Hands-on dplyr tutorial for faster data manipulation in R

dplyr is a new R package for data manipulation. Using a series of examples on a dataset you can download, this tutorial covers the five basic dplyr "verbs" as well as a dozen other dplyr functions.

Tutorial contents:
1. Introduction to dplyr (starts at 0:00)
2. Loading dplyr and the example dataset (starts at 2:29)
3. Understanding "local data frames" (starts at 3:23)
4. Verb #1: `filter` (starts at 5:17)
5. Verb #2: `select`, plus `contains`, `starts_with`, `ends_with`, `matches` (starts at 7:54)
6. Using chaining syntax for more readable code (starts at 9:34)
7. Verb #3: `arrange` (starts at 12:53)
8. Verb #4: `mutate` (starts at 13:55)
9. Verb #5: `summarise`, plus `group_by`, `summarise_each`, `n`, `n_distinct`, `tally` (starts at 15:31)
10. Window functions: `min_rank`, `top_n`, `lag` (starts at 26:47)
11. Convenience functions: `sample_n`, `sample_frac`, `glimpse` (starts at 32:44)
12. Connecting to databases (starts at 34:21)

== RESOURCES ==

== LET'S CONNECT! ==

[douglaspiresmartins]

You rock dude, 6 year later and this vid is still really really helpful.

[bryanwu]

This is great! I really love the fact that you show the "base R" approach as a comparison. Looking forward to more vids.

[dwadelson]

very clear, straightforward, succinct, and helpful.  i particularly liked the way the dplyr approach was contrasted with the base R approach for each of the examples.  excellent.  thank you.

[mago]

This is, with out question, one of the best R tutorials i have seen! Thank you!

[nakul]

That was a well spent 40 minutes. Very neat, precise, and easy to understand. You have my additional gratitude for comparing the dpylr to the base R functions, which helped in visualizing why dpylr is practical. Thank You!

[faseehahmed]

8 years old, still the best tutorial on dplyr.

[チャリンコ爺]

I've been using dplyr for a while now and I still learned some new and useful stuff from this video. Thanks very much for sharing.

[keithtyrrell]

I'm fairly new to R and didn't really know where to start for my current project. I think I learned about 90% of what I need to know from this video. Thank you very much!

[yongyanchen]

Thanks a lot! As a greenhand to R, your presentation is really helpful to me! Great!

[fghj-zhcv]

Your tutorial is meticulous, clear and useful for those who are used to basic R approach but feels a need to learn dplyr package. This not lengthy video does help me to write R-code in an efficient and convenient manner. Thanks.

[johnsheehan]

Thank you for putting these tutorials together. They are FANTASTIC for the R newbie. And I particularly love that you have the R Markdown version that we can keep for reference.

[nurmohammad]

It's just time-worthy step from my side to watch this video. I pay my gratitude to your nice work. Thanks a lot.

[bir_dilim_bilim]

Thanks for this video! I really loved the way you explain things. Very clear and step-by-step approach. Will definitely watch more of your videos to get better in R!

[shivibhatia]

This is simply fantastic. I have gone through so many forums - R help, Stats exchange etc etc and in most of the cases ppl bluntly say go and brush ur skills and don't believe in answering easy questions posted by newbies. However in the video above you have taken the pain to explain step by step how dyply is working. Really helped.
In 11:31 we are filtering where DepDelay  >60 so can we do a similar between 2 columns. I did so but received an error as they were factors. Thanks,

[prempasanha]

The way you explained the commands comparing to Base R help us to appreciate dplyr. Thanks. Good job

[bikashpokharel]

I just started 38:56 mins ago, now I am a R expert. Thanks a lot

[macanbhaird]

I am teaching statistics and data manipualtion and this is most useful! Thanks for this.

[fet]

26:47
10. Window functions: `min_rank`, `top_n`, `lag` (starts at 26:47)
32:44
11. Convenience functions: `sample_n`, `sample_frac`, `glimpse` (starts at 32:44)
34:21
12. Connecting to databases (starts at 34:21)

[different]

I have learned more about how to neatly and efficiently using more than one functions to apply on more than one columns at once.
Really appreciate your organized illustration!!!
Thank you so
Anticipating for more lessons from

[gregoryhorne]

The dplyr package should simplify exploratory data analysis when used in conjunction with the graphics packages (base, ggplot2, or lattice). Excellent introductory tutorial.