```
df <- tibble(x = runif(6), y = runif(6), z = runif(6))
# Compute the mean of x, y, z in each row
df %>% rowwise() %>% mutate(m = mean(c(x, y, z)))
# use c_across() to more easily select many variables
df %>% rowwise() %>% mutate(m = mean(c_across(x:z)))
# Compute the minimum of x and y in each row
df %>% rowwise() %>% mutate(m = min(c(x, y, z)))
# In this case you can use an existing vectorised function:
df %>% mutate(m = pmin(x, y, z))
# Where these functions exist they'll be much faster than rowwise
# so be on the lookout for them.
# rowwise() is also useful when doing simulations
params <- tribble(
~sim, ~n, ~mean, ~sd,
1, 1, 1, 1,
2, 2, 2, 4,
3, 3, -1, 2
)
# Here I supply variables to preserve after the summary
params %>%
rowwise(sim) %>%
summarise(z = rnorm(n, mean, sd))
# If you want one row per simulation, put the results in a list()
params %>%
rowwise(sim) %>%
summarise(z = list(rnorm(n, mean, sd)))
```

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