如果没有Newts数据或PCA对象，我们将从创建一个示例开始。

加载包

library(palmerpenguins) # Data for this example
library(ggplot2)
library(dplyr)
library(tidyr)
library(broom)
library(ggrepel)

字符串

Palmer Penguins数据的主成分分析

glimpse(penguins)
#> Rows: 344
#> Columns: 8
#> $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
#> $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
#> $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
#> $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
#> $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
#> $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
#> $ sex               <fct> male, female, female, NA, female, male, female, male…
#> $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

peng_pca <- prcomp(~ bill_length_mm + bill_depth_mm + flipper_length_mm + body_mass_g, data = penguins, scale. = TRUE)

peng_pca
#> Standard deviations (1, .., p=4):
#> [1] 1.6594442 0.8789293 0.6043475 0.3293816
#> 
#> Rotation (n x k) = (4 x 4):
#>                          PC1          PC2        PC3        PC4
#> bill_length_mm     0.4552503 -0.597031143 -0.6443012  0.1455231
#> bill_depth_mm     -0.4003347 -0.797766572  0.4184272 -0.1679860
#> flipper_length_mm  0.5760133 -0.002282201  0.2320840 -0.7837987
#> body_mass_g        0.5483502 -0.084362920  0.5966001  0.5798821

型

创建前两个PC的散点图

peng_pca %>%
  augment(newdata = penguins) %>%
  ggplot(aes(x = .fittedPC1,
             y = .fittedPC2,
             color = species)) +
  geom_point() +
  theme_bw() +
  coord_equal()
#> Warning: Removed 2 rows containing missing values (`geom_point()`).

型
x1c 0d1x的数据

创建载荷图

tidy(peng_pca, 2, matrix = 'v') %>%
  mutate(PC = paste0('PC', PC)) %>%
  pivot_wider(names_from = PC, values_from = value) %>%
  ggplot() +
  geom_segment(aes(x = 0, y = 0, xend = PC1, yend = PC2)) +
  geom_label_repel(aes(x = PC1, y = PC2, label = column)) +
  coord_equal() +
  theme_bw()

型

的
创建于2023-12-12带有reprex v2.0.2

在一些额外的搜索之后，我找到了一些可以回答我的问题的东西。为了在我的PCA双标图中可视化蝾螈存在或不存在的水池，我使用了以下代码。

# Conducting a PCA using the prcomp()function instead of the rad()function
heavymetals_pca<-prcomp(heavymetals,scale. = TRUE)

# Code for the actual plot
library(ggfortify) 
autoplot(heavymetals_pca, data = Newts, colour = 'Newts_present',
         loadings = TRUE, loadings.colour = 'blue',
         loadings.label = TRUE, loadings.label.size = 3)

字符串
我希望它也能帮助一些和我有同样问题的人。结果：PCA biplot with clear presence absence visualised