Deleting incomplete cases across multiple rows in R studio - r

Question

Say I have a longitudinal data set as below
ID <- c(1, 1, 2, 2, 3, 3, 4, 4)
time <- c(1, 2, 1, 2, 1, 2, 1, 2)
value <- c(7, 5, 9, 2, NA, 3, 7, NA)
mydata <- data.frame(ID, time, value)
ID time value
1 1 1 7
2 1 2 5
3 2 1 9
4 2 2 2
5 3 1 NA
6 3 2 3
7 4 1 7
8 4 2 NA
In this data-set, we have 4 cases with data at two time-points (let's say pre and post treatment)
Something I want to do is set criteria to delete any case that are not complete for both time-points. In this example, I would want to delete ID3 (who is missing timepoint 1), and ID4 (who is missing timepoint 2). Like below:
ID time value
1 1 1 7
2 1 2 5
3 2 1 9
4 2 2 2
I am not having much luck. I've tried variants of complete.cases() or which() to no avail
I'm still new to R, and would be hugely appreciative if anyone could help me out
Edit: Thank you Ronak for answering my question. Upon reflection of my real data, I have encountered a second problem. My actual data is more reflected by the below:
ID <- c(1, 1, 2, 2, 3, 3, 4, 4, 5, 6, 7, 8)
time <- c(1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1)
value <- c(7, 5, 9, 2, NA, 3, 7, NA, 8, 9, 7, 6)
mydata <- data.frame(ID, time, value)
ID time value
1 1 1 7
2 1 2 5
3 2 1 9
4 2 2 2
5 3 1 NA
6 3 2 3
7 4 1 7
8 4 2 NA
9 5 1 8
10 6 1 9
11 7 1 7
12 8 1 6
Where I would also want to remove cases 5, 6, 7 and 8. These IDs have an entry for Time 1, but not Time 2. Hopefully this makes sense
Thanks a heap

Answer 1

If you switch your data to wide format (where each time point is represented as its own column), then you can use na.omit. Using dplyr and tidyr functions:
library(dplyr)
mydata <- mydata %>%
tidyr::spread(key=time, value=value) %>% # reformat to wide
na.omit() %>% # delete cases with missingness on any variable (i.e. any time point)
tidyr::gather(key="time", value="value", -ID) # put it back in long format
> mydata
ID time value
1 1 1 7
2 2 1 9
3 1 2 5
4 2 2 2
Note that this will work (it will keep only cases with complete data for both time 1 and time 2) even when you have a time point missing without an explicit NA present in the data, like this:
> mydata
ID time value
1 1 1 7
2 1 2 5
3 2 1 9
4 2 2 2
5 3 1 NA
6 3 2 3
7 4 1 7
8 4 2 NA
9 5 1 8
10 6 1 9
11 7 1 7
12 8 1 6

Answer 2

You can do this easily with sqldf.
library(sqldf)
sqldf(' select * from (select ID, count(*) as cnt from mydata where value is not null group by id having cnt >1 ) t1 inner join mydata t2 on t1.ID=t2.ID')
You would select those id having a count greater than 1 and who doesn't have NA in their values and then join back with the original data.

Answer 3

#Ronak already provided
mydata[!mydata$ID %in% mydata$ID[is.na(mydata$value)], ]
For the second part, you can just group over each id and filter on their frequency
k2 <- data.frame(table(mydata$ID))
k2$Var1[k2$Freq > 1]
and then do something like
mydata[mydata$ID %in% k2$Var1[k2$Freq > 1],]

Answer 4

See the updated answer
# Eliminates ID cases with NA
mydata = mydata[!mydata$ID %in% mydata[!complete.cases(mydata) ,]$ID, ]
library(plyr)
# counts all the IDs
cnt = count(mydata, "ID")
# Eliminates any ID that doesn't have 2 observations
mydata[mydata$ID %in% cnt[cnt$freq == 2, ]$ID, ]
ID time value
1 1 1 7
2 1 2 5
3 2 1 9
4 2 2 2