Principal components analysis in R

[@vplougoboy]

Noone explains R better than Hefin. Give this man a medal already!!

[@PhinaLovesMusic]

I'm in graduate school and you just explained PCA better than my professor. GOD BLESS YOU!!!!

[@HarmonicaTool]

5 year old video still one of the best I found on the topic on YT. Thumbs up

[@sadian3392]

I had listened to several other lectures on this topic but the pace and the detail covered in this video is simply the best. Please keep up the good work!

[@hefinrhys8572]

Thanks Sadia! Glad to be of help.

[@maitivandenbosch1541]

Never a tutorial about PCA so clear and simply. Thanks

[@rebecai.m.6670]

OMG, this tutorial is perfection, I´m serious. You make it sound so easy and you explain every single step. Also, that is the prettiest plot I´ve seen. Thank you so much for this.

[@hefinrhys8572]

You're very welcome! If you like pretty plots, check out my video on using ggplot2 ;) kzbin.info/www/bejne/Z3jQgmh4maabfZY

[@Rudblattner]

I never comments on videos, but you really saved me here. Nothing was working on my dataset and this came smoothly. Well done on the explanations too, everything as crystal clear.

[@WatchMacro16]

Finally a perfect tutorial for POA in Rstudio. Thanks mate!

[@jackiemwaniki1266]

How i came across this video a week before ,my final year, project due date is a miracle. Thank you so much Hefin Rhys.

[@mohamedadow8153]

Jackie Mwaniki doing?

[@jackiemwaniki1266]

@@mohamedadow8153 my topic is on Macroeconomic factors and the stock prices using the APT framework.

[@user-kb6ui2sh5v]

really useful video thank you, I've just started my MSc project using PCA, so thank you for this. I will be following subsequent videos.

[@chinmoysarangi9399]

I have my exam in 2 days and Your video saved me tons of effort in combing through so many other articles and videos explaining PCA. A BIG Thank You! Hope you do many more videos and impart your knowledge to newbies like me. :)

[@timisoutdoors]

Quite literally, the best tutorial I've ever seen on an advanced multivariate topic. Job well done, sir!

[@shantanutamuly6932]

Excellent tutorial. I have used this for analysis of my research. Thanks a lot for sharing your valuable knowledge.

[@Axle_Tavish]

Explained everything one might need. If only every tutorial on KZbin is like this one!

[@tylerripku8222]

The best run through I've seen for using and understanding PCA.

[@johnkaruitha2527]

Great help, been doing my own work following step by step this tutorial...the whole night

[@johnmandrake8829]

its so funny I don't think you realize but myPR "my pyaar" in Urdu/Hindi means my love. Thank you for an amazing and extremely helpful video

[@lilmune]

In all honesty this is the best tutorial I've seen in months. Nice job!

[@ditshegoralefeta1315]

I've been going through your tutorials and I'm so impressed. Legend!!!

[@jackpumpunifrimpong-manso6523]

Excellent! Words cannot show how grateful I am!

[@fabriziomauri9109]

Damn, your accent is hypnotic! The explanation is good too!

[@hefinrhys8572]

Thanks! 😘

[@siktrading3117]

This tutorial is outstanding. Excellent explanation! Thank you very much!!!

[@glenndejucos3891]

This video gave a major leap in my study. Thanks.

[@0xea31c0]

The explanation is just perfect. Thank you.

[@nrlzt9443]

really love your explanantion! thank you so much for your video, really helpful and i can understand it! keep it up! looking forward to your many more upcoming videos

[@lisakaly6371]

In fact I found out how to overcome the multicolinearity , by using the eigen values of PC1 and PC2! I love PCA!

[@elenavlasenko5452]

I can say for sure that it´s the best explanation I´ve ever seen!! Go on and I would be really grateful if you make one of Time Series and Forecasting :)

[@hefinrhys8572]

Thanks Elena! Thank you also for the feedback; I may make a video on time series in the future.

[@HDgamesFTW]

Best explanation I’ve found so far! Thanks mate, legend!

