Join Data with dplyr in R (6 Examples) | inner, left, righ, full, semi & anti

In this video I'm showing you how to merge data frames with the dplyr package in R. The video includes six different join functions, i.e. inner_join, left_join, right_join, full_join, and anti_join. For each function, I show a reproducible example as well as a graphic, which illustrates how the data is merged.
Also check out this tutorial on the join dplyr functions: statisticsglob...
In this tutorial, you can find further examples, which are a bit more complex (e.g. merging multiple data frame or joining by multiple columns).
Have fun with the video and let me know in the comments, if you have any feedback or questions.

[@rakhimebrams3912]

The all in one explaination at 1.00 min,, thats all i needed. thank you.....! best explaination.

[@StatisticsGlobe]

Awesome to hear that you found what you were looking for Rakhim! Also, Thanks a lot for the kind words! :)

[@lanimontalvo4220]

😊000pp⁰​@@StatisticsGlobe

[@eking3469]

Even without any explanation, your diagram in 0:50 self-explain itself. Very CLEAR! Thanks for your work.

[@StatisticsGlobe]

Glad it was helpful! Thanks for the kind words!

[@carlwratten6200]

One of the best videos to understand what the different joins are.

[@StatisticsGlobe]

Thanks Carl, I'm glad that you liked it :)

[@macanbhaird1966]

Excellent overview. I was going around in circles and you explained this very well. Thank you!

[@StatisticsGlobe]

Thanks a lot for your comments here and at the homepage Andrew. It's awesome to get such positive feedback!

[@davebowman9000]

Thanks! DataCamp kind of glosses over this and I got lost. I prefer the visual explanation you gave way better!

[@matthias.statisticsglobe]

Hey Dave, thanks for the feedback. Glad to hear that the visual explanations in the videos are helpful for you!

[@jezdel6298]

thanks for this video. very simple explanation. I love that you indicate that this function is in dplyr package. whenever I find other new functions that interest me, it is frustrating to see online discussions that doesn't always indicate from which package the function comes from.

[@matthias.statisticsglobe]

Hi! Thanks a lot for the wonderful response, glad to hear that you like our instructions. If you have any other suggestions to improve them, please let us know!

[@joelrodriguez1232]

Excellent video. I now understand SQL better by watching a video on R.

[@StatisticsGlobe]

Haha that's great to hear! :D Thanks for the comment!

[@jetcurioso6349]

I really love this kind of tutorial. Just sweet & concise explanation

[@StatisticsGlobe]

Thank you very much, glad you liked it!

[@MrRamaeri]

Really excellent video, many thanks; you are the best R programmer. Very nice very good 👍 🥇🥇🥇🥇🥇🥇💯💯💯💯

[@StatisticsGlobe]

Wow, thanks a lot for the very kind words Ram! Glad you like my videos! :)

[@Adeyeye_seyison]

Thanks a million sir for all you do and share...

[@matthias.statisticsglobe]

Hey Seyison, thank you very much for the feedback and your support! Glad the content is helpful!

[@mariamaroni8104]

Thank you genious!!! I needed this, since I am doing my first steps in Data Science. Hugs from Argentina!

[@StatisticsGlobe]

Thanks a lot Maria, glad to hear that it helped! Greetings back to Argentina and a happy new year from Germany

[@asmanoj1]

Thank you so much.
Simple & neat with beautiful explanation!

[@StatisticsGlobe]

Thanks Manoj, glad it was helpful!

[@zahiissam]

Very Good explanation!keep sharing your knowledge.Thank you

[@StatisticsGlobe]

Thanks a lot for the motivating words Issam :)

[@Faycal59]

Thank you for the tuto. it's useful

[@matthias.statisticsglobe]

That's great to hear! Thanks for the positive feedback!

[@MrWonszBoa]

very helpful, thank you.

[@StatisticsGlobe]

That's great to hear. Glad it was helpful!

[@liviasacchi5584]

Very clear and concise, thank you.
So useful!

[@StatisticsGlobe]

Thank you Livia, great to hear that it helped! :) Regards, Joachim

[@meseretmuche6984]

remarkable lecture
Dear Dr, please give us a video tutorial related to meta analysis of continuous data for ecological data management

[@cansustatisticsglobe]

Hello Meseret,
Thank you for your feedback. We are going to consider your suggestion for our future work.
Regards,
Cansu

[@comfortchukwuere5830]

Wow. Thank you so much. Great summary

[@StatisticsGlobe]

Glad it was helpful! :)

[@felixolaya3526]

Excellent video, thanks for sharing.

[@StatisticsGlobe]

Glad you enjoyed it Felix! :)

[@boitshokodiale6443]

Thanks for the tutorials they are very helpful. Can you do a video on calculating number of days in a month e.g.forms are logged on a monthly basis, however, I want to track how many are processed in 30 days.

[@StatisticsGlobe]

Hey Boitshoko, thank you for the kind words, and for the tutorial request. I've just created such a tutorial on the website: statisticsglobe.com/find-out-number-days-month-r Regards, Joachim

[@MRDS12]

Great video - helped me use join function

[@StatisticsGlobe]

Thanks for the nice comment Binaya! Great to hear!

