Web Scraping Earnings Calendar-CodePudding

  url <- "https://finance.yahoo.com/calendar/earnings?from=2022-12-04&to=2022-12-10&day=2022-12-06"

download_table <- function(url) {
  url_file <- GET(url)
  web_page_parsed <- htmlParse(url_file)
  tables <- readHTMLTable(web_page_parsed)
}

url_file <- GET(url)
web_page_parsed <- htmlParse(url_file)
tables <- readHTMLTable(web_page_parsed)
print(head(tables))

I used this one for yahoo and it worked. But I tried this for:

url <- "https://www.benzinga.com/calendars/earnings"

download_table <- function(url) {
  url_file <- GET(url)
  web_page_parsed <- htmlParse(url_file)
  tables <- readHTMLTable(web_page_parsed)
}

url_file <- GET(url)
web_page_parsed <- htmlParse(url_file)
tables <- readHTMLTable(web_page_parsed)
print(head(tables))
tables$`NULL`

And I got no tables as result but this:

> print(head(tables))
$`NULL`
  Date time ticker Quarter Prior EPS Est EPS Actual EPS EPS Surprise
1 Date time ticker Quarter Prior EPS Est EPS Actual EPS EPS Surprise
  Prior Rev Est Rev Actual Rev Rev Surprise Get Alert
1 Prior Rev Est Rev Actual Rev Rev Surprise Get Alert

$`NULL`
  V1   V2   V3   V4   V5   V6   V7   V8   V9  V10  V11  V12  V13
1                                                               
2    <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>

> tables$`NULL`
  Date time ticker Quarter Prior EPS Est EPS Actual EPS EPS Surprise
1 Date time ticker Quarter Prior EPS Est EPS Actual EPS EPS Surprise
  Prior Rev Est Rev Actual Rev Rev Surprise Get Alert
1 Prior Rev Est Rev Actual Rev Rev Surprise Get Alert
>

If i search in the source code for example the tickers I cant find them. So I cant use the rvest package to scrap them.

Has anyone a idea how to do this with benzinga?

Thank you and KR

Web Scraping Bezinga Earnings Calender with rvest and httpr

CodePudding user response：

The data is pulled from an API that you can see in the network section (inspect element in the developer tools).

The link is as follows:

https://api.benzinga.com/api/v2.1/calendar/earnings?token=1c2735820e984715bc4081264135cb90&parameters[date_from]=2023-01-25&parameters[date_to]=2023-01-25&parameters[tickers]=&pagesize=1000

You can then create a function that alter the dates and filter for the tickers ([tickers]) of interest. I wrote on here as a suggestion with httr2 where the function takes from_date and to_date as input.

library(tidyverse)
library(httr2)

get_earnings <- function(from_date, to_date) {
  str_c(
    "https://api.benzinga.com/api/v2.1/calendar/earnings?token=1c2735820e984715bc4081264135cb90&parameters[date_from]=",
    from_date,
    "&parameters[date_to]=",
    to_date,
    "&parameters[tickers]=&pagesize=1000"
  ) %>%
    request() %>%
    req_headers(accept = "application/json") %>%
    req_perform() %>%
    resp_body_json(simplifyVector = TRUE) %>%
    pluck("earnings") %>%
    as_tibble() %>%
    type_convert()
}

get_earnings(from_date = "2023-01-01", to_date = "2023-01-25")

# A tibble: 387 × 25
   currency date       date_confirmed   eps eps_est eps_prior eps_surprise eps_surprise_per…
   <chr>    <date>              <int> <dbl>   <dbl>     <dbl>        <dbl>             <dbl>
 1 USD      2023-01-25              1  0.91    0.58      0.57         0.33            0.569 
 2 USD      2023-01-25              1 NA       1.27      1.42        NA              NA     
 3 USD      2023-01-25              1  1       0.97      0.92         0.03            0.0309
 4 USD      2023-01-25              1  1.01    1.13      0.95        -0.12           -0.106 
 5 USD      2023-01-25              1  0.69   NA         0.93        NA              NA     
 6 USD      2023-01-25              1  0.12    0.13      0.16        -0.01           -0.0769
 7 USD      2023-01-25              1  1.5     1.43      1.05         0.07            0.049 
 8 USD      2023-01-25              1  1.1     0.98      0.69         0.12            0.122 
 9 USD      2023-01-25              1  0.02    0.01     -0.65         0.01            1     
10 USD      2023-01-25              1  0.42    0.44      0.5         -0.02           -0.0455
# … with 377 more rows, and 17 more variables: eps_type <chr>, exchange <chr>, id <chr>,
#   importance <int>, name <chr>, notes <chr>, period <chr>, period_year <int>,
#   revenue <dbl>, revenue_est <dbl>, revenue_prior <dbl>, revenue_surprise <dbl>,
#   revenue_surprise_percent <dbl>, revenue_type <chr>, ticker <chr>, time <time>,
#   updated <int>