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Jun 26th, 2019
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  1. glimpse(merged_dat2)
  2. Observations: 12
  3. Variables: 3
  4. Groups: Brand, topic [12]
  5. $ Brand <fct> Samsung, BLU, Apple, Samsung, BLU, Apple, Samsung, Apple, BLU, Samsung, BLU, Apple
  6. $ topic <int> 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4
  7. $ term <chr> "phone data months brand far anymore hard scratches funtions intuitive much everyth", "pho...
  8.  
  9. structure(list(Brand = structure(c(3L, 2L, 1L, 3L, 2L, 1L, 3L,
  10. 1L, 2L, 3L, 2L, 1L), .Label = c("Apple", "BLU", "Samsung"), class = "factor"),
  11. topic = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L),
  12. term = c("phone data months brand far anymore hard scratches funtions intuitive much everyth",
  13. "phone data months far alone ear life little ram everything loud againnow battery day nit notifications november pick reviews seconds someplace stop usually want well bueno",
  14. "phone brand always replacement refund camera fully kindly problems",
  15. "fast option answer buy number excel charg thanks lot", "fast product couple dont monei properly waste fact memory plenty sometimes walk sel shipping command asleep charge crap customer elseill ever garbageso sleep sure yesterday",
  16. "touch image category response", "phon even service work doesnt issues time works quality databas support warranty fantasticthanx issue problem wellno better love",
  17. "phon even inch though still need now apps things wonder",
  18. "even service work doesnt issues time works dependably looks bang gift blu ring away bunch itselfthen look morning night personally pops process volume",
  19. "just never weeks flash price supose purchase rep samsung water phoneworks truely vers cell",
  20. "just screen never weeks att hour junk right texts maybe photos business thus back call first issuesso meat music wifi muy",
  21. "screen weeks exactly trustful trip virtually seller week"
  22. )), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
  23. ), row.names = c(NA, -12L), vars = c("Brand", "topic"), labels = structure(list(
  24. Brand = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L,
  25. 3L, 3L), .Label = c("Apple", "BLU", "Samsung"), class = "factor"),
  26. topic = c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L)), class = "data.frame", row.names = c(NA,
  27. -12L), vars = c("Brand", "topic"), labels = structure(list(Brand = structure(c(1L,
  28. 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L), .Label = c("Apple",
  29. "BLU", "Samsung"), class = "factor"), topic = c(1L, 2L, 3L, 4L,
  30. 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L)), row.names = c(NA, -12L), class = "data.frame", vars = c("Brand",
  31. "topic"), drop = TRUE, indices = list(c(24L, 25L, 26L, 27L, 28L,
  32. 29L, 36L, 69L, 70L, 71L, 72L, 73L, 74L, 75L), 114:117, c(120L,
  33. 121L, 125L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L),
  34. c(185L, 186L, 192L, 221L, 222L, 223L, 224L, 225L, 226L),
  35. c(6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
  36. 18L, 19L, 20L, 21L, 22L, 23L, 32L, 34L, 38L, 39L, 47L, 48L,
  37. 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L,
  38. 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L), c(77L, 78L, 79L,
  39. 80L, 81L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L,
  40. 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L,
  41. 110L, 111L, 112L, 113L), c(123L, 124L, 128L, 129L, 131L,
  42. 132L, 133L, 135L, 137L, 139L, 140L, 142L, 153L, 154L, 155L,
  43. 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L,
  44. 166L, 167L, 168L), c(179L, 180L, 181L, 182L, 183L, 184L,
  45. 188L, 190L, 191L, 204L, 205L, 206L, 207L, 208L, 209L, 210L,
  46. 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L
  47. ), c(0L, 1L, 2L, 3L, 4L, 5L, 30L, 31L, 33L, 35L, 37L, 40L,
  48. 41L, 42L, 43L, 44L, 45L, 46L), c(76L, 82L, 83L, 84L, 85L,
  49. 86L, 87L, 88L, 89L), c(118L, 119L, 122L, 126L, 127L, 130L,
  50. 134L, 136L, 138L, 141L, 143L, 144L, 145L, 146L, 147L, 148L,
  51. 149L, 150L, 151L, 152L), c(177L, 178L, 187L, 189L, 193L,
  52. 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L
  53. )), group_sizes = c(14L, 4L, 11L, 9L, 44L, 29L, 28L, 26L,
  54. 18L, 9L, 20L, 15L), biggest_group_size = 44L, labels = structure(list(
  55. Brand = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L,
  56. 3L, 3L), .Label = c("Apple", "BLU", "Samsung"), class = "factor"),
  57. topic = c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L)), row.names = c(NA,
  58. -12L), class = "data.frame", vars = c("Brand", "topic"), drop = TRUE, indices = list(
  59. 54L, 47L, 69L, 83L, 40L, 174L, 103L, 204L, 159L, 213L, 155L,
  60. 55L, 151L, 157L, 35:36, 68L, 160L, 211L, 84L, 214L, 72L,
  61. 116L, 203L, 87L, 104L, 102L, 80:81, 105L, 106L, 30:32, 144L,