[@HDgamesFTW]

Uploaded the script as well what a guy

[@brunopiato]

Great video. Very instructive. Please keep making them

[@brunocamargodossantos5049]

Thanks for the the video, it helped me a lot!! Your explanation is very didactic!

[@tankstube09]

Very nice tutorial, nicely explained and really complete, looking forward to learn more in R with other of your vids, thank you for the tremendous help!

[@hefinrhys8572]

Thank you! I'm glad it helped.

[@em70171]

This is gold. I absolutely love you for this

[@chris-qm2tq]

Excellent walkthrough. Thank you!

[@blackpearlstay]

Thank you so much for this SUPER helpful video. (P.S. The explanation with the iris dataset was especially convenient for me as I'm working on a dataset with dozens of recorded plant traits:D)

[@andreamonge5025]

Thank you so much for the very clear and concise explanation!

[@vagabond197979]

Added to my stats/math playlist! Very useful.

[@arunkumarmallik9091]

Thanks for nice and easy way of explanation.It really helps me a lot.

[@himand11]

Thank you so so much!! You just saved the day and helped me really understand my homework for predictive analysis.

[@OZ88]

Ok so the Sepal.Width contributes mostly over 80% to the PC2 and the other three to PC1 more. 14:32 and so Sepal Width is fair enough as an info to separate setosa in the next plot. Isn't it also advisable to apply pca to linear problems?

[@hefinrhys8572]

You're correct about the relative contributions of the variables to each principal component. The Setosa species is discriminated from the other two species mainly by PC1, to which sepal.width contributes less that than the other variables. As PCA is a linear dimension reduction technique, it will best reveal clusters of cases that are linearly separable, but PCA is still a valid and useful approach to compress information, even in situations where this isn't true, or when we don't know about the structures in the data. Non-linear techniques such as t-SNE and UMAP are excellent at revealing non-linearly-separable clusters of cases in data, but interpreting their axes is very difficult/impossible.

[@biochemistry9729]

Thank you so much! This is GREAT! You explained very clearly and smoothly.

[@rVnikov]

Excellent tutorial Hefin. Hooked and subscribed...

[@hefinrhys9234]

Vesselin Nikov thank you! Feel free to let me know if there are other topics you'd like to see covered.

[@florama5210]

It is a really nice and clear tutorial! Thanks a lot,​ Hefin~

[@hefinrhys8572]

You're welcome Flora! Thank you!

[@kasia9904]

when i generate the PCA with the code explained @ 20:46 my legend appears as a gradient rather than the separate values (as in your three different species appearing in red, blue green. how can i change this?

[@kevinroberts5703]

thank you so much for this video. incredibly helpful.

[@shafiqullaharyan261]

Perfect! Never seen such explanation

[@Fan-vk9gx]

You are really a life saver! Thank you!

[@testchannel5805]

Very nice, guys hit the subscribe button, the best explanation so far.

[@murambiwanyati3607]

Great teacher you are, thanks

[@mativillagran1684]

thank you so much! you are the best, very clear explanation.

[@mustafa_sakalli]

Finally understood this goddamn topic! Thank you dude

[@timothystewart7300]

Fantastic video Hefin! thanks

[@SUMITKUMAR-hj8im]

a perfect tutorial for PCA... Thank you

[@sandal-city-pet-clinic-1]

simple and clear. very good

[@fatimaelmansouri9338]

Super well-explained, thank you!

[@aliosmanturgut102]

Very informative and clear Thanks.

[@harryainsworth6923]

this tutorial is slap bang fuckin perfect, god bless you, you magnificant bastard

[@hefinrhys8572]

😘

[@harryainsworth6923]

@@hefinrhys8572 stats assignment due in 12 hours and you saved me alot of hassle

[@christianberntsen3856]

10:21 - When using "prcomp", the calculation is done by a singular value decomposition. So, these are not actually eigenvectors, right?

