如何在稀疏点之间插入数据以在R&plot中绘制轮廓图

Question

我想根据第一张图中以下彩色点的浓度数据在xy平面上创建等高线图.我在每个高度都没有角点,所以我需要将浓度外推到xy平面的边缘(xlim = c(0,335),ylim = c(0,426)).

点的html文件可以在这里找到:https://leeds365-my.sharepoint.com/:u:/ r/personal /cenmk_leeds_ac_uk/Files/Files/HECOIRA /Chamber%20CO2%20Experiments/Sensors.html？csf = 1&E = HiX8fF

dput(df)
structure(list(Sensor = structure(c(11L, 12L, 13L, 14L, 15L, 
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 
29L, 1L, 3L, 4L, 5L, 6L, 8L, 30L, 31L, 32L, 33L, 34L, 35L), .Label = c("N1", 
"N2", "N3", "N4", "N5", "N6", "N7", "N8", "N9", "Control", "A1", 
"A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", 
"A19", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "R1", 
"R2", "R3", "R4", "R5", "R6"), class = "factor"), calCO2 = c(2237, 
2389.5, 2226.5, 2321, 2101.5, 1830.5, 2418, 2356.5, 435, 2345.5, 
2376, 2451, 2397, 2466, 2518.5, 2087, 2463, 2256.5, 2345.5, 3506, 
2950, 3386, 2511, 2385, 3441, 2473, 2357.5, 2052.5, 2318, 1893.5, 
2251), x = c(83.75, 167.5, 167.5, 167.5, 251.25, 167.5, 251.25, 
251.25, 0, 83.75, 251.25, 167.5, 251.25, 83.75, 83.75, 83.75, 
83.75, 251.25, 167.5, 335, 0, 0, 335, 167.5, 167.5, 167.5, 0, 
335, 335, 167.5, 167.5), y = c(213, 319.5, 319.5, 110, 319.5, 
213, 110, 110, 356, 213, 319.5, 110, 213, 110, 319.5, 319.5, 
110, 213, 213, 0, 0, 426, 426, 426, 0, 213, 213, 70, 213, 426, 
0), z = c(155, 50, 155, 155, 155, 226, 50, 155, 178, 50, 50, 
50, 50, 155, 50, 155, 50, 155, 50, 0, 0, 0, 0, 0, 0, 0, 130, 
50, 120, 130, 130), Type = c("Airnode", "Airnode", "Airnode", 
"Airnode", "Airnode", "Airnode", "Airnode", "Airnode", "Airnode", 
"Airnode", "Airnode", "Airnode", "Airnode", "Airnode", "Airnode", 
"Airnode", "Airnode", "Airnode", "Airnode", "Naveed", "Naveed", 
"Naveed", "Naveed", "Naveed", "Naveed", "Rotronic", "Rotronic", 
"Rotronic", "Rotronic", "Rotronic", "Rotronic")), .Names = c("Sensor", 
"calCO2", "x", "y", "z", "Type"), row.names = c(NA, -31L), class = "data.frame")

require(plotly)

plot_ly(data = subset(df,z==0), x=~x,y=~y, z=~calCO2, type = "contour") %>%
  layout(
    xaxis = list(range = c(340, 0), autorange = F, autorange="reversed"), 
    yaxis = list(range = c(0, 430)))

我想找到这样的东西.任何帮助将非常感激.

Answer 1

首先,您必须考虑使用+ -30点不足以获得您在示例中可以看到的那些干净的分离层.说,让我们开始工作:

首先,您可以监督您的数据,以便猜测这些图层的形状.在这里,您可以很容易地看到较低的z值具有较高的CO2值.

require(dplyr)
require(plotly)
require(akima)
require(plotly)
require(zoo)
require(raster)

plot_ly(df, x=~x,y=~y, z=~z, color =~calCO2)

重要的是你必须定义你将拥有的图层.这些层必须通过整个表面上的值插值来制作.所以:

• 定义您为每个图层使用的数据.
• 插值z和calCO2的值.这很重要,因为这是两件不同的事情.z插值将使图形的sape和calCO2将产生颜色(浓度或其他).在(https://plot.ly/r/3d-surface-plots/)的图像中,颜色和z在这里表示相同,我想你想要表示z的表面并用calCO2着色它.这就是为什么你需要为两者插值.插值方法是一个世界,在这里我只做了一个简单的插值,我用平均值填充了NA.

