Question

我有一个输出像这样的数据帧的函数：
df：

# A tibble: 100 x 7
   test_idx train_user_id test_user_id train_idx     D isSame D_rank
      <int>         <int>        <int>     <int> <dbl> <lgl>   <dbl>
 1        5             1            1         2  51.3 TRUE        1
 2        1             1            1         4  53.0 TRUE        1
 3        5             1            1         4  56.9 TRUE        2
 4        1             1            1         5  61.6 TRUE        2
 5        1             1            1         3  63.1 TRUE        3
 6        1             1            1         2  66.7 TRUE        4
 7        5             1            1         5  66.8 TRUE        3
 8        5             1            1         3  67.7 TRUE        4
 9        9             1            2         4  85.3 FALSE       1
10        1             1            1         1  86.1 TRUE        5
# ... with 90 more rows

现在，我想将不同的D_ranks传递给ggplot以便为1-5中的每一个绘制一个合适的图：

for i = 1:5:

ggplot(df %>% filter(D_rank == i), aes(x = D, fill = isSame)) + 
geom_histogram(position = "dodge", bins = 50)

在这种情况下，是否有任何类似地图的函数可以执行此操作并生成5个图（通常为N）？

请告知我如何使用功能编程技术来做到这一点。

数据

df <- 
  structure(list(test_idx = c(5L, 1L, 5L, 1L, 1L, 1L, 5L, 5L, 9L, 1L, 2L, 9L, 2L, 4L, 5L, 2L, 
                              2L, 7L, 2L, 7L, 4L, 9L, 7L, 3L, 7L, 
                              4L, 9L, 4L, 8L, 9L, 3L, 4L, 6L, 3L, 3L, 6L, 8L, 7L, 8L, 3L, 8L, 
                              6L, 6L, 6L, 8L, 10L, 10L, 10L, 10L, 10L, 9L, 7L, 8L, 7L, 7L, 
                              8L, 6L, 8L, 6L, 5L, 9L, 8L, 9L, 6L, 7L, 6L, 3L, 5L, 2L, 3L, 1L, 
                              4L, 2L, 3L, 1L, 9L, 4L, 2L, 8L, 9L, 2L, 3L, 5L, 3L, 5L, 4L, 7L, 
                              2L, 6L, 1L, 1L, 1L, 4L, 5L, 10L, 4L, 10L, 10L, 10L, 10L), 
                 train_user_id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                   1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                   1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                   1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                   2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                   2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                   2L, 2L, 2L), 
                 test_user_id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                  2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 
                                  2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 
                                  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                  2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                  1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
                                  2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L), 
                 train_idx = c(2L, 4L, 4L, 5L, 3L, 2L, 5L, 3L, 4L, 1L, 5L, 3L, 3L, 3L, 1L, 4L, 1L, 
                               3L, 2L, 1L, 5L, 2L, 2L, 3L, 5L, 2L, 5L, 4L, 3L, 1L, 5L, 1L, 1L, 
                               1L, 2L, 2L, 5L, 4L, 1L, 4L, 2L, 3L, 5L, 4L, 4L, 3L, 4L, 1L, 5L, 
                               2L, 8L, 9L, 9L, 8L, 10L, 10L, 10L, 8L, 9L, 8L, 10L, 6L, 9L, 6L, 
                               6L, 8L, 7L, 10L, 8L, 10L, 8L, 10L, 10L, 9L, 10L, 7L, 8L, 6L, 
                               7L, 6L, 9L, 8L, 9L, 6L, 6L, 9L, 7L, 7L, 7L, 6L, 7L, 9L, 6L, 7L, 
                               7L, 7L, 8L, 9L, 10L, 6L), 
                 D = c(51.3423229472633, 53.0102437725091, 
                       56.9151660778132, 61.5523467358413, 63.1196128125039, 66.7242855371326, 
                       66.7691111976302, 67.743214491629, 85.2525195097706, 86.0735262618907, 
                       87.369258138824, 88.6330505796213, 92.2078873349342, 93.7741850369463, 
