Finding "outliers" in a group

Question

I am working with hospital discharge data. All hospitalizations (cases) with the same Patient_ID are supposed to be of the same person. However I figured out that there are Pat_ID's with different ages and both sexes.

Imagine I have a data set like this:

Case_ID <- 1:8
Pat_ID <- c(rep("1",4), rep("2",3),"3")
Sex <- c(rep(1,4), rep(2,2),1,1)
Age <- c(rep(33,3),76,rep(19,2),49,15)
Pat_File <- data.frame(Case_ID, Pat_ID, Sex,Age)

Case_ID Pat_ID Sex Age
1       1      1   33
2       1      1   33
3       1      1   33
4       1      1   76
5       2      2   19
6       2      2   19
7       2      1   49
8       3      1   15 

It was relatively easy to identify Pat_ID's with cases that differ from each other. I found these ID's by calculating an average for age and/or sex (coded as 1 and 2) with help of the function aggregate and then calculated the difference between the average and age or sex. I would like to automatically remove/identify cases where age or sex deviate from the majority of the cases of a patient ID. In my example I would like to remove cases 4 and 7.

Answer 1

You could try

library(data.table)

Using Mode from Is there a built-in function for finding the mode?

  Mode <- function(x) {
    ux <- unique(x)
    ux[which.max(tabulate(match(x, ux)))]
 }

setDT(Pat_File)[, .SD[Age==Mode(Age) & Sex==Mode(Sex)] , by=Pat_ID]
#    Pat_ID Case_ID Sex Age
#1:      1       1   1  33
#2:      1       2   1  33
#3:      1       3   1  33
#4:      2       5   2  19
#5:      2       6   2  19
#6:      3       8   1  15

Testing other cases,

 Pat_File$Sex[6] <- 1
 Pat_File$Age[4] <- 16
 setDT(Pat_File)[, .SD[Age==Mode(Age) & Sex==Mode(Sex)] , by=Pat_ID]
 #    Pat_ID Case_ID Sex Age
 #1:      1       1   1  33
 #2:      1       2   1  33
 #3:      1       3   1  33
 #4:      2       6   1  19
 #5:      3       8   1  15

Answer 2

This method works, I believe, though I doubt it's the quickest or most efficient way.

Essentially I split the dataframe by your grouping variable. Then I found the 'mode' for the variables you're concerned about. Then we filtered those observations that didn't contain all of the modes. We then stuck everything back together:

library(dplyr) # I used dplyr to 'filter' though you could do it another way
temp <- split(Pat_File, Pat_ID)

Mode.Sex <- lapply(temp, function(x) { temp1 <- table(as.vector(x$Sex)); names(temp1)[temp1 == max(temp1)]})

Mode.Age <- lapply(temp, function(x) { temp1 <- table(as.vector(x$Age)); names(temp1)[temp1 == max(temp1)]})

temp.f<-NULL
for(i in 1:length(temp)){
  temp.f[[i]] <- temp[[i]] %>% filter(Sex==Mode.Sex[[i]] & Age==Mode.Age[[i]])
}

do.call("rbind", temp.f)

#  Case_ID Pat_ID Sex Age
#1       1      1   1  33
#2       2      1   1  33
#3       3      1   1  33
#4       5      2   2  19
#5       6      2   2  19
#6       8      3   1  15