[@hefinrhys8572]

SVD still finds eigenvectors as it's a generalization of eigen-decomposition. This might be useful: web.mit.edu/be.400/www/SVD/Singular_Value_Decomposition.htm

[@christianberntsen3856]

@@hefinrhys8572 Thank you answering! I will look into it.

[@rockcandy28]

Hello! Thanks for the video, just a question how would you modify the code if you have NA values? In advance, thank you!

[@Badwolf_82]

Thank you so much for this tutorial, it really helped me!

[@stephravelo]

Hi, i wonder if it's possible to put label in each points? I tried geom_text but i get error

[@hefinrhys8572]

Yes you should be able to. What have you tried? If you have a column called names with the label for each point, something like this should work: ggplot(df, aes(PC1, PC2, label = names)) + geom_text() Or use geom_label() if you prefer. You can also check out the ggrepel package if you have many overlapping points.

[@stephravelo]

@@hefinrhys8572 I have 18 observations and 9 variables w/represented my environmental parameters. I successfully produced the ggplot figure. But I wanted to put a label in all the points in the figure to know what variables cluster together. i tried your suggestion but it gives me the numerical value, not the environmental variables. Any other suggestion?

[@aminsajid123]

Amazing video! Thanks for the explaining everything very simply. Could you please do a video on PLS-DA?

[@esterteran2872]

Good tutorial!I have learnt a lot. Thanks !

[@tonyrobinson9046]

Outstanding. Thank you.

[@metadelabegaz6279]

Sweet baby Jesus. Thank you for making this video!

[@hefinrhys8572]

You're very welcome!

[@alessandrorosati969]

How is it possible to generate outliers uniformly in the p-parallelotope defined by the coordinate-wise maxima and minima of the ‘regular’ observations in R?

[@DesertHash]

At 5:50, don't you mean that if we measured sepal width in kilometers then it would appear LESS important? Because if we measured it in kilometers instead of millimeters, our numerical values will be smaller and vary far less, making it less important in the context of PCA. Thank you for this video.

[@hefinrhys8572]

Yes, you're absolutely correct! What I meant to say was that if that length was kilometers, but we neasured it in millimeters, then it would be given greater importance. But yes, larger values are given greater importance.

[@DesertHash]

@@hefinrhys8572 Alright, thanks for the reply and for the video!

[@blessingtate9387]

You "R" AWESOME!!!

[@galk32]

amazing video, thank you

[@kmowl1994]

Very helpful, thanks!

[@salvatoregiordano2511]

Hi Hefin, Thanks for this tutorial. What do we do if PC1 and PC2 can only explain around 50% of the variation? Do we also include PC3 and PC4? If so, how?

[@maf4421]

Thank you Hefin Rhys for explaining PCA in detail. Can you please explain how to find weights of a variable by PCA for making a composite index? Is it rotation values that are for PC1, PC2, etc.? For example, if I have (I=w1*X+w2*Y+w3*Z) then how to find w1, w2, w3 by PCA.

[@stinkbomb13]

Error in svd(x, nu = 0, nv = k) : infinite or missing values in 'x' ???

[@yayciencia]

Thank you! This was very helpful to me

[@jackiemwaniki1266]

Thank again. Quick one....Would you mind also doing the Fama and Macbeth Analysis without using the KenFrench Dataframe?

[@Sunny-China3]

Very informative video. Can you tell me? When i m plotting the last plot ggplot it showed error like . R said there is no package called digest. How to deal with it kindly advise.

[@stephaniefaithravelo3510]

Hi Hefin, can I put a percentage in the PCA 1 and PC2 in the x and y-axis? How to do that?

[@JibHyourinmaru]

If my biological data only has numbers(1,2 & 3 digits) and a lot of zeros, do I need to scale also?

[@stephaniefaithravelo3510]

Hey Hefin, I wonder if you can also do a tutorial of PCA producing triplot graph?

[@patriciaamado9897]

can I put the loadings scores in the ggplot, as well?

[@fsxaviator]

Where did you define PC1 and PC2 (where you use them in the ggplot)? I'm getting "Error: object 'PC1' not found"

[@AcademicActuary]

Great presentation! However, why did you not binarize the categorical variable first, and then do the subsequent analysis? Thanks!

