使用 R shiny 中的对数刻度减少 plotly 散点图中的网格线数量
Reduce number of gridlines in plotly scatter plots with log scale in R shiny
我已经构建了以下测试应用程序,我解决了将刻度标签作为科学注释的问题,但我现在想减少网格线的数量以仅放置在 "main"刻度线,即具有文本标签的刻度线。
这个问题是根据之前
的讨论/评论发布的
我想找到一种适用于 2D 和 3D 绘图散点图的方法,因为我正在使用这两种方法。
这是 3D 应用程序。
library(shiny)
library(plotly)
shinyApp(
ui = fluidPage( plotlyOutput('plot') ),
server = function(input, output) {
output$plot <- renderPlotly ({
mtcars <- rbind(mtcars, mtcars*1000, mtcars/1000) #create data with big logarithmic range
maxlog <- round(log10(max(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0],mtcars[['cyl']][mtcars[['cyl']]>0])), digits = 0) +1 # determine max log needed
minlog <- round(log10(min(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0],mtcars[['cyl']][mtcars[['cyl']]>0])), digits = 0) -1 # determine min log needed
logrange <- (maxlog - minlog)*9 +1 # get the distance between smallest and largest log power
tval <- sort(as.vector(sapply(seq(1,9), function(x) x*10^seq(minlog, maxlog)))) #generates a sequence of numbers in logarithmic divisions
ttxt <- rep("",length(tval)) # no label at most of the ticks
ttxt[seq(1,logrange,9)] <- formatC(tval, format = "e", digits = 2)[seq(1,logrange,9)] # every 9th tick is labelled
p <- plot_ly(source = 'ThresholdScatter')
p <- add_trace(p, data = mtcars,
x = mtcars[['mpg']],
y = mtcars[['disp']],
z = mtcars[['cyl']],
type = 'scatter3d',
mode = 'markers',
marker = list(size = 2))
p <- layout(p, autosize = F, width = 500, height = 500,
scene = list(yaxis = list(type="log",
zeroline=F, showline=T,
ticks="outside",
tickvals=tval,
ticktext=ttxt),
xaxis = list(type="log",
zeroline=F, showline=T,
ticks="outside",
tickvals=tval,
ticktext=ttxt),
zaxis = list(type="log",
zeroline=F, showline=T,
ticks="outside",
tickvals=tval,
ticktext=ttxt),
camera = list(eye = list(x = -1.5, y = 1.5, z = 1.5))))
})
}
)
相同,但在 2D 中
library(shiny)
library(plotly)
shinyApp(
ui = fluidPage( plotlyOutput('plot') ),
server = function(input, output) {
output$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))
})
}
)
对于二维散点图,您可以使用 layout
中的 shapes
选项绘制自己的网格线。然后,您还可以使用 showgrid = FALSE
.
抑制网格线
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$plot <- renderPlotly ({
mtcars <- rbind(mtcars, mtcars*1000, mtcars/1000) #create data with big logarithmic range
maxlog <- round(log10(max(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0])), digits = 0) +1 # determine max log needed
minlog <- round(log10(min(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0])), digits = 0) -1 # determine min log needed
logrange <- (maxlog - minlog)*9 +1 # get the distance between smallest and largest log power
tval <- sort(as.vector(sapply(seq(1,9), function(x) x*10^seq(minlog,
maxlog)))) #generates a sequence of numbers in logarithmic divisions
ttxt <- rep("",length(tval)) # no label at most of the ticks
ttxt[seq(1,logrange,9)] <- formatC(tval, format = "e", digits = 2)[seq(1,logrange,9)] # every 9th tick is labelled
p <- plot_ly(source = 'ThresholdScatter')
p <- add_trace(p, data = mtcars,
