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Mar 30th, 2017
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  1. MemberGroup <- structure(c(1L, 1L, 1L, 1L, 1L,
  2. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
  3. 13L, 13L, 13L, 13L, 13L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
  4. 25L, 25L, 25L, 25L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L,
  5. 37L, 37L, 37L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L,
  6. 49L, 49L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L,
  7. 61L), .Label = c("Bogus1_Group1",
  8. "Bogus1_Group1", "Bogus1_Group1",
  9. "Bogus1_Group1", "Bogus1_Group1",
  10. "Bogus1_Group1", "Bogus1_Group1",
  11. "Bogus1_Group1", "Bogus1_Group1",
  12. "Bogus1_Group1", "Bogus1_Group1",
  13. "Bogus1_Group1", "Bogus2_Group1",
  14. "Bogus2_Group1", "Bogus2_Group1",
  15. "Bogus2_Group1", "Bogus2_Group1",
  16. "Bogus2_Group1", "Bogus2_Group1",
  17. "Bogus2_Group1", "Bogus2_Group1",
  18. "Bogus2_Group1", "Bogus2_Group1",
  19. "Bogus2_Group1", "Bogus3_Group1",
  20. "Bogus3_Group1", "Bogus3_Group1",
  21. "Bogus3_Group1", "Bogus3_Group1",
  22. "Bogus3_Group1", "Bogus3_Group1",
  23. "Bogus3_Group1", "Bogus3_Group1",
  24. "Bogus3_Group1", "Bogus3_Group1",
  25. "Bogus3_Group1", "Bogus4_Group2",
  26. "Bogus4_Group2", "Bogus4_Group2",
  27. "Bogus4_Group2", "Bogus4_Group2",
  28. "Bogus4_Group2", "Bogus4_Group2",
  29. "Bogus4_Group2", "Bogus4_Group2",
  30. "Bogus4_Group2", "Bogus4_Group2",
  31. "Bogus4_Group2", "Bogus5_Group2",
  32. "Bogus5_Group2", "Bogus5_Group2",
  33. "Bogus5_Group2", "Bogus5_Group2",
  34. "Bogus5_Group2", "Bogus5_Group2",
  35. "Bogus5_Group2", "Bogus5_Group2",
  36. "Bogus5_Group2", "Bogus5_Group2",
  37. "Bogus5_Group2", "Bogus6_Group3",
  38. "Bogus6_Group3", "Bogus6_Group3",
  39. "Bogus6_Group3", "Bogus6_Group3",
  40. "Bogus6_Group3", "Bogus6_Group3",
  41. "Bogus6_Group3", "Bogus6_Group3",
  42. "Bogus6_Group3", "Bogus6_Group3",
  43. "Bogus6_Group3"), class = "factor");
  44.  
  45. Display <- c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  46. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  47. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  48. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  49. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L);
  50.  
  51. Oid <- c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  52. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
  53. 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
  54. 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
  55. 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L);
  56.  
  57. AgeBucket <- structure(c(1L, 7L, 13L, 19L, 25L, 31L, 37L,
  58. 43L, 49L, 55L, 61L, 67L, 1L, 7L, 13L, 19L, 25L, 31L, 37L,
  59. 43L, 49L, 55L, 61L, 67L, 1L, 7L, 13L, 19L, 25L, 31L, 37L,
  60. 43L, 49L, 55L, 61L, 67L, 1L, 7L, 13L, 19L, 25L, 31L, 37L,
  61. 43L, 49L, 55L, 61L, 67L, 1L, 7L, 13L, 19L, 25L, 31L, 37L,
  62. 43L, 49L, 55L, 61L, 67L, 1L, 7L, 13L, 19L, 25L, 31L, 37L,
  63. 43L, 49L, 55L, 61L, 67L), .Label = c("10-14", "10-14", "10-14",
  64. "10-14", "10-14", "10-14", "15-19", "15-19", "15-19", "15-19",
  65. "15-19", "15-19", "20-24", "20-24", "20-24", "20-24", "20-24",
  66. "20-24", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29",
  67. "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "35-39",
  68. "35-39", "35-39", "35-39", "35-39", "35-39", "40-44", "40-44",
  69. "40-44", "40-44", "40-44", "40-44", "45-49", "45-49", "45-49",
  70. "45-49", "45-49", "45-49", "50-54", "50-54", "50-54", "50-54",
  71. "50-54", "50-54", "55-59", "55-59", "55-59", "55-59", "55-59",
  72. "55-59", "60-64", "60-64", "60-64", "60-64", "60-64", "60-64",
  73. "65-69", "65-69", "65-69", "65-69", "65-69", "65-69"), class = "factor");
  74.  
  75. BinMin <- c(10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
  76. 60L, 65L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
  77. 60L, 65L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
  78. 60L, 65L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
  79. 60L, 65L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
  80. 60L, 65L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
  81. 60L, 65L);
  82.  
