In the last blog, we have learned how to create “Dynamic Maps Using ggplot2“. In this article, we will explore more into the 3D visualization in R programming language by using the plot3d package.

在上一个博客中，我们学习了如何创建“ 使用ggplot2使用动态地图 ” 。 在本文中，我们将使用plot3d包对R编程语言中的3D可视化进行更多探索。

The plot3d package can be used to generate stunning 3-D plots in R. It can generate an interesting array of plots, but in this recipe, we will focus on creating 3-D scatterplots. These arise in situations where we have three variables, and we want to plot the triplets of values on the x–y–z space.

plot3d包可用于在R中生成令人惊叹的3-D图。它可以生成有趣的图阵列，但是在本食谱中，我们将重点介绍创建3-D散点图。 这些出现在我们有三个变量的情况下，我们想在x – y – z空间上绘制三元组值。

We will use a specific dataset to plot them into fancy plots using the plot3d package. The following steps are implemented to create 3D visualization in R.

我们将使用plot3d包使用特定的数据集将它们绘制成精美的图。 执行以下步骤以在R中创建3D可视化 。

Step 1: Install the required packages which are needed for 3D visualization in R.

步骤1：在R中安装3D可视化所需的必需软件包。

> install.packages("rgl")
Installing package into ‘C:/Users/admin/Documents/R/win-library/3.6’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.6/rgl_0.100.30.zip'
Content type 'application/zip' length 4253430 bytes (4.1 MB)
downloaded 4.1 MB
 
package ‘rgl’ successfully unpacked and MD5 sums checked
 
The downloaded binary packages are in
	C:\Users\admin\AppData\Local\Temp\Rtmpymt5Jd\downloaded_packages
> install.packages("plot3D")
Installing package into ‘C:/Users/admin/Documents/R/win-library/3.6’
(as ‘lib’ is unspecified)
package ‘plot3D’ successfully unpacked and MD5 sums checked
 
The downloaded binary packages are in
	C:\Users\admin\AppData\Local\Temp\Rtmpymt5Jd\downloaded_packages

Include the required libraries in the mentioned workspace.

在上述工作区中包括所需的库。

> library(plot3D)
> library(rgl)

Step 2: We will use the dataset named “income.csv” which includes all the necessary parameters which are needed for understanding income rates of every employee.

步骤2：我们将使用名为“ income.csv ”的数据集，其中包括了解每个员工的收入率所需的所有必要参数。

Step 3: Analyze the data structure of the dataset with the mentioned attributes.

步骤3：使用上述属性分析数据集的数据结构。

> str(income)
'data.frame':	30 obs. of  5 variables:
 $ names    : Factor w/ 30 levels "brady","brandy",..: 27 29 14 17 10 26 22 24 30 18 ...
 $ Education: num  21.6 18.3 12.1 17 19.9 ...
 $ Seniority: num  113 119 101 188 20 ...
 $ Income   : num  99.9 92.6 34.7 78.7 68 ...
 $ gender   : int  1 1 0 0 1 1 1 1 1 1 ...

Step 4: It is important to understand the five-point summary of data before proceeding further. Visualization requires a lookout on bivariate and univariate analysis which is clearly understood with a five-point summary of data.

步骤4：在继续进行之前，了解数据的五点摘要很重要。 可视化需要监视双变量和单变量分析，并通过五点数据摘要清楚地了解。

> summary(income)
      names      Education       Seniority          Income     
 brady   : 1   Min.   :10.00   Min.   : 20.00   Min.   :17.61  
 brandy  : 1   1st Qu.:12.48   1st Qu.: 44.83   1st Qu.:36.39  
 brian   : 1   Median :17.03   Median : 94.48   Median :70.80  
 brittany: 1   Mean   :16.39   Mean   : 93.86   Mean   :62.74  
 bruce   : 1   3rd Qu.:19.93   3rd Qu.:133.28   3rd Qu.:85.93  
 charles : 1   Max.   :21.59   Max.   :187.59   Max.   :99.92  
 (Other) :24                                                   
     gender      
 Min.   :0.0000  
 1st Qu.:0.0000  
 Median :1.0000  
 Mean   :0.6333  
 3rd Qu.:1.0000  
 Max.   :1.0000

Step 5: Let us with bivariate analysis which focusses on 2-dimensional data and scatter plot is considered as an easy method to create the same.

