The Sleeper API is pretty extensive. If there is something you’d like to access that’s beyond the current scope of ffscrapr, you can use the lower-level “sleeper_getendpoint” function to create a GET request and access the data, while still using the authentication and rate-limiting features I’ve already created.
Here is an example of how you can call one of the endpoints - in this case, let’s pull Sleeper’s trending players data!
We’ll start by opening up this page, https://docs.sleeper.app/#trending-players, which is the documentation page for this particular endpoint. From here, we can see that Sleeper’s documentation says the endpoint is:
https://api.sleeper.app/v1/players/<sport>/trending/<type>?lookback_hours=<hours>&limit=<int>
On first glance, you can see that it takes two parameters within the endpoint call itself (sport and type) and we can further adjust the query with HTTP parameters lookback_hours and limit. The sleeper_getendpoint function already has the https://api.sleeper.app/v1/ part encoded, so all we’ll need to do is pass in the remaining part of the URL as the endpoint, and pass the HTTP parameters in as arguments to the function (these are case sensitive!)
We can use the glue package to parameterise this, although you can also use base R’s paste function just as easily.
type <- "add"
query <- glue::glue('players/nfl/trending/{type}')
query
#> players/nfl/trending/add
response_trending <- sleeper_getendpoint(query,lookback_hours = 48, limit = 10)
#> Using request.R from "ffscrapr"
str(response_trending, max.level = 1)
#> List of 3
#> $ content :List of 10
#> $ query : chr "https://api.sleeper.app/v1/players/nfl/trending/add?lookback_hours=48&limit=10"
#> $ response:List of 9
#> ..- attr(*, "class")= chr "response"
#> - attr(*, "class")= chr "sleeper_api"Along with the parsed content, the function also returns the query and the response that was sent by the server. These are helpful for debugging, but we can turn the content into a dataframe with some careful application of the tidyverse.
df_trending <- response_trending %>%
purrr::pluck("content") %>%
dplyr::bind_rows()
head(df_trending)
#> # A tibble: 6 x 2
#> player_id count
#> <chr> <int>
#> 1 4187 270285
#> 2 6156 142272
#> 3 1169 129162
#> 4 5100 84788
#> 5 4381 64578
#> 6 333 62680This isn’t very helpful without knowing who these players are, so let’s pull the players endpoint in as well - this one has a convenient function!
players <- sleeper_players() %>%
select(player_id, player_name, pos, team, age)
trending <- df_trending %>%
left_join(players, by = "player_id")
trending
#> # A tibble: 10 x 6
#> player_id count player_name pos team age
#> <chr> <int> <chr> <chr> <chr> <dbl>
#> 1 4187 270285 Brian Hill RB ATL 25.3
#> 2 6156 142272 Benny Snell RB PIT 23
#> 3 1169 129162 Robert Griffin QB BAL 31
#> 4 5100 84788 Jordan Wilkins RB IND 26.6
#> 5 4381 64578 Taysom Hill QB NO 30.5
#> 6 333 62680 Ryan Fitzpatrick QB MIA 38.2
#> 7 NYG 57945 <NA> DEF NYG NA
#> 8 4147 52537 Samaje Perine RB CIN 25.4
#> 9 3976 50102 Mitchell Trubisky QB CHI 26.5
#> 10 943 45866 Kyle Rudolph TE MIN 31.2There - this means something to us now! As of this writing (2020-11-10), Kalen Ballage was the most added player. Haven’t we been bitten by this before?