I used the dplyr do function to apply a kernel regression smoother to a 3
column data table (grouping index, x, y) with about 7 M rows and 45000
groups.
This runs quickly, about 1-2 minutes.
It creates an data table (44,326 by 2) - grouping index, kernel smoothing
output.
The kernel smoothing output is a list of two element lists (x, smoothed y).
I used a for loop to unlist this into a data table.
for (i in 1:nrow(do object)) {
df <- bind_rows(list(df,
data.frame(grouping index = do object[i],
x = do object[[i]]$x,
y = do object[[i]]$smoothed
y)))
}
This takes about 100 minutes.
Any guidance for a faster (more elegant?) solution will be appreciated.
Nathan
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