Principal Component Analysis in R Programming | How to Apply PCA | Step-by-Step Tutorial & Example
ฝัง
- เผยแพร่เมื่อ 27 มิ.ย. 2023
- This video explains how to apply a Principal Component Analysis (PCA) in R. More details: statisticsglobe.com/principal...
The video is presented by Cansu Kebabci, a data scientist and statistician at Statistics Globe. Find more information about Cansu here: statisticsglobe.com/cansu-keb...
In the video, Cansu explains the steps and application of the Principal Component Analysis in R. Watch the video to learn more on this topic!
Here can you find the first part of this series:
Introduction to Principal Component Analysis (Pt. 1 - Theory): • Introduction to Princi...
Links to the tutorials mentioned in the video:
Can PCA be Used for Categorical Variables? (Alternatives & Example): statisticsglobe.com/pca-categ...
PCA Using Correlation & Covariance Matrix (Examples): statisticsglobe.com/pca-corre...
Biplot of PCA in R (Examples): statisticsglobe.com/biplot-pca-r
R code of this video:
install.packages("MASS")
install.packages("factoextra")
install.packages("ggplot2")
Load Libraries
library(MASS)
library(factoextra)
library(ggplot2)
Import biopsy data
data(biopsy)
dim(biopsy)
Structure of Data
str(biopsy)
summary(biopsy)
Delete Cases with Missingness
biopsy_nomiss ＜- na.omit(biopsy)
Exclude Categorical Data
biopsy_sample ＜- biopsy_nomiss[,-c(1,11)]
Run PCA
biopsy_pca ＜- prcomp(biopsy_sample,
scale = TRUE)
Summary of Analysis
summary(biopsy_pca)
Elements of PCA object
names(biopsy_pca)
Std Dev of Components
biopsy_pca$sdev
Eigenvectors
biopsy_pca$rotation
Std Dev and Mean of Variables
biopsy_pca$center
biopsy_pca$scale
Principal Component Scores
biopsy_pca$x
Scree Plot of Variance
fviz_eig(biopsy_pca,
addlabels = TRUE,
ylim = c(0, 70))
Biplot with Default Settings
fviz_pca_biplot(biopsy_pca)
Biplot with Labeled Variables
fviz_pca_biplot(biopsy_pca,
label="var")
Biplot with Colored Groups
fviz_pca_biplot(biopsy_pca,
label="var",
habillage = biopsy_nomiss$class)
Biplot with Customized Colored Groups and Variables
fviz_pca_biplot(biopsy_pca,
label="var",
habillage = biopsy_nomiss$class,
col.var = "black") +
scale_color_manual(values=c("orange", "purple"))
Follow me on Social Media:
Facebook - Statistics Globe Page: / statisticsglobecom
Facebook - R Programming Group for Discussions & Questions: / statisticsglobe
Facebook - Python Programming Group for Discussions & Questions: / statisticsglobepython
LinkedIn - Statistics Globe Page: / statisticsglobe
LinkedIn - R Programming Group for Discussions & Questions: / 12555223
LinkedIn - Python Programming Group for Discussions & Questions: / 12673534
Twitter: / joachimschork
Instagram: / statisticsglobecom
TikTok: / statisticsglobe
Great stuff. Clearly explained and easy to follow for a somewhat complicated analysis. Thanks for this!
Hello,
I am so glad to hear that. You are welcome!
Best,
Cansu
Really helpful tutorial.Thank you Cansu and Joachim!!
Hello Darryl,
I am glad that the tutorial was helpful for you. Welcome!
Best,
Cansu
This was an excellent presentation, and doubly-good when paired with the intro one. I agree with others that taking the final solution and being able to unscale and unrotate to get back to an original variable solution would have made this off the charts great!
Hello hello!
Thank you for your encouraging feedback. I am not sure if I got your last point. Are you interested in finding original values from te calculated principal components?
Best,
Cansu
Thank you Joachim and Cansu. This is a good overview of PCA.
Hello John,
I'm glad to hear that you're interested. If you're also considering learning how to perform PCA in Python, be sure not to miss the upcoming tutorial in this series
Best,
Cansu
Well done! This was simple and straightforward
Thanks a lot, glad you found it helpful! :)
Thank you so much for sharing valuable information about PCA!
Hello!
You are very welcome! You can always visit our Statistics Globe webpage: statisticsglobe.com/ for further details about PCA.
Best,
Cansu
If this is Part 2 please label the video "Part 2" so it easy for people to watch them in sequence.
Thanks
Hey, thanks for the feedback, Greg! You can also watch this video without watching the first one, if you are only interested in how to apply PCA in R.
Thanks a lot for this valuable video! It was very easy to follow the explanations.
Thanks, Kalemao for your kind words. We are happy that you found the video helpful!
I will use this example in my classes! Thank you very much.
Hello,
I am glad that you liked the example. You are welcome.
Bes,
Cansu
Welcome Kansu! Excellent tutor . I love her way of teaching. Just 😮
Hello!
Thank you for such kind words. Feel free to let me know if you have any questions about this topic.
Best,
Cansu
@@cansustatisticsglobe Sure. I would love to see you in principal coordinate analysis . Is it okay?
@@wakjiratesfahun3682 Noted!
Thank you Statistics globe for the insightful and informative video. Please keep posting these kind of tutorials.