这是代码:

## Define your layers in z range (by hand or use quantiles, percentiles, etc.)
df1 <- subset(df, z >= 0 & z <= 125) #layer between 0 and 150m
df2 <- subset(df, z > 125)           #layer between 150 and max

#interpolate values for each layer and for z and co2
z1 <- interp(df1$x, df1$y, df1$z, extrap = TRUE, duplicate = "mean") #interp z layer 1 with spline interp
ifelse(anyNA(z1$z) == TRUE, z1$z[is.na(z1$z)] <- mean(z1$z, na.rm = TRUE), NA) #fill na cells with mean value

z2 <- interp(df2$x, df2$y, df2$z, extrap = TRUE, duplicate = "mean") #interp z layer 2 with spline interp
ifelse(anyNA(z2$z) == TRUE, z2$z[is.na(z2$z)] <- mean(z2$z, na.rm = TRUE), NA) #fill na cells with mean value

c1 <- interp(df1$x, df1$y, df1$calCO2, extrap = F, linear = F, duplicate = "mean") #interp co2 layer 1 with spline interp
ifelse(anyNA(c1$z) == TRUE, c1$z[is.na(c1$z)] <- mean(c1$z, na.rm = TRUE), NA) #fill na cells with mean value

c2 <- interp(df2$x, df2$y, df2$calCO2, extrap = F, linear = F, duplicate = "mean") #interp co2 layer 2 with spline interp
ifelse(anyNA(c2$z) == TRUE, c2$z[is.na(c2$z)] <- mean(c2$z, na.rm = TRUE), NA) #fill na cells with mean value

#THE PLOT
p <- plot_ly(showscale = TRUE) %>%
    add_surface(x = z1$x, y = z1$y, z = z1$z, cmin = min(c1$z), cmax = max(c2$z), surfacecolor = c1$z) %>%
    add_surface(x = z2$x, y = z2$y, z = z2$z, cmin = min(c1$z), cmax = max(c2$z), surfacecolor = c2$z) %>%
    add_trace(data = df, x = ~x, y = ~y, z = ~z, mode = "markers", type = "scatter3d", 
              marker = list(size = 3.5, color = "red", symbol = 10))%>%
    layout(title="Stack Exchange Plot")
p

Answer 2

正如 Cesar 指出的那样，您需要定义要在此 3d 系统中插入的“层”。

在这里，我提出了一种假设一层的方法（即 - 我使用沿 z 方向的所有点）。查看您的值表将帮助您定义中断发生的位置。您可以为您定义的每个“层”重复使用下面的代码。

> table(d$z)

  0  50 120 130 155 178 226 
  7  10   1   3   8   1   1 

既然你在处理空间数据，那么让我们使用 R 中的空间对象来解决这个问题。

首先，我将您的数据复制/粘贴到一个名为d.

# make d into a SpatialPointsDataFrame object
library(sp)
coords <- d[, c("x", "y")]
s      <- SpatialPointsDataFrame(coords = coords, data = d)

# interpolate with a thin plate spline 
# (or another interpolation method: kriging, inverse distance weighting). 
library(raster)
library(fields)
tps <- Tps(coordinates(s), as.vector(d$calCO2))
p   <- raster(s)
p   <- interpolate(p, tps)

# plot raster, points, and contour lines
plot(p)
plot(s, add=T)
contour(p, add=T) 

您可以想象根据z点的值将数据拆分为多个层，然后重新运行此代码以生成每个层的插值。请务必阅读各种插值方法，以确定哪种方法最适合您的系统。一旦你有了这些层，把这些数据移植到上面显示的 ploty 中就不需要太多的工作了。

编辑：采用 base --> ggplot --> plotly 很简单：

# ggplot
library(ggplot2)
p <- ggplot(as.data.frame(p, xy = TRUE), aes(x, y, fill = layer)) + 
  geom_tile() + 
  geom_contour(aes(z = layer), color = "white") + 
  scale_fill_viridis_c() + 
  theme_minimal()

这是有关添加轮廓标签的一些说明。

把它变成一个交互式的 plotly 对象。

library(plotly)
ggplotly(p)

第一篇文章中的代码将您带到 3d。