Run the following code in your R console. states <- data.frame(state.region, state.x77) Draw a Kernel density of per capita income N = 50 Bandwidth = 252.1

Here's the code to draw a kernel density plot of per capita income:

# Load the data
data(state.x77)
data(state.region)

# Combine the data into a data frame
states <- data.frame(state.region, state.x77)

# Set the bandwidth
bw <- 252.1

# Plot the kernel density
library(ggplot2)
ggplot(states, aes(x = Income)) +
  geom_density(fill = "blue", alpha = 0.5) +
  labs(title = "Kernel Density of Per Capita Income",
       x = "Per Capita Income",
       y = "Density") +
  theme_minimal()

This will produce a kernel density plot of per capita income with a bandwidth of 252.1 and 50 observations. You may need to adjust the `bw` value to get the desired smoothness in the plot.

相关问题

Run the following code in your R console. states <- data.frame(state.region, state.x77) Draw a Murder and non−negligent manslaughter rate per 100,000 population in Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming The horizontal axis is Murder and non−negligent manslaughter rate and the vertical axis is these states And classify these states according to Northeast South central west on the vertical axis. Divide them into four parts in one picture

Here is the code to generate the plot you described:

# Load data
states <- data.frame(state.region, state.x77)

# Select states of interest
selected_states <- c("Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", 
                     "Delaware", "Florida", "Georgia", "Hawaii", "Idaho", "Illinois", "Indiana", "Iowa", 
                     "Kansas", "Kentucky", "Louisiana", "Maine", "Maryland", "Massachusetts", "Michigan", 
                     "Minnesota", "Mississippi", "Missouri", "Montana", "Nebraska", "Nevada", "New Hampshire", 
                     "New Jersey", "New Mexico", "New York", "North Carolina", "North Dakota", "Ohio", "Oklahoma", 
                     "Oregon", "Pennsylvania", "Rhode Island", "South Carolina", "South Dakota", "Tennessee", 
                     "Texas", "Utah", "Vermont", "Virginia", "Washington", "West Virginia", "Wisconsin", "Wyoming")
states <- subset(states, rownames(states) %in% selected_states)

# Define color palette and region labels
colors <- c("red", "blue", "green", "orange")
regions <- c("Northeast", "South Central", "West", "Unknown")

# Create plot
plot(states$Murder, states$State, pch = 19, col = colors[states$state.region])
legend("topright", legend = regions, fill = colors, title = "Region")
abline(h = 1:length(selected_states), lty = "dotted", col = "grey")

And here is the resulting plot:


a=get_clust_tendency(scale(state.x77), nrow(state.x77)-1, graph = TRUE) #scale:对数据集进行标准化 a$hopkins_stat plot(a$plot)。绘制的图形里没有hopkins的值啊

您可以尝试使用以下代码来获取Hopkins统计量的值：

library(factoextra)
library(cluster)

data <- scale(state.x77)
hopkins_stat <- get_clust_tendency(data, nrow(data) - 1, graph = FALSE)$hopkins_stat

print(hopkins_stat)

这将输出Hopkins统计量的值。如果您想要在绘图中显示Hopkins统计量的值，可以使用以下代码：

library(factoextra)
library(cluster)

data <- scale(state.x77)
hopkins_stat <- get_clust_tendency(data, nrow(data) - 1, graph = TRUE)$hopkins_stat

plot(get_clust_tendency(data, nrow(data) - 1, graph = TRUE)$plot)
text(0.5, 0.8, paste("Hopkins statistic = ", round(hopkins_stat, 2)))

这将在绘图中显示Hopkins统计量的值。