步骤5：让我们进行专注于二维数据的双变量分析，而将散点图视为创建该变量的简便方法。

> scatter3D(x = inc$Education, y = inc$Income, z =inc$Seniority,
+           colvar = inc$Income,
+           pch = 16, cex = 1.5, xlab = "Education", ylab = "Income",
+           zlab = "Seniority", theta = 60, d = 2,clab = c("Income"),
+           colkey = list(length = 0.5, width = 0.5, cex.clab = 0.75,
+                         dist = -.08, side.clab = 3)
+           ,main = "Relationship Between Income , Education and Seniority")

Here, we are plotting “Education” in the x-axis, “Income” in the y-axis and the “Seniority” level in the z-axis. The distinct legends are created based on the range of income parameters. The plot is helpful to show the relationship between Income, Education, and Society.

在这里，我们在x轴上绘制“教育”，在y轴上绘制“收入”，在z轴上绘制“高级”水平。 根据收入参数范围创建不同的图例。 该图有助于显示收入，教育和社会之间的关系。

We can add more effects to the mentioned 3D plot with plane surfaces and ranges depicted in a specific order. Consider that we want to implement a linear regression model to establish the relationship between Seniority, Education and Income we can create a predictive model for the same.

我们可以使用特定顺序描绘的平面和范围为上述3D图添加更多效果。 考虑到我们要实现线性回归模型以建立资历，教育和收入之间的关系，我们可以为此创建一个预测模型。

Step 6: Create a predictive model with the help of the RGL package. RGL is the 3D real-time rendering package in the R programming language. It provides high-level functions to create an interactive graph. To create a 3D plot of linear regression, we need to create a predictive model of the same.

步骤6：借助RGL软件包创建预测模型。 RGL是R编程语言中的3D实时渲染包。 它提供了高级功能来创建交互式图形。 要创建线性回归的3D图，我们需要创建相同的预测模型。

> lmin = lm(inc$Income~inc$Education+inc$Seniority)
> lmin
 
Call:
lm(formula = inc$Income ~ inc$Education + inc$Seniority)
 
Coefficients:
  (Intercept)  inc$Education  inc$Seniority  
     -50.0856         5.8956         0.1729  
> est = coef(lmin)
> a = est["inc$Education"]
> b = est["inc$Seniority"]
> c=-1
> d= est["(Intercept)"]
> est
  (Intercept) inc$Education inc$Seniority 
  -50.0856387     5.8955560     0.1728555 
> a
inc$Education 
     5.895556 
> b
inc$Seniority 
    0.1728555 
> c
[1] -1
> d
(Intercept) 
  -50.08564

Step 7: Once the required parameters for linear regression are taken into consideration, we can create an interactive graph where we plot the data points as a scattered graph and later embed the linear regression model in them.

步骤7：一旦考虑了线性回归所需的参数，我们就可以创建一个交互式图形，在其中将数据点绘制为散点图，然后将线性回归模型嵌入其中。

> plot3d(inc$Education,inc$Seniority,inc$Income, type = "s",
+        col = "blue", xlab = "Education",ylab = "Income",zlab =
+            "Seniority",box = FALSE)

Now we will embed the linear regression line as mentioned below:

现在，我们将嵌入线性回归线，如下所示：

planes3d(a,b,c,d, alpha = 0.5, col = "red")

Step 8: We can create a surface plot that defines the volume and intensity of the data. Following steps are implemented to create a specific plot as mentioned below:

