clean up dplyr and reshape2 imports

R/alluvialClones.R

@@ -64,8 +64,7 @@
 #' [hcl.pals][grDevices::hcl.pals].
 #'
 #' @importFrom ggalluvial StatStratum geom_flow geom_stratum to_lodes_form geom_alluvium
-#' @importFrom dplyr %>% mutate
-#' 
+#'
 #' @export
 #' @concept SC_Functions
 #' @return Alluvial ggplot comparing clone distribution.

R/clonalCluster.R

@@ -37,7 +37,6 @@
 #' @importFrom stringdist stringdist
 #' @importFrom igraph set_vertex_attr V union
 #' @importFrom plyr join
-#' @importFrom dplyr bind_rows summarize
 #' @importFrom stringr str_replace_all
 #' @importFrom rlang %||%
 #' @importFrom SummarizedExperiment colData<- colData

R/clonalDiversity.R

@@ -67,7 +67,7 @@
 #' @param return.boots export boot strapped values calculated - 
 #' will automatically exportTable = TRUE.
 #' @param skip.boots remove down sampling and boot strapping from the calculation.
-#' @importFrom reshape2 melt
+#'
 #' @importFrom dplyr sample_n
 #' @export
 #' @concept Visualizing_Clones

R/clonalHomeostasis.R

@@ -30,8 +30,7 @@
 #' environment in addition to the visualization.
 #' @param palette Colors to use in visualization - input any 
 #' [hcl.pals][grDevices::hcl.pals].
-#' @importFrom reshape2 melt
-#' @importFrom dplyr bind_rows
+#'
 #' @export
 #' @concept Visualizing_Clones
 #' @return ggplot of the space occupied by the specific proportion of clones

R/clonalNetwork.R

@@ -48,10 +48,10 @@
 #' @param exportClones Exports a table of clones that are shared
 #' across multiple identity groups and ordered by the total number
 #' of clone copies.
-#' @param palette Colors to use in visualization - input any 
+#' @param palette Colors to use in visualization - input any
 #' [hcl.pals][grDevices::hcl.pals].
 #' @importFrom igraph graph_from_data_frame V `V<-`
-#' @importFrom dplyr %>% group_by select summarize_all count n across all_of desc
+#' @importFrom dplyr summarize_all count across all_of desc
 #' @importFrom tidygraph as_tbl_graph activate
 #' @importFrom ggraph ggraph geom_edge_bend  geom_node_point scale_edge_colour_gradientn circle guide_edge_colourbar
 #' @importFrom stats setNames

R/clonalOccupy.R

@@ -35,8 +35,7 @@
 #' environment in addition to the visualization
 #' @param palette Colors to use in visualization - input any 
 #' [hcl.pals][grDevices::hcl.pals]
-#' @importFrom dplyr %>% group_by mutate count
-#' @importFrom reshape2 melt
+#' @importFrom dplyr count
 #' @export
 #' @concept SC_Functions
 #' @return Stacked bar plot of counts of cells by clone frequency group

R/clonalOverlap.R

@@ -57,7 +57,6 @@
 #' @param palette Colors to use in visualization - input any 
 #' [hcl.pals][grDevices::hcl.pals]
 #' @importFrom stringr str_to_title
-#' @importFrom reshape2 melt
 #' @importFrom stats quantile
 #' @export
 #' @concept Visualizing_Clones

R/clonalProportion.R

@@ -31,9 +31,6 @@
 #' @param palette Colors to use in visualization - input any 
 #' [hcl.pals][grDevices::hcl.pals]
 #'
-#' @importFrom reshape2 melt
-#' @importFrom dplyr bind_rows n
-#'
 #' @export
 #' @concept Visualizing_Clones
 #' @return ggplot of the space occupied by the specific rank of clones

