed" ) )) |>
+      nrMeasured <- mstats |> dplyr::select(-one_of(c(base::setdiff(x_summaries,"nrMeasured"),"imputed" ) )) |>
         tidyr::spread(interaction, nrMeasured, sep = ".nrMeasured.") |>
         arrange(!!!syms(pid)) |> dplyr::ungroup()
 
-      meanAbundance <- mstats |> dplyr::select(-one_of(c(setdiff(x_summaries,"meanAbundance"),"imputed" ) )) |>
+      meanAbundance <- mstats |> dplyr::select(-one_of(c(base::setdiff(x_summaries,"meanAbundance"),"imputed" ) )) |>
         tidyr::spread(interaction, meanAbundance, sep = ".meanAbundance.") |>
         arrange(!!!syms(pid)) |> dplyr::ungroup()
 
-      meanAbundanceImputed <- mstats |> dplyr::select(-one_of(setdiff(x_summaries,"imputed" ) )) |>
+      meanAbundanceImputed <- mstats |> dplyr::select(-one_of(base::setdiff(x_summaries,"imputed" ) )) |>
         tidyr::spread(interaction, .data$imputed, sep = ".imputed.") |>
         arrange(!!!syms(pid)) |> dplyr::ungroup()