geneView_wilson.Rmd

### {{ title }}

<!-- Component created on {{ date }} -->

```{r}
{{ env_id }} <- readRDS("envs/{{ env_id }}.rds")

is_shiny <- identical(knitr::opts_knit$get("rmarkdown.runtime"), "shiny")
```

```{r, eval=!is_shiny}
# Parameters for wilson::create_geneview()
# params <- list()
"%ni%" <- Negate("%in%")
additional_arguments <- {{ env_id }}$additional_arguments

if("plot.type" %ni% names({{ env_id }}$additional_arguments)){
  additional_arguments$plot.type <- "line"
}
if("facet.target" %ni% names({{ env_id }}$additional_arguments)){
  additional_arguments$facet.target <- "gene"
}
if("facet.cols" %ni% names({{ env_id }}$additional_arguments)){
  additional_arguments$facet.cols <- 3
}
if("color" %ni% names({{ env_id }}$additional_arguments)){
  color <- RColorBrewer::brewer.pal(8, "Accent")
  additional_arguments$color <- color
}

# force static
additional_arguments$plot.method <- "static"

# set variables
countTable <- {{ env_id }}$countTable
group_by <- {{ env_id }}$group_by[1]

# create data.tables "data" and "grouping" from provided data
data <- data.table::data.table("features" = rownames(countTable), countTable)
grouping <- data.table::data.table("keys" = colnames(countTable), "factor" = group_by[[1]])

additional_arguments$data <- data
additional_arguments$grouping <- grouping

# Provide data for download
#i2dash::embed_var(data)

# Render plot
output_list <- do.call(wilson::create_geneview, additional_arguments)
plot <- output_list$plot
plot
```

```{r, eval=is_shiny}
################ UI ####################
ui_list <- list()

# select type of plot
ui_list <- rlist::list.append(ui_list,
                                selectInput("select_type_{{ env_id }}", label = "Type of Plot:",
                                            choices = c("line", "box", "violin", "bar"), selected = "line"))

# subset features
ui_list <- rlist::list.append(ui_list,
                                selectInput("select_subset_{{ env_id }}",
                                  label = "Select features:",
                                  choices = rownames({{ env_id }}$countTable),
                                  multiple = TRUE)
                              )

# selection field for group_by
if ({{ env_id }}$group_by_selection){
  ui_list <- rlist::list.append(ui_list,
                                selectInput("select_group_by_{{ env_id }}", label = "Select grouping:",
                                            choices = names({{ env_id }}$group_by)))
}
# selection grouping by
ui_list <- rlist::list.append(ui_list,
                                selectInput("select_by_{{ env_id }}",
                                  label = "Grouping by:",
                                  choices = c("gene", "condition"),
                                  selected = "gene",
                                  multiple = FALSE)
                              )
# selection column number of plot
ui_list <- rlist::list.append(ui_list,
                                sliderInput("colnumber_{{ env_id }}", label = "Number of plot columns:", min = 1, max = 7, value = 3)
                              )

# Download link
ui_list <- rlist::list.append(ui_list, tags$div(tags$br(), downloadButton('downloadData_{{ env_id }}', 'Download data')))

################# Server ####################
# if component is a reciever
if({{ env_id }}$compId %in% edgeTable$reciever){
  
  # set variables
  reciever_{{ env_id }} <- {{ env_id }}$compId
  transmitter_to_{{ env_id }} <- edgeTable[reciever=={{ env_id }}$compId]$transmitter

  df_{{ env_id }} <- shiny::reactive({
    
    # Parameters for wilson::create_geneview()
    # params <- list()
    "%ni%" <- Negate("%in%")
    additional_arguments <- {{ env_id }}$additional_arguments
    
      # type of plot
    additional_arguments$plot.type <- input$select_type_{{ env_id }}
    
    # type of grouping by
    additional_arguments$facet.target <- input$select_by_{{ env_id }}
    
    # number of columns in plot
    additional_arguments$facet.cols <- input$colnumber_{{ env_id }}
    
    if("color" %ni% names({{ env_id }}$additional_arguments)){
      color <- RColorBrewer::brewer.pal(8, "Accent")
      additional_arguments$color <- color
    }
    
    additional_arguments$plot.method <- "static"
    
    # Set values for 'group_by' and 'countTable'
    if( !{{ env_id }}$group_by_selection ) {
      group_by <- {{ env_id }}$group_by[1]
    } else {
      group_by <- {{ env_id }}$group_by[input$select_group_by_{{ env_id }}]
    }
    countTable <- {{ env_id }}$countTable
    
    # subset countTable according to transmitted sample keys
    plot_transmitter_string <- paste0("plot_env_", transmitter_to_{{ env_id }})
    keys_transmitter_string <- paste0("df_env_", transmitter_to_{{ env_id }}, "()$key")
    selection_transmitter <- plotly::event_data("plotly_selected", source = plot_transmitter_string)$key
    if(!is.null(selection_transmitter)){
      if(all(selection_transmitter %in% colnames(countTable))){
        countTable <- subset(countTable, select = selection_transmitter)
