Get my FREE cheat sheets for R programming and statistics (including transcripts of these lessons) here: www.learnmore365.com/courses/rprogramming-resource-library

00:01 Cleaning your data involves systematic exploration, cleaning, manipulation, visualization, and analysis. 01:44 Installing packages in R expands functionality. 05:31 Converting character variable to factor variable in R 07:31 Using the factor function to swap levels in R 11:11 Understanding the difference between 'or' and 'and' in filtering data. 13:03 Handling missing data is crucial for accurate analysis. 17:02 Understanding how to handle missing values in data sets is crucial for data cleaning in R. 18:49 Handle missing data with nuanced approach, not just sweeping deletion 22:16 Identify and handle duplicates in data frames 24:04 Selecting and filtering data using base R method

Life saver using this vidoe in a last min dash to finish some coursework

We really appreciate your best KZfaq channel for learning R we looking forward to see more especially for survival analysis, parametric and non parametric tests

I do love your enthusiasm Greg, it really keeps me interested in watching through to the end!

Your tutorials are unarguably the most explicit and practical for the use of R. Beyond using R as a tool, you explain a lot of statistical concepts. Thanks for all you do, I've learned a lot from your channel

Honestly love all your videos. Detailed explanation and yet simple and straightforward. Keep up the great work.

Thank you so much Dr Martin. As a beginner , the way you explain the R programming makes me loving that language more and easy to deal with coding. Keep up with more great videos.

Thank you so much, you're such a huge help! I dont think i would pass my 'digital data analysis' course without your channel

Incredible channel, the material is better than any course I have paid for. The delivery and the breakdown of topics into separate videos are perfect for learning. Thank you for sharing your expertise and time.

Excellent! Thanks for putting out such great content that's not only useful, but easy to follow!

Out of all the online classes and videos I have done, I WISH I STARTED WITH THIS ONE!! Thank you!

I don't usually comment, but this man here is the best I've come across on youtube,. Damn, too good

I am here for the "super duper easy"... Keep up the great work Dr, thnaks.

Very useful thank you - especially the section on NAs and recoding. Also appreciate the editing, effects, sound quality and close ups of the code. I’ve recently been using the star wars database for my English teaching lessons to help students with the interrogative. How tall is R2D2? How much does Darth Vader weigh? Etc. One request if possible - Times, Dates and TimeDates. Thank you again, your videos have been very helpful.

Very clear, useful and interesting. I'm just getting into R and this helped me understand how it can be used for sensible data cleaning.

You are an awesome teacher. I want to give you a hug right now! 😊Thank you for making it so easy to foloow through.

Easy, Peasy, Lemon Squeezy! Best R Programming Channel Keep it going!

You really make programming seem "easy-peasy lemon squeezy", keep it up!

You sir have an incredible voice for teaching. Glad I found your channel

Hi there, Greg! Thanks a lot for these videos, love your style. I've learned A LOT!

Thank you so much for creating so much accessible and engaging content. For a beginner the way you teach is very clear and easy to understand and your passion for R has made me love it even more! (I also appreciate being hyped up for learning by some drum and bass in the intro)

Excellent lectures and a good lecturer also

Very helpful. the perfect mixture between the background ideas ( what data to dismiss) and the R way to do so. Hope to see more videos. Enjoy X-mas

You're amazing at teaching this. Thank you!

Truly helpful! Amazing video for tidyverse

Amazing tutorial!! Thank you so much! ⭐️⭐️⭐️⭐️⭐️

oh my god man you are a godsent!!!! I've been learning R in the google data course from Coursera and they don't teach much.

This is brilliant, I am on my third video and I am amazed at how easy this is made to seem👏

YOU ARE SUCH A GOOD TEACHER... THANK YOU

Very useful Greg, such a big help for at R-n00b

You're a hell of a teacher! congrats!!

Such good videos for learning R programming and such a nice series. When is the next episode about manipulating your data comming out? Can't wait for it!

Thank you so much for the great and useful videos

Love this. I didn't know there was an in-house R dataset for star wars.

Thank you so much! love all your videos! simple and straightforward! with detailed explanation.

Great content. Recode is superseded… we need a new video on this topic 🙂

You channel is a life saver man. Thank you

Thanks for the lessons so far. Love it

Thanks a lot sir. I can't be grateful enough for your videos

Much appreciated. Amazing skills in such a simplified way thank you.

Yo you arer the best programing youtube chanel bro

best practice lecture for R

Thank you so much. Your videos are very helpful.

Thank you very much Sir. This was quite easy to understand as a beginner. Would you kindly maybe make a series of these videos for us beginners because you have many videos and we wouldn't know which video to watch after this one. I hope that makes sense. Anyway thank you a lot for these videos

Thank you, Dr Martin

Thumbs up for you Greg!

Just a heads up to everyone, at the end of the vid when you're doing the recode bit, you might hit this error: Error: Problem with `mutate()` column `gender`. i `gender = recode(gender, masculine = 1, feminine = 2)`. x unused arguments (masculine = 1, feminine = 2) if you get this error, force R to use dplyr's version of recode like this: starwars %>% select(name, gender) %>% mutate(gender_coded = dplyr::recode(gender, "masculine"=1, "feminine"=2)) I'm not sure why I had to do this, as I had already run the library(tidyverse) command, but replacing recode with dplyr::recode sorted it out.

