I am trying to aggregate a large data set by two identification variables (i.e., id1 and id2). My aggregate command in R drops the entire row if missing a value on any of the columns. For instance, even though row #1 has been observed on all, but one column (missing on only 1 of 20 columns), row #1 is dropped during the aggregation process. I was wondering if there is a way to aggregate (get mean or sums) the rows by the two identification variables without dropping the entire row for missing on 1 or 2 columns.
Here is my current R code for aggregating:
dfagg1
You might consider using the dplyr package within the "tidyverse".
Using your example:
library(tidyverse)
output %
group_by( id1, id2 ) %>%
summarize( mn = mean( x ), N = n( x ) )
The group_by will include NA entries by default. These can be dropped by including using the option .drop = TRUE in the group by function.
You can simply use the `subset' command to select a part of the full data set with certain conditions.
dfagg1
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