In loosolab/wilson: Web-Based Interactive Omics Visualization

{{ env_id }} = readRDS("envs/{{ env_id }}.rds")

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

# Parameters for wilson::create_scatterplot
"%ni%" <- Negate("%in%")
additional_arguments <- {{ env_id }}$additional_arguments

# force interctive parameter
additional_arguments$plot.method <- "interactive"

if("density" %ni% names(additional_arguments)){
  additional_arguments$density <- F
}
if("line" %ni% names(additional_arguments)){
  additional_arguments$line <- F
}
if("categorized" %ni% names(additional_arguments)){
  additional_arguments$categorized <- F
}
# Set values for 'x'
x <- {{ env_id }}$x[1]

# Set values for 'y'
y <- {{ env_id }}$y[1]

# Set values for 'colour_by'
if (!is.null({{ env_id }}$colour_by)){
  colour_by <- {{ env_id }}$colour_by[1]
}

# Set values for id'
id <- c(1:length(x[[1]]))

# Create a data.frame
df <- data.frame(id, x, y)

# if colour_by provided
if(!is.null({{ env_id }}$colour_by)){
  df["colour_by"] <- colour_by
  # if colour_by is character
  if(is.character(df[["colour_by"]])){
    additional_arguments$categorized <- T
  # if colour_by is factor
  } else if (is.factor(df[["colour_by"]])){
    additional_arguments$categorized <- T
    df["colour_by"] <- droplevels(df["colour_by"])
  # if colour_by is numeric
  } else if (is.numeric(df[["colour_by"]])){
    if("categorized" %in% names({{ env_id }}$additional_arguments)){
      additional_arguments$categorized <- {{ env_id }}$additional_arguments$categorized
    }
  }
}

# color
if(additional_arguments$categorized){
  # categorical (qualitative) color palettes
  if("color" %ni% names({{ env_id }}$additional_arguments)){
    color <- RColorBrewer::brewer.pal(8, "Accent")
    additional_arguments$color <- color
  }
} else {
  # sequential (one-sided) color palettes
  if("color" %ni% names({{ env_id }}$additional_arguments)){
    color <- RColorBrewer::brewer.pal(9, "YlOrRd")
    additional_arguments$color <- color
  }
}

# Create data.table from data.frame
dt <- data.table::setDT(df)
additional_arguments$data <- dt

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

# Render plot
output_list <- do.call(wilson::create_scatterplot, additional_arguments)
gg <- output_list$plot
gg$x$layout$height <- 0
gg$x$layout$width <- 0

gg
# convert to plotly object for automatic resizing
#plotly::ggplotly(gg)

ui_list <- list()
# selection field for x
if ({{ env_id }}$x_selection){
  ui_list <- rlist::list.append(ui_list,
                                selectInput("select_x_{{ env_id }}", label = "Select data for x axis:",
                                            choices = names({{ env_id }}$x)))
}

# selection field for y
if ({{ env_id }}$y_selection){
  ui_list <- rlist::list.append(ui_list,
                                selectInput("select_y_{{ env_id }}", label = "Select data for y axis:",
                                            choices = names({{ env_id }}$y)))
}

# selection field for colour_by
if ({{ env_id }}$colour_by_selection){
  ui_list <- rlist::list.append(ui_list,
                                selectInput("select_colour_{{ env_id }}", label = "Select colouring:",
                                            choices = names({{ env_id }}$colour_by)))
}

# Checkbox and selection field for colour by feature
if (!is.null({{ env_id }}$expression)) {
  ui_list <- rlist::list.append(ui_list,
                                tags$div(checkboxInput("expr_checkbox_{{ env_id }}", label = "Colour by feature", value = FALSE),
                                         selectInput("select_feature_{{ env_id }}", label = NULL, choices = rownames({{ env_id }}$expression))
  ))
}

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

#
# Create reactive data table
#
df_{{ env_id }} <- shiny::reactive({

  # Parameters for wilson::create_scatterplot
  "%ni%" <- Negate("%in%")
  additional_arguments <- {{ env_id }}$additional_arguments