[@the_escapist]

Perfect explanation ...Thank you so much!

[@StatisticsGlobe]

Glad you liked it Belen :)

[@jaybagdisite]

thank you very much sir.

[@StatisticsGlobe]

Most welcome Jay, glad it helped!

[@ostione]

I love the visuals. Very helpfull!

[@StatisticsGlobe]

Glad to hear it Ostione, thank you!

[@ryanschneider8958]

fantastic explanation!

[@StatisticsGlobe]

Thanks a lot Ryan!

[@nitufahmidakhalique2698]

Thank you. it was really helpful.

[@StatisticsGlobe]

Thank you Fahmida, glad to hear that!

[@puspitalestarikhanna8582]

thanks, thanks, thanks !!!!

[@StatisticsGlobe]

You're very welcome Puspita! :)

[@aaryadeshpande1621]

Thank you very much!

[@StatisticsGlobe]

You're welcome Aarya!

[@DrumMcC]

Perfect, thank you

[@StatisticsGlobe]

You are very welcome mate!

[@deeptimittal6547]

Nice video. I want to know the rationale behind using stringsasfactors.

[@StatisticsGlobe]

Thank you, glad you like it! When this video was made, character strings were automatically converted to factors in data.frame creation. To retain them as characters, I used stringsAsFactors = FALSE. This behavior has been updated in newer R versions, eliminating the need for this specification.

[@Sofono]

Thanks :)

[@StatisticsGlobe]

No problem Safouane! :)

[@wolfgangi]

I'm a recent subscriber to your channel. Love your content. I have a question, what is the difference between bind_row and inner_join?

[@StatisticsGlobe]

Hey Wolfgang, bind_row adds rows to a data frame without changing the order of the values. In contrast, the join functions add columns based on an ID. Regards, Joachim

[@wolfgangi]

@@StatisticsGlobe Thank you for the answer! Love your content I'm learning a lot from your videos!

[@StatisticsGlobe]

That's really great to hear! Thanks for the kind words Wolfgang!

[@willhelm95]

Good video! Thanks!

[@StatisticsGlobe]

Thanks Guillermo!

[@jakobtraneibsen3016]

What Liva Sacchi said. Thank you very much.

[@StatisticsGlobe]

You are welcome Jakob and thanks for the comment! :)

[@andremeiner1365]

Bomben Video!

[@StatisticsGlobe]

Haha danke Andre :)

[@vincenzo4259]

Thanks

[@StatisticsGlobe]

It's my pleasure!

[@Imsulit28]

Great

[@StatisticsGlobe]

Thanks Marc!

[@efrainrodriguez1324]

Why would one get duplicate cases for left_join

[@cansustatisticsglobe]

Hello!
Are you getting undesired duplicates? If so, consider the following solutions, to avoid unintentional duplication:
Always inspect your data before and after the join.
Understand the nature of the columns you're joining on.
Consider using inner_join(), semi_join(), or anti_join() if they are more appropriate for your specific use case.
If you expect one-to-one matches, but get duplicates, investigate the reasons before proceeding with further analysis.
Best,
Cansu

[@aquilatb1124]

There is a way to full_anti_join?
exemple, go to the minute 8:33
I would like something like:
ID X1 X2
1 a2 NA
2 NA b2
In other words: all that are not in both tables. A exlusive full join.
the result will be same that your in full_join except by the second line bcs the second line has data in both tables.
ofc for this single exemple need just filter a full join where ID != 2 .
But i not mean about a filter i really would like a full_anti_join bringing values of 2 tables that are exclusives.

[@StatisticsGlobe]

Hey Aquila, I'm not sure if this would be provided as a function itself, but you may use the following R code to get your desired result:
data_full_anti

[@aquilatb1124]

@@StatisticsGlobe tyvm :D

[@k.charith373]

what if need to use more than one reference to merge? Please educate me.. thank you sir

[@StatisticsGlobe]

Hey, what do you mean with "reference"? Regards, Joachim

[@k.charith373]

@@StatisticsGlobe the merge process is done using a common coloum (e.g. Common ID), what if I need to use two coloums to merge?

[@StatisticsGlobe]

Thanks for the clarification! Is this what you are looking for? statisticsglobe.com/merge-data-frames-by-two-id-columns-in-r

[@k.charith373]

@@StatisticsGlobe exactly sir... thanks, this is great.. :)

[@StatisticsGlobe]

You are very welcome, glad it helped! :)

[@tvvt005]

What are meant by vertical and horizontal merges in R?

[@Ifeanyi.StatisticsGlobe]

Hi Tvvt005. Vertical merging of two tables means combining two tables by their rows. That is, stacking one table on top of the other table. A function like rbind( ) can be used to perform this operation. For the operation to be successful, both tables must have the same number of columns.
Horizontal merging of two tables means combining two tables by their columns. A function like cbind( ) can be used to accomplish this. For this operation to be successful, both tables must have the same number of rows.
I hope this helps!

[@WahranRai]

Too light (not too much data.) your exemple !

[@StatisticsGlobe]

Hey WahranRai, Thank you for the feedback. I kept the example as simple as possible to make it easier to unterstand. However, you could apply exactly the same R codes to more complex data sets.