R/clonalSizeDistribution.R

@@ -30,34 +30,34 @@
 #' 
 #' @examples
 #' #Making combined contig data
-#' combined <- combineTCR(contig_list, 
-#'                         samples = c("P17B", "P17L", "P18B", "P18L", 
+#' combined <- combineTCR(contig_list,
+#'                         samples = c("P17B", "P17L", "P18B", "P18L",
 #'                                     "P19B","P19L", "P20B", "P20L"))
 #' clonalSizeDistribution(combined, cloneCall = "strict", method="ward.D2")
 #'
-#' @param input.data The product of [combineTCR()], 
+#' @param input.data The product of [combineTCR()],
 #' [combineBCR()], or [combineExpression()].
-#' @param cloneCall How to call the clone - VDJC gene (**gene**), 
+#' @param cloneCall How to call the clone - VDJC gene (**gene**),
 #' CDR3 nucleotide (**nt**), CDR3 amino acid (**aa**),
-#' VDJC gene + CDR3 nucleotide (**strict**) or a custom variable 
-#' in the data. 
-#' @param chain indicate if both or a specific chain should be used - 
+#' VDJC gene + CDR3 nucleotide (**strict**) or a custom variable
+#' in the data.
+#' @param chain indicate if both or a specific chain should be used -
 #' e.g. "both", "TRA", "TRG", "IGH", "IGL".
-#' @param threshold Numerical vector containing the thresholds 
+#' @param threshold Numerical vector containing the thresholds
 #' the grid search was performed over.
 #' @param method The clustering parameter for the dendrogram.
 #' @param group.by The variable to use for grouping.
 #' @param exportTable Returns the data frame used for forming the graph.
-#' @param palette Colors to use in visualization - input any 
+#' @param palette Colors to use in visualization - input any
 #' [hcl.pals][grDevices::hcl.pals].
-#' @importFrom dplyr bind_rows
+#'
 #' @importFrom ggdendro dendro_data segment label
 #' @importFrom stats hclust optim pgamma as.dist
 #' @export
 #' @concept Visualizing_Clones
 #' @return ggplot dendrogram of the clone size distribution
 #' @author Hillary Koch
-
+#'
 clonalSizeDistribution <- function(input.data,
                                    cloneCall ="strict", 
                                    chain = "both", 

R/combineContigs.R

@@ -193,8 +193,7 @@ combineTCR <- function(input.data,
 #' @param filterNonproductive This option will allow for the removal of 
 #' nonproductive chains if the variable exists in the contig data. Default
 #' is set to TRUE to remove nonproductive contigs.
-#' @importFrom dplyr %>% mutate
-#' @importFrom assertthat assert_that is.flag
+#'
 #' @export
 #' @concept Loading_and_Processing_Contigs
 #' @return List of clones for individual cell barcodes

R/combineExpression.R

@@ -45,11 +45,10 @@
 #' @param addLabel This will add a label to the frequency header, allowing
 #' the user to try multiple group.by variables or recalculate frequencies after 
 #' subsetting the data.
-#' @importFrom dplyr bind_rows %>% summarise left_join mutate select n all_of coalesce
+#' @importFrom dplyr left_join all_of coalesce
 #' @importFrom  rlang %||% sym :=
 #' @importFrom SummarizedExperiment colData<- colData
 #' @importFrom S4Vectors DataFrame
-#' @importFrom assertthat assert_that is.string is.flag
 #' @export
 #' @concept SC_Functions
 #' @return Single-cell object with clone information added to meta data

R/exportClones.R

@@ -25,7 +25,6 @@
 #' return a data.frame
 #' @param dir directory location to save the csv
 #' @param file.name the csv file name
-#' @importFrom dplyr bind_rows
 #' @importFrom utils write.csv
 #' @export
 #' @concept Loading_and_Processing_Contigs
@@ -57,8 +56,8 @@ exportClones <- function(input.data,
   write.csv(mat, file = filepath)
 }
 
-#' @importFrom dplyr %>% group_by mutate
-.TCRmatchExport<- function(input.data) {
+
+.TCRmatchExport <- function(input.data) {
 
   input.data <- .data.wrangle(input.data, NULL, "CTgene", "TRB")
 

R/percentAA.R

@@ -21,8 +21,7 @@
 #' @param aa.length The maximum length of the CDR3 amino acid sequence. 
 #' @param exportTable Returns the data frame used for forming the graph.
 #' @param palette Colors to use in visualization - input any [hcl.pals][grDevices::hcl.pals].
-#' @importFrom reshape2 melt
-#' @importFrom dplyr mutate_at %>% mutate_if
+#' @importFrom dplyr mutate_at mutate_if
 #' @export
 #' @concept Summarize_Repertoire
 #' @return ggplot of stacked bar graphs of amino acid proportions

R/percentGenes.R

@@ -24,7 +24,6 @@
 #' @param exportTable Returns the data frame used for forming the graph.
 #' @param palette Colors to use in visualization - input any 
 #' [hcl.pals][grDevices::hcl.pals].
-#' @importFrom reshape2 melt
 #' @export
 #' @concept Summarize_Repertoire
 #' @return ggplot of percentage of indicated genes as a heatmap

R/percentKmer.R

@@ -27,7 +27,6 @@
 #' @param exportTable Returns the data frame used for forming the graph.
 #' @param palette Colors to use in visualization - input any 
 #' [hcl.pals][grDevices::hcl.pals]
-#' @importFrom reshape2 melt
 #' @importFrom stats mad
 #' @export
 #' @concept Summarize_Repertoire

