如何计算群集中某个状态到群集中心的距离？ [英] How can I calculate the distance of a state within a cluster from the center of the cluster?
问题描述
我有28个州的样本。
我想将它们绘制在一个群集中，确定中心，然后计算每年每个州到中心的距离。
I have a sample of 28 states. I want to plot them in one cluster, identify the center, and then calculate the distance of every state from the center, per year.
我的输入文件类似于以下内容：
第一栏：国家
第二栏：年（从2008年到2017年）
第三栏：PI（指数）
my input file resemble the following: first column: Country second column: Year (from 2008 to 2017) third column: PI (index)
问题1：
我得到了错误：
eval（e，x，parent.frame（））中的错误：我运行时找不到对象'mydata.year'
：
table_2008 = subset（ table1，mydata.year == 2008）
Question 1: I am getting the error: Error in eval(e, x, parent.frame()) : object 'mydata.year' not found when I run: table_2008 = subset(table1, mydata.year ==2008)
问题2：
哪种代码最适合计算状态到群集中心的距离。
Question 2: Which code is best suited to calculate the distance of a state from the center of the cluster.
请在下面找到我的代码。
我希望有人能提供帮助。
Please find my code below. I hope someone can help.
谢谢。
代码：
heisenberg< ;-read.csv（文件= C：/用户/ TA /桌面/R4./PI4.csv，head=TRUE，sep=，）
rm（list = ls（） ）
heisenberg <- read.csv(file="C:/Users/TA/Desktop/R4./PI4.csv",head=TRUE,sep=",") rm(list=ls())
mydata = read.csv("C:/Users/TA/Desktop/R4./PI4.csv",sep = ",", header=TRUE)
mydata$Country
mydata$Category
mydata$PI
data_cluster = data.frame(mydata$Country,mydata$Category,mydata$PI)
write.csv(data_cluster,"C:/Users/TA/Desktop/R4./OutputPI.csv", row.names = FALSE)
table1 = data_cluster
#plot(uk_line[,4])
table1 = na.omit(table1)
within_results = ts(,start = c(2008), end = c(2017), frequency = 1)
within_resultsbetweenss = ts(,start = c(2008), end = c(2017), frequency = 1)
within_results_withinss = matrix(data= NA, nrow = 10, ncol = 4)
#nrow = years, ncols = number of clusters
#colnames(mydata, c("Country","Year"))
#YEAR 2008
#SELECTING A GIVEN YEAR (subset of rows such that year = 2008)
table_2008 = subset(table1, mydata.year ==2008)
table_2008
data2008_clus = table_2008[,3:ncol(table_2008)]
#NAMING THE ROWS USING THE COUNTRY NAMES
rownames(data2008_clus) = table_2008$mydata.Country
data2008_clus
plot(table_2008)
wss <- (nrow(data2008_clus)-1)*sum(apply(data2008_clus,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(data2008_clus,
centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")
# Compute k-means with k = 1
fit1=kmeans(x = data2008_clus,centers = 1)
fit1$cluster
fviz_cluster(fit1,data = data2008_clus)
fit1$withinss
fit1$totss
fit1$betweenss
table_2008$cluster = factor(fit1$cluster)
centers=as.data.frame(fit1$centers)
table_2008
within_results[1] = fit1$totss
within_resultsbetweenss[1] = fit1$betweenss
within_results_withinss[1,] = fit1$withinss
within_results_withinss[1,] = fit1$withinss
plot(within_results)
plot(within_resultsbetweenss)
plot(within_results_withinss)
# Print the results
print(km.res)
table_2008
mydata_struct = structure（
list（
Year = c（2008L，2008L，2008L，2008L，2008L，2008L），
国家=结构（
1：6，
。标签= c（
奥地利，
比利时，
保加利亚，
克罗地亚，
塞浦路斯，
捷克，
丹麦，
爱沙尼亚，
芬兰 ；，
法国，
德国，
希腊，
匈牙利，
爱尔兰，$ b $ quot ；意大利，
拉脱维亚，
立陶宛，
卢森堡，
马耳他，
荷兰，
波兰，
葡萄牙，
罗马尼亚，
斯洛伐克，
斯洛文尼亚，
西班牙，
瑞典，
英国
），
class = factor
），
Prosperity.Index = c（79.4，
