How do I count icons using rvest?

February 26, 2023

I want to count the number of overall stars for each player on this page: https://cbgm.news/stats/CONN_Ratings.html

Here’s my rvest code:

library(tidyverse)
library(rvest)

url <- "https://cbgm.news/stats/CONN_Ratings.html"

scrape <- url %>% 
  read_html() %>% 
  html_nodes("td:nth-child(19)")

scrape

This returns:

{xml_nodeset (14)}
 [1] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
 [2] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
 [3] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
 [4] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
 [5] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
 [6] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
 [7] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
 [8] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
 [9] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
[10] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
[11] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
[12] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
[13] <td>\n<i class="star yellow icon"></i><i class="star yellow ic ...
[14] <td><i class="star half yellow icon"></i></td>\n

How do I convert the xml_nodeset to a df/tibble that allows for mutating and counting the number of star icons?

I appreciate any help with this puzzle!

>Solution :

You could make a small function that looks for stars (full and half) and returns the number. Then use mutate() to add a new column stars which holds the application of that function to each element of scrape.

f <- function(s) {
  return(str_count(as.character(s), "star yellow") + str_count(as.character(s), "star half")/2)
}

Now, use rvest::html_table() along with mutate()

rvest::html_table(url %>% read_html)[[1]] %>% 
  mutate(OVERALL = sapply(scrape,f))

Output:

     NUM POS   PLAYER    FGI   FGJ    FT   SCR   PAS   HDL   ORB   DRB   DEF   BLK   STL  DRFL    DI    IQ   ATH OVERALL
   <int> <chr> <chr>   <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>   <dbl>
 1     1 PG    Marek …    36    76    54    73    81    50    66    72    65    72    75    21    53    42    58     4  
 2    14 PG    Brian …    10    90    72    50    74    32    71    71    69    53    82    32    65    57    91     3.5
 3    15 PG    Morris…    25    85    56    71    53    60    10    53    76    10    53    28    72    47    76     2  
 4    12 SG    Ryan M…    31    78    96    74    46    38    50    43    71    46    40    35    61    45    75     3.5
 5    21 SG    Lenny …    10    90    67    50    60    49    56    71    58    60    66    39    56    38    69     3  
 6     5 SG    Fred M…    10    83    61    71    30    23    10    78    63    10    16    39    61    38    87     2  
 7    23 SF    Will B…    35    73    58    74    66    38    70    72    52    60    74    21    42    46    30     4  
 8    51 SF    Lyly L…    51    76    83    84    75    32    66    81    61    70    85    24    52    47    60     5  
 9    42 SF    Joe Ch…    58    50    80    70    56    39    53    53    78    10    53    21    54    52    78     2  
10    40 PF    Richar…    63    50    41    72    71    32    79    78    65    71    71    54    39    43    72     4  
11    30 PF    Ammer …    56    54    81    63    60    23    72    72    78    66    56    35    50    54    58     3.5
12    54 C     Xavier…   100    33    36   100    76    16    96    91    76   100    87    61    28    41    73     5  
13    45 C     Brad L…    91    38    56    60    63    19    75    76    78    82    70    58    30    28    68     4  
14    10 C     Ed Str…    68    40    45    10    10    10    13    10    10    10    10    24    17    16    10     0.5