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  1. value_1 year_1 value_2 year_2 id_key
  2. 1 0.8572629 2006_2007 0.8352446 2006_2007 2006_2007_21267
  3. 2 0.9955628 2017_2018 0.9851993 2017_2018 2017_2018_1111711
  4. 3 0.9336878 2012_2013 0.9865080 2012_2013 2012_2013_1140536
  5. 4 0.8965862 2017_2018 0.9877127 2017_2018 2017_2018_832988
  6. 5 0.9659160 2012_2013 0.9855530 2012_2013 2012_2013_715096
  7. 6 0.9788319 2012_2013 0.5560681 2012_2013 2012_2013_875045
  8.  
  9. kclust <- x %>%
  10. as_tibble() %>%
  11. group_by(year_1, year_2) %>%
  12. nest(.key = "value")
  13. kclust
  14.  
  15. # A tibble: 13 x 3
  16. year_1 year_2 value
  17. <chr> <chr> <list>
  18. 1 2006_2007 2006_2007 <tibble [3 x 3]>
  19. 2 2017_2018 2017_2018 <tibble [11 x 3]>
  20. 3 2012_2013 2012_2013 <tibble [11 x 3]>
  21. 4 2010_2011 2010_2011 <tibble [11 x 3]>
  22. 5 2014_2015 2014_2015 <tibble [12 x 3]>
  23. 6 2011_2012 2011_2012 <tibble [9 x 3]>
  24. 7 2013_2014 2013_2014 <tibble [11 x 3]>
  25. 8 2016_2017 2016_2017 <tibble [7 x 3]>
  26. 9 2009_2010 2009_2010 <tibble [6 x 3]>
  27. 10 2008_2009 2008_2009 <tibble [3 x 3]>
  28. 11 2007_2008 2007_2008 <tibble [7 x 3]>
  29. 12 2015_2016 2015_2016 <tibble [5 x 3]>
  30. 13 2018_2019 2018_2019 <tibble [4 x 3]>
  31.  
  32. [[12]]
  33. # A tibble: 5 x 3
  34. value_1 value_2 id_key
  35. <dbl> <dbl> <chr>
  36. 1 0.943 0.887 2015_2016_1024478
  37. 2 0.861 0.571 2015_2016_816284
  38. 3 0.759 0.959 2015_2016_1260221
  39. 4 0.756 0.921 2015_2016_101829
  40. 5 0.981 0.936 2015_2016_709519
  41.  
  42. [[13]]
  43. # A tibble: 4 x 3
  44. value_1 value_2 id_key
  45. <dbl> <dbl> <chr>
  46. 1 0.927 0.959 2018_2019_6201
  47. 2 0.888 0.950 2018_2019_1274494
  48. 3 0.962 0.995 2018_2019_1011657
  49. 4 0.982 0.921 2018_2019_78814
  50.  
  51. k_means_centers = 2
  52.  
  53. kclust <- x %>%
  54. as_tibble() %>%
  55. group_by(year_1, year_2) %>%
  56. nest(.key = "value") %>%
  57. filter(map_int(value, nrow) > 4) %>%
  58. mutate(kmeans = map(value, ~kmeans(.x[[1]],
  59. centers = k_means_centers, iter.max = 10, nstart = 1)),
  60. tidied = map(kmeans, tidy),
  61. glanced = map(kmeans, glance),
  62. augmented = map2(kmeans, value, augment))
  63.  
  64. x <- structure(list(value_1 = c(0.857262918412708, 0.995562776151855,
  65. 0.93368775296229, 0.896586197519892, 0.965915992432594, 0.978831872921186,
  66. 0.931391986938977, 0.92860612171699, 0.942462944742556, 0.762633664061804,
  67. 0.929314203239609, 0.857555211754759, 0.942672735583934, 0.975237093000455,
  68. 0.472863198177383, 0.83842400391849, 0.526669740477171, 0.952190151229782,
  69. 0.519623395661802, 0.981457763792911, 0.91428464980769, 0.954400181141033,
  70. 0.840051106034647, 0.867699181854421, 0.89115631348807, 0.729514655086613,
  71. 0.659568217442908, 0.955200325383147, 0.88820423579156, 0.777402590109491,
  72. 0.943172514612716, 0.944933061146504, 0.476284928268558, 0.946901135343463,
  73. 0.780230224813699, 0.909629399821505, 0.865760792222491, 0.773621382484436,
  74. 0.836554542942252, 0.850980529158788, 0.527814114655505, 0.90791799831592,
  75. 0.882265024462087, 0.952685269154299, 0.891211744870977, 0.976456274127145,
  76. 0.90126924436977, 0.969111672067112, 1, 1, 0.968113370208161,
  77. 0.916126244980983, 0.933883953373501, 0.980900126347656, 0.924480004726964,
  78. 0.967149874304775, 1, 0.933612247290514, 0.982568394222027, 0.987764537202365,
  79. 0.898088994752593, 0.943973029406048, 0.926659428845797, 0.982663249602368,
  80. 0.0116889359524374, 0.985030805938289, 0.888240767289578, 0.779528122930639,
  81. 0.99485244406698, 0.82816655776856, 0.861023758791347, 1, 0.664694109407606,
  82. 0.960818825051411, 0.75945031696856, 0.763886276158968, 0.835629553075541,
  83. 0.846110310875411, 0.755847711756697, 0.67196568780797, 0.961888544525641,