Thanks a lot, will do! :)
The part 1 video does this well I think.
Thanks for your video.
Hey Preeya! Thanks for your comment, hope the video has been helpful!
Love u guys 🙌🏼
Thank you very much! 🙂
Excellent presentation of PCA analyses by Cansu. You may want to alert the reader that either MASS or factoextra libraries affect the tidyverse select() function. I had to reinstall tidyverse after completing this session. On another point, I wonder if Cansu will describe the process to rotate the factor solution. I assume the example dealt with an unrotated solution.
Hello Alberto,
I am happy to hear that you liked the tutorial. Thank you for the feedback.
I have double-checked the functions of the packages. There shouldn't be any function called select() in either of the packages to mask the select() function. However, there can be complex interactions between packages, and it is conceivable that other functions in these packages may somehow interfere with select(). If you are having issues, one way to potentially avoid them is to explicitly use the dplyr::select() syntax when you want to use the select() function.
Regarding the second point, rotation is often used in factor analysis, like EFA, SEM, etc., to ease the interpretation of the factors. However, rotating may undermine the purpose of PCA, which is to capture as much variability as possible with the first few components. In other words, the first few rotated components might not explain as much of the variance in the data as the original principal components do. That is why, in practice, it's often more useful to interpret the loadings of the original variables on the principal components directly
I hope it is clear. Please let me know if I am missing something, or in case of any further questions.
Best,
Cansu
Very helpful! Is there a way to only project the observations in the space instead of a biplot of both the observations and variables?
Hello!
I am glad that you found the tutorial helpful. Sure! You can use scatterplots for that. Please see the tutorials on our website: statisticsglobe.com/scatterplot-pca-r
Best,
Cansu
Muchas graciaaaas
Thank you very much for the positive feedback. Hope the video has been helpful.
Very understandable. Thanks a lot. But one question.. Isn't it necessary before the PCA to make a Kaiser-Meyer-Olkin test?
Hello Mustafa,
The Kaiser-Meyer-Olkin test is more commonly used for other factor analysis techniques like EFA and CFA. It may also be a useful tool for assessing the adequacy of your data for PCA. However, in practice, PCA usually proceeds without it, as PCA is used more for pattern recognition and dimensionality reduction rather than strict factor extraction like in EFA and CFA.
Best,
Cansu
can a dataset consisting of the principal components and the target variable be used to perform machine learning techniques?
Hello Alessandro,
Yes, you can.
Best,
Cansu
Hi is there any way of doing PCA for different variables with unequal numbers of values without omitting?
Hello!
Do you mean variables with different lengths of inputs? If so, yes, you can, but the lacking inputs would be treated as missing values. Then, dealing with missingness comes into play. You should decide on a missingness handling method. The most straightforward but hazardous one is omitting the cases with missingness. For more advanced methods, see statisticsglobe.com/missing-data/.
Best,
Cansu
Nice video! How can one remove principal components to simplfy the model?
Hello Nico!
I am glad that you liked the video. If you want to extract, let's say, the first two principal components to simplify your dataset. You can do the following for the dataset in this tutorial.
# Principal Component Scores
biopsy_pca$x
# Retrieve the first two components
biopsy_pca$x[,1:2]
As you can see, it is a simple dataset subsetting operation.
Best,
Cansu
Thank you!
Welcome @@Nico_boost !
What variable will we use as an independent variable as an index? I mean, what is the biopsy index variable we will use for the analysis?
I'm not an expert in the biopsy field, but as far as I know, the biopsy index variable used for the analysis will typically be the first principal component derived from the PCA on the set of dummy variables. This component captures the most significant variation in the data, serving as a comprehensive index.
Can we use PCA to make an index using a set of dummy variables?
Hey! Yes, PCA can be used to create an index from a set of dummy variables by transforming them into principal components that summarize the underlying patterns.
Nice intro. If you had added at least some basic interpretation of the results would be even better.
Hey!
Thank you for your feedback. It is the second video of our PCA series. I explain how to interpret the results in our first video, which you can find here: th-cam.com/video/DngS4LNNzc8/w-d-xo.html. I hope it helps!
Best,
Cansu
Is it possible that we perform PCA without using factoextra library? Relying solely on tidyverse?
Hello Melody!
Yes, you don't need to install the factoextra package. It is just needed to visualize the results easily. You can also use only ggplot2 for the visualization, but then you need to write more lines of code to obtain the same visual. Please let me know if you still have some questions.
Best,
Cansu
@@cansustatisticsglobeGot it!! Thanks for the reply !!
Welcome @@freya_yuen !
if resultant variables are overlapping, what can be done to ripple that. i used geom_text_repel, nudge_x = c(4, 5, 6), # Adjust horizontal position for each label
nudge_y = c(0, 0, 0) # Adjust vertical position for each label
etc. But nothing was working. may be i was using it in correctly.
kindly guide me for the same please
Hi Ambujmishra. Sorry about the late response to your comment. Do you still require assistance with the problem?
@@Ifeanyi.StatisticsGlobe yes
Everyone makes simple videos of PCA. No one can explain well what PCA and dimension reduction means. Maybe the video makers also don’t understand what PCA is
Hey, do you have a specific question that is not answered by the video? We are happy to help. Regards, Joachim