ggplot2堆积条形图：按多个维度重新排序条形图

时间：2018-04-12 18:22:18

标签： r ggplot2

我有一个名为data_words的数据集，如下所示（这些只是几行）：

             Word     Cognate TestingMoment     Score freq
1        aambeeld     Cognate         Main2 0.3500000   10
2        aambeeld     Cognate         Main4 0.7670000   10
3           bezem     Cognate         Main2 1.0000000    5
4           bezem     Cognate         Main4 1.0000000    5
5    broodrooster Non-cognate         Main2 0.5428571   14
6    broodrooster Non-cognate         Main4 0.5714286   14
7            buis Non-cognate         Main2 0.4545455   11
8            buis Non-cognate         Main4 0.6363636   11

我正在创建一个到目前为止如下所示的条形图： Bar chart

这是生成情节的代码：

ggplot(data_words %>% group_by(Word, Cognate) %>% arrange(desc(Score)), 
       aes(x = reorder(Word, -Score), 
           y = Score * 100, 
           fill = Cognate)) + 
  geom_bar(aes(group = TestingMoment, 
               colour = TestingMoment), 
           stat = "identity", 
           position = "identity", 
           alpha = 0.5) +
  geom_text(aes(label = freq), 
            size = 3, 
            position = position_nudge(x=0.1, y=3), 
            data = filter(data_words, TestingMoment == "Main4")) +
  theme(axis.text.x = element_text(size = 13),
        axis.text.y = element_text(size = 10),
        text = element_text(size = 15)) +
  labs(x = "Word\n", y = "\nAverage score at Main2 and Main4") +
  scale_fill_manual(values=c("#000000", "#56B4E9")) +
  scale_colour_manual(values=c("white", "white"), guide=FALSE) +
  coord_flip()

即使我看过关于这个主题的其他问题，我仍然有两件事情在努力解决并且尚未能够解决这个问题

1）条形图目前按照Main2和Main4的分数平均值排序。这使得图形非常混乱。如何通过Main2或Main4的值对其进行排序？

2）当点1被处理时，我希望那些具有相同分数的条目按字母顺序排序。数据帧已按字母顺序排序，但由于我在最后翻转坐标，因此单词的顺序与我想要的方式完全相反。我怎么能这样做？

