R语言 根据变量是负还是正区分geom_path中的线型

13z8s7eq  于 2023-05-20  发布在  其他
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在该数据中：

  1. data <- structure(list(IPU_id = c(2L, 2L, 116L, 116L, 352L, 352L, 481L, 
  2. 481L, 583L, 583L, 1077L, 1077L, 1394L, 1394L, 1447L, 1447L, 1538L, 
  3. 1538L, 1982L, 1982L, 2459L, 2459L, 2729L, 2729L, 2763L, 2763L, 
  4. 2809L, 2809L, 3162L, 3162L, 3165L, 3165L, 3188L, 3188L, 3249L, 
  5. 3249L, 3454L, 3454L, 3618L, 3618L, 3780L, 3780L, 3796L, 3796L, 
  6. 3972L, 3972L, 4407L, 4407L, 4779L, 4779L, 5061L, 5061L, 6599L, 
  7. 6599L, 6614L, 6614L, 6977L, 6977L, 7135L, 7135L, 7462L, 7462L, 
  8. 8338L, 8338L, 8352L, 8352L, 8511L, 8511L, 8529L, 8529L, 9160L, 
  9. 9160L, 9524L, 9524L, 9547L, 9547L, 9966L, 9966L, 10147L, 10147L, 
  10. 10393L, 10393L, 10507L, 10507L, 10894L, 10894L, 10950L, 10950L, 
  11. 11550L, 11550L, 11563L, 11563L, 11571L, 11571L, 11718L, 11718L, 
  12. 11741L, 11741L, 11912L, 11912L, 12062L, 12062L, 12112L, 12112L, 
  13. 12116L, 12116L, 12129L, 12129L, 12161L, 12161L, 12718L, 12718L, 
  14. 12767L, 12767L, 12800L, 12800L, 12809L, 12809L, 13580L, 13580L, 
  15. 13829L, 13829L, 13979L, 13979L, 14002L, 14002L, 14609L, 14609L, 
  16. 18862L, 18862L, 19116L, 19116L, 19804L, 19804L, 20654L, 20654L, 
  17. 24181L, 24181L, 26543L, 26543L, 26562L, 26562L, 27002L, 27002L, 
  18. 27050L, 27050L, 27068L, 27068L, 27199L, 27199L, 27354L, 27354L, 
  19. 27632L, 27632L, 27677L, 27677L, 27697L, 27697L, 28288L, 28288L
  20. ), comparison = c(0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 
  21. 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  22. 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 
  23. 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
  24. 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 
  25. 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 
  26. 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  27. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
  28. 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 
  29. 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 
  30. 0L, 0L, 0L, 0L, 0L), name = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 
  31. 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
  32. 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
  33. 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
  34. 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
  35. 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
  36. 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
  37. 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
  38. 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
  39. 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
  40. 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), levels = c("Answerer", 
  41. "NotAnswerer"), class = "factor"), value = c(-0.0431558441558438, 
  42. 1.70782692307692, 0.684046511627906, 0.423953124999999, 0.302268456375839, 
  43. -0.309683333333333, 0.792993421052627, -0.871427152317877, 2.55586178861788, 
  44. -0.85030081300813, -0.241509900990097, 0.026694999999999, 0.880453020134229, 
  45. -0.298657718120805, -0.0876920529801324, -0.250344370860927, 
  46. -6.02329885057471, -8.22881976744186, -6.48995, 1.035525, 9.35135416666668, 
  47. -2.60511643835616, 0.643900662251654, 0.167112582781379, 0.572819078947369, 
  48. -2.0765165562914, 0.462728395061728, -0.141359756097561, 0.76575, 
  49. -1.60808333333333, -0.894208609271523, -0.786271523178809, 0.754248201438848, 
  50. -0.160610714285716, -0.338933884297521, -1.21221008403362, 6.022116, 
  51. -0.524599999999991, 0.425596774193548, 0.163237012987012, 0.189037634408601, 
  52. -2.02574175824177, 0.1978, 1.16077962962963, -1.45908116883117, 
  53. 0.964378289473683, 0.421305555555556, 0.540716783216783, 0.16981683168317, 
  54. -0.798830000000001, -5.24771590909091, 0.00527067669172944, -0.0771428571428602, 
  55. 1.30525, -1.54231666666667, -0.87164, -1.6384238410596, -0.363324503311258, 
  56. 0.46028145695364, -0.10932119205298, 0.925983050847458, 2.02564406779661, 
  57. 0.527496212121212, -0.55037786259542, 0.075385869565218, 0.672869565217391, 
  58. -3.55737931034484, 0.261267857142857, 0.362056291390728, 0.239115894039735, 
  59. 0.116307947019869, 0.216654605263158, 0.732013422818791, 0.709734899328861, 
  60. 0.362520134228187, 0.092070469798658, 1.32523305084746, 0.207796610169491, 
  61. -0.440720930232559, -1.91472265625, 0.596914473684205, 0.68512582781457, 
  62. 3.48474796747966, -0.790289156626509, 0.858354651162783, 4.8597356321839, 
  63. 4.24323509933774, -1.68983774834437, 1.95657432432433, -1.80357432432432, 
  64. 0.134786821705425, -0.463600775193796, -0.222075342465754, -0.137592465753425, 
  65. 7.38660927152317, -4.3473, 0.935841059602654, -8.79423026315788, 
  66. -0.123965034965031, 0.139277777777776, 0.435614678899091, -0.0393564814814806, 
  67. 2.04550442477876, 0.544691304347827, 0.398654929577465, -1.11304929577466, 
  68. 0.509702702702702, -5.98748648648649, 1.33939403973509, -11.2562350993377, 
  69. 0.402916666666664, -2.24811394557824, 6.45144, -1.86279591836734, 
  70. -2.86064569536425, -0.778245033112582, 2.69490604026845, 0.175989999999999, 
  71. -0.0124668874172132, 0.594857615894042, 2.14110317460318, 4.49728225806452, 
  72. -8.18573913043478, -0.88930530973451, -0.753973856209148, 0.496088235294117, 
  73. -0.402965346534655, 0.181366336633662, -0.451240000000001, 1.91439, 
  74. -0.633221476510065, 0.292332214765101, -8.19086139455783, -0.494641176470588, 
  75. 0.908275000000001, -10.3464235714286, -0.206032894736843, -7.97076158940397, 
  76. 1.47209302325581, -1.92366091954023, -0.704506622516558, -0.226966887417218, 
  77. 2.37554, 2.51840333333333, 1.99034437086093, 11.2908609271523, 
  78. 1.10772023809525, -3.53189880952381, 0.220096026490063, -0.898711920529798, 
  79. 0.700872093023255, -11.4639803921569, -3.12411920529801, 8.04174685430463, 
  80. -6.07138333333333, 1.2730100671141, -1.07498230088496, 0.277022123893805, 
  81. -3.62009154929577, 2.01665277777778), name_num = c(1.1325, 1.8675, 
  82. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  83. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  84. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  85. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  86. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  87. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  88. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  89. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  90. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  91. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  92. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  93. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  94. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  95. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  96. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  97. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  98. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  99. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  100. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675, 
  101. 1.1325, 1.8675, 1.1325, 1.8675, 1.1325, 1.8675)), row.names = c(NA, 
  102. -160L), class = c("tbl_df", "tbl", "data.frame"))

