A demonstration the effect of increasing number groups on mutate() in dplyr.

cost_of_groups_dplyr.R

library(ggplot2)
library(dplyr)

system.time({
nycflights13::flights %>%
  mutate(timestring = lubridate::date(time_hour))
})
#0 groups
#   user  system elapsed 
#   0.048   0.000   0.047 

system.time({
  nycflights13::flights %>%
    group_by(carrier) %>%
    mutate(timestring = lubridate::date(time_hour))
})
#16 groups
#user  system elapsed 
#0.068   0.000   0.070 

system.time({
  nycflights13::flights %>%
    group_by(day) %>%
    mutate(timestring = lubridate::date(time_hour))
})
#365 groups
#   user  system elapsed 
#0.06    0.00    0.06 

system.time({
  nycflights13::flights %>%
    group_by(origin, dest, sched_dep_time) %>%
    mutate(timestring = lubridate::date(time_hour))
})
#13 017 groups
#   user  system elapsed 
# 1.344   0.012   1.355 
#

system.time({
  nycflights13::flights %>%
    group_by(tailnum, flight) %>%
    mutate(timestring = lubridate::date(time_hour))
})
#179 858 groups
#    user  system elapsed 
# 20.36    0.30   20.66
system.time({
  nycflights13::flights %>%
    group_by(row_number(flight)) %>%
    mutate(timestring = lubridate::date(time_hour))
})
#336 776 groups
#    user  system elapsed 
# 38.896   0.496  39.396 

time_df <- data_frame(
groups = c(0, 16, 365, 13017, 179858, 336776),
time = c(0.048, 0.068, 0.06, 1.344, 20.36, 38.896)
)

ggplot(time_df) + geom_line(mapping = aes(x=groups, y=time)) + 
  geom_point(mapping = aes(x=groups, y=time)) +
  labs(x="Number of groups", y="Seconds to mutate()", title="The Cost of Groups") +
  scale_x_continuous(labels = comma) +
  theme_minimal()