  84. 0.969861418360086, 1, 0.974427258663929, 0.831055915618247, 0.93600722049079,
  85. 0.966106024456998, 0.901338903666627, 0.683877040965164, 0.979457749060513,
  86. 0.943143340661096, 1, 0.98087163513475, 0.769732004988478, 0.968733750777874,
  87. 0.937393276158036, 0.982135855939805, 1, 0.00987183002576516,
  88. 0.764641730481701), year_1 = c("2006_2007", "2017_2018", "2012_2013",
  89. "2017_2018", "2012_2013", "2012_2013", "2012_2013", "2010_2011",
  90. "2014_2015", "2011_2012", "2013_2014", "2016_2017", "2013_2014",
  91. "2011_2012", "2011_2012", "2013_2014", "2009_2010", "2009_2010",
  92. "2013_2014", "2010_2011", "2016_2017", "2016_2017", "2017_2018",
  93. "2013_2014", "2014_2015", "2014_2015", "2017_2018", "2008_2009",
  94. "2007_2008", "2012_2013", "2015_2016", "2017_2018", "2007_2008",
  95. "2012_2013", "2014_2015", "2009_2010", "2006_2007", "2010_2011",
  96. "2010_2011", "2014_2015", "2012_2013", "2017_2018", "2006_2007",
  97. "2013_2014", "2009_2010", "2007_2008", "2012_2013", "2010_2011",
  98. "2014_2015", "2017_2018", "2017_2018", "2011_2012", "2013_2014",
  99. "2016_2017", "2013_2014", "2014_2015", "2011_2012", "2013_2014",
  100. "2010_2011", "2012_2013", "2007_2008", "2017_2018", "2018_2019",
  101. "2013_2014", "2012_2013", "2014_2015", "2018_2019", "2009_2010",
  102. "2008_2009", "2010_2011", "2015_2016", "2010_2011", "2011_2012",
  103. "2007_2008", "2015_2016", "2008_2009", "2011_2012", "2013_2014",
  104. "2015_2016", "2010_2011", "2018_2019", "2016_2017", "2016_2017",
  105. "2011_2012", "2016_2017", "2010_2011", "2017_2018", "2014_2015",
  106. "2007_2008", "2014_2015", "2009_2010", "2017_2018", "2015_2016",
  107. "2010_2011", "2014_2015", "2012_2013", "2018_2019", "2007_2008",
  108. "2011_2012", "2014_2015"), value_2 = c(0.83524458245376, 0.985199346676161,
  109. 0.98650800423171, 0.987712680219121, 0.985552973109259, 0.55606807703455,
  110. 0.993081550565629, 0.942324451054759, 0.874951001978959, 0.972242235849801,
  111. 0.960561835073607, 0.745948805820105, 0.797055662541724, 0.977508894088148,
  112. 0.712233681864871, 0.285060053385682, 0.905730331400375, 0.93571084346821,
  113. 0.790305033705714, 0.958722926473936, 0.962776635511766, 0.992608325470545,
  114. 0.474283965476535, 0.806366773701265, 0.904730345643149, 0.862254279087857,
  115. 0.984488707157245, 0.892241046229236, 0.714442964628943, 0.807622124741829,
  116. 0.887170731681905, 0.954684589806249, 0.9211778417945, 0.948974567771373,
  117. 0.965125469708914, 0.886108424878785, 0.942065878654209, 0.66663307765255,
  118. 0.90331177434957, 0.976829922293502, 0.95848533971269, 0.956127315051688,
  119. 0.650750852737616, 0.9999724828739, 0.826005013210071, 0.959980346940766,
  120. 0.978304048122191, 0.975422514331076, 0.792199553496305, 0.461104040127036,
  121. 0.997962170857627, 0.968897881428091, 0.820571356084491, 0.99183854174536,
  122. 0.937073215517585, 0.993271661681666, 0.862602069969553, 0.941823773454386,
  123. 0.984268864412331, 0.983876968226894, 0.760177556170661, 0.926285514876429,
  124. 0.959350334184441, 0.996409752091077, 0.0403289662596409, 0.994749999506845,
  125. 0.950154051313514, 0.916520797550305, 0.728271849187279, 0.89835975825379,
  126. 0.571018894293857, 0.971731331958454, 0.810499095029711, 0.887497351434693,
  127. 0.958925181726699, 0.893189038016587, 0.875143741543741, 0.833284214217249,
  128. 0.921240338805686, 0.926586130283117, 0.994798572238072, 0.980971763292719,
  129. 0.964016005572769, 0.989376580856801, 0.935519257737914, 0.922845605574439,
  130. 0.996381524259124, 0.0351359902186695, 0.953643584869029, 0.937802352885434,
  131. 0.902249244386311, 0.719887783612443, 0.936028294902931, 0.809292272844584,
  132. 0.974049350800454, 0.781649033147858, 0.920733566350649, 0.998781417653825,
