Optimized Solution

Here is the optimized solution using data.table:

library(data.table)

# Define the columns to filter by
cols <- paste0("Val", 1:2)

# Sort the desired columns by group while sending NAs to the end
setDT(data)[, (cols) := lapply(.SD, sort, na.last = TRUE), .SDcols = cols, by = .(Var1, Var2)]

# Define an index which checks for rows with NAs in all columns
indx <- rowSums(is.na(data[, cols, with = FALSE])) < length(cols)

# Simple subset by condition
data[indx]

Explanation

This solution takes advantage of data.table’s performance and flexibility. Here’s how it works:

1. We define the columns to filter by (cols) in a vector format.
2. We sort the desired columns by group while sending NAs to the end using the .SDcols argument. This ensures that the rows with NAs are sent to the end of each group.
3. We create an index (indx) which checks for rows with NAs in all columns using rowSums(is.na(data[, cols, with = FALSE])).
4. Finally, we subset the original data by this index to get the desired result.

Benefits

• This solution is much faster and more efficient than the original code.
• It uses data.table’s built-in performance features to optimize the sorting and indexing steps.
• The syntax is concise and easy to read.

Overall, this optimized solution provides a significant improvement in performance and efficiency compared to the original code.

Last modified on 2024-11-24