How to scrape tables inside a comment tag in html

Question

I am trying to scrape from http://www.basketball-reference.com/teams/CHI/2015.html using rvest. I used selectorgadget and found the tag to be #advanced for the table I want. However, I noticed it wasn't picking it up. Looking at the page source, I noticed that the tables are inside an html comment tag <!--

What is the best way to get the tables from inside the comment tags? Thanks!

Edit: I am trying to pull the 'Advanced' table: http://www.basketball-reference.com/teams/CHI/2015.html#advanced::none

Answer 1

Ok..got it.

library(stringi)
library(knitr)
library(rvest)


 any_version_html <- function(x){
       XML::htmlParse(x)
    }
a <- 'http://www.basketball-reference.com/teams/CHI/2015.html#advanced::none'
b <- readLines(a)
c <- paste0(b, collapse = "")
d <- as.character(unlist(stri_extract_all_regex(c, '<table(.*?)/table>', omit_no_match = T, simplify = T)))

e <- html_table(any_version_html(d))


> kable(summary(e),'rst')
======  ==========  ====
Length  Class       Mode
======  ==========  ====
9       data.frame  list
2       data.frame  list
24      data.frame  list
21      data.frame  list
28      data.frame  list
28      data.frame  list
27      data.frame  list
30      data.frame  list
27      data.frame  list
27      data.frame  list
28      data.frame  list
28      data.frame  list
27      data.frame  list
30      data.frame  list
27      data.frame  list
27      data.frame  list
3       data.frame  list
======  ==========  ====


kable(e[[1]],'rst')


===  ================  ===  ====  ===  ==================  ===  ===  =================================
No.  Player            Pos  Ht     Wt  Birth Date          Â    Exp  College                          
===  ================  ===  ====  ===  ==================  ===  ===  =================================
 41  Cameron Bairstow  PF   6-9   250  December 7, 1990    au   R    University of New Mexico         
  0  Aaron Brooks      PG   6-0   161  January 14, 1985    us   6    University of Oregon             
 21  Jimmy Butler      SG   6-7   220  September 14, 1989  us   3    Marquette University             
 34  Mike Dunleavy     SF   6-9   230  September 15, 1980  us   12   Duke University                  
 16  Pau Gasol         PF   7-0   250  July 6, 1980        es   13                                    
 22  Taj Gibson        PF   6-9   225  June 24, 1985       us   5    University of Southern California
 12  Kirk Hinrich      SG   6-4   190  January 2, 1981     us   11   University of Kansas             
  3  Doug McDermott    SF   6-8   225  January 3, 1992     us   R    Creighton University    


## Realized we should index with some names...but this is somewhat cheating as we know the start and end indexes for table titles..I prefer to parse-in-the-dark.

# Names are in h2-tags
e_names <- as.character(unlist(stri_extract_all_regex(c, '<h2(.*?)/h2>', simplify = T)))
e_names <- gsub("<(.*?)>","",e_names[grep('Roster',e_names):grep('Salaries',e_names)])
names(e) <- e_names
kable(head(e$Salaries), 'rst')

===  ==============  ===========
 Rk  Player          Salary     
===  ==============  ===========
  1  Derrick Rose    $18,862,875
  2  Carlos Boozer   $13,550,000
  3  Joakim Noah     $12,200,000
  4  Taj Gibson      $8,000,000 
  5  Pau Gasol       $7,128,000 
  6  Nikola Mirotic  $5,305,000 
===  ==============  ===========

Answer 2

You can use the XPath comment() function to select comment nodes, then reparse their contents as HTML:

library(rvest)

# scrape page
h <- read_html('http://www.basketball-reference.com/teams/CHI/2015.html')

df <- h %>% html_nodes(xpath = '//comment()') %>%    # select comment nodes
    html_text() %>%    # extract comment text
    paste(collapse = '') %>%    # collapse to a single string
    read_html() %>%    # reparse to HTML
    html_node('table#advanced') %>%    # select the desired table
    html_table() %>%    # parse table
    .[colSums(is.na(.)) < nrow(.)]    # get rid of spacer columns

df[, 1:15]
##    Rk           Player Age  G   MP  PER   TS%  3PAr   FTr ORB% DRB% TRB% AST% STL% BLK%
## 1   1        Pau Gasol  34 78 2681 22.7 0.550 0.023 0.317  9.2 27.6 18.6 14.4  0.5  4.0
## 2   2     Jimmy Butler  25 65 2513 21.3 0.583 0.212 0.508  5.1 11.2  8.2 14.4  2.3  1.0
## 3   3      Joakim Noah  29 67 2049 15.3 0.482 0.005 0.407 11.9 22.1 17.1 23.0  1.2  2.6
## 4   4     Aaron Brooks  30 82 1885 14.4 0.534 0.383 0.213  1.9  7.5  4.8 24.2  1.5  0.6
## 5   5    Mike Dunleavy  34 63 1838 11.6 0.573 0.547 0.181  1.7 12.7  7.3  9.7  1.1  0.8
## 6   6       Taj Gibson  29 62 1692 16.1 0.545 0.000 0.364 10.7 14.6 12.7  6.9  1.1  3.2
## 7   7   Nikola Mirotic  23 82 1654 17.9 0.556 0.502 0.455  4.3 21.8 13.3  9.7  1.7  2.4
## 8   8     Kirk Hinrich  34 66 1610  6.8 0.468 0.441 0.131  1.4  6.6  4.1 13.8  1.5  0.6
## 9   9     Derrick Rose  26 51 1530 15.9 0.493 0.325 0.224  2.6  8.7  5.7 30.7  1.2  0.8
## 10 10       Tony Snell  23 72 1412 10.2 0.550 0.531 0.148  2.5 10.9  6.8  6.8  1.2  0.6
## 11 11    E'Twaun Moore  25 56  504 10.3 0.504 0.273 0.144  2.7  7.1  5.0 10.4  2.1  0.9
## 12 12   Doug McDermott  23 36  321  6.1 0.480 0.383 0.140  2.1 12.2  7.3  3.0  0.6  0.2
## 13 13    Nazr Mohammed  37 23  128  8.7 0.431 0.000 0.100  9.6 22.3 16.1  3.6  1.6  2.8
## 14 14 Cameron Bairstow  24 18   64  2.1 0.309 0.000 0.357 10.5  3.3  6.8  2.2  1.6  1.1