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(This article was first published on <strong><a href="http://www.datacamp.com/community/blog/new-r-course-writing-efficient-r-code"> DataCamp Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Hello R users, we’ve got a brand new course today: <a rel="nofollow" href="https://www.datacamp.com/courses/writing-efficient-r-code?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Writing Efficient R Code</a> by <a rel="nofollow" href="https://www.datacamp.com/instructors/colingillespie?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Colin Gillespie</a>.</p>

<p>The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time and get insights as quickly as possible.</p>

<p><center><a rel="nofollow" href="https://campus.datacamp.com/courses/writing-efficient-r-code/the-art-of-benchmarking?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><strong>Take me to chapter 1!</strong></a></center></p>
<p>Writing Efficient R Code features interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will make you a master in writing efficient, quick, R code!</p>
<p><a rel="nofollow" href="https://www.datacamp.com/courses/writing-efficient-r-code?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img alt="" src="https://i1.wp.com/community.datacamp.com.s3.amazonaws.com/community/production/ckeditor_assets/pictures/665/content_1200x628_new_course_6.png?w=456" data-recalc-dims="1"></a></p>
<p><strong>What you’ll learn: </strong></p>
<p><strong>Chapter 1: The Art of Benchmarking</strong></p>
<p>In order to make your code go faster, you need to know how long it takes to run.</p>
<p><strong>Chapter 2: Fine Tuning – Efficient Base R</strong></p>
<p>R is flexible because you can often solve a single problem in many different ways. Some ways can be several orders of magnitude faster than the others.</p>
<p><strong>Chapter 3: Diagnosing Problems – Code Profiling</strong></p>
<p>Profiling helps you locate the bottlenecks in your code.</p>
<p><strong>Chapter 4: Turbo Charged Code – Parallel Programming</strong></p>
<p>Some problems can be solved faster using multiple cores on your machine. This chapter shows you how to write R code that runs in parallel.</p>

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<a rel="nofollow" href="https://www.datacamp.com/courses/writing-efficient-r-code?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><strong>Learn how to write efficient R code today!</strong></a>
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