如何按组进行透视_更广泛并将每个表导出为csv，同时删除R中具有单个值的空列和行？

Question

让我们考虑下面的数据框

dat0

。
使用

pivot_wider

，我可以将其转置为

dat1

表，然后为每组

biomarker

导出 csv（link）：

dat1 <- pivot_wider(dat0, names_from = analyser, values_from = c(result)) # then
dat1 %>%
  group_by(biomarker) %>%
  group_walk(~ write_csv(.x, paste0(.y$biomarker, ".csv")))

但是，我希望在所有最终 CSV 中：

1. 空列被消除
2. 只有一个结果的行（无论分析器是什么）也被删除（以便仅保留配对结果）

3. NA

   单元格显示为空

感谢您的帮助

初始数据

dat0
# A tibble: 30 x 4
   id         biomarker analyser result
   <chr>      <chr>     <chr>     <dbl>
 1 0240307415 BICAR     A1           27
 2 0240307373 BICAR     A1           23
 3 0240326970 AMMO      A2          657
 4 0240326976 AMMO      A2          603
 5 0240322181 HAPTO     A3            6
 6 0240322185 HAPTO     A3            4
 7 0240322191 HAPTO     A3            4
 8 0240326976 AMMO      B1          744
 9 0240326970 AMMO      B1          623
10 0240326981 AMMO      B1          550
11 0240326975 AMMO      B1          490
12 0240307415 BICAR     B1           28
13 0240307415 BICAR     B2           27
14 0240307373 BICAR     B2           23
15 0240322181 HAPTO     B2            5
16 0240322185 HAPTO     B2            5
17 0240322191 HAPTO     B2            4
18 0240326976 AMMO      C1          772
19 0240326970 AMMO      C1          649
20 0240326981 AMMO      C1          570
21 0240326975 AMMO      C1          509
22 0240307415 BICAR     C1           29
23 0240252218 BICAR     C1           25
24 0240307417 BICAR     C1           23
25 0240307415 BICAR     C2           28
26 0240307373 BICAR     C2           23
27 0240322181 HAPTO     C2            5
28 0240322185 HAPTO     C2            5
29 0240322191 HAPTO     C2            4
30 0240322151 HAPTO     C2            4

dput

dat0 <-
structure(list(id = c("0240307415", "0240307373", "0240326970", 
"0240326976", "0240322181", "0240322185", "0240322191", "0240326976", 
"0240326970", "0240326981", "0240326975", "0240307415", "0240307415", 
"0240307373", "0240322181", "0240322185", "0240322191", "0240326976", 
"0240326970", "0240326981", "0240326975", "0240307415", "0240252218", 
"0240307417", "0240307415", "0240307373", "0240322181", "0240322185", 
"0240322191", "0240322151"), biomarker = c("BICAR", "BICAR", 
"AMMO", "AMMO", "HAPTO", "HAPTO", "HAPTO", "AMMO", "AMMO", "AMMO", 
"AMMO", "BICAR", "BICAR", "BICAR", "HAPTO", "HAPTO", "HAPTO", 
"AMMO", "AMMO", "AMMO", "AMMO", "BICAR", "BICAR", "BICAR", "BICAR", 
"BICAR", "HAPTO", "HAPTO", "HAPTO", "HAPTO"), analyser = c("A1", 
"A1", "A2", "A2", "A3", "A3", "A3", "B1", "B1", "B1", "B1", "B1", 
"B2", "B2", "B2", "B2", "B2", "C1", "C1", "C1", "C1", "C1", "C1", 
"C1", "C2", "C2", "C2", "C2", "C2", "C2"), result = c(27, 23, 
657, 603, 6, 4, 4, 744, 623, 550, 490, 28, 27, 23, 5, 5, 4, 772, 
649, 570, 509, 29, 25, 23, 28, 23, 5, 5, 4, 4)), row.names = c(NA, 
-30L), class = "data.frame")

Answer 1

一种选择是先

group_nest

，然后

pivot_wider

，然后迭代每个嵌套框架以删除所有

NA

列。

dat1 <- dat0 |>
  group_nest(biomarker) |>
  mutate(data = lapply(data, function(d) pivot_wider(d, names_from = analyser, values_from = result)))
dat1 |>
  group_by(biomarker) |>
  group_walk(~ write_csv(.x$data[[1]], paste0(.y$biomarker, ".csv")))

制作过程：

list.files(pattern = "csv$")
# [1] "AMMO.csv"  "BICAR.csv" "HAPTO.csv"
lapply(list.files(pattern = "csv$"), readr::read_csv, show_col_types = FALSE)
# [[1]]
# # A tibble: 4 × 4
#   id            A2    B1    C1
#   <chr>      <dbl> <dbl> <dbl>
# 1 0240326970   657   623   649
# 2 0240326976   603   744   772
# 3 0240326981    NA   550   570
# 4 0240326975    NA   490   509
# [[2]]
# # A tibble: 4 × 6
#   id            A1    B1    B2    C1    C2
#   <chr>      <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 0240307415    27    28    27    29    28
# 2 0240307373    23    NA    23    NA    23
# 3 0240252218    NA    NA    NA    25    NA
# 4 0240307417    NA    NA    NA    23    NA
# [[3]]
# # A tibble: 4 × 4
#   id            A3    B2    C2
#   <chr>      <dbl> <dbl> <dbl>
# 1 0240322181     6     5     5
# 2 0240322185     4     5     5
# 3 0240322191     4     4     4
# 4 0240322151    NA    NA     4

（我知道我正在将 base-

lapply

与

purrr

函数混合在一起......老习惯。

purrr

变体没有任何问题:-)