[@anjangowdas2541]

Thank you, it was very helpful.

[@Marinkaasje]

I run into the error when running line 17 (in the download file): Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 510, 382. What it going wrong?

[@Actanonverba01]

Clear and straight forward, good work! Bully for you! Lol

[@hellthraser550]

How can i input desired fonts and font size in that graph ?

[@Jjhukri]

Amazing video Hefin, there are lot of details covered in 27 min video, we just have to be careful not to miss any second of the video. I have a question: How does the scores are calculated for each PC's ? Why do we have to check the correlation between the variables and the PC1 & PC2 ? what value it adds practically ?

[@Orange-xw4lt]

Hi, good job but If I have an input data as a wave how can I take and separate the values ​​of the crests starting from a certain threshold?

[@rafaeu7904]

How can I see the residuals? And correlate with scores

[@heartfighters2055]

just brilliant

[@Emmyb]

this video is fab thank you!

[@hefinrhys8572]

Thank you Emily! Happy dimension reduction!

[@rafaborkowski580]

How can I upload my data into RStudio to work with ?

[@tiberiusjimbo9176]

Thank you. Very helpful.

[@mohammadtuhinali1430]

Many thanks for your efforts to make this complex issue much easier for us. Could you enlight me to understand group similarly and dissimilarity using pca?

[@amggwarrior]

Thank you for this very clear video. Question about interpretation: I get just the 1 cluster in my ggplot, what does this mean? that all my variables relate to the same construct (component) and that they cant really be differentiated?

[@hefinrhys8572]

So when you apply PCA to your own data and plot the first two components, you see just a single cloud of data? This would indicate that you don't have distinct, linearly-separable sub-classes of cases in your dataset. PCA will still compress the majority of the information of your many variables into a smaller number of variables, so even if it doesn't reveal a class structure in your data, it can still be beneficial for dimension reduction.

[@amggwarrior]

@@hefinrhys8572 thanks for the quick reply. Yes I only see a single cloud. I am not using PCA for dimension reduction - just using it to explore my data before including these variables into a SEM. In particular, I wanted to see if it makes sense to relate these 5 variables to a single latent variable in my SEM. All the loadings for PC1 are 0.7 or 0. 8, or more, and PC1 captures 0.7 of variation. Can I take this result as support for considering these 5 variables as part of the same measuring model (linked to the same latent variable) in my SEM? theoretically it makes sense to, but I wanted to see if the data supported this. I have never done PCA or SEM so no idea if I am doing this right.

[@lisakaly6371]

Thank you for this great video. can you show how to seek multicolinearity or treat multicolinearity with PCA ? I have a data set with 40 variables with high intercorrelation because of cross reactivity . VIF and matrix correlation doesnt work probably because of multiple comparison ....:(((

[@EV4UTube]

Can I confess something that baffles me? Because, I see this all the time. OK, so you, personally, are motivated to share your knowledge with the world, right? I mean, you took time, effort, energy, focus, planning, equipment, software, etc. to prepare this explanation and exercises. You screen-captured it, you set up your microphone, you edited the video, you did all this enormous amount of work. You're clearly motivated. Yet, when it actually comes time to deliver that instruction, you think it is 100% acceptable to place all your code into an absolutely miniscule fraction of the entire screen. Like, pretty-close to 96% of the screen is 'dead-space' from the perspective of the learner. The size of the typeface is miniscule (depending on your viewing system). It would be like producing a major blockbuster film, but then publishing it at the size of a postage stamp. Surely, it would be possible for you to 'zoom-into' that section of the IDE to show people what it was you were typing - the operators, the functions, the arugments, etc. I'm not really picking on you, individually, per se. I see this happen all the time with instructors of every stripe. I have this insane idea that instruction has much, much less to do with the insturctor's ability to demonstrate their knowledge to an uninformed person and has much, much more to do with the instructor's ability to 'meet' the student 'where' they are and to carry the student from a place of relative ignoracne (about a specific topic) to a place of relative competence. One of the best tools for assessing whether you're meeting that criteria is to PRETEND that you know nothing about the topic - then watch your own video (stripping-out all the assumptions you would automatically make about what is going on based on your existing knowledge). If you didn't have a 48" monitor and excellent eye-sight, would you be able to see what was being written? Like... why would you do that? If writing of the code IS NOT important - don't bother showing it. If writing of the code IS important, then make it (freaking) visible and legible. This really baffles me. I guess instructors are so "in-their-own-head" when they're delivering content, they don't take time to realize that no one can see what is happening. . It just baffles me how often I see this.