x = mtcars[['mpg']],
y = mtcars[['disp']],
type = 'scatter',
mode = 'markers',
marker = list(size = 2))
p <- layout(p,autosize = F, width = 500, height = 500,
yaxis = list(type="log",
zeroline=F, showline=T, showgrid=F,
ticks="outside",
tickvals=tval,
ticktext=ttxt),
xaxis = list(type="log",
zeroline=F, showline=T, showgrid=F,
ticks="outside",
tickvals=tval,
ticktext=ttxt),
shapes = lapply(10^(-1:6), hline))
})
}
)
不幸的是,我认为您不能在 3d 图中使用这种方法,因为形状没有 z 维度。您可以使用 add_lines
而不是形状来做类似的事情,但这不会那么整洁。
在 Python 中,对于 3D 图,在 scene
字典中指定所有 Layout 属性,如下所示:
layout = go.Layout(
margin=dict(
l=0,
r=0,
b=0,
t=0
),
scene=dict(
xaxis=dict(
type='log',
autorange=True,
title='L1'))
)
我假设 R 中的最新版本的 plotly 具有相同的功能。
我已经构建了以下测试应用程序,我解决了将刻度标签作为科学注释的问题,但我现在想减少网格线的数量以仅放置在 "main"刻度线,即具有文本标签的刻度线。
这个问题是根据之前
我想找到一种适用于 2D 和 3D 绘图散点图的方法,因为我正在使用这两种方法。
这是 3D 应用程序。
library(shiny)
library(plotly)
shinyApp(
ui = fluidPage( plotlyOutput('plot') ),
server = function(input, output) {
output$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))))
})
}
)
相同,但在 2D 中
library(shiny)
library(plotly)
shinyApp(
ui = fluidPage( plotlyOutput('plot') ),
server = function(input, output) {
output$plot <- renderPlotly ({
mtcars <- rbind(mtcars, mtcars*1000, mtcars/1000) #create data with big logarithmic range
maxlog <- round(log10(max(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0])), digits = 0) +1 # determine max log needed
minlog <- round(log10(min(mtcars[['mpg']][mtcars[['mpg']]>0], mtcars[['disp']][mtcars[['disp']]>0])), digits = 0) -1 # determine min log needed
logrange <- (maxlog - minlog)*9 +1 # get the distance between smallest and largest log power
tval <- sort(as.vector(sapply(seq(1,9), function(x) x*10^seq(minlog,
maxlog)))) #generates a sequence of numbers in logarithmic divisions
ttxt <- rep("",length(tval)) # no label at most of the ticks
ttxt[seq(1,logrange,9)] <- formatC(tval, format = "e", digits = 2)[seq(1,logrange,9)] # every 9th tick is labelled
p <- plot_ly(source = 'ThresholdScatter')
p <- add_trace(p, data = mtcars,
x = mtcars[['mpg']],
y = mtcars[['disp']],
type = 'scatter',
mode = 'markers',
marker = list(size = 2))
p <- layout(p,autosize = F, width = 500, height = 500,
yaxis = list(type="log",
zeroline=F, showline=T,
ticks="outside",
tickvals=tval,
ticktext=ttxt),
xaxis = list(type="log",
zeroline=F, showline=T,
ticks="outside",
tickvals=tval,
ticktext=ttxt))
})
}
)
对于二维散点图,您可以使用 layout
中的 shapes
选项绘制自己的网格线。然后,您还可以使用 showgrid = FALSE
.
shinyApp(
ui = fluidPage( plotlyOutput('plot') ),
server = function(input, output) {
hline <- function(y = 0, color = "grey", width=0.1) {
list(type = "line", x0 = 0, x1 = 1, xref = "paper",
y0 = y, y1 = y, line = list(color = color, width=width))
}
output$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,我认为您不能在 3d 图中使用这种方法,因为形状没有 z 维度。您可以使用 add_lines
而不是形状来做类似的事情,但这不会那么整洁。
在 Python 中,对于 3D 图,在 scene
字典中指定所有 Layout 属性,如下所示:
layout = go.Layout(
margin=dict(
l=0,
r=0,
b=0,
t=0
),
scene=dict(
xaxis=dict(
type='log',
autorange=True,
title='L1'))
)
我假设 R 中的最新版本的 plotly 具有相同的功能。