76.1，62，65.1，69.9，70.9）
），
row.names = c（NA， 6L），
class = data.frame；
）
mydata_struct = structure( list( Year = c(2008L, 2008L, 2008L, 2008L, 2008L, 2008L), Country = structure( 1:6, .Label = c( "Austria", "Belgium", "Bulgaria", "Croatia", "Cyprus", "Czechia", "Denmark", "Estonia", "Finland", "France", "Germany", "Greece", "Hungary", "Ireland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Malta", "Netherlands", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Sweden", "United Kingdom" ), class = "factor" ), Prosperity.Index = c(79.4, 76.1, 62, 65.1, 69.9, 70.9) ), row.names = c(NA, 6L), class = "data.frame" )
推荐答案
在接下来的几年中，我们可以生成随机值。由于您的数据已完成，因此无需执行此操作。我只是想创建类似于您的数据：
We can generate random values for the rest of the years. You do not need to do this since your data is complete. I'm just trying to create data that resembles yours:
mydata_struct = structure( list( Year = c( 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L,
2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L,
2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2009L ),
Country = structure( c( 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 1L ),
.Label = c( "Austria", "Belgium", "Bulgaria", "Croatia", "Cyprus", "Czechia", "Denmark",
"Estonia", "Finland", "France", "Germany", "Greece", "Hungary", "Ireland", "Italy",
"Latvia", "Lithuania", "Luxembourg", "Malta", "Netherlands", "Poland", "Portugal",
"Romania", "Slovakia", "Slovenia", "Spain", "Sweden", "United Kingdom" ),
class = "factor" ), Prosperity.Index = c( 79.4, 76.1, 62, 65.1, 69.9, 70.9, 83.2, 73.5,
81.2, 75.9, 79.9, 66, 66.7, 78.9, 69.6, 67.7, 66.6, 79.9, 73.4, 81.2, 66.9, 71, 62.6,
68.2, 72.7, 72.6, 82.8, 78, 79.4 ) ), row.names = c(NA, 29L), class = "data.frame" )
现在我们创建其他年份的数据和数据框繁荣通过复制第一年的数据：
Now we create data for the other years and a data frame Prosperity by copying the data for the first year:
names <- rep(mydata_struct$Country[1:28], 10)
years <- rep(2008:2017, each=28)
prosp <- rep(mydata_struct$Prosperity.Index[1:28], 10)
Prosperity <- data.frame(Country=names, Year=years, PI=prosp)
现在，我们将模糊其他年份并增加一种趋势不断增加的繁荣：
Now we will fuzz the other years and add a trend toward increasing prosperity:
set.seed(42)
Prosperity$PI <- rnorm(280, prosp, rnorm(280, 2, .25)) + (years - years[1]) * rnorm(280, 1, .25)
此处是您从实际数据开始的地方。首先我们可以得到一些统计数据：
Here is where you start with your actual data. First we can get some statistics:
options(digits=4)
with(Prosperity, tapply(PI, Year, mean)) # Mean PI for each year
# 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
# 73.02 73.74 74.50 76.50 76.13 77.95 78.33 79.55 80.85 81.71
with(Prosperity, tapply(PI, Year, sd)) # Standard deviation for PI for each year
# 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
# 6.861 6.422 6.840 6.935 6.582 6.592 8.331 6.777 7.489 8.044
with(Prosperity, tapply(PI, Year, max) - tapply(PI, Year, min)) # Range in PI for each year
# 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
# 28.24 23.24 22.20 24.83 23.99 23.26 27.97 22.77 27.78 30.66
最后一些情节：
plot(PI~Year, Prosperity) # Plot all values
boxplot(PI~Year, Prosperity) # Boxplots
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