                       95.1397157285217, 96.2026472448337, 96.9228466441253, 97.2558549437615, 
                       97.3898735538098, 97.5469166830202, 97.5751612411753, 97.9406556127815, 
                       98.9451888862382, 98.9681351156223, 99.766578399879, 100.017199435986, 
                       101.975347576293, 103.097872757076, 105.041685508915, 105.057005748779, 
                       105.496841451745, 106.125579523708, 107.9382547829, 108.612088134172, 
                       108.68293031272, 109.695378380029, 111.325786796424, 111.881553810468, 
                       112.348809216118, 112.888668376943, 113.49128839933, 116.77296297582, 
                       118.888803029784, 120.903835929009, 122.27718661991, 156.423902775119, 
                       167.503309969173, 169.030474185564, 175.189202023024, 180.788562965559, 
                       48.5496720637138, 51.6482423994687, 53.4542060530927, 58.0128616909946, 
                       58.7971141941753, 60.2587315942066, 61.8538635626938, 62.222929248795, 
                       62.3865335970707, 63.0977052264393, 64.9664647267018, 65.5753112984812, 
                       67.0733197397097, 68.7553373383392, 70.3274318273383, 70.9561586831357, 
                       71.1969721821622, 71.9521437091054, 72.2811149112368, 72.7021293248461, 
                       73.1399655726917, 73.4746352292488, 75.5818665806328, 75.6687872897965, 
                       77.5832030879899, 77.9322604688026, 79.7123489004008, 80.1066357588347, 
                       80.1507338530155, 80.4540641900277, 80.5021221779844, 82.5711795226778, 
                       83.1590737400302, 83.279098613812, 83.4857610266722, 83.5491091101213, 
                       83.5564732281585, 84.1257094245289, 84.1402634222038, 88.6441050623217, 
                       90.2958220505986, 90.3323019629058, 91.8753888005348, 92.2496048477023, 
                       92.3323541396661, 93.3607071270989, 96.8024552048253, 102.947477697813, 
                       102.959949397485, 116.434702831121), 
                 isSame = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, 
                            TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, 
                            TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, 
                            TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
                            FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, 
                            TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, 
                            TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
                            FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, 
                            FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, 
                            FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE), 
                 D_rank = c(1, 1, 2, 2, 3, 4, 3, 4, 1, 5, 1, 2, 2, 1, 5, 3, 4, 1, 5, 2, 2, 3, 
                            3, 1, 4, 3, 4, 4, 1, 5, 2, 5, 1, 3, 4, 2, 2, 5, 3, 5, 4, 3, 4, 
                            5, 5, 1, 2, 3, 4, 5, 1, 1, 1, 2, 3, 2, 1, 3, 2, 1, 2, 4, 3, 3, 
                            4, 4, 1, 2, 1, 2, 1, 1, 2, 3, 2, 4, 2, 3, 5, 5, 4, 4, 3, 5, 4, 
                            3, 5, 5, 5, 3, 4, 5, 4, 5, 1, 5, 2, 3, 4, 5)), 
            class = c("tbl_df", "tbl", "data.frame"), 
            row.names = c(NA, -100L))

Answer 1

split，按df$D_rank来输入数据，然后尝试imap。 map或lapply也可以使用，但是imap可以轻松地遍历列表和该列表的名称-名称由.y捕获（请参见下文））。这似乎很有用，因为例如可以使用.y作为情节标题的一部分。

library(purrr)
library(ggplot2)
plot_list <- imap(split(df, df$D_rank), ~ {
  ggplot(.x, aes(x = D, fill = isSame)) +
           geom_histogram(position = "dodge", bins = 50) +
    labs(title = paste0("ID ", .y))
})
plot_list$`5`

通过向包装器传递R中的一组参数来生成N个ggplot图。

1 个答案: