frame with 10 rows and 3 columns
a b c
1 1 201 1
2 2 202 1
3 3 203 1
4 4 204 1
5 5 205 4
6 6 206 5
7 7 207 4
8 8 208 4
9 9 209 8
10 10 210 5
I want to delete all rows where the same value in the column "c" repeated less than 3 times.
In this example I want to remove rows 6, 9 and 10. (my real data.frame has 5000 rows and 25 cols)
I tried to do it using the function rle, but I keep getting the wrong solution.
any help? thanks!
Building on Joshua's answer:
Data[Data$c %in% names(which(table(Data$c) > 2)), ]
Correct me if I'm wrong, but it seems like you want all the rows where the value in column c occurs more than twice. "Repeated" makes me think that they need to occur consecutively, which is what rle
is for, but you would only want rows 1-4 if that was what you were trying to do.
That said, the code below finds the rows where the value in column c occurs more than 2 times. I'm sure this can be done more elegantly, but it works.
lines <-
"a b c
1 201 1
2 202 1
3 203 1
4 204 1
5 205 4
6 206 5
7 207 4
8 208 4
9 209 8
10 210 5"
Data <- read.table(con <- textConnection(lines), header=TRUE); close(con)
cVals <- data.frame(table(Data$c))
Rows <- Data$c %in% cVals[cVals$Freq > 2,1]
Data[Rows,]
# a b c
#1 1 201 1
#2 2 202 1
#3 3 203 1
#4 4 204 1
#5 5 205 4
#7 7 207 4
#8 8 208 4
Using unsplit is probably the easiest way to project a grouped aggregate (in this case using table to get counts, but see tapply for the general case) out to the original data.
subset(Data, with(Data, unsplit(table(c), c)) >= 3)
Equivalently and more similar to Erik's:
Data[unsplit(table(Data$c), Data$c) >= 3, ]
Here is a solution using ave
:
Data[ave(Data$c, Data$c, FUN = length) > 2, ]
or using ave
with subset
:
subset(Data, ave(c, c, FUN = length) > 2)