具有单个输入变量的kmeans聚类图

Question

我有一些看起来像这样的数据

   id_row year_row      value
1 1031296     2012 0.13908350
2 1031296     2013 0.11825776
3 1031296     2014 0.03925923
4 1031296     2015 0.07821547
5 1031296     2016 0.04694897
6 1031296     2017 0.07790232

我可以按年份过滤并运行kmeans

kmdata <- results %>%
  filter(year_row == "2010")

km <- kmeans(as.vector(kmdata$value), centers = 4, iter.max = 10, nstart = 1)
km

但是我想计算每年的公里数，并查看每个id_row如何随时间变化簇。

由于数据不是矩阵，因此在尝试绘制模型时会出错。

library(cluster) clusplot(kmdata$value, km$clusters, color=T, shade=T, labels=2, lines=0)

Error in is.list(s.x.2d) : x is not a data matrix

我为此使用“好”的方法吗？我在网上查看并找到了一些kmeans示例，发现许多示例使用多个inputs，而我所拥有的只是一个cosine相似输入。

##         Murder Assault UrbanPop     Rape
## Alabama 1.2426   0.783   -0.521 -0.00342
## Alaska  0.5079   1.107   -1.212  2.48420
## Arizona 0.0716   1.479    0.999  1.04288

数据：

structure(list(id_row = c("1031296", "1031296", "1031296", "1031296", 
"1031296", "1031296", "1031296", "1031296", "1130310", "1130310", 
"1130310", "1130310", "1130310", "1130310", "1130310", "1130310", 
"1130310", "1130310", "1130310", "1130310", "1130310", "1130310", 
"1130310", "1137411", "1137411", "1336920", "1336920", "1336920", 
"1336920", "1336920", "1336920", "1336920", "1336920", "1336920", 
"1336920", "1336920", "1336920", "1336920", "1336920", "1336920", 
"1336920", "1336920", "1336920", "1336920", "1413329", "1413329", 
"1413329", "1413329", "1413329", "1413329", "1413329", "1413329", 
"1413329", "1413329", "1413329", "1413329", "1413329", "1413329", 
"1413329", "1413329", "1413329", "1413329", "1413329", "16732", 
"16732", "16732", "16732", "16732", "16732", "16732", "16732", 
"16732", "16732", "16732", "16732", "16732", "16732", "16732", 
"21344", "21344", "21344", "21344", "21344", "21344", "21344", 
"21344", "21344", "21344", "21344", "21344", "21344", "21344", 
"21344", "29989", "29989", "29989", "29989", "313616", "313616", 
"46989", "46989", "46989", "46989", "46989", "46989", "46989", 
"46989", "46989", "5513", "5513", "5513", "5513", "5513", "5513", 
"5513", "5513", "5513", "5513", "5513", "5513", "5513", "5513", 
"5513", "5513", "716823", "716823", "716823", "716823", "716823", 
"716823", "716823", "716823", "716823", "716823", "789073", "789073", 
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"789073", "789073", "789073", "789073", "797468", "797468", "797468", 
"797468", "797468", "797468", "797468", "797468", "797468", "797468", 
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"80661", "80661", "80661", "80661", "80661", "80661", "80661", 
"80661", "80661", "80661", "80661", "80661", "80661", "80661", 
"80661", "866787", "866787", "866787", "866787", "866787", "866787", 
"866787", "866787", "866787", "866787", "866787", "866787", "866787", 
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"882184", "91142", "91142", "91142", "91142", "91142", "91142", 
"91142", "91142", "91142", "91142", "91142", "91142", "91142", 
"91142", "91142", "91142", "91142", "912595", "95521", "95521", 
"95521", "95521", "95521", "95521", "95521", "95521", "95521", 
"95521", "95521", "95521"), year_row = c("2012", "2013", "2014", 
"2015", "2016", "2017", "2018", "2019", "2004", "2005", "2006", 
"2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", 
"2015", "2016", "2017", "2018", "2003", "2004", "2001", "2002", 
"2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", 
"2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", 
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"2006", "2007", "2008", "2005", "2005", "2006", "2006", "2007", 
"2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", 
"2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", 
"2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", 
"2004", "2005", "2006", "2009", "2010", "2011", "2012", "2013", 
"2014", "2015", "2016", "2016", "2017", "2017", "2018", "2019", 
"2006", "2006", "2007", "2007", "2008", "2008", "2009", "2010", 
"2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", 
"2019", "2016", "2017", "2018", "2019", "2003", "2004", "2005", 
"2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", 
"2014", "2015", "2016", "2017", "2018", "2019", "2018", "2006", 
"2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", 
"2017", "2018", "2019"), value = c(0.139083502412409, 0.11825775641964, 
0.0392592265955874, 0.0782154662932015, 0.0469489736719239, 0.0779023179300866, 
0.0228012955999517, 0.0854168153956153, 0.999737539238827, 0.0443179732423611, 
0.0390309184765143, 0.0922585629702825, 0.0403666403458272, 0.0382194133579655, 
0.042698343847385, 0.0685255449505098, 0.0675200147346398, 0.0187881296791695, 
0.0429479468414007, 0.079743052611441, 0.0320744404500168, 0.0144941429460794, 
0.119160368459038, 0.0925697035527265, 0.083984708174856, 0.996283500380756, 
0.107778943258269, 0.173435313229931, 0.0900909715473757, 0.0197546332298797, 
0.144120296067433, 0.158299486589792, 0.186295755413315, 0.101668114945428, 
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0.0378571859484953, 0.0409750669985696, 0.0835845366556822, 0.0461210474287448, 
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0.04872107872219, 0.0428538370791108, 0.0702414653935244, 0.0509909654321864, 
0.021307630695821, 0.0487040360447408, 0.041478962700618, 0.0899399982611924, 
0.0596779333637508, 0.0594380923275606, 0.0260485423561843, 0.0227124484448211, 
0.0283345344486783, 0, 0, 0.987417394803821, 0.977452829626341, 
0.0935080361786257, 0.0399062483581079, 0.0597891120112862, 0.315545198466048, 
0.163328528827512, 0.0874148150892009, 0.0510720020721022, 0.0667940605980389, 
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0.058743275128742)), row.names = c(NA, -230L), class = "data.frame")

Answer 1

您可以使用nest创建嵌套的小标题，然后将kmeans应用于每个组：

library(tidyverse)
x <- results %>% 
  as_tibble() %>% 
  select(-id_row) %>% 
  group_by(year_row) %>% 
  nest(.key = "value") %>%
  filter(map_int(value, nrow)> 4) %>% 
  mutate(kmeans = map(value, ~kmeans(.x[[1]], centers = 4, iter.max = 10, nstart = 1)))

请注意，我筛选了一些年份，因为它们没有足够的观测值。

然后您可以制作一个集群图：

cluster::clusplot(x$value[[1]], x$kmeans[[1]]$cluster)