Very helpful! Thank you, sir!:D

I can't get the line to work: filter(hair_color %in% c(“blond”, “brown”) & height < 180) Error: unexpected symbol in " filter(hair_color c" > height < 180)

For the replace NA, it just filter the dataset but it doesn't change anything from the dataset. So the dataset remained not cleaned.

Thanks a lot!

this tutorial is Excellent! thank you!

GREAT VIDEO

i dont know why when i am running the complete cases code , it is showing error that " . " object is not found . Please help sir starwars|> select(name,gender,hair_color,height)|> filter(!complete.cases(.)) I ran this code

Thank you very very very much!! Really appreciate your wonderful video. 👍👍👍

Warning in install.packages("tidyverse") : 'lib = "C:/Program Files/R/R-4.2.3/library"' is not writable Error in install.packages("tidyverse") : unable to install packages do you have any insight why I get this message? I'm starting with R for a statistics class and I see you recommend this package? my laptop is archaic...

Bravo, this content was very good

Thank you for this. Very helpful.

Solid tut. Thank you.

Hi, Love your videos they are so helpful. Could you do a video on loops in r ?? Thanks!

What if the integer was a chr data type and you want to change it to an integer or double

You probably mention that you are looking at the whole observation for Duplicates - but I was merrily making my own example vector with a duplicate name, however with different ages. Took me a while to realise to use below to get the same as you: friends[!duplicated(friends$Names) , ] or friends %>% distinct(Names) Great content 🙏

Thank you so much, you're such a great help! please show us how to create Dashboards via Shiny. Thanks a lot.

Thank you for the video sir

Appreciate your helpful videos

Perfect lecture ! benefited a lot.

When you start to explain how to find complete and incomplete cases at 16:09, what do you do if you want to find incomplete cases for the entire dataset? Would you just omit the "select" portion of the code?

thanks . I've really appreciate your video

How can you convert data type using mutate in dplyr?

Can anybody help me with how to disaggregate data that exists in the same column? i.e. in this example lets say you wanted to have a second column(or new variable) for secondary hair color for those values which contain a primary then a secondary hair color i.e "brown, grey", "auburn, white" etc. I am actually working on a file which contains addresses and in many cases the apartment number is not actually separated into another column. However, it should be for the import into the database I am working on and therefore I want to try to create logic to clean and disaggregate these pieces into separate columns. Any help would be greatly appreciated.

You Sir, are a legend!

I'm having a problem where I want to mutate two variables with values 0, 1 and NA into a new variable with the sum of 0 and 1, however, R in my case counts NA as 0. Are there an easy fix to this, to exclude the NA?

Really excellent PT. Then how R is associated with Python algorithm?

variable types select and filter find and deal with missing data find and deal with duplicates recode values

Thank you for this amazing resource. Very helpful for someone like myself who is learning R without any meaningful stats experience aside from a semester at uni. Is anyone learning along able to share their experience of using the mutate and recode functions. I haven't had any success using this whilst following along with this video and a previous one when trying to recode the gender to M and F, or 1 and 2. I've had to work around using : starwars %>% select(name,gender) %>% mutate(gender=if_else(gender=="masculine", "1", "2")) But I'd really like to know what I'm doing wrong using recode as I think my code looks the same as Greg's! starwars %>% select(name,gender) %>% mutate(gender=recode(gender, "masculine"=1, "feminine"=2))

Excellente~! Thanks -

Love your videos, man - they're clear and concise, easy to follow! Would you be open to creating content based on the Google Data Analytics Cert? One of the case studies they have is about a fictional bike study called Cyclistic. And there is only one person on YT who does it in an R (Caribou Data Science) but it's not as seamless or clear as you make your videos out to be! :)

You explained it thoroughly ❤❤

Just curious is there any reason why you haven't enabled coloured brackets as per the last update it would make the code easier to read

Thank you!! 👑

I'm a newbie to r, is there a open community where people can help you with your work

Doc Martin! As usual an excellent video! I have one question regarding the recoding. In case of binary cases is it not typical to do 0 and 1 in order to be able to some stats on the data? Thanks and Happy Holidays!

If you are here from Tom's Bayesian stats class, give this video a thumbs up!

Thanks so much for the engaging yet very useful video! Can i ask why the following chunk of code is not able to recode missing value to the assigned value? starwars %>% select(name, gender) %>% mutate(gender2= if_else(gender=="masculine",1,if_else(is.na(gender),3,2))). Thanks a lot in advancE!

Thank you very much! Very helpful

And how do you undo a command that you had already executed?

Pretty handy tips

How come I am just discovering this channel?

Could you do a video on data management using R please

what is the difference between a data frame and a tibble

Hello Sir, can you please make a video about pachage shiny on medical data ? Thanks a lot, great videos !!

Thank you!

Thanks a lot.

great! thank you very much

super thanks

Thank you so much!

Never thought Jason Statham is an "R" Expert

love it

Hello can you make playlist about OOP in R ?