  133. 0.0617975853732401, 0.883026179946989), year_2 = c("2006_2007",
  134. "2017_2018", "2012_2013", "2017_2018", "2012_2013", "2012_2013",
  135. "2012_2013", "2010_2011", "2014_2015", "2011_2012", "2013_2014",
  136. "2016_2017", "2013_2014", "2011_2012", "2011_2012", "2013_2014",
  137. "2009_2010", "2009_2010", "2013_2014", "2010_2011", "2016_2017",
  138. "2016_2017", "2017_2018", "2013_2014", "2014_2015", "2014_2015",
  139. "2017_2018", "2008_2009", "2007_2008", "2012_2013", "2015_2016",
  140. "2017_2018", "2007_2008", "2012_2013", "2014_2015", "2009_2010",
  141. "2006_2007", "2010_2011", "2010_2011", "2014_2015", "2012_2013",
  142. "2017_2018", "2006_2007", "2013_2014", "2009_2010", "2007_2008",
  143. "2012_2013", "2010_2011", "2014_2015", "2017_2018", "2017_2018",
  144. "2011_2012", "2013_2014", "2016_2017", "2013_2014", "2014_2015",
  145. "2011_2012", "2013_2014", "2010_2011", "2012_2013", "2007_2008",
  146. "2017_2018", "2018_2019", "2013_2014", "2012_2013", "2014_2015",
  147. "2018_2019", "2009_2010", "2008_2009", "2010_2011", "2015_2016",
  148. "2010_2011", "2011_2012", "2007_2008", "2015_2016", "2008_2009",
  149. "2011_2012", "2013_2014", "2015_2016", "2010_2011", "2018_2019",
  150. "2016_2017", "2016_2017", "2011_2012", "2016_2017", "2010_2011",
  151. "2017_2018", "2014_2015", "2007_2008", "2014_2015", "2009_2010",
  152. "2017_2018", "2015_2016", "2010_2011", "2014_2015", "2012_2013",
  153. "2018_2019", "2007_2008", "2011_2012", "2014_2015"), id_key = c("2006_2007_21267",
  154. "2017_2018_1111711", "2012_2013_1140536", "2017_2018_832988",
  155. "2012_2013_715096", "2012_2013_875045", "2012_2013_891024", "2010_2011_815556",
  156. "2014_2015_39911", "2011_2012_1123360", "2013_2014_916365", "2016_2017_26172",
  157. "2013_2014_732485", "2011_2012_1551152", "2011_2012_1095073",
  158. "2013_2014_709804", "2009_2010_65100", "2009_2010_1018963", "2013_2014_20388",
  159. "2010_2011_1115222", "2016_2017_1646383", "2016_2017_1567892",
  160. "2017_2018_1368007", "2013_2014_205520", "2014_2015_851968",
  161. "2014_2015_46989", "2017_2018_1116521", "2008_2009_23217", "2007_2008_79879",
  162. "2012_2013_709804", "2015_2016_1024478", "2017_2018_1062379",
  163. "2007_2008_877890", "2012_2013_51396", "2014_2015_1064728", "2009_2010_1026214",
  164. "2006_2007_58492", "2010_2011_820027", "2010_2011_2488", "2014_2015_1458891",
  165. "2012_2013_40545", "2017_2018_1326801", "2006_2007_36270", "2013_2014_1140536",
  166. "2009_2010_1396009", "2007_2008_42582", "2012_2013_1637459",
  167. "2010_2011_794323", "2014_2015_4904", "2017_2018_701221", "2017_2018_934612",
  168. "2011_2012_47111", "2013_2014_352947", "2016_2017_1613103", "2013_2014_34408",
  169. "2014_2015_890801", "2011_2012_875570", "2013_2014_812074", "2010_2011_1466258",
  170. "2012_2013_723612", "2007_2008_5513", "2017_2018_30625", "2018_2019_6201",
  171. "2013_2014_1024305", "2012_2013_1466258", "2014_2015_814453",
  172. "2018_2019_1274494", "2009_2010_2488", "2008_2009_106640", "2010_2011_33213",
  173. "2015_2016_816284", "2010_2011_1267238", "2011_2012_1451505",
  174. "2007_2008_38777", "2015_2016_1260221", "2008_2009_1001082",
  175. "2011_2012_817473", "2013_2014_1274057", "2015_2016_101829",
  176. "2010_2011_1020569", "2018_2019_1011657", "2016_2017_789388",
  177. "2016_2017_1004434", "2011_2012_1156039", "2016_2017_1350031",
  178. "2010_2011_205520", "2017_2018_42582", "2014_2015_812074", "2007_2008_1039684",
  179. "2014_2015_751652", "2009_2010_1047699", "2017_2018_101829",
  180. "2015_2016_709519", "2010_2011_861878", "2014_2015_832428", "2012_2013_74208",
  181. "2018_2019_78814", "2007_2008_922224", "2011_2012_314808", "2014_2015_3673"
  182. )), row.names = c(NA, -100L), class = "data.frame")
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