步骤8：我们可以创建一个表面图来定义数据的数量和强度。 执行以下步骤来创建特定图，如下所述：

> inc= read.csv("income2.csv")
> View(inc)
> row.names(inc)= inc$names
> inc
            names Education Seniority   Income gender
tim           tim  21.58621 113.10345 99.91717      1
tom           tom  18.27586 119.31034 92.57913      1
kim           kim  12.06897 100.68966 34.67873      0
mary         mary  17.03448 187.58621 78.70281      0
hary         hary  19.93103  20.00000 68.00992      1
thomas     thomas  18.27586  26.20690 71.50449      1
peter       peter  19.93103 150.34483 87.97047      1
romeo       romeo  21.17241  82.06897 79.81103      1
wald         wald  20.34483  88.27586 90.00633      1
matt         matt  10.00000 113.10345 45.65553      1
pam           pam  13.72414  51.03448 31.91381      0
pamela     pamela  18.68966 144.13793 96.28300      0
larry       larry  11.65517  20.00000 27.98250      1
karl         karl  16.62069  94.48276 66.60179      1
brian       brian  10.00000 187.58621 41.53199      1
dan           dan  20.34483  94.48276 89.00070      1
sim           sim  14.13793  20.00000 28.81630      0
kristin   kristin  16.62069  44.82759 57.68169      0
chiu         chiu  16.62069 175.17241 70.10510      1
bruce       bruce  20.34483 187.58621 98.83401      1
brady       brady  18.27586 100.68966 74.70470      1
brandy     brandy  14.55172 137.93103 53.53211      0
charles   charles  17.44828  94.48276 72.07892      1
timothy   timothy  10.41379  32.41379 18.57067      0
jerry       jerry  21.58621  20.00000 78.80578      1
garry       garry  11.24138  44.82759 21.38856      1
jena         jena  19.93103 168.96552 90.81404      0
ram           ram  11.65517  57.24138 22.63616      1
brittany brittany  12.06897  32.41379 17.61359      0
milly       milly  17.03448 106.89655 74.61096      0

Here, we convert the dataset into a separate vector and we also created the index based on the names of candidates. Text3d is the function that adds text to the plane surface. The text represents actual data representation. As we converted the row names with names of candidates, it becomes easy to display text in the 3D plot.

在这里，我们将数据集转换为单独的向量，并且还基于候选名称创建了索引。 Text3d是将文本添加到平面的功能。 文本表示实际数据表示。 当我们用候选名称转换行名称时，在3D图中显示文本变得很容易。

> text3D(x = inc$Education, y = inc$Income, z =inc$Seniority,
+        colvar = inc$Income,labels= row.names(inc),
+        pch = 16, cex = 0.8, xlab = "Education", ylab = "Income",
+        zlab = "Seniority", theta = 60, d = 2,clab = c("Income"),
+        colkey = list(length = 0.5, width = 0.5, cex.clab = 0.75,
+                      dist = -.08, side.clab = 3)
+        ,bty = "g")

Step 9: We can convert the values in proper labels with distinct colors. This helps to visualize the 3D plot more easily and distinctly with a specific color range.

步骤9：我们可以将值转换为具有不同颜色的适当标签。 这有助于在特定颜色范围内更轻松，更清晰地可视化3D图。

> text3D(x = inc$Education, y = inc$Income, z =inc$Seniority,
+        colvar= inc$gender,col = c("red","black"),labels= row.names(inc),
+        pch = 16, cex = 0.8,xlab = "Education", ylab = "Income",
+        zlab = "Seniority", theta = 60, d = 2,clab = c("Income"),
+        bty = "g", colkey = FALSE)
> legend("topright", fill = c("red", "black"), legend=
+            c("Female","Male"), bty = "n")

等高线图 (Contour Plots)

Contour plots visually represent the intensity of the plot. The color and graphical representation help in the visual analysis of data.

