Youtube view counts of Linear Algebra lectures

I am learning linear algebra these days by watching the excellent series of lectures taught by Prof. Gilbert Strang at Youtube. During this journey, I think it would be interesting to look how many view count for all lectures. I expect the view counts will decline for later lectures.

Alright, first load some R packages in order to get data from Youtube.

library(plyr)
library(dplyr)
library(rvest) # for webpage scripting
library(stringr) # string handling
library(ggplot2) # plotting
library(knitr)

Then I searched online to find out the url of the playlist for all lectures. To find the correct CSS part, I followed this tutorial.

# the playlist first
url = html("https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8")
lectures = html_nodes(url, ".yt-uix-tile-link")
# length(lectures) # 35 vedio
# get lecture names
lec_names = html_text(lectures) %>% 
  sapply(function(x) str_replace(x, "^.*Lec ([b0-9]*) .*", "\\1")) %>% 
  unname() %>% as.character()
lec_names[lec_names == "24b"] = 24.5
lec_names = as.numeric(lec_names)

Then, get urls for all lectures and extract their view counts.

# get url for all lectures
url_all = ldply(lectures, function(x){
  paste0("https://www.youtube.com", html_attr(x, name = "href"))
})

# for each lecture, get the view count
view_all = sapply(url_all$V1, function(x){
  print(x)
  xx = html(x)
  view_count = html_nodes(xx, ".watch-view-count") %>% html_text() %>%
    gsub(",", "", .) %>% 
    as.numeric()
  lect_descrip = html_nodes(xx, "#eow-description") %>% html_text() %>% 
    gsub("^(.*)View the complete.*$", "\\1", .) %>% str_trim()
  print(lect_descrip)
  list(view_count, as.character(lect_descrip))
})

Now, combine lecture names with their view counts.

# combine lecture names with view count
view = unlist(view_all[(1:length(view_all)) %% 2 == 1])
# remove some notes that start with *.
descrip = unlist(view_all[(1:length(view_all)) %% 2 == 0])
descrip = sapply(descrip, function(x){
 if(str_detect(x, "\\*")){
   str_replace(x, "^(.*)\\*+.*$", "\\1")
 } else{
   x
 }
})
dat = data_frame(lec = lec_names, view = view, description = descrip)
kable(data.frame(lec = lec_names, view = format(view, big.mark = ","), description = descrip), format = "html")

view	description
1,471,018	Lecture 1: The Geometry of Linear Equations.
421,456	Lecture 2: Elimination with Matrices.
359,628	Lecture 3: Multiplication and Inverse Matrices.
304,766	Lecture 4: Factorization into A = LU
210,564	Lecture 5: Transposes, Permutations, Spaces R^n.
199,004	Lecture 6: Column Space and Nullspace.
151,769	Lecture 7: Solving Ax = 0: Pivot Variables, Special Solutions.
138,087	Lecture 8: Solving Ax = b: Row Reduced Form R.
151,718	Lecture 9: Independence, Basis, and Dimension.
132,971	Lecture 10: The Four Fundamental Subspaces.
104,452	Lecture 11: Matrix Spaces; Rank 1; Small World Graphs.
82,273	Lecture 12: Graphs, Networks, Incidence Matrices.
77,250	Lecture 13: Quiz 1 Review.
108,408	Lecture 14: Orthogonal Vectors and Subspaces.
99,687	Lecture 15: Projections onto Subspaces.
96,329	Lecture 16: Projection Matrices and Least Squares.
95,593	Lecture 17: Orthogonal Matrices and Gram-Schmidt.
90,094	Lecture 18: Properties of Determinants.
79,339	Lecture 19: Determinant Formulas and Cofactors.
85,189	Lecture 20: Cramer's Rule, Inverse Matrix, and Volume.
159,954	Lecture 21: Eigenvalues and Eigenvectors.
109,883	Lecture 22: Diagonalization and Powers of A.
84,893	Lecture 23: Differential Equations and exp(At).
84,173	Lecture 24: Markov Matrices; Fourier Series.*
36,172	Lecture 24b : Quiz 2 Review.*
59,755	Lecture 25: Symmetric Matrices and Positive Definiteness.*
62,566	Lecture 26: Complex Matrices; Fast Fourier Transform.
58,041	Lecture 27: Positive Definite Matrices and Minima.
70,082	Lecture 28: Similar Matrices and Jordan Form.
85,714	Lecture 29: Singular Value Decomposition.
99,162	Lecture 30: Linear Transformations and Their Matrices.
61,037	Lecture 31: Change of Basis; Image Compression.
36,158	Lecture 32: Quiz 3 Review.
55,596	Lecture 33: Left and Right Inverses; Pseudoinverse.
50,540	Lecture 34: Final Course Review.

Finally, let’s plot it.

# plot
ggplot(dat, aes(x = lec, y = view)) +
  geom_point(color = "red", size = 2) + 
  geom_line(color = "blue") +
  labs(x = "Lectures", y = "Youtube view count",
       title = "Youtube view counts of Linear Algebra lectures taught by 
       Gilbert Strang, Srping 2005")

Imgur

Wow, the first lecture has 1,471,030 by far (2015-06-21-23:00 Central Time)! However, the view count of the second lecture is about one million lower than the first one. It will be interesting to find out why lecture 21 and 22 have more view counts than their neighbors (I am getting their, at lecture 14 now! – Eigenvalues!). The last lecture has about 50K views. Does this mean about 50K people finished all lectures?

It clearly shows how hard it is to be persistent.