R:绘制大于原始数据的轴显示值 [英] R: Plot Axis Display Values Larger than the Original Data

问题描述

我使用的是 R 编程语言.我正在这里学习有关数据可视化的教程:

如此处所见，此 3D 图中有一个点为y = 97".我不确定这怎么可能，因为原始数据框d"中的任何值都没有.在任何地方都接近 97.我通过查看原始数据框d"中每个变量的单独分布来确定这一点:

#plot 个体密度情节(密度(d $ a)，主要=密度图"，col=红色")线(密度(d $ b)，col =蓝色")线(密度(d $ c)，col =绿色")图例(左上角"，c(a"，b"，c")，text.col=c(红色"、蓝色"、绿色"))

如这里所见，原始数据框d"中的任何变量(a、b、c)都没有有任何接近 97 的值.

因此，我的问题是:谁能解释一下点 (x = 0 , y = 97, z =25.326) 怎么可能出现在这个 3D 图中?

谢谢

解决方案

我不确定这是否能解决问题 - 但使用与之前的 stackoverflow 帖子相同的逻辑:

现在，在这个可视化图中看到的所有值似乎都包含在原始数据框中.

但是，我仍然不确定这两个图之间的基本(和数学)区别是什么:

我很想知道别人怎么说.

谢谢

I am using the R programming language. I am following a tutorial on data visualization over here: https://plotly.com/r/3d-surface-plots/

I created my own data and made a 3D plot:

library(plotly)

set.seed(123)

#generate data
a = rnorm(100,10,10)
b = rnorm(100,5,5)
c = rnorm(100,5,10)
d = data.frame(a,b,c)

#3d plot
fig <- plot_ly(z = ~as.matrix(d))
fig <- fig %>% add_surface()

#view plot
fig

As seen here, there is a point on this 3D plot where "y = 97". I am not sure how this is possible, seeing how none of the values within the original data frame "d" are anywhere close to 97. I made sure of this by looking at the individual distributions of each variable in the original data frame "d":

#plot individual densities 

plot(density(d$a), main = "density plots", col = "red")
lines(density(d$b), col = "blue")
lines(density(d$c), col = "green")

legend( "topleft", c("a", "b", "c"), 
text.col=c("red", "blue", "green") )

As seen here, none of the variables (a,b,c) from the original data frame "d" have any values that are close to 97.

Thus, my question: can someone please explain how is it possible that the point (x = 0 , y = 97, z =25.326) appears on this 3D plot?

Thanks

解决方案

I am not sure if this will resolve the problem - but using the same logic from this previous stackoverflow post: 3D Surface with Plot_ly in r, with x,y,z coordinates

library(plotly)
set.seed(123)

#generate data
a = rnorm(100,10,10)
b = rnorm(100,5,5)
c = rnorm(100,5,10)
d = data.frame(a,b,c)


data = d

plot_ly() %>% 
  add_trace(data = data,  x=data$a, y=data$b, z=data$c, type="mesh3d" ) 

Now, it appears that all values seen in this visual plot are contained in the original data frame.

However, I am still not sure what is the fundamental (and mathematical) difference between both of these plots:

I am curious to see what others have to say.

Thanks

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