使用R Shiny中的对数刻度减少绘图散点图中的网格线数量 [英] Reduce number of gridlines in plotly scatter plots with log scale in R shiny

问题描述

我已经构建了以下测试应用程序，在该应用程序中，我解决了该问题，以将刻度线标签作为科学注释获得，但是现在我希望将网格线的数量减少到仅放置在主要"刻度线处，即有文字标签的标签. 该问题是基于对先前

I've build the following test app where I solve the issue to get the tick labels as scientific annotation, but I would now like to reduce the number of grid lines to only be placed at the "main" ticks, i.e. the ones that have a text label. This question was posted based on discussion / comment on this previous SO question

我想找到一种适用于2D和3D散点图的方法，因为我同时使用了这两种方法.

I would like to find a way that works for both 2D and 3D plotly scatter plots since I am using both.

这是3D应用程序.

    library(shiny)
    library(plotly)

    shinyApp(
      ui = fluidPage( plotlyOutput('plot') ),

      server = function(input, output) {
        output$plot <- renderPlotly ({

          mtcars <- rbind(mtcars, mtcars*1000, mtcars/1000)  #create data with big logarithmic range
          maxlog <- round(log10(max(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0],mtcars[['cyl']][mtcars[['cyl']]>0])), digits = 0) +1 # determine max log needed
          minlog <- round(log10(min(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0],mtcars[['cyl']][mtcars[['cyl']]>0])), digits = 0) -1 # determine min log needed
          logrange <- (maxlog - minlog)*9 +1 # get the distance between smallest and largest log power
          tval <- sort(as.vector(sapply(seq(1,9), function(x) x*10^seq(minlog, maxlog)))) #generates a sequence of numbers in logarithmic divisions
          ttxt <- rep("",length(tval))  # no label at most of the ticks
          ttxt[seq(1,logrange,9)] <- formatC(tval, format = "e", digits = 2)[seq(1,logrange,9)] # every 9th tick is labelled


          p <- plot_ly(source = 'ThresholdScatter')
          p <- add_trace(p, data = mtcars, 
                      x = mtcars[['mpg']], 
                      y = mtcars[['disp']],
                      z = mtcars[['cyl']],
                      type = 'scatter3d', 
                      mode = 'markers',
                      marker = list(size = 2)) 

      p <- layout(p, autosize = F, width = 500, height = 500,
                  scene = list(yaxis = list(type="log",
                                            zeroline=F, showline=T, 
                                            ticks="outside",
                                            tickvals=tval,
                                            ticktext=ttxt),
                               xaxis = list(type="log",
                                            zeroline=F, showline=T, 
                                            ticks="outside",
                                            tickvals=tval,
                                            ticktext=ttxt),
                               zaxis = list(type="log",
                                            zeroline=F, showline=T, 
                                            ticks="outside",
                                            tickvals=tval,
                                            ticktext=ttxt),
                               camera = list(eye = list(x = -1.5, y = 1.5, z = 1.5))))
    })
  }
    )

，但相同，但为二维

        library(shiny)
        library(plotly)

        shinyApp(
          ui = fluidPage( plotlyOutput('plot') ),

          server = function(input, output) {
            output$plot <- renderPlotly ({

                  mtcars <- rbind(mtcars, mtcars*1000, mtcars/1000)  #create data with big logarithmic range
                  maxlog <- round(log10(max(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0])), digits = 0) +1 # determine max log needed
                  minlog <- round(log10(min(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0])), digits = 0) -1 # determine min log needed
                  logrange <- (maxlog - minlog)*9 +1 # get the distance between smallest and largest log power
                  tval <- sort(as.vector(sapply(seq(1,9), function(x) x*10^seq(minlog, 

    maxlog)))) #generates a sequence of numbers in logarithmic divisions
              ttxt <- rep("",length(tval))  # no label at most of the ticks
              ttxt[seq(1,logrange,9)] <- formatC(tval, format = "e", digits = 2)[seq(1,logrange,9)] # every 9th tick is labelled


              p <- plot_ly(source = 'ThresholdScatter')
              p <- add_trace(p, data = mtcars, 
                             x = mtcars[['mpg']], 
                             y = mtcars[['disp']],
                             type = 'scatter', 
                             mode = 'markers',
                             marker = list(size = 2)) 

              p <- layout(p,autosize = F, width = 500, height = 500,
                          yaxis = list(type="log",
                                         zeroline=F, showline=T, 
                                         ticks="outside",
                                         tickvals=tval,
                                         ticktext=ttxt),
                          xaxis = list(type="log",
                                       zeroline=F, showline=T, 
                                       ticks="outside",
                                       tickvals=tval,
                                       ticktext=ttxt))
            })
          }


  )

推荐答案

对于2D散点图，可以使用layout中的shapes选项绘制自己的网格线.然后，您还可以使用showgrid = FALSE取消网格线.

For the 2D scatterplot, you can draw your own grid lines using the shapes option in layout. You also then suppress the gridlines using showgrid = FALSE.

shinyApp(
  ui = fluidPage( plotlyOutput('plot') ),

  server = function(input, output) {

    hline <- function(y = 0, color = "grey", width=0.1) {
      list(type = "line", x0 = 0, x1 = 1, xref = "paper",
        y0 = y, y1 = y, line = list(color = color, width=width))
    }

    output$plot <- renderPlotly ({
      mtcars <- rbind(mtcars, mtcars*1000, mtcars/1000)  #create data with big logarithmic range
      maxlog <- round(log10(max(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0])), digits = 0) +1 # determine max log needed
      minlog <- round(log10(min(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0])), digits = 0) -1 # determine min log needed
      logrange <- (maxlog - minlog)*9 +1 # get the distance between smallest and largest log power
      tval <- sort(as.vector(sapply(seq(1,9), function(x) x*10^seq(minlog, 

        maxlog)))) #generates a sequence of numbers in logarithmic divisions
      ttxt <- rep("",length(tval))  # no label at most of the ticks
      ttxt[seq(1,logrange,9)] <- formatC(tval, format = "e", digits = 2)[seq(1,logrange,9)] # every 9th tick is labelled

      p <- plot_ly(source = 'ThresholdScatter')
      p <- add_trace(p, data = mtcars, 
        x = mtcars[['mpg']], 
        y = mtcars[['disp']],
        type = 'scatter', 
        mode = 'markers',
        marker = list(size = 2)) 

      p <- layout(p,autosize = F, width = 500, height = 500,
        yaxis = list(type="log",
          zeroline=F, showline=T, showgrid=F,
          ticks="outside",
          tickvals=tval,
          ticktext=ttxt),
        xaxis = list(type="log",
          zeroline=F, showline=T, showgrid=F,
          ticks="outside",
          tickvals=tval,
          ticktext=ttxt),
        shapes = lapply(10^(-1:6), hline))
    })
  }
)

不幸的是，我不认为您不能在3d图中使用此方法，因为形状没有z维度.您可以使用add_lines代替形状来做类似的事情，但这不会那么整洁.

Unfortunately, I don't think you can use this approach in the 3d plot, as shapes do not have a z dimension. You could do something similar using add_lines instead of shapes, but this won't be quite as neat.

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