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I'm new to web-scraping. Was using the SelectorGadget and rvest to take the data from "https://fbref.com/en/comps/9/defense/Premier-League-Stats". I'm unable to take the data from the players table.

The code I'm using

url <- "https://fbref.com/en/comps/9/defense/Premier-League-Stats"
def_stats <- url %>% read_html() %>% html_nodes("#stats_defense") %>% html_table()

But this is returning an empty list. If I use url %>% read_html() %>% html_table() it just returns the Teams stats table.

I tried using various css selectors, "#stats_defense", #div_stats_defense, and even selecting the individual columns of the table. But it's returning an empty list.

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I have been able to scrape the table with the following code :

library(RSelenium)

shell('docker run -d -p 4446:4444 selenium/standalone-firefox')
remDr <- remoteDriver(remoteServerAddr = "localhost", port = 4446L, browserName = "firefox")
remDr$open()
remDr$navigate("https://fbref.com/en/comps/9/defense/Premier-League-Stats")

web_Obj_Table <- remDr$findElement("id", "all_stats_squads_defense")
text_Table <- web_Obj_Table$getElementText()[[1]]
strsplit(text_Table, "\n")

[[1]]
 [1] "Squad Defensive Actions 2022-2023 Premier League"                                                                
 [2] "Share & Export"                                                                                                  
 [3] "GlossaryToggle Per90 Stats"                                                                                      
 [4] "Squad Stats Opponent Stats"                                                                                      
 [5] "Tackles Challenges Blocks"                                                                                       
 [6] "Squad # Pl 90s Tkl TklW Def 3rd Mid 3rd Att 3rd Tkl Att Tkl% Lost Blocks Sh Pass Int Tkl+Int Clr Err"            
 [7] "Arsenal 26 30.0 448 275 193 164 91 193 368 52.4 175 283 68 215 195 643 486 18"                                   
 [8] "Aston Villa 26 31.0 534 285 263 207 64 265 520 51.0 255 373 103 270 269 803 567 12"                              
 [9] "Bournemouth 31 31.0 502 307 254 201 47 206 471 43.7 265 417 147 270 289 791 848 9"                               
[10] "Brentford 25 31.0 490 286 225 196 69 215 505 42.6 290 376 141 235 288 778 695 11"                                
[11] "Brighton 26 29.0 494 271 221 191 82 207 407 50.9 200 296 73 223 242 736 333 11"                                  
[12] "Chelsea 32 31.0 628 374 310 223 95 280 544 51.5 264 379 86 293 294 922 560 14"                                   
[13] "Crystal Palace 26 31.0 579 354 299 210 70 231 440 52.5 209 392 103 289 311 890 683 5"                            
[14] "Everton 28 31.0 589 356 307 211 71 253 483 52.4 230 421 148 273 298 887 701 17"                                  
[15] "Fulham 29 30.0 483 272 226 173 84 224 427 52.5 203 356 96 260 269 752 581 9"                                     
[16] "Leeds United 28 30.0 684 399 309 279 96 332 678 49.0 346 421 101 320 316 1000 563 12"                            
[17] "Leicester City 28 31.0 560 337 266 239 55 257 554 46.4 297 420 134 286 309 869 607 8"                            
[18] "Liverpool 27 29.0 440 279 189 185 66 201 477 42.1 276 282 64 218 264 704 434 13"                                 
[19] "Manchester City 22 30.0 382 220 146 160 76 172 359 47.9 187 278 74 204 180 562 298 6"                            
[20] "Manchester Utd 26 29.0 505 294 279 170 56 232 442 52.5 210 385 119 266 267 772 625 6"                            
[21] "Newcastle Utd 26 30.0 493 300 218 201 74 251 499 50.3 248 365 94 271 267 760 564 9"                              
[22] "Nott'ham Forest 33 30.0 500 296 276 163 61 228 439 51.9 211 391 125 266 258 758 729 5"                           
[23] "Southampton 31 31.0 590 342 297 243 50 244 475 51.4 231 426 115 311 343 933 658 6"                               
[24] "Tottenham 27 31.0 492 283 256 169 67 212 442 48.0 230 344 116 228 273 765 604 15"                                
[25] "West Ham 25 29.0 469 250 222 175 72 201 413 48.7 212 322 106 216 307 776 616 7"                                  
[26] "Wolves 31 31.0 570 320 317 189 64 241 496 48.6 255 394 139 255 231 801 672 10"                                   
[27] "Totals may not be complete for all senior-level play, see coverage note."                                        
[28] "Expected Goals (xG explained) and other Advanced Data provided by Opta, and is available for these competitions."
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