等高线图直观地表示了该图的强度。 颜色和图形表示有助于数据的可视化分析。

> x = y = seq(-3,3, length.out = 10)
> x
 [1] -3.0000000 -2.3333333 -1.6666667 -1.0000000 -0.3333333  0.3333333
 [7]  1.0000000  1.6666667  2.3333333  3.0000000
> y
 [1] -3.0000000 -2.3333333 -1.6666667 -1.0000000 -0.3333333  0.3333333
 [7]  1.0000000  1.6666667  2.3333333  3.0000000
> f = function(x,y){ z= (y^2-x^2)}
> m = outer(x,y,f)
> 
> image2D(m)
> image2D(m, contour = TRUE)
> 
> persp3D(z = m, contour = TRUE)
> persp3D(z = volcano, contour = TRUE)
> 
> library(rgl)
> c = terrain.colors(5)
> persp3d(z = volcano, contour = TRUE, col = c)

The output of the contour plot is mentioned below:

等高线图的输出如下所述：

The interactive 3d plot is mentioned below:

交互式3d图如下所示：

We can also create a plot with an animation feature which increases the interactive rate. For this, it is important to install an “animation” package which helps in creating the plots as desired.

我们还可以创建带有动画功能的绘图，以提高交互速率。 为此，重要的是安装一个“动画”程序包，该程序包可以帮助创建所需的绘图。

> install.packages("plotrix")
Installing package into ‘C:/Users/admin/Documents/R/win-library/3.6’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.6/plotrix_3.7-7.zip'
Content type 'application/zip' length 1132324 bytes (1.1 MB)
downloaded 1.1 MB
 
package ‘plotrix’ successfully unpacked and MD5 sums checked
 
The downloaded binary packages are in
	C:\Users\admin\AppData\Local\Temp\RtmpSCUS7b\downloaded_packages
> 
> install.packages("animation")
Installing package into ‘C:/Users/admin/Documents/R/win-library/3.6’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.6/animation_2.6.zip'
Content type 'application/zip' length 548202 bytes (535 KB)
downloaded 535 KB
 
package ‘animation’ successfully unpacked and MD5 sums checked
 
The downloaded binary packages are in
	C:\Users\admin\AppData\Local\Temp\RtmpSCUS7b\downloaded_packages
> 
> library(plot3D)
> library(plotrix)
 
Attaching package: ‘plotrix’
 
The following object is masked from ‘package:rgl’:
 
    mtext3d
 
> library(animation)
> 
> x = y = seq(0,2*pi, length.out = 100)
> 
> z = mesh(x,y)
> u = z$x
> v = z$y
> 
> m= (sin(u)*sin(2*v)/2)
> n = (sin(2*u)*cos(v)*cos(v))
> o = (cos(2*u)*cos(v)*cos(v))
> 
> surf3D(m, n,o, colvar = o, border = "black",colkey = FALSE,box = TRUE)
> 
> surf3D(m, n,o, colvar = o, border = "black",colkey = TRUE, box = TRUE,theta = 60)
> 
> surf3D(m, n,o, colvar = o, border = "black",colkey = TRUE,box = TRUE,theta = 100)
> 
> library("animation")
> saveHTML({
+     for (i in 1:100 ){
+         x = y = seq(0,2*pi, length.out = 100)
+         z = mesh(x,y)
+         u = z$x
+         v = z$y
+         m= (sin(u)*sin(2*v)/2)
+         n = (sin(2*u)*cos(v)*cos(v))
+         o = (cos(2*u)*cos(v)*cos(v))
+         surf3D(m, n,o, colvar = o, border = "black",colkey = FALSE,
+                theta = i, box = TRUE)
+     }
+ },interval = 0.1, ani.width = 500, ani.height = 1000)

The ranges of the graphs are depicted below:

图的范围如下所示：

The file is saved with .html extension and represents the graphical animation of the values in three co-ordinates.

该文件以.html扩展名保存，并以三个坐标表示值的图形动画。

So, this was all about 3D visualization in R programming!

因此，这一切都与R编程中的3D可视化有关！

In the next section, we will be going to learn about Data Wrangling and Visualization in R programming language.

在下一节中，我们将学习R编程语言中的数据整理和可视化 。

Originally published https://blog.eduonix.com on March 27, 2020

最初 于 2020 年 3月27日发布 https://blog.eduonix.com