[@EV4UTube]

If 'zooming-in' is not easily achieved, the least instructors could do is go into the preferences of the IDE and jack-up the size of the text so that it would be reasonably legible on a screen typical of, say, a laptop or tablet. It just seems like such a low-hanging fruit, and easy fix to facilitate learning and ensure legibility.

[@Pancho96albo]

@@EV4UTube chill out dude

[@zahrasattari8738]

Thanks a lot for a great video. Could you possibly guide me to a source with info on performing cross validation in R after doing PCA on the data? Possibly as clear as yours:) I've been searching and mostly came across guides on how it's performed after doing PLS-DA. I'm preparing a report for a modeling course and am asked to provide (describe and perform) a validation step.

[@hefinrhys8572]

So cross-validation is only useful for supervised learning modelling, because we have a ground truth to evaluate the model performance against. PCA is an unsupervised algorithm, (it's really just a transformation of the data), and it doesn't make predictions. In contrast, PLS-DA is a supervised classification algorithm. To train it, you need to start with labelled data. So in this case, cross-validation is a useful tool to evaluate model performance, because we can compare the model predictions to the ground truth. Does that make sense?

[@zahrasattari8738]

@@hefinrhys8572 Sure it does. Thanks a lot! If I'm right PCA is considered modeling when it comes to picking the appropriate number of PCs that best describe the data. And as it is meant to be reported for a course project and I need to include/perform a validation step I am pushing to get somewhere with it without having to include PLS-DA in the whole story.. I guess I can still consider the step with "making sure I have picked the right number of PCs for my model", as a validation step, am I right about this? I came across this link, which I guess is close to what I'm looking for www.r-bloggers.com/2018/10/obtaining-the-number-of-components-from-cross-validation-of-principal-components-regression/

[@hefinrhys8572]

Ah ok, well of you're using principal components as predictors in a supervised model, then you can use cross validation to guide the number of components you should include. For example, you train a model with the first 5 principal components and use cross-validation to evaluate the performance of this model, then try the first 4, then 3 and so on. You can pick the number of principal components that gives you the best cross validation performance. This is essentially a feature selection problem. You can do this manually, or the mlr package in R can help you achieve this by creating a 'wrapped learner'.

[@zahrasattari8738]

thanks again.. hope I can get somewhere with it:)

[@zahrasattari8738]

@@hefinrhys8572 Hi it's me again:) I am using your nice PCA plot code for plotting some data with limited number of samples and the idea is to show they are not grouped based on some variable (which they are not). Then I'd like to show in my plot the sample numbers. This is probably a very basic question, I was wondering how I could insert the sample numbers in the plot. (I'm guessing I should add something to the geom_point line (?))

[@abhiagni242]

Thanks for the video..helped a lot :)

[@hefinrhys9234]

ABHI agni Glad it helped :) Feel free to give feedback on other topics that would be useful.

[@hoseinmousavi4890]

Thanks for your nice job! I have a question. I have a biostat data. As you told in this video, we do not need to know what is our variable for colour grouping! Actually, I have a problem, and it does not work for me! aes(x = PC1, y = PC2 , col= ??? ) I really appreciate it if you reply me back!

[@djangoworldwide7925]

Great tutorial but it leaves me with the question, what do i do with it? Is this just the begining of a K means classification that gives me an idea of the proper k?

[@djangoworldwide7925]

Lol you just replied in 26:00... Thank you so much!