Merge pull request rstudio-education#11 from strboul/master

Insert missing curly brackets in system.time call

Author: garrettgman

speed.rmd

@@ -256,25 +256,25 @@ You can dramatically increase the speed of your `for` loops by doing two things
 Second, make sure that any storage objects that you use with the loop are large enough to contain _all_ of the results of the loop. For example, both loops below will need to store one million values. The first loop stores its values in an object named `output` that begins with a length of _one million_:
 
 ```r
-system.time(
+system.time({
   output <- rep(NA, 1000000) 
   for (i in 1:1000000) {
     output[i] <- i + 1
   }
-)
+})
 ##   user  system elapsed 
 ##  1.709   0.015   1.724 
 ```
 
 The second loop stores its values in an object named `output` that begins with a length of _one_. R will expand the object to a length of one million as it runs the loop. The code in this loop is very similar to the code in the first loop, but the loop takes _37 minutes_ longer to run than the first loop:
 
 ```r
-system.time(
+system.time({
   output <- NA 
   for (i in 1:1000000) {
     output[i] <- i + 1
   }
-)
+})
 ##     user   system  elapsed 
 ## 1689.537  560.951 2249.927
 ```
@@ -505,4 +505,4 @@ knitr::include_graphics("images/hopr_1004.png")
 
 A well-rounded data scientist will need to be able to solve each of these problems in many different situations. By learning to program in R, you have mastered the logistical problem, which is a prerequisite for solving the tactical and strategic problems.
 
-If you would like to learn how to reason with data, or how to transform, visualize, and explore your data sets with R tools, I recommend the book [_R for Data Science_](http://r4ds.had.co.nz/), the companion volume to this book. _R for Data Science_ teaches a simple workflow for transforming, visualizing, and modeling data in R, as well as how to report results with the R Markdown package.
+If you would like to learn how to reason with data, or how to transform, visualize, and explore your data sets with R tools, I recommend the book [_R for Data Science_](http://r4ds.had.co.nz/), the companion volume to this book. _R for Data Science_ teaches a simple workflow for transforming, visualizing, and modeling data in R, as well as how to report results with the R Markdown package.