这个图：

  1. library(tidyverse)
  2. library(tidyquant)
  3. library(ggdist)
  4. library(ggthemes)
  6. box_width <- .12
  7. jitter_width <- .025
  9. pj <- position_jitter(seed = 1, width = jitter_width, height = 0)
  11. data %>%
  12.     
  13.   ggplot(aes(x = name, y = value, fill = name)) +
  14.   
  15.    # half-violin:
  16.    stat_halfeye(
  17.      aes(
  18.        justification = ifelse(name == "Answerer", 1.2, -.2),
  19.        side = ifelse(name == "Answerer", "left", "right")
  20.      ),
  21.      adjust = 0.5,
  22.      .width = 0,
  23.      point_colour = NA
  24.    ) +
  25.   
  26.   # boxplot:
  27.   geom_boxplot(
  28.     width = box_width,
  29.     alpha = 0.5,
  30.   ) +
  31.   scale_fill_manual(values = c(
  32.     "Answerer" = "slateblue",
  33.     "NotAnswerer" = "lawngreen"
  34.   )) +
  35.   guides(fill = "none", color = "none")+
  36.   
  37.   # data points:
  38.   geom_point(
  39.     aes(x = name_num, color = factor(comparison)),
  40.     alpha = 0.4, col = "grey40",
  41.     position = pj
  42.   ) +
  44.   # connecting lines:
  45.   geom_path(aes(x = name_num, group = IPU_id, color = factor(comparison)),
  46.             alpha = 0.4,
  47.             col = "grey40",
  48.             position = pj,
  49.   )

我想根据变量comparison是负（0）还是正（1）来区分geom_path中的线型：例如，如何得到折线，其中comparison is positive and a **dotted line** where the比较`为负？

r

1条答案

按热度按时间
k5ifujac

k5ifujac1#

您必须将数据集中的某些内容Map到geom_path()调用中的linetype美学。在共享的数据集中，没有一个很好的方法来做到这一点，所以这里有一个方法，通过这些步骤：

  • 使用此斜率信息汇总您的数据（“NotAnswerer”-“Answerer”的结果），
  • 将新的汇总数据合并回原始数据集
  • 重写geom_path()部分以Map到linetype，以及
  • 手动设置linetype以匹配新值

下面是完整的代码，并有一些注解可以帮助您：

  1. # calculating slopes
  2. df <- data %>% select(IPU_id, comparison, name, value) %>%
  3.   pivot_wider(names_from = "name", values_from = "value") %>%
  4.   mutate(slope_num = NotAnswerer - Answerer)
  6. # summarizing and gathering together the slope per each IPU_id
  7. df <- df %>%
  8.   group_by(IPU_id) %>%
  9.   summarize(slope_name = ifelse(sign(slope_num) < 0, "negative", "positive"))
  11. # merging dataset back - keeping all values in original dataset
  12. merged <- left_join(data, df, by="IPU_id")

这是情节的新代码。为了简洁起见，我在这里只表示geom_path()部分。

  1. geom_path(
  2.     aes(
  3.       x = name_num, group = IPU_id,
  4.       color = factor(comparison),
  5.       linetype = slope_name),  # here's our new mapping
  6.     show.legend = FALSE,   # note, removing legend here
  7.     alpha = 0.4, col = "grey40",
  8.     position = pj
  9.   ) +
  11.   # manually setting values to map to slope
  12.   scale_linetype_manual(values=c("negative"="dashed", "positive"="solid"))

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