отображение списков и масштабирование с тем же масштабом из другого списка - PullRequest
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отображение списков и масштабирование с тем же масштабом из другого списка

0 голосов
/ 21 октября 2019

У меня есть два списка, которые выглядят так: 

[[1]]
<100/1/149>

[[2]]
<100/1/149>

К которым можно получить доступ с помощью: 

library(rsample)
map(d, ~ analysis(.x))
map(d, ~ assessment(.x))

Похоже: 

[[1]]
# A tibble: 100 x 5
   time       ID        Value   out    NA
   <date>     <chr>     <dbl> <dbl> <dbl>
 1 2016-06-01 CAT1   0            0     0
 2 2016-06-02 CAT1  -0.00511      0     0
 3 2016-06-03 CAT1  -0.0110       0     0
 4 2016-06-06 CAT1  -0.00802      0     0
 5 2016-06-07 CAT1   0.000140     0     0
 6 2016-06-08 CAT1   0.0162       1     0
 7 2016-06-09 CAT1   0.000412     1     1
 8 2016-06-10 CAT1  -0.0126       0     1
 9 2016-06-13 CAT1  -0.00146      1     0
10 2016-06-14 CAT1  -0.000125     1     1
# ... with 90 more rows

[[2]]
# A tibble: 100 x 5
   time       ID        Value   out    NA
   <date>     <chr>     <dbl> <dbl> <dbl>
 1 2016-06-02 CAT1  -0.00511      0     0
 2 2016-06-03 CAT1  -0.0110       0     0
 3 2016-06-06 CAT1  -0.00802      0     0
 4 2016-06-07 CAT1   0.000140     0     0
 5 2016-06-08 CAT1   0.0162       1     0
 6 2016-06-09 CAT1   0.000412     1     1
 7 2016-06-10 CAT1  -0.0126       0     1
 8 2016-06-13 CAT1  -0.00146      1     0
 9 2016-06-14 CAT1  -0.000125     1     1
10 2016-06-15 CAT1   0.000905     0     1
# ... with 90 more rows

> map(d, ~ assessment(.x))
[[1]]
# A tibble: 1 x 5
  time       ID      Value   out    NA
  <date>     <chr>   <dbl> <dbl> <dbl>
1 2016-10-21 CAT1  0.00301     1     0

[[2]]
# A tibble: 1 x 5
  time       ID     Value   out    NA
  <date>     <chr>  <dbl> <dbl> <dbl>
1 2016-10-24 CAT1  0.0172     1     1

Япытаюсь применить к данным функцию центра и масштаба. 

Scale_Me <- function(x){
  (x - mean(x, na.rm = TRUE)) / sd(x, na.rm = TRUE)
}

Я хочу применить функцию Scale_Me к столбцу Value каждого из списков analysis с одинаковыми Scale_Meфункция, применяемая к данным assessment. То есть я хочу применить функцию Scale_Me к списку [[1]] A tibble: 100 x 5 и с этими значениями применить ее к [[1]] A tibble: 1 x 5. Затем сделайте то же самое для [[2]] A tibble: 100 x 5 и примените его к [[2]] A tibble: 1 x 5 и т. Д.

Я пытался использовать map, но, похоже, не могу получить такое же масштабирование для применения к данным assessment.

Данные: 