  # force to use interactive parameter
  additional_arguments$plot.method <- "interactive"

  if("density" %ni% names(additional_arguments)){
    additional_arguments$density <- F
  }
  if("line" %ni% names(additional_arguments)){
    additional_arguments$line <- F
  }
  if("categorized" %ni% names(additional_arguments)){
    additional_arguments$categorized <- F
  }
  # Set values for 'x'
  if( !{{ env_id }}$x_selection ) {
    x <- {{ env_id }}$x[1]
  } else {
    x <- {{ env_id }}$x[input$select_x_{{ env_id }}]
  }
  # Set values for 'y'
  if( !{{ env_id }}$y_selection ) {
    y <- {{ env_id }}$y[1]
  } else {
    y <- {{ env_id }}$y[input$select_y_{{ env_id }}]
  }
  # Set values for 'colour_by'
  if (!{{ env_id }}$colour_by_selection){
    colour_by <- {{ env_id }}$colour_by[1]
  } else {
    colour_by <- {{ env_id }}$colour_by[input$select_colour_{{ env_id }}]
  }
  # Set values for id'
  id <- c(1:length(x[[1]]))

  # Create a data.frame
  df <- data.frame(id, x, y)

  # if checkbox for expression exists
  if(!is.null(input$expr_checkbox_{{ env_id }})){
    if(input$expr_checkbox_{{ env_id }}){
      df["colour_by"] <- {{ env_id }}$expression[input$select_feature_{{ env_id }},]
    } else {
      if(!is.null({{ env_id }}$colour_by)){
        df["colour_by"] <- colour_by
        # if colour_by is character
        if(is.character(df[["colour_by"]])){
          additional_arguments$categorized <- T
        # if colour_by is factor
        } else if (is.factor(df[["colour_by"]])){
          additional_arguments$categorized <- T
          df["colour_by"] <- droplevels(df["colour_by"])
        # if colour_by is numeric
        } else if (is.numeric(df[["colour_by"]])){
          if("categorized" %in% names({{ env_id }}$additional_arguments)){
            additional_arguments$categorized <- {{ env_id }}$additional_arguments$categorized
          }
        }
      }
    }
  } else {
    # if colour_by provided
    if(!is.null({{ env_id }}$colour_by)){
      df["colour_by"] <- colour_by
      # if colour_by is character
      if(is.character(df[["colour_by"]])){
        additional_arguments$categorized <- T
      # if colour_by is factor
      } else if (is.factor(df[["colour_by"]])){
        additional_arguments$categorized <- T
        df["colour_by"] <- droplevels(df["colour_by"])
      # if colour_by is numeric
      } else if (is.numeric(df[["colour_by"]])){
        if("categorized" %in% names({{ env_id }}$additional_arguments)){
          additional_arguments$categorized <- {{ env_id }}$additional_arguments$categorized
        }
      }
    }
  }
  # color
  if(additional_arguments$categorized){
    # categorical (qualitative) color palettes
    if("color" %ni% names({{ env_id }}$additional_arguments)){
      color <- RColorBrewer::brewer.pal(8, "Accent")
      additional_arguments$color <- color
    }
  } else {
    # sequential (one-sided) color palettes
    if("color" %ni% names({{ env_id }}$additional_arguments)){
      color <- RColorBrewer::brewer.pal(9, "YlOrRd")
      additional_arguments$color <- color
    }
  }

  # Create data.table from data.frame
  dt <- data.table::setDT(df)
  additional_arguments$data <- dt

  return(list("params" = additional_arguments, "data" = dt))
})

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

#
# Output
#
output$plot_{{ env_id }} <- plotly::renderPlotly({
    output_list <- do.call(wilson::create_scatterplot, df_{{ env_id }}()$params)
    gg <- output_list$plot

    # convert to plotly object for automatic resizing
    gg$x$layout$height <- 0
    gg$x$layout$width <- 0

    gg
})

#
# Layout of component
#
shiny::fillCol(flex = c(NA, 1),
        do.call(shiny::inputPanel, ui_list),
        plotly::plotlyOutput("plot_{{ env_id }}", height = "100%")
)