        # subset group_by according to countTable
        group_by <- group_by[[1]][which(colnames(countTable) == selection_transmitter)]
        group_by <- list("group_by" = group_by)
      } else {
        false_keys <- which(!(selection_transmitter %in% colnames(countTable)))
        print("The following keys are not in the countTable:")
        print(selection_transmitter[false_keys])
        if(!is.null(ncol(selection_transmitter[-false_keys]))){
          selection_transmitter <- selection_transmitter[-false_keys]
          countTable <- subset(countTable, select = selection_transmitter)
          # subset group_by according to countTable
          group_by <- group_by[[1]][which(colnames(countTable) == selection_transmitter)]
          group_by <- list("group_by" = group_by)
        }
      }
    }
    
    # subset countTable by chosen features
    if(!is.null(input$select_subset_{{ env_id }})){
      subset_features <- input$select_subset_{{ env_id }}
      if(length(subset_features) > 1){
        countTable <- countTable[subset_features,]
      } else if(length(subset_features) == 1){
        countTable <- countTable[subset_features,,drop = FALSE]
      }
    }
    
    # create data.tables "data" and "grouping" from provided data
    data <- data.table::data.table("features" = rownames(countTable), countTable)
    grouping <- data.table::data.table("keys" = colnames(countTable), "factor" = group_by[[1]])
    download_dt <- data.table::data.table("features" = rownames(countTable), "factor" = group_by[[1]], countTable)
    additional_arguments$data <- data
    additional_arguments$grouping <- grouping
    #additional_arguments$width <- 20
    #additional_arguments$height <- 15 
    
    return(list("params" = additional_arguments, "data" = download_dt))
  })
} else {
  df_{{ env_id }} <- shiny::reactive({
    
    # Parameters for wilson::create_geneview()
    # params <- list()
    "%ni%" <- Negate("%in%")
    additional_arguments <- {{ env_id }}$additional_arguments
    
      # type of plot
    additional_arguments$plot.type <- input$select_type_{{ env_id }}
    
    # type of grouping by
    additional_arguments$facet.target <- input$select_by_{{ env_id }}
    
    # number of columns in plot
    additional_arguments$facet.cols <- input$colnumber_{{ env_id }}
    
    if("color" %ni% names({{ env_id }}$additional_arguments)){
      color <- RColorBrewer::brewer.pal(8, "Accent")
      additional_arguments$color <- color
    }
    
    additional_arguments$plot.method <- "static"
    
    # Set values for 'group_by' and 'countTable'
    if( !{{ env_id }}$group_by_selection ) {
      group_by <- {{ env_id }}$group_by[1]
    } else {
      group_by <- {{ env_id }}$group_by[input$select_group_by_{{ env_id }}]
    }
    countTable <- {{ env_id }}$countTable
    
    # subset countTable by chosen features
    if(!is.null(input$select_subset_{{ env_id }})){
      subset_features <- input$select_subset_{{ env_id }}
      if(length(subset_features) > 1){
        countTable <- countTable[subset_features,]
      } else if(length(subset_features) == 1){
        countTable <- countTable[subset_features,,drop = FALSE]
      }
    }
    #
    # create data.tables "data" and "grouping" from provided data
    data <- data.table::data.table("features" = rownames(countTable), countTable)
    grouping <- data.table::data.table("keys" = colnames(countTable), "factor" = group_by[[1]])
    download_dt <- data.table::data.table("features" = rownames(countTable), "factor" = group_by[[1]], countTable)
    additional_arguments$data <- data
    additional_arguments$grouping <- grouping
    #additional_arguments$width <- 20
    #additional_arguments$height <- 15
    return(list("params" = additional_arguments, "data" = download_dt))
  })
}

# Download
output$downloadData_{{ env_id }} <- downloadHandler(
  filename =  paste('data-', Sys.Date(), '.csv', sep=''),
  content = function(file) {
    write.csv(df_{{ env_id }}()$data, file)
  }
)

# works as transmitter with brushopt
plot_{{ env_id }} <- shiny::reactive({
    if(!is.null(input$select_subset_{{ env_id }})){
      output_list <- do.call(wilson::create_geneview, df_{{ env_id }}()$params)
      dt <- output_list$plot$data
      return(dt)
    }
})

# Output
output$plot_{{ env_id }} <- renderPlot({
    if(!is.null(input$select_subset_{{ env_id }})){
      output_list <- do.call(wilson::create_geneview, df_{{ env_id }}()$params)
      gg <- output_list$plot
      gg
      #p <- plotly::ggplotly(gg)
      #p
    }
    # convert to plotly object for automatic resizing
    
})

# Layout of component
shiny::fillRow(flex = c(NA, 1),
        shinyWidgets::dropdownButton(div(style='max-height: 350px; overflow-x: auto;',do.call(shiny::inputPanel, ui_list)),
                       circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
                       tooltip = shinyWidgets::tooltipOptions(title = "Click, to change plot settings:")),
        plotOutput("plot_{{ env_id }}", width = "100%",click = "plot1_click",
        brush = brushOpts(
          id = "plot1_brush"
        ))
)
```