  83. per <- c(0.387832699619772, 0.317991631799163,
  84. 0.123521681997372, 0.0947368421052632, 0.0972359328726555,
  85. 0.0937873357228196, 0.103455162772119, 0.108816521048451,
  86. 0.114578206516389, 0.116530727610434, 0.12681131040965, 0.110502997133177,
  87. 0.20532319391635, 0.259414225941423, 0.258869908015769, 0.232456140350877,
  88. 0.16436327739388, 0.0949820788530466, 0.0728979428799681,
  89. 0.0557320624834525, 0.0484299635790924, 0.0429621254946297,
  90. 0.0460562387405029, 0.0406567630961689, 0.0342205323193916,
  91. 0.0502092050209205, 0.0801576872536137, 0.171929824561404,
  92. 0.262092793682132, 0.328853046594982, 0.387257839025364,
  93. 0.42176330420969, 0.450241165469042, 0.462731163692159, 0.479439179133704,
  94. 0.445660672400313, 0.0418250950570342, 0.0167364016736402,
  95. 0.00919842312746386, 0.00701754385964912, 0.00740375123395854,
  96. 0.00627240143369176, 0.00279608548032754, 0.00238284352660842,
  97. 0.0016733930504971, 0.00121133812484858, 0.00109657711286912,
  98. 0.000260620276257493, 0.102661596958175, 0.0711297071129707,
  99. 0.0315374507227332, 0.0254385964912281, 0.0108588351431392,
  100. 0.00686977299880526, 0.00499300978629918, 0.00357426528991263,
  101. 0.00246087213308397, 0.00185738512476783, 0.00117490404950262,
  102. 0.00130310138128746, 0.608365019011407, 0.54602510460251,
  103. 0.314060446780552, 0.292982456140351, 0.285291214215202,
  104. 0.253584229390681, 0.226682644297983, 0.231665342864707,
  105. 0.237621813170588, 0.246305418719212, 0.265058353567792,
  106. 0.252541047693511);
  107.  
  108. Ct <- c(102L, 152L, 94L, 108L, 197L,
  109. 314L, 518L, 822L, 1164L, 1443L, 1619L, 424L, 54L, 124L, 197L,
  110. 265L, 333L, 318L, 365L, 421L, 492L, 532L, 588L, 156L, 9L,
  111. 24L, 61L, 196L, 531L, 1101L, 1939L, 3186L, 4574L, 5730L,
  112. 6121L, 1710L, 11L, 8L, 7L, 8L, 15L, 21L, 14L, 18L, 17L, 15L,
  113. 14L, 1L, 27L, 34L, 24L, 29L, 22L, 23L, 25L, 27L, 25L, 23L,
  114. 15L, 5L, 160L, 261L, 239L, 334L, 578L, 849L, 1135L, 1750L,
  115. 2414L, 3050L, 3384L, 969L);
  116.  
  117. InRange <- c(263L, 478L, 761L,
  118. 1140L, 2026L, 3348L, 5007L, 7554L, 10159L, 12383L, 12767L,
  119. 3837L, 263L, 478L, 761L, 1140L, 2026L, 3348L, 5007L, 7554L,
  120. 10159L, 12383L, 12767L, 3837L, 263L, 478L, 761L, 1140L, 2026L,
  121. 3348L, 5007L, 7554L, 10159L, 12383L, 12767L, 3837L, 263L,
  122. 478L, 761L, 1140L, 2026L, 3348L, 5007L, 7554L, 10159L, 12383L,
  123. 12767L, 3837L, 263L, 478L, 761L, 1140L, 2026L, 3348L, 5007L,
  124. 7554L, 10159L, 12383L, 12767L, 3837L, 263L, 478L, 761L, 1140L,
  125. 2026L, 3348L, 5007L, 7554L, 10159L, 12383L, 12767L, 3837L);
  126.  
  127.  
  128. df <- as.data.frame(list(MemberGroup,Display,Oid,AgeBucket,BinMin,per,Ct,InRange))
  129.  
  130. library(ggplot2)
  131.  
  132. p <- ggplot(data=df, aes(x=BinMin, y=per, color = MemberGroup))#, order = as.numeric(Oid))
  133. p <- p + geom_line(aes(linetype=MemberGroup, color=MemberGroup, size=1))
  134. p <- p + theme(legend.position=c(.4,.8)) + guides(size = "none")
  135. p <- p + theme(text = element_text(size = 30))
  136. p <- p + scale_y_continuous(labels = scales::percent, breaks=seq(0, .80, .05))
  137. p <- p + scale_x_continuous(breaks=seq(10,65,5), labels = unique(all_labs) )
  138. p <- p + theme(axis.text.x=element_text(angle=50,vjust=0.5))
  139. p <- p + scale_color_manual(values=c("#ff1919", "#ff4c4c", "#ff9999","#76ee00","#32cd32","#1874cd"))
  140. p <- p + scale_linetype_manual(values = c("dashed","dashed","dashed","dotted","dotted","dotted"))
  141. p <- p + xlab("XXX Range")
  142. p <- p + ylab("%")
  143. p <- p + theme(legend.background = element_rect(fill = "transparent"),legend.key = element_rect(fill = "transparent",color ="transparent"))
  144. p <- p + guides(color = guide_legend(override.aes = list(size=1)))
  145. p <- p + theme(legend.key.width = unit(3,"cm"))
  146. p <- p + theme(legend.title=element_blank())
  147. p
  148.  
  149. library(gtable)
  150. library(grid)
  151. # Get the ggplot grob
  152. g = ggplotGrob(p)
  153.  
  154. # Get the legend
  155. leg = g$grobs[[which(g$layout$name == "guide-box")]]$grobs[[1]]
  156.  
  157. # Add rown to the legend gtable
  158. leg = gtable_add_rows(leg, unit(1, "lines"), pos = 8) # Space below "Group 2"
  159. leg = gtable_add_rows(leg, unit(1, "lines"), pos = 6) # Space below "Group 1"
  160.  
  161. # Put the legned back into the plot
  162. g$grobs[[which(g$layout$name == "guide-box")]]$grobs[[1]] = leg
  163.  
  164. # Draw it
  165. grid.newpage()
  166. grid.draw(g)
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