如何基于应用于大量列的“不等于”条件对数据帧进行子集化？

Question

我是R的新手，目前正尝试根据预定义的排除标准对数据进行子集分析。我目前正在尝试删除ICD-10编码的所有患有痴呆症的病例。问题是，有多个变量包含有关每个人的疾病状况的信息（约70个变量），尽管由于它们以相同的方式编码，因此可以对所有变量应用相同的条件。

一些模拟数据：

#Create dataframe containing simulated data
df = data.frame(ID = c(1001, 1002, 1003, 1004, 1005,1006,1007,1008,1009,1010,1011),
                    disease_code_1 = c('I802','H356','G560','D235','B178','F011','F023','C761','H653','A049','J679'),
                    disease_code_2 = c('A071','NA','G20','NA','NA','A049','NA','NA','G300','G308','A045'),
                    disease_code_3 = c('H250','NA','NA','I802','NA','A481','NA','NA','NA','NA','D352'))

#data is structured as below:

     ID disease_code_1 disease_code_2 disease_code_3
1  1001           I802           A071           H250
2  1002           H356             NA             NA
3  1003           G560            G20             NA
4  1004           D235             NA           I802
5  1005           B178             NA             NA
6  1006           F011           A049           A481
7  1007           F023             NA             NA
8  1008           C761             NA             NA
9  1009           H653           G300             NA
10 1010           A049           G308             NA
11 1011           J679           A045           D352

在这里，我试图删除所有“ disease_code”变量中带有“痴呆症代码”的病例。

#Remove cases with dementia from dataframe (e.g. F023, G20)
Newdata_df <- subset(df, (2:4 != "F023"|"G20"|"F009"|"F002"|"F001"|"F000"|"F00"|    
                    "G309"| "G308"|"G301"|"G300"|"G30"| "F01"|"F018"|"F013"|
                    "F012"| "F011"| "F010"|"F01"))

我收到的错误是：

Error in 2:4 != "F023" | "G20" : 
  operations are possible only for numeric, logical or complex types

理想情况下，子集数据帧应如下所示：

     ID disease_code_1 disease_code_2 disease_code_3
1  1001           I802           A071           H250
2  1002           H356             NA             NA
4  1004           D235             NA           I802
5  1005           B178             NA             NA
8  1008           C761             NA             NA
11 1011           J679           A045           D352

我知道我的代码中有一个错误，尽管我不确定如何正确地解决它。尽管到目前为止还没有运气，但我尝试了其他几种方法（使用dplyr）。

任何帮助是极大的赞赏！

Answer 1

一种dplyr可能性可能是：

df %>%
 filter_at(vars(2:4), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",    
            "G309", "G308","G301","G300","G30", "F01","F018","F013",
            "F012", "F011", "F010","F01")))

    ID disease_code_1 disease_code_2 disease_code_3
1 1001           I802           A071           H250
2 1002           H356             NA             NA
3 1004           D235             NA           I802
4 1005           B178             NA             NA
5 1008           C761             NA             NA
6 1011           J679           A045           D352

在本例中，它检查 2:4 列中是否包含任何给定代码。

或者：

df %>%
 filter_at(vars(contains("disease_code")), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",    
            "G309", "G308","G301","G300","G30", "F01","F018","F013",
            "F012", "F011", "F010","F01")))

在这种情况下，它检查任何具有名称的列是否disease_code包含任何给定的代码。