I have data like this, where some "name" occurs more than three times:
df <- data.frame(name = c("a", "a", "a", "b", "b", "c", "c", "c", "c"), x = 1:9)
name x
1 a 1
2 a 2
3 a 3
4 b 4
5 b 5
6 c 6
7 c 7
8 c 8
9 c 9
I wish to subset (filter) the data based on number of rows (observations) within each level of the name
variable. If a certain level of name
occurs more than say 3 times, I want to remove all rows belonging to that level. So in this example, we would drop observations where name == c
, since there are > 3
rows in that group:
name x
1 a 1
2 a 2
3 a 3
4 b 4
5 b 5
I wrote this code, but can't get it to work.
as.data.frame(table(unique(df)$name))
subset(df, name > 3)
First, two base
alternatives. One relies on table
, and the other on ave
and length
. Then, two data.table
ways.
table
tt <- table(df$name)
df2 <- subset(df, name %in% names(tt[tt < 3]))
# or
df2 <- df[df$name %in% names(tt[tt < 3]), ]
If you want to walk it through step by step:
# count each 'name', assign result to an object 'tt'
tt <- table(df$name)
# which 'name' in 'tt' occur more than three times?
# Result is a logical vector that can be used to subset the table 'tt'
tt < 3
# from the table, select 'name' that occur < 3 times
tt[tt < 3]
# ...their names
names(tt[tt < 3])
# rows of 'name' in the data frame that matches "the < 3 names"
# the result is a logical vector that can be used to subset the data frame 'df'
df$name %in% names(tt[tt < 3])
# subset data frame by a logical vector
# 'TRUE' rows are kept, 'FALSE' rows are removed.
# assign the result to a data frame with a new name
df2 <- subset(df, name %in% names(tt[tt < 3]))
# or
df2 <- df[df$name %in% names(tt[tt < 3]), ]
ave
and length
As suggested by @flodel:
df[ave(df$x, df$name, FUN = length) < 3, ]
data.table
: .N
and .SD
:library(data.table)
setDT(df)[, if (.N < 3) .SD, by = name]
data.table
: .N
and .I
:setDT(df)
df[df[, .I[.N < 3], name]$V1]
See also the related Q&A Count number of observations/rows per group and add result to data frame.