rowwise filtering in dplyr - r

Question

I wanted to use dplyr instead of apply,1 in order to filter a dataset rowwise according to a logical expression, ie for this example I´d like to remove all rows that have one or more values of 99.
However, I was surprised by the poor performance in dplyr. Any ideas if I can speed this up in dplyr? Also, I would have thought that the rowwise function would pipe the individual rows, but apparently not (see below). How can I use the rowwise function?
library(tidyverse)
s <- tibble(rows = seq(from = 250, to = 5000, by = 250)) #my original dataset has 400K rows...
s$num <- map(s$rows, ~ rnorm(.x * 6))
s$num <-
map(s$num, ~ replace(.x, sample(1:length(.x), size = length(.x) / 20), 99))
s$mat <- map(s$num, ~ as_data_frame(matrix(.x, ncol = 6)))
help_an <- function(vec) {
browser()
return(!any(vec == 99))
}
help_dp_t <- function(df) {
clo1 <- proc.time()
a <- as_data_frame(t(df)) %>% summarise_all(help_an)
df2 <- filter(df, t(a)[, 1])
b <- tibble(time = (proc.time() - clo1)[3], df = list(df2))
return(b)
}
s$dplyr <- map(s$mat, ~ dplyr::mutate(help_dp_t(.x)))
help_lap <- function(df) {
clo1 <- proc.time()
a_base <- df[apply(df, 1, function(x)
! any(x == 99)), ]
b <- tibble(time = (proc.time() - clo1)[3], df = list(a_base))
return(b)
}
s$lapply <- map(s$mat, ~ mutate(help_lap(.x)))
s$equal_dplyr_lapply <-
map2_lgl(s$dplyr, s$lapply, ~ all.equal(.x$df, .y$df))
s$dplyr_time <- map_dbl(s$dplyr, "time")
s$lapply_time <- map_dbl(s$lapply, "time")
ggplot(gather(s, ... = c(7, 8)), aes(x = rows, y = value, color = key)) +
geom_line()
I tried the following with rowwise, but the rowwise pipe does not send a vector, but the entire df to the help_an function.
help_dp_r <- function(df) {
clo1 <- proc.time()
df2 <-
df %>% rowwise() %>% mutate(cond = help_an(.)) ### . is not passed on as a vector, but the entire df??
b <- tibble(time = (proc.time() - clo1)[3], df = list(df2))
}
s$dplyr_r <- map(s$mat, ~ dplyr::mutate(help_dp_r(.x)))