R：使用dplyr基于列值的子集data.frame

Question

library(dplyr)
mydat1 <- data.frame(ID = c(1, 1, 2, 2),
                    Gender = c("Male", "Female", "Male", "Male"),
                    Score = c(30, 40, 20, 60))
mydat1 %>%
  group_by(ID, Gender) %>%
  slice(which.min(Score))

# A tibble: 3 x 3
# Groups:   ID, Gender [3]
     ID Gender Score
  <dbl> <fctr> <dbl>
1     1 Female    40
2     1   Male    30
3     2   Male    20

I'm trying to group the rows by ID and Gender . And then I want to only keep the row with the lowest Score . The above code works perfectly because when ID == 2 , I only kept the entry with the lower score.

mydat2 <- data.frame(ID = c(1, 1, 2, 2),
                    Gender = c("Male", "Female", "Male", "Male"),
                    Score = c(NA, NA, 20, 60))

mydat2 %>%
  group_by(ID, Gender) %>%
  slice(which.min(Score))

# A tibble: 1 x 3
# Groups:   ID, Gender [1]
     ID Gender Score
  <dbl> <fctr> <dbl>
1     2   Male    20

However, when I have NAs, which.min doesn't work like I want it to because it'll not return a valid index. Instead, all of my ID == 1 entries are erased. My desired output in this scenario is:

# A tibble: 1 x 3
# Groups:   ID, Gender [1]
     ID Gender Score
  <dbl> <fctr> <dbl>
1     1 Female    NA
2     1   Male    NA
1     2   Male    20

How can I modify my code to account for this?

Edit:

df2 <- structure(list(pubmed_id = c(23091106L, 23091106L), Gender = structure(c(4L, 
                                                                                4L), .Label = c("", "Both", "female", "Female", "Male"), class = "factor"), 
                      Total_Carrier = c(NA, 1107)), class = c("grouped_df", "tbl_df", 
                                                              "tbl", "data.frame"), row.names = c(NA, -2L), vars = "pubmed_id", drop = TRUE, indices = list(
                                                                0:1), group_sizes = 2L, biggest_group_size = 2L, labels = structure(list(
                                                                  pubmed_id = 23091106L), class = "data.frame", row.names = c(NA, 
                                                                                                                              -1L), vars = "pubmed_id", drop = TRUE, .Names = "pubmed_id"), .Names = c("pubmed_id", 
                                                                                                                                                                                                       "Gender", "Total_Carrier"))

> df2
# A tibble: 2 x 3
# Groups:   pubmed_id [1]
  pubmed_id Gender Total_Carrier
      <int> <fctr>         <dbl>
1  23091106 Female            NA
2  23091106 Female          1107

In this example, I would want the desired output to only contain row 2 (ie the row with carrier sample size of 1107). However, I get the following result:

> df2 %>%
   group_by(pubmed_id, Gender) %>%
   slice(which.min(Total_Carrier) || 1)

# A tibble: 1 x 3
# Groups:   pubmed_id, Gender [1]
  pubmed_id Gender Total_Carrier
      <int> <fctr>         <dbl>
1  23091106 Female            NA

Answer 1

which.min ignores the missing values, and returns integer(0) when the input vector contains solely NA s. You can add a condition check in the slice , ie when all Scores are NA s in a group, pick the first row:

mydat2 %>%
     group_by(ID, Gender) %>%
     slice({idx <- which.min(Score); if(length(idx) > 0) idx else 1})

# A tibble: 3 x 3
# Groups:   ID, Gender [3]
#     ID Gender Score
#  <dbl> <fctr> <dbl>
#1     1 Female    NA
#2     1   Male    NA
#3     2   Male    20

Answer 2

You could also use arrange to sort your scores within your groups, and then slice to select the first row of each group. That way, if there are only NAs in the group, you would still select the first row:

mydat2 %>%
group_by(ID, Gender) %>%
arrange(ID,Gender,Score) %>%
slice(1)
     ID Gender Score
  <dbl> <fctr> <dbl>
1     1 Female    NA
2     1   Male    NA
3     2   Male    20

Answer 3

Here is another option with which and pmin

mydat2 %>%
   group_by(ID, Gender) %>% 
   slice(pmin(1, which(Score == min(Score, na.rm = TRUE))[1], na.rm = TRUE))
# A tibble: 3 x 3
# Groups:   ID, Gender [3]
#      ID Gender Score
#   <dbl> <fctr> <dbl>
#1     1 Female    NA
#2     1   Male    NA
#3     2   Male    20

Answer 4

A solution using data.table

library(data.table)
setDT(mydat2)
mydat2[, .(Score = sort(Score)[1]), by = .(ID, Gender)]
#    ID Gender Score
# 1:  1   Male    NA
# 2:  1 Female    NA
# 3:  2   Male    20