所以我正在解决一个问题，希望我在R中获取地震数据，并将其分离为给定深度的因子向量，然后创建不同类别的箱形图。然后对其运行Kruskal沃利斯测试。
这就是我的全部

library(dplyr)
library(tidyr)
library(ggplot2)

# Load the quakes data
data(quakes)

# Create a new factor vector defining the depths of each event according to the given categories
quakes <- quakes %>%
  mutate(depth_cat = cut(depth, breaks = c(0, 200, 400, 680), labels = c("[0,200]", "(200,400]", "(400,680]")))

# Use a boxplot to visually compare the distributions of the number of detecting stations, split according to the three categories of depth
ggplot(quakes, aes(x = depth_cat, y = stations)) +
  geom_boxplot() +
  labs(x = "Depth Category", y = "Number of Detecting Stations")

# Since the distributions of the number of detecting stations appear to be skewed, we will use a Kruskal-Wallis test to compare the distributions of the three depth categories.
kruskal.test(stations ~ depth_cat, data = quakes, na.action = na.exclude)

#The output of the Kruskal-Wallis test shows a p-value of less than 0.01, which indicates that there is strong evidence of a difference in the distributions of the number of detecting stations between the three depth categories. Therefore, we reject the null hypothesis that the distributions are the same and conclude that there is a significant difference in the number of detecting stations between the different depth categories.

这段代码工作，并使用资源，这是我如何解决它。我需要帮助编码使用基本R函数，而不是我有什么，因为这是什么分配指定。