You can use the microbenchmark package in R to compare the execution time of different expressions.

You can use the following syntax to do so:

library(microbenchmark)

#compare execution time of two different expressions
microbenchmark(
  expression1,
  expression2)
)

The following example shows how to use this syntax in practice.

Example: Using microbenchmark() in R

Suppose we have the following data frame in R that contains information about points scored by players on various basketball teams:

#make this example reproducible
set.seed(1)

#create data frame
df <- data.frame(team=rep(c('A', 'B'), each=500),
                 points=rnorm(1000, mean=20))

#view data frame
head(df)

  team   points
1    A 19.37355
2    A 20.18364
3    A 19.16437
4    A 21.59528
5    A 20.32951
6    A 19.17953

Now suppose we would like to calculate the mean points scored by players on each team using two different methods:

• Method 1: Use aggregate() from Base R
• Method 2: Use group_by() and summarise_at() from dplyr

We can use the microbenchmark() function to measure how long it takes for each of these expressions to execute:

library(microbenchmark)
library(dplyr)

#time how long it takes to calculate mean value of points by team
microbenchmark(
  aggregate(df$points, list(df$team), FUN=mean),
  df %>% group_by(team) %>% summarise_at(vars(points), list(name = mean))
)

Unit: milliseconds
                                                                    expr
                         aggregate(df$points, list(df$team), FUN = mean)
 df %>% group_by(team) %>% summarise_at(vars(points), list(name = mean))
      min       lq     mean   median        uq      max neval cld
 1.307908 1.524078 1.852167 1.743568  2.093813  4.67408   100  a 
 6.788584 7.810932 9.946286 8.914692 10.239904 56.20928   100   b

The microbenchmark() function executes each expression 100 times and measures the following metrics:

• min: Minimum time it took to execute
• lq: Lower quartile (25th percentile) time it took to execute
• mean: Mean time it took to execute
• median: Median time it took to execute
• uq: Upper quartile (75th percentile) time it took to execute
• max: Maximum time it took to execute
• neval: Number of times each expression was evaluated

Typically we only look at either the mean or median time it took to execute each expression.

From the output we can see:

• It took a mean time of 1.852 milliseconds to calculate the mean points by team using the base R method.
• It took a mean time of 9.946 milliseconds to calculate the mean points by team using the dplyr method.

Based on these results, we would conclude that the base R method is significantly faster.

library(microbenchmark)
library(dplyr)

#time how long it takes to calculate mean value of points by team
results <- microbenchmark(
  aggregate(df$points, list(df$team), FUN=mean),
  df %>% group_by(team) %>% summarise_at(vars(points), list(name = mean))
)

#create boxplot to visualize results
boxplot(results, names=c('Base R', 'dplyr'))

From the boxplots we can see that the dplyr method takes longer, on average, to calculate the mean points value by team.

Note: In this example we used the microbenchmark() function to compare the execution time of two different expressions, but you can compare as many expressions as you’d like in practice.