R dplyr在删除重复元素的同时将轴旋转为长形式

我有一个数据框，看起来是这样的：

  |  id   |  nct_id    |minimum_age| maximum_age |criteria_rank |   criteria                             
1 |6516355| NCT04293180| 2 Years |     50 Years|    Inclusion Criteria_1| criteria 1 description
2 |6516355| NCT04293180| 2 Years |     50 Years|    Inclusion Criteria_2| criteria 2 description
3 |6516355| NCT04293180| 2 Years |     50 Years|    Exclusion Criteria_1| criteria 3 description
4 |6531830| NCT04091700| 18 Years|    45 Years |    Inclusion Criteria_1| criteria 1 description
5 |6531830| NCT04091700| 18 Years|    45 Years |    Inclusion Criteria_2| criteria 2 description
6 |6531830| NCT04091700| 18 Years|    45 Years |    Exclusion Criteria_1| criteria 3 description
7 |6531830| NCT04091700| 18 Years|    45 Years |    Exclusion Criteria_2| criteria 4 description

  |V1                  |                V2       |              V3           |          
1 |id                  |              6516355    |             6531830       |        
2 |nct_id              |              NCT04293185|            NCT04091737    |        
3 |minimum_age         |           2 Years       |              18 Years     |         
4 |maximum_age         |           50 Years      |              45 Years     |         
5 |Inclusion Criteria_1| criteria 1 description. | criteria 1 description.   |
6 |Inclusion Criteria_2| criteria 2 description. | criteria 2 description.   |
7 |Exclusion Criteria_1| criteria 3 description. | criteria 3 description.   |
8 |Exclusion Criteria_2| NA                      | criteria 4 description.   |

我想将其转换为如下所示的数据帧：

  |  id   |  nct_id    |minimum_age| maximum_age |criteria_rank |   criteria                             
1 |6516355| NCT04293180| 2 Years |     50 Years|    Inclusion Criteria_1| criteria 1 description
2 |6516355| NCT04293180| 2 Years |     50 Years|    Inclusion Criteria_2| criteria 2 description
3 |6516355| NCT04293180| 2 Years |     50 Years|    Exclusion Criteria_1| criteria 3 description
4 |6531830| NCT04091700| 18 Years|    45 Years |    Inclusion Criteria_1| criteria 1 description
5 |6531830| NCT04091700| 18 Years|    45 Years |    Inclusion Criteria_2| criteria 2 description
6 |6531830| NCT04091700| 18 Years|    45 Years |    Exclusion Criteria_1| criteria 3 description
7 |6531830| NCT04091700| 18 Years|    45 Years |    Exclusion Criteria_2| criteria 4 description

  |V1                  |                V2       |              V3           |          
1 |id                  |              6516355    |             6531830       |        
2 |nct_id              |              NCT04293185|            NCT04091737    |        
3 |minimum_age         |           2 Years       |              18 Years     |         
4 |maximum_age         |           50 Years      |              45 Years     |         
5 |Inclusion Criteria_1| criteria 1 description. | criteria 1 description.   |
6 |Inclusion Criteria_2| criteria 2 description. | criteria 2 description.   |
7 |Exclusion Criteria_1| criteria 3 description. | criteria 3 description.   |
8 |Exclusion Criteria_2| NA                      | criteria 4 description.   |

基本上，我希望将数据帧转换为一个长的形式，并在保留包含和排除标准的同时去掉常见的重复元素。这只是数据的一个样本，但我有几个nct_id，每个nct_id的包含和排除标准数量不同，因此包含排除标准最多的nct_id将决定创建多少包含排除标准行。包含/排除标准较少的nct_id将填写NA（如示例最后一行所示）。在本例中，第一列具有行名称

弄不清楚。提前感谢您的指点

样本数据如下：

structure(list(id = c(6516355, 6516355, 6516355, 6531830, 6531830, 
6531830, 6531830), nct_id = c("NCT04293180", "NCT04293180", "NCT04293180", 
"NCT04091700", "NCT04091700", "NCT04091700", "NCT04091700"), 
    minimum_age = c("2 Years", "2 Years", "2 Years", "18 Years", 
    "18 Years", "18 Years", "18 Years"), maximum_age = c("50 Years", 
    "50 Years", "50 Years", "45 Years", "45 Years", "45 Years", 
    "45 Years"), criteria_rank = c("Inclusion Criteria_1", "Inclusion Criteria_2", 
    "Exclusion Criteria_1", "Inclusion Criteria_1", "Inclusion Criteria_2", 
    "Exclusion Criteria_1", "Exclusion Criteria_2"), criteria = c("criteria 1 description", 
    "criteria 2 description", "criteria 3 description", "criteria 1 description", 
    "criteria 2 description", "criteria 3 description", "criteria 4 description"
    )), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -7L), spec = structure(list(cols = list(
    id = structure(list(), class = c("collector_double", "collector"
    )), nct_id = structure(list(), class = c("collector_character", 
    "collector")), minimum_age = structure(list(), class = c("collector_character", 
    "collector")), maximum_age = structure(list(), class = c("collector_character", 
    "collector")), criteria_rank = structure(list(), class = c("collector_character", 
    "collector")), criteria = structure(list(), class = c("collector_character", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 1L), class = "col_spec"))

---

不完全是列名称

V1

，

V2

，

V3

-这里我使用

id

作为每个记录的列名，而属性名称存储在

变量

列中

library(dplyr)
library(tidyr)

data %>%   
  # to remove criteria_rank and store the criteria description under
  # values of of criteria_rank instead.
  pivot_wider(id_cols = c(id, nct_id, minimum_age, maximum_age),
             names_from = "criteria_rank", values_from = "criteria") %>%
  # Put data in long format where all the criteria is store in variables column
  # and thier values store in values column
  pivot_longer(names_to = "variables", values_to = "values",
               cols = c("nct_id", "maximum_age", "minimum_age") |
                 contains("Criteria")) %>%
  # convert data into wide format with the id is column name
  pivot_wider(id_cols = variables, names_from = "id", values_from = "values")

输出

# A tibble: 7 x 3
  variables            `6516355`              `6531830`             
  <chr>                <chr>                  <chr>                 
1 nct_id               NCT04293180            NCT04091700           
2 maximum_age          50 Years               45 Years              
3 minimum_age          2 Years                18 Years              
4 Inclusion Criteria_1 criteria 1 description criteria 1 description
5 Inclusion Criteria_2 criteria 2 description criteria 2 description
6 Exclusion Criteria_1 criteria 3 description criteria 3 description
7 Exclusion Criteria_2 NA                     criteria 4 description

#一个tible:7 x 3
变量'6516355``6531830`
1 nct_id NCT04293180 NCT04091700
2最大年龄50岁45岁
3最低年龄2岁18岁
4纳入标准\u 1标准1描述标准1描述
5纳入标准2标准2描述标准2描述
6排除标准\u 1标准3说明标准3说明
7排除标准2 NA标准4说明