R/percentVJ.R

@@ -20,7 +20,6 @@
 #' @param exportTable Returns the data frame used for forming the graph
 #' @param palette Colors to use in visualization - input any 
 #' [hcl.pals][grDevices::hcl.pals].
-#' @importFrom reshape2 melt
 #' @export
 #' @concept Summarize_Repertoire
 #' @return ggplot of percentage of V and J gene pairings as a heatmap

R/positionalProperty.R

@@ -41,7 +41,6 @@
 #' @param exportTable Returns the data frame used for forming the graph
 #' @param palette Colors to use in visualization - input any [hcl.pals][grDevices::hcl.pals]
 #' @importFrom stats qt
-#' @importFrom dplyr %>% summarise n group_by 
 #' @export
 #' @concept Summarize_Repertoire
 #' @return ggplot of line graph of diversity by position

R/scRepertoire-package.R

@@ -9,5 +9,8 @@
 #' @import ggplot2
 #' @importFrom assertthat assert_that is.count is.flag is.string
 #' @importFrom stringr str_sort str_split
+#' @importFrom dplyr %>% select mutate filter arrange bind_rows group_by n
+#' @importFrom dplyr summarise summarize
+#' @importFrom reshape2 melt
 ## usethis namespace: end
 NULL

R/startracDiversity.R

@@ -39,8 +39,7 @@
 #' @param group.by The variable in the meta data to group by, often samples.
 #' @param exportTable Returns the data frame used for forming the graph.
 #' @param palette Colors to use in visualization - input any [hcl.pals][grDevices::hcl.pals].
-#' @importFrom reshape2 melt
-#' @importFrom dplyr %>% mutate group_by
+#'
 #' @export
 #' @concept SC_Functions
 #' @return ggplot object of Startrac diversity metrics

R/utils.R

@@ -57,7 +57,7 @@
   })
 }
 
-#' @importFrom dplyr %>% group_by arrange desc ungroup count mutate
+#' @importFrom dplyr desc ungroup count
 #' @importFrom rlang ensym !!
 .clone.counter <- function(meta,
                            group.by, 
@@ -164,7 +164,6 @@
     df
 }
 
-#' @importFrom dplyr bind_rows
 #' @keywords internal
 .bound.input.return <- function(df) {
   if (is_seurat_or_se_object(df)) {
@@ -264,7 +263,7 @@
 }
 
 #Removing extra clones in barcodes with > 2 productive contigs
-#' @importFrom dplyr group_by %>% slice_max
+#' @importFrom dplyr slice_max
 #' @keywords internal
 .filteringMulti <- function(x) {
     x <- x %>%
@@ -290,7 +289,7 @@
 
 
 #Filtering NA contigs out of single-cell expression object
-#' @importFrom dplyr %>% transmute
+#' @importFrom dplyr transmute
 #' @importFrom SingleCellExperiment colData
 #' @keywords internal
 .filteringNA <- function(sc) {
@@ -312,7 +311,6 @@
 
 #Organizing list of contigs for visualization
 #' @keywords internal
-#' @importFrom dplyr %>% group_by n summarise
 .parseContigs <- function(df, i, names, cloneCall) {
     data <- df[[i]]
     data1 <- data %>% 
@@ -371,7 +369,6 @@
 # but now also constructs Con.df and runs the parseTCR algorithm on it, all in Rcpp
 #' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding, Qile Yang
 #' @keywords internal
-#' @importFrom dplyr %>% arrange
 .constructConDfAndParseTCR <- function(data2) {
   rcppConstructConDfAndParseTCR(
     data2 %>% dplyr::arrange(., chain, cdr3_nt),
@@ -488,7 +485,6 @@
 
 #Sorting the V/D/J/C gene sequences for T and B cells
 #' @importFrom stringr str_c str_replace_na
-#' @importFrom dplyr bind_rows mutate %>%
 #' @keywords internal
 .makeGenes <- function(cellType, data2, chain1, chain2) {
     if(cellType %in% c("T")) {
@@ -575,7 +571,6 @@ is_df_or_list_of_df <- function(x) {
 # nucleotide sequence.
 #' @importFrom stringdist stringdist
 #' @importFrom igraph graph_from_data_frame components graph_from_edgelist
-#' @importFrom dplyr bind_rows filter
 #' @keywords internal
 .lvCompare <- function(dictionary, gene, chain, threshold, exportGraph = FALSE) {
   overlap <- NULL

R/vizGenes.R

@@ -33,7 +33,7 @@
 #' @param palette Colors to use in visualization - input any 
 #' [hcl.pals][grDevices::hcl.pals].
 #' @importFrom stats sd
-#' @importFrom dplyr bind_rows %>% group_by mutate ungroup summarise
+#' @importFrom dplyr ungroup
 #' @export
 #' @concept Visualizing_Clones
 #' @return ggplot bar diagram or heatmap of gene usage