从具有特定条件的 dataframe 获取不同的行

Question

I have a dataframe:

ID    Name    Value 
1     John    17
1             17
2             NULL
3             NULL
4    Mike     35
4    Mike     NULL  
5    Leo      22
5    Leo      

I want there be only rows with unique ID. I need to keep those rows with maximally full columns. So, desired result is:

ID    Name    Value 
1     John    17
2             NULL
3             NULL
4    Mike     35
5    Leo      22

As you see, all ID are kept, but now there are only hose with maximally full columns. How could i do that?

I tried df[complete_cases(df),], but it removed fully empty rows (ID 2 and 3)

Answer 1

I would use dplyr::distinct() in a pipe like that:

df %>% distinct(ID, .keep_all = TRUE) (.keep_all = TRUE to keep other columns)

or in base R

df[,duplicated(df$ID),]

both would do the job

Answer 2

Using aggregate with option na.action=na.pass .

aggregate(cbind(Value, Name) ~ ID, dat, el, na.action=na.pass)
#   ID Value Name
# 1  1    17 John
# 2  2  NULL <NA>
# 3  3  NULL <NA>
# 4  4    35 Mike
# 5  5    22  Leo

---

Data:

dat <- structure(list(ID = c(1L, 1L, 2L, 3L, 4L, 4L, 5L, 5L), Name = c("John", 
NA, NA, NA, "Mike", "Mike", "Leo", "Leo"), Value = c("17", "17", 
"NULL", "NULL", "35", NA, "22", "NULL")), class = "data.frame", row.names = c(NA, 
-8L))