list(structure(list(data = structure(list(structure(c(16953, 
16954, 16955, 16958, 16959, 16960, 16961, 16962, 16965, 16966, 
16967, 16968, 16969, 16972, 16973, 16974, 16975, 16976, 16979, 
16980, 16981, 16982, 16983, 16987, 16988, 16989, 16990, 16993, 
16994, 16995, 16996, 16997, 17000, 17001, 17002, 17003, 17004, 
17007, 17008, 17009, 17010, 17011, 17014, 17015, 17016, 17017, 
17018, 17021, 17022, 17023, 17024, 17025, 17028, 17029, 17030, 
17031, 17032, 17035, 17036, 17037, 17038, 17039, 17042, 17043, 
17044, 17045, 17046, 17050, 17051, 17052, 17053, 17056, 17057, 
17058, 17059, 17060, 17063, 17064, 17065, 17066, 17067, 17070, 
17071, 17072, 17073, 17074, 17077, 17078, 17079, 17080, 17081, 
17084, 17085, 17086, 17087, 17088, 17091, 17092, 17093, 17094, 
17095, 17098, 17099, 17100, 17101, 17102, 17105, 17106, 17107, 
17108, 17109, 17112, 17113, 17114, 17115, 17116, 17119, 17120, 
17121, 17122, 17123, 17126, 17127, 17128, 17130, 17133, 17134, 
17135, 17136, 17137, 17140, 17141, 17142, 17143, 17144, 17147, 
17148, 17149, 17150, 17151, 17154, 17155, 17156, 17157, 17158, 
17162, 17163, 17164, 17165), class = "Date"), c("CAT1", "CAT1", 
"CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", 
"CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", 
"CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", 
"CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", 
"CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", 
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0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0), c(0, 0, 
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)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-149L), .Names = c("time", "ID", "Value", "out", NA)), in_id = 1:100, 
    out_id = 101L, id = structure(list(id = "Slice01"), row.names = c(NA, 
    -1L), class = c("tbl_df", "tbl", "data.frame"))), class = c("rsplit", 
"rof_split")), structure(list(data = structure(list(structure(c(16953, 
16954, 16955, 16958, 16959, 16960, 16961, 16962, 16965, 16966, 
16967, 16968, 16969, 16972, 16973, 16974, 16975, 16976, 16979, 
16980, 16981, 16982, 16983, 16987, 16988, 16989, 16990, 16993, 
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17095, 17098, 17099, 17100, 17101, 17102, 17105, 17106, 17107, 
17108, 17109, 17112, 17113, 17114, 17115, 17116, 17119, 17120, 
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17135, 17136, 17137, 17140, 17141, 17142, 17143, 17144, 17147, 
17148, 17149, 17150, 17151, 17154, 17155, 17156, 17157, 17158, 
17162, 17163, 17164, 17165), class = "Date"), c("CAT1", "CAT1", 
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)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-149L), .Names = c("time", "ID", "Value", "out", NA)), in_id = 2:101, 
    out_id = 102L, id = structure(list(id = "Slice02"), row.names = c(NA, 
    -1L), class = c("tbl_df", "tbl", "data.frame"))), class = c("rsplit", 
"rof_split")))

РЕДАКТИРОВАТЬ:

Для analysis У меня есть: 

[[1]]
# A tibble: 100 x 5
   time       ID        Value   out    NA
   <date>     <chr>     <dbl> <dbl> <dbl>
 1 2016-06-01 CAT1   0            0     0
 2 2016-06-02 CAT1  -0.00511      0     0
 3 2016-06-03 CAT1  -0.0110       0     0
 4 2016-06-06 CAT1  -0.00802      0     0
 5 2016-06-07 CAT1   0.000140     0     0
 6 2016-06-08 CAT1   0.0162       1     0
 7 2016-06-09 CAT1   0.000412     1     1
 8 2016-06-10 CAT1  -0.0126       0     1
 9 2016-06-13 CAT1  -0.00146      1     0
10 2016-06-14 CAT1  -0.000125     1     1
# ... with 90 more rows

Для assessment У меня есть. 

[[1]]
# A tibble: 1 x 5
  time       ID      Value   out    NA
  <date>     <chr>   <dbl> <dbl> <dbl>
1 2016-10-21 CAT1  0.00301     1     0

Я пытаюсь сделать так, чтобы эти два применяли одну и ту же функцию масштабирования, я попытался bind_rows() и применить функцию масштабирования, а затем снова разделить ее.

1 Ответ

1 голос
/ 21 октября 2019

Если вы хотите использовать масштабированные значения analysis, чтобы затем масштабировать значения assesement, сохраните их как промежуточный список. Затем используйте purrr::map2() для итерации по assesement и промежуточному списку. 

library(tidyverse)

scale_values <- map(d,
    ~ analysis(.x) %>% 
      as_tibble(., .name_repair = "universal") %>% 
      summarise(mu = mean(Value, na.rm = TRUE),
                sd = sd(Value, na.rm = TRUE)))

scaled_analysis <- map(d,
    ~ analysis(.x) %>% 
      as_tibble(., .name_repair = "universal") %>% 
      mutate(Value = Scale_Me(Value)))

scaled_assessment <- map2(d, scale_values,
     ~ assessment(.x) %>% 
       as_tibble(., .name_repair = "universal") %>% 
       mutate(Value = (Value - .y$mu) / .y$sd))
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