  62. 56L, 153L, 134:135, 90:91, 48L, 107L, 122:125, 108L, 46L,
  63. 52L, 221L, 86L, 95L, 147L, 37:39, 76:77, 215L, 193L, 73L,
  64. 43L, 109L, 156L, 41L, 205L, 115L, 169L, 44L, 148L, 136:137,
  65. 216L, 161L, 206L, 177:183, 74L, 49L, 50L, 162L, 154L, 89L,
  66. 53L, 152L, 209L, 217L, 96L, 92L, 33:34, 163L, 45L, 218L,
  67. 220L, 172L, 187:188, 164L, 57L, 58L, 59L, 173L, 85L, 82L,
  68. 165L, 118:121, 0:29, 200L, 210L, 60L, 97L, 166L, 194L, 149L,
  69. 75L, 167L, 78:79, 93L, 196L, 143L, 51L, 71L, 197L, 70L, 117L,
  70. 61L, 207L, 158L, 198L, 42L, 184:186, 62L, 100L, 225L, 126:129,
  71. 101L, 110L, 63L, 98L, 171L, 64L, 195L, 145L, 111:112, 208L,
  72. 88L, 175L, 170L, 212L, 138:140, 114L, 223L, 201L, 222L, 65L,
  73. 202L, 224L, 168L, 99L, 66L, 146L, 94L, 199L, 226L, 189:192,
  74. 67L, 150L, 219L, 176L, 130:133, 141:142, 113L), group_sizes = c(1L,
  75. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
  76. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 1L, 1L,
  77. 2L, 2L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L,
  78. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 7L, 1L,
  79. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
  80. 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 30L, 1L, 1L, 1L,
  81. 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  82. 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  83. 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  84. 1L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 4L, 2L, 1L), biggest_group_size = 30L, labels = structure(list(
  85. term = c("againnow", "alone", "always", "answer", "anymore",
  86. "apps", "asleep", "att", "away", "back", "bang", "battery",
  87. "better", "blu", "brand", "bueno", "bunch", "business", "buy",
  88. "call", "camera", "category", "cell", "charg", "charge",
  89. "command", "couple", "crap", "customer", "data", "databas",
  90. "day", "dependably", "doesnt", "dont", "ear", "elseill",
  91. "even", "ever", "everyth", "everything", "exactly", "excel",
  92. "fact", "fantasticthanx", "far", "fast", "first", "flash",
  93. "fully", "funtions", "garbageso", "gift", "hard", "hour",
  94. "image", "inch", "intuitive", "issue", "issues", "issuesso",
  95. "itselfthen", "junk", "just", "kindly", "life", "little",
  96. "look", "looks", "lot", "loud", "love", "maybe", "meat",
  97. "memory", "monei", "months", "morning", "much", "music",
  98. "muy", "need", "never", "night", "nit", "notifications",
  99. "november", "now", "number", "option", "personally", "phon",
  100. "phone", "phoneworks", "photos", "pick", "plenty", "pops",
  101. "price", "problem", "problems", "process", "product", "properly",
  102. "purchase", "quality", "ram", "refund", "rep", "replacement",
  103. "response", "reviews", "right", "ring", "samsung", "scratches",
  104. "screen", "seconds", "sel", "seller", "service", "shipping",
  105. "sleep", "someplace", "sometimes", "still", "stop", "supose",
  106. "support", "sure", "texts", "thanks", "things", "though",
  107. "thus", "time", "touch", "trip", "truely", "trustful", "usually",
  108. "vers", "virtually", "volume", "walk", "want", "warranty",
  109. "waste", "water", "week", "weeks", "well", "wellno", "wifi",
  110. "wonder", "work", "works", "yesterday")), row.names = c(NA,
  111. -158L), class = "data.frame", vars = "term", drop = TRUE))), indices = list(
  112. c(2L, 8L, 40L, 41L, 42L, 43L, 44L, 45L, 46L), 81:84, c(86L,
  113. 89L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L), c(139L,
  114. 144L, 173L, 174L, 175L, 176L, 177L, 178L), c(1L, 4L, 6L,
  115. 10L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
  116. 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L), c(48L,
  117. 49L, 50L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
  118. 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L
  119. ), c(88L, 91L, 93L, 95L, 97L, 99L, 101L, 112L, 113L, 114L,
  120. 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L,
  121. 125L, 126L, 127L), c(137L, 138L, 141L, 143L, 156L, 157L,
  122. 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L,
  123. 168L, 169L, 170L, 171L, 172L), c(0L, 3L, 5L, 7L, 9L, 11L,
  124. 12L, 13L, 14L, 15L, 16L, 17L), c(47L, 51L, 52L, 53L, 54L,
  125. 55L, 56L, 57L, 58L), c(85L, 87L, 90L, 92L, 94L, 96L, 98L,
  126. 100L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L,
  127. 111L), c(136L, 140L, 142L, 145L, 146L, 147L, 148L, 149L,
  128. 150L, 151L, 152L, 153L, 154L, 155L)), drop = TRUE, group_sizes = c(9L,
  129. 4L, 10L, 8L, 26L, 25L, 23L, 21L, 12L, 9L, 18L, 14L), biggest_group_size = 26L), indices = list(
  130. 2L, 5L, 7L, 11L, 1L, 4L, 8L, 10L, 0L, 3L, 6L, 9L), drop = TRUE, group_sizes = c(1L,
  131. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), biggest_group_size = 1L)
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