以下是所有数据：

structure(list(Word = structure(c(1L, 1L, 2L, 2L, 4L, 4L, 5L, 
5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 11L, 11L, 12L, 12L, 13L, 
13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L, 17L, 19L, 19L, 20L, 20L, 
21L, 21L, 22L, 22L, 23L, 23L, 24L, 24L, 25L, 25L, 26L, 26L, 27L, 
27L, 28L, 28L, 29L, 29L, 30L, 30L, 31L, 31L, 32L, 32L, 35L, 35L, 
36L, 36L, 37L, 37L, 38L, 38L, 40L, 40L, 41L, 41L, 42L, 42L, 43L, 
43L, 44L, 44L, 45L, 45L, 46L, 46L, 47L, 47L, 48L, 48L, 49L, 49L, 
50L, 50L, 51L, 51L, 52L, 52L, 53L, 53L, 54L, 54L, 55L, 55L, 56L, 
56L, 57L, 57L, 58L, 58L, 59L, 59L, 60L, 60L, 61L, 61L, 62L, 62L, 
63L, 63L, 64L, 64L, 65L, 65L, 66L, 66L, 67L, 67L, 68L, 68L, 69L, 
69L, 70L, 70L, 71L, 71L, 73L, 73L, 74L, 74L, 75L, 75L, 76L, 76L, 
77L, 77L, 78L, 78L, 79L, 79L, 80L, 80L, 81L, 81L, 82L, 82L, 83L, 
83L), .Label = c("aambeeld", "bezem", "brandblusser", "broodrooster", 
"buis", "citruspers", "dienblad", "dobber", "dweil", "emmer", 
"garde", "gesp", "gieter", "gum", "heggenschaar", "hengel", "hes", 
"kaars", "kapstok", "keppel", "kist", "klapper", "klos", "knikker", 
"knuffel", "kooi", "kous", "kraag", "kroon", "kruiwagen", "kruk", 
"kurk", "kussen", "kwast", "lantaarn", "lessenaar", "mijter", 
"onderzetter", "pak", "passer", "peddel", "pet", "pruik", "puntenslijper", 
"rammelaar", "reddingsvest", "rietje", "rits", "romper", "sambabal", 
"schort", "schroef", "servet", "skelter", "slab", "slang", "slinger", 
"speen", "speldje", "spijker", "spuit", "staf", "stamper", "stelt", 
"stofzuiger", "stokpaard", "stolp", "tamboerijn", "tol", "tooi", 
"toverstaf", "tuinbroek", "tulband", "vergiet", "veter", "vijl", 
"vijzel", "waaier", "wafelijzer", "wip", "zaag", "zeis", "zwemvleugel"
), class = "factor"), Cognate = structure(c(1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 
1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("Cognate", "Non-cognate"), class = "factor"), 
    TestingMoment = structure(c(2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 
    3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 
    3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 
    2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 
    3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 
    2L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 
    3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 
    3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 
    3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 
    3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 
    3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L), .Label = c("Post", "Main2", 
    "Main4", "FollowUp"), class = "factor"), Score = c(0.35, 
    0.767, 1, 1, 0.542857142857143, 0.571428571428571, 0.454545454545455, 
    0.636363636363636, 1, 1, 1, 1, 0.866666666666667, 0.666666666666667, 
    1, 1, 0.042, 0.108666666666667, 0.383333333333333, 0.25, 
    0.871538461538462, 0.512307692307692, 0.9175, 1, 1, 1, 0.946666666666667, 
    0.493333333333333, 0.585, 0.5, 0.362307692307692, 0.692307692307692, 
    0.5, 0.416666666666667, 0.003, 1, 1, 1, 0.35, 0.616666666666667, 
    1, 1, 1, 1, 0.785714285714286, 0.714285714285714, 0.227272727272727, 
    0.181818181818182, 1, 0.9, 1, 1, 0.504666666666667, 0.908, 
    0.9375, 1, 0.666666666666667, 0.944444444444444, 0.75, 0.6425, 
    0.686, 0.871333333333333, 0.335, 0.335, 1, 1, 0.666666666666667, 
    0.6, 0.571428571428571, 0.857142857142857, 1, 1, 0.7, 0.6375, 
    0.648666666666667, 0.678666666666667, 0.71, 1, 0.9, 0.8, 
    0.75, 0.583333333333333, 1, 1, 0.83, 0.003, 0.902666666666667, 
    0.917333333333333, 0.261538461538462, 0.384615384615385, 
    0.42, 0.353, 0.93, 0.9475, 0.214285714285714, 0.612142857142857, 
    0.533333333333333, 0.316666666666667, 1, 0.833333333333333, 
    0.857142857142857, 0.785714285714286, 0.732142857142857, 
    0.357142857142857, 0.285714285714286, 0.857142857142857, 
    0.559285714285714, 0.785714285714286, 0.25, 0.25, 0.75, 1, 
    0.903333333333333, 1, 0.573571428571429, 0.532857142857143, 
    1, 1, 1, 1, 0.25, 0.003, 0.916666666666667, 1, 0.316666666666667, 
    0.533333333333333, 0.714285714285714, 0.857142857142857, 
    0.7225, 0.695, 0.593333333333333, 0.279333333333333, 1, 1, 
    0.230769230769231, 0.153846153846154, 0.003, 0.003, 0.269285714285714, 
    0.342857142857143, 0.666666666666667, 0.866666666666667, 
    0.698571428571429, 0.841428571428571, 1, 0.916666666666667, 
    0.757272727272727, 0.533636363636364, 0.555833333333333, 
    0.479166666666667, 0.99, 0.99), freq = c(10L, 10L, 5L, 5L, 
    14L, 14L, 11L, 11L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 1L, 15L, 
    15L, 15L, 15L, 13L, 13L, 4L, 4L, 1L, 1L, 15L, 15L, 4L, 4L, 
    13L, 13L, 6L, 6L, 1L, 1L, 2L, 2L, 15L, 15L, 1L, 1L, 11L, 
    11L, 14L, 14L, 11L, 11L, 10L, 10L, 1L, 1L, 15L, 15L, 4L, 
    4L, 9L, 9L, 12L, 12L, 15L, 15L, 2L, 2L, 11L, 11L, 15L, 15L, 
    7L, 7L, 1L, 1L, 8L, 8L, 15L, 15L, 1L, 1L, 1L, 1L, 12L, 12L, 
    10L, 10L, 1L, 1L, 15L, 15L, 13L, 13L, 10L, 10L, 8L, 8L, 14L, 
    14L, 15L, 15L, 12L, 12L, 14L, 14L, 14L, 14L, 7L, 7L, 14L, 
    14L, 3L, 3L, 4L, 4L, 3L, 3L, 14L, 14L, 1L, 1L, 8L, 8L, 4L, 
    4L, 12L, 12L, 15L, 15L, 7L, 7L, 4L, 4L, 15L, 15L, 1L, 1L, 
    13L, 13L, 1L, 1L, 14L, 14L, 15L, 15L, 7L, 7L, 12L, 12L, 11L, 
    11L, 12L, 12L, 10L, 10L)), .Names = c("Word", "Cognate", 
"TestingMoment", "Score", "freq"), row.names = c(NA, -152L), class = "data.frame")

1 个答案:

答案 0 :(得分：1)

试试这个：

library(dplyr)
Main2_order <- data_words %>%
    filter(TestingMoment == 'Main2') %>%
    arrange(Score,Word) %>%
    pull(Word) %>%
    as.character()

Main4_order <- data_words %>%
    filter(TestingMoment == 'Main4') %>%
    arrange(Score,Word) %>%
    pull(Word) %>%
    as.character()

data_words %>% 
    group_by(Word, Cognate) %>% 
    arrange(desc(Score)) %>%
ggplot(data = ., 
             aes(x = factor(Word,levels = rev(Main2_order)), 
                    y = Score * 100, 
                    fill = Cognate)) + 
    geom_bar(aes(group = TestingMoment, 
                             colour = TestingMoment), 
                     stat = "identity", 
                     position = "identity", 
                     alpha = 0.5) +
    geom_text(aes(label = freq), 
                        size = 3, 
                        position = position_nudge(x=0.1, y=3), 
                        data = filter(data_words, TestingMoment == "Main4")) +
    theme(axis.text.x = element_text(size = 13),
                axis.text.y = element_text(size = 10),
                text = element_text(size = 15)) +
    labs(x = "Word\n", y = "\nAverage score at Main2 and Main4") +
    scale_fill_manual(values=c("#000000", "#56B4E9")) +
    scale_colour_manual(values=c("white", "white"), guide=FALSE) +
    coord_flip()

我只在该示例中的ggplot代码中插入了Main2_order，但您可以换掉另一个用于其他排序。

要通过Main4获得更复杂的排序，那么Main2然后Word可能会尝试：

library(tidyr)
Main2_Main4_order <- data_words %>% 
 select(Word,TestingMoment,Score) %>%
 spread(key = TestingMoment,value = Score) %>%
 arrange(Main4,Main2,Word) %>%
 pull(Word) %>%
 as.character()
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