I have a large data frame where I have a grouping variable and then lots of other variable columns. I want to calculate the mean of each variable by group - but I want to take account of the proportion of missing data. If there is >75% of the data then calculate the mean, if not return `NA`

.

My actual data has many more columns than the test data below. This approach seems to be pretty quick. My question is whether there is a quicker way?

```
# number of groups
n <- 100000
dat <- data.frame(grp = factor(rep(1:n, each = 10)),
var1 = rep(c(1:8, NA, NA), times = n),
var2 = rep(c(1:7, NA, NA, NA), times = n)
)
# summarise by group, calculate mean if enough data
res <- dat %>%
group_by(grp) %>%
summarise_each(funs(ifelse(length(na.omit(.)) / length(.) > 0.75,
mean(., na.rm = TRUE), NA)))
```

Thanks

David

Answer:

Here is an option that is almost 5x faster:

```
system.time(
res0 <- dat %>%
group_by(grp) %>%
summarise_each(
funs(
ifelse(
length(na.omit(.)) / length(.) > 0.75,
mean(., na.rm = TRUE), NA)
) )
)
# user system elapsed
# 7.27 0.00 7.29
system.time(
res1 <- dat %>%
group_by(grp) %>%
summarise_each(
funs(
if(sum(is.na(.)) / length(.) < 0.25) mean(., na.rm=TRUE)
else NA
) )
)
# user system elapsed
# 1.59 0.00 1.60
all.equal(res0, res1)
# [1] TRUE
```

And an extra 2x speed increase with `data.table`

:

```
system.time(
res2 <- setDT(dat)[,
lapply(
.SD,
function(x)
if(sum(is.na(x)) / .N < 0.25) mean(x, na.rm=TRUE) else NA
),
by=grp]
)
# user system elapsed
# 0.76 0.00 0.76
all.equal(res0, setDF(res2))
# [1] TRUE
```

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