Microbenchmark revenues

You work as a Data Analyst for an online seller. You have queried sales data for different products sold during a month. This is available in your workspace as a list, ls_sales. Each element of this list is a vector of revenues for a given product.

You would like to see how the revenue grew day by day. This mean calculating a cumulative sum. Base R has a function called cumsum() to do the job. But you would like to see if parallelization can help. You want to apply cumsum() to every element of ls_sales sequentially and in parallel and compare the results. parallel and microbenchmark packages have been loaded for you.

This exercise is part of the course

Parallel Programming in R

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Exercise instructions

Pass the sequential and parallel versions as arguments to a microbenchmark() call.
Generate a cluster of all available cores minus two.
Use the cluster to apply cumsum() to ls_sales in parallel using parLapply().
Stop the cluster once computation is done.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Pass the code to microbenchmark
___
  
  "lapply" = lapply(ls_sales, cumsum),
  
  "parLapply" = {
    # Make a cluster of all cores minus two
    cluster <- ___
    # Use cluster to apply in parallel
    parLapply(cluster, ___, ___)
    # Stop cluster
    ___
  },
  times = 3
___

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