问题
I have data like (a,b,c)
a b c
1 2 1
2 3 1
9 2 2
1 6 2
where 'a' range is divided into n (say 3) equal parts and aggregate function calculates b values (say max) and grouped by at 'c' also.
So the output looks like
a_bin b_m(c=1) b_m(c=2)
1-3 3 6
4-6 NaN NaN
7-9 NaN 2
Which is MxN where M=number of a bins, N=unique c samples or all range
How do I approach this? Can any R package help me through?
回答1:
There would be easier ways.
If your dataset is dat
res <- sapply(split(dat[, -3], dat$c), function(x) {
a_bin <- with(x, cut(a, breaks = c(1, 3, 6, 9), include.lowest = T, labels = c("1-3",
"4-6", "7-9")))
c(by(x$b, a_bin, FUN = max))
})
res1 <- setNames(data.frame(row.names(res), res),
c("a_bin", "b_m(c=1)", "b_m(c=2)"))
row.names(res1) <- 1:nrow(res1)
res1
a_bin b_m(c=1) b_m(c=2)
1 1-3 3 6
2 4-6 NA NA
3 7-9 NA 2
回答2:
A combination of aggregate, cut and reshape seems to work
df <- data.frame(a = c(1,2,9,1),
b = c(2,3,2,6),
c = c(1,1,2,2))
breaks <- c(0, 3, 6, 9)
# Aggregate data
ag <- aggregate(df$b, FUN=max,
by=list(a=cut(df$a, breaks, include.lowest=T), c=df$c))
# Reshape data
res <- reshape(ag, idvar="a", timevar="c", direction="wide")
回答3:
I would use a combination of data.table and reshape2 which are both fully optimized for speed (not using for loops from apply family).
The output won't return the unused bins.
v <- c(1, 4, 7, 10) # creating bins
temp$int <- findInterval(temp$a, v)
library(data.table)
temp <- setDT(temp)[, list(b_m = max(b)), by = c("c", "int")]
library(reshape2)
temp <- dcast.data.table(temp, int ~ c, value.var = "b_m")
## colnames(temp) <- c("a_bin", "b_m(c=1)", "b_m(c=2)") # Optional for prettier table
## temp$a_bin<- c("1-3", "7-9") # Optional for prettier table
## a_bin b_m(c=1) b_m(c=2)
## 1 1-3 3 6
## 2 7-9 NA 2
来源：https://stackoverflow.com/questions/24350113/groupby-bins-and-aggregate-in-r