考虑数据:
library(data.table)
library(magrittr)
vec1 <- c("Iron", "Copper")
vec2 <- c("Defective", "Passed", "Error")
set.seed(123)
a1 <- sample(x = vec1, size = 20, replace = T)
b1 <- sample(x = vec2, size = 20, replace = T)
set.seed(1234)
a2 <- sample(x = vec1, size = 20, replace = T)
b2 <- sample(x = vec2, size = 20, replace = T)
DT <- data.table(
c(1:20), a1, b1, a2, b2
) %>% .[order(V1)]
names(DT) <- c("id", "prod_name_1", "test_1", "prod_name_2", "test_2")
我需要过滤 test_1
或 test_2
值为“通过”
的行。因此,如果这些列都没有指定的值,则删除该行。通过dplyr
,我们可以使用filter_at()
动词:
> # dplyr solution...
>
> cols <- grep(x = names(DT), pattern = "test", value = T, ignore.case = T)
>
>
> DT %>%
+ dplyr::filter_at(.vars = grep(x = names(DT), pattern = "test", value = T, ignore.case = T),
+ dplyr::any_vars(. == "Passed")) -> DT.2
>
> DT.2
id prod_name_1 test_1 prod_name_2 test_2
1 3 Iron Passed Copper Defective
2 5 Copper Passed Copper Defective
3 7 Copper Passed Iron Passed
4 8 Copper Passed Iron Error
5 11 Copper Error Copper Passed
6 14 Copper Error Copper Passed
7 16 Copper Passed Copper Error
酷。在 data.table
中是否有类似的方法来执行此操作?
这是我得到的最接近的:
> lapply(seq_along(cols), function(x){
+
+ setkeyv(DT, cols[[x]])
+
+ DT["Passed"]
+
+ }) %>%
+ do.call(rbind,.) %>%
+ unique -> DT.3
>
> DT.3
id prod_name_1 test_1 prod_name_2 test_2
1: 3 Iron Passed Copper Defective
2: 5 Copper Passed Copper Defective
3: 8 Copper Passed Iron Error
4: 16 Copper Passed Copper Error
5: 7 Copper Passed Iron Passed
6: 11 Copper Error Copper Passed
7: 14 Copper Error Copper Passed
>
> identical(data.table(DT.2)[order(id)], DT.3[order(id)])
[1] TRUE
你们有更优雅的解决方案吗?最好是包含在诸如 dplyr::filter_at()
之类的动词中的内容。
最佳答案
我们可以在.SDcols
中指定'cols',循环遍历Data.table的子集(.SD
)来比较该值是否“通过”,使用 |
将其减少
为单个向量
,并对行进行子集化
res2 <- DT[DT[, Reduce(`|`, lapply(.SD, `==`, "Passed")), .SDcols = cols]]
与OP帖子中的dplyr
输出进行比较
identical(as.data.table(res1), res2)
#[1] TRUE
关于r - data.table 相当于 dplyr::filter_at,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/48668509/