Matrix 使用plotly软件包将多个平行平面添加到三维打印（datacamp练习）

我试图用plotly软件包在3D绘图中可视化两个平行平面，对于那些可以访问Datacamp的人来说，这是一个练习：

在构建此图的过程中，您需要创建一个矩阵来拟合与分类变量plane_new=条件为new，plane_used=条件为used的平面。问题是，我用作输入的两个数据集对应于两个级别的条件，具有不同的观察数量。我似乎不知道如何才能使这些矩阵具有可比性，从而使平面作为图形中的几何对象正确拟合

我希望R-wizard能帮我一把；。这是我的密码：

# libraries
library(openintro) #exemplary datasets
library(modelr) #multivariate methods
library(broom) #tidy
library(ggplot2) #visualizing data
library(plotly) #visualizing models in 3D

## Fit the model

lm_ext <- lm(totalPr ~ duration + startPr + cond,           # Interpretation: With every 1 unit increase of auction duration (unit = day), the price of the game decreases with .51 units in the response variable (total price), when keeping startPr constant. The eventual value of the predicted value also depends on condition (categorical), for which the y-intercept is different
             data = marioKart)          


## Visualize the model (including predictions)

marioKart_ss_new <- subset(marioKart, cond == "new")        # To visualize planes in a 3D graph in plotly, the dataframe needs to be split in the number of levels of the categorical variable
marioKart_ss_used <- subset(marioKart, cond == "used")

duration_new <- as.vector(marioKart_ss_new$duration)        # These vectors represent the linear model for condition = new
startPr_new <- as.vector(marioKart_ss_new$startPr)

duration_used <- as.vector(marioKart_ss_used$duration)      # These vectors represent the linear model for condition = used
startPr_used <- as.vector(marioKart_ss_used$startPr)

lm_new <- lm(totalPr ~ duration + startPr,                  # Create two linear models
             data = marioKart_ss_new) 
lm_used <- lm(totalPr ~ duration + startPr, 
             data = marioKart_ss_used) 

grid_new <- marioKart_ss_new %>%                            # Make two grids with all combinations of the levels of the two numerical explanatory variables
              data_grid(duration = 
                          seq_range(duration, by = 1),
                        startPr = 
                          seq_range(startPr, by = 1))
grid_used <- marioKart_ss_used %>%  
              data_grid(duration = 
                          seq_range(duration, by = 1),
                        startPr = 
                          seq_range(startPr, by = 1))

lm_new <- lm(totalPr ~ duration + startPr,                  # Make two seperate models based on the two levels of the categorical explanatory variable
             data = marioKart_ss_new) 
lm_used <- lm(totalPr ~ duration + startPr,                
             data = marioKart_ss_used) 

pred_new <- augment(lm_new, newdata = grid_new)             # Predictions
pred_used <- augment(lm_used, newdata = grid_used) 

plane_new <- matrix(pred_new$.fitted,                       # Matrix of preditions as input for planes
                    nrow = 70,
                    ncol = 70)

plane_used <- matrix(pred_used$.fitted,                     
                     nrow = 55,
                     ncol = 55)

plot <- plot_ly(data = marioKart,                           # 3D plot of datapoints
                z = ~totalPr, 
                x = ~duration, 
                y = ~startPr, 
                opacity = 0.6) %>%
  add_markers(color = ~cond) 

plot %>%                                                    # Add planes
  add_surface(x = ~duration_new,                            ### NOT WORKING, WAIT FOR DATACAMP
              y = ~startPr_new, 
              z = ~plane_new, 
              showscale = FALSE) %>%
  add_surface(x = ~duration_used, 
              y = ~duration_used, 
              z = ~plane_used, 
              showscale = FALSE)

---

此处没有代码向导，但要求相同的内容：

library(tidyverse)
library(modelr)

grid <- mario_kart %>%
  modelr::data_grid(
    duration = seq_range(duration, n = 70),
    startPr = seq_range(startPr, n = 70),
    cond
  )

library(broom)

tidy_planes <- mod %>%
  augment(newdata = grid)

x <- unique(grid$duration)
y <- unique(grid$startPr)

plane0 <- tidy_planes %>%
  filter(cond == "new") %>%
  pull(.fitted) %>%
  matrix(nrow = length(x), byrow = TRUE)

plane1 <- tidy_planes %>%
  filter(cond == "used") %>%
  pull(.fitted) %>%
  matrix(nrow = length(x), byrow = TRUE)

---

是的，就是这样！谢谢你，本杰明