What I usually do in these cases is to
- get each row
<li> - and then use xpaths to navigate from each
<li>element down to the element of interest and map it to column.
It's a bit tidious but you save yourself the trouble of cleaning up the merged texts.
On the page use F12 -> Elements to inspect the tables' structure using the button (1).
You can right click on elements within the structure -> "copy xpath" to get the path for each html_element() call. So from each <li> element we go down the xpaths to get to the column data.
library(rvest)
# get rows 'li' of table to iterate over them
rows <- read_html("https://gainblers.com/mx/quinielas/progol-revancha/", encoding = "UTF-8") |>
html_element(xpath= '//*[@id="content_seccionb"]/div[1]/ul') |>
html_nodes("li")
# helper function to get the text from a nodes child found by xpath
from_xpath <- \(x, path) x |> html_element(xpath = path) |> html_text(trim = TRUE)
foo <- rows |>
purrr::map_df(~ {
list(
nr = from_xpath(.x, "div[1]/span"),
partidos1 = from_xpath(.x, "div[1]/p/span[1]"),
partidos2 = from_xpath(.x, "div[1]/p/span[3]"),
L1 = from_xpath(.x, "div[2]/span"),
L2 = from_xpath(.x, "div[2]/strong"),
E1 = from_xpath(.x, "div[3]/span"),
E2 = from_xpath(.x, "div[3]/strong"),
V1 = from_xpath(.x, "div[4]/span"),
V2 = from_xpath(.x, "div[4]/strong"),
pron1 = from_xpath(.x, "div[5]/div[1]"),
pron2 = from_xpath(.x, "div[5]/div[2]")
)
}) |>
data.frame() |>
subset(!is.na(partidos1)) # filter out header row
giving
nr partidos1 partidos2 L1 L2 E1 E2 V1 V2 pron1 pron2
2 1 México Japón 2,42 39% 3,50 27% 2,72 34% L V
3 2 USA Corea del Sur 2,22 43% 3,20 30% 3,40 28% L <NA>
4 3 Juárez FC Pachuca 3,87 24% 3,55 26% 1,79 51% V <NA>
5 4 Tigres UANL Monterrey 1,53 59% 3,88 23% 5,31 17% L <NA>
6 5 Dorados De Sinaloa Irapuato 2,96 31% 3,40 27% 2,23 42% L V
7 6 Tampico Madero Tapatio 1,60 58% 3,90 24% 5,13 18% L <NA>
8 7 Tepatitlan de Morelos Leones Negros 2,03 46% 3,38 27% 3,40 27% L <NA>
9 8 Irlanda Hungría 2,75 36% 3,26 30% 2,90 34% L V
10 9 Grecia Dinamarca 2,82 35% 3,24 30% 2,84 35% L V
11 10 Estoril Praia Santa Clara 2,77 34% 3,10 31% 2,75 35% L V
12 11 St. Louis City Dallas FC 2,00 49% 4,15 24% 3,55 28% L <NA>
13 12 Sporting Kansas City Austin FC 2,49 39% 3,75 26% 2,67 36% L V
14 13 Deportivo Coruña Sporting Gijón 2,20 44% 3,25 30% 3,70 26% L E
15 14 Burgos Las Palmas 2,43 40% 3,12 31% 3,25 30% L V
16 15 Burgos Las Palmas 2,43 40% 3,12 31% 3,25 30% L V
17 16 Pumas Toluca % % % <NA> <NA>
18 17 Tlaxcala FC Oaxaca 1,73 54% 4,00 24% 4,25 22% L <NA>
19 18 Atlante Correcaminos 1,31 71% 5,05 18% 9,10 10% L <NA>
20 19 Turquía España 6,00 17% 4,61 22% 1,60 62% V <NA>
21 20 Israel Italia 8,70 11% 4,91 20% 1,43 69% V <NA>
22 21 Zaragoza Valladolid 2,65 37% 3,25 30% 2,90 33% L V
23 22 Almería Racing Santander 1,90 51% 3,84 25% 4,00 24% L <NA>