Jak o tym (stosując base
R):
dt$size="small"
a=aggregate(dt$thread~dt$diameter, dt, max)[,"dt$thread"]
dt[dt$thread %in% a,]$size="large"
OUTPUT
diameter thread size
1 1 4 small
2 1 6 large
3 1 4 small
4 2 5 small
5 2 7 large
6 3 9 large
DANE
dt=structure(list(diameter = c(1L, 1L, 1L, 2L, 2L, 3L), thread = c(4L,
6L, 4L, 5L, 7L, 9L)), .Names = c("diameter", "thread"), class = "data.frame", row.names = c(NA,
-6L))
BENCHMARK
library(dplyr)
library(microbenchmark)
dt=structure(list(diameter = c(1L, 1L, 1L, 2L, 2L, 3L), thread = c(4L,
6L, 4L, 5L, 7L, 9L)), .Names = c("diameter", "thread"), class = "data.frame", row.names = c(NA,
-6L))
func_ZachTurn <- function(data){data %>% group_by(diameter) %>% mutate(size=ifelse(thread==max(thread),"large","small"))}
func_m0h3n <- function(dt){dt$size="small";a=aggregate(dt$thread~dt$diameter, dt, max)[,"dt$thread"];dt[dt$thread %in% a,]$size="large";dt}
func_Psidom <- function(df){data.table::setDT(df);df[, size := c("small", "large")[(thread == max(thread)) + 1L], .(diameter)];df[];}
f <- function(x) (if(length(x)==1) 1L else x == max(x)) + 1L
func_docendo.discimus <- function(dat){dat$size <- c("small", "large")[ave(dat$thread, dat$diameter, FUN = f)];dat;}
func_Ernest.A <- function(df){df$size <- factor(unsplit(lapply(split(df$thread, df$diameter), function(x) ifelse(x == max(x), 'large', 'small')), df$diameter));df;}
r <- func_ZachTurn(dt)
all(r == func_m0h3n(dt))
# [1] TRUE
all(r == func_docendo.discimus(dt))
# [1] TRUE
all(r == func_Ernest.A(dt))
# [1] TRUE
all(r == as.data.frame(func_Psidom(dt)))
# [1] TRUE
microbenchmark(func_ZachTurn(dt), func_m0h3n(dt), func_docendo.discimus(dt), func_Ernest.A(dt), func_Psidom(dt))
# Unit: microseconds
# expr min lq mean median uq max neval
# func_ZachTurn(dt) 3477.835 3609.147 3833.5482 3679.079 3860.6490 7136.169 100
# func_m0h3n(dt) 4436.367 4601.042 4879.2726 4743.474 4859.8150 8578.031 100
# func_docendo.discimus(dt) 854.168 923.673 999.2991 956.180 992.9645 4422.252 100
# func_Ernest.A(dt) 1032.101 1086.636 1165.4361 1129.195 1167.9040 4882.057 100
# func_Psidom(dt) 1537.245 1622.577 1731.0602 1678.822 1742.3395 5424.840 100
+1 do sporządzania roztworu Ave, w każdym razie wydaje mi się bardziej czytelny: 'DAT $ wielkość <- ave (DAT $ wątek, DAT $ średnica FUN = function (x) ifelse (x == max (x), "duży", "mały")) ' – digEmAll
@digEmAll, to dobra alternatywa. Po prostu próbowałem uniknąć 'ifelse' przez grupę –