library(ffscrapr)
library(dplyr)
library(purrr)
library(glue)
#>
#> Attaching package: 'glue'
#> The following object is masked from 'package:dplyr':
#>
#> collapse
The Sleeper API is pretty extensive, and I haven’t written out a function for every single combination of the endpoints. If there is something you’d like to access, 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>
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.
We’ll need to fill out the other parameters as per the documentation: sport is NFL
, type is either add
or drop
, lookback_hours is optional in hours, and limit is optional in number of rows. We can use the glue
package to parameterise this, although you can also use base R’s paste function just as easily.
query <- glue::glue('players/nfl/trending/add')
query
#> players/nfl/trending/add
response_trending <- sleeper_getendpoint(query)
str(response_trending, max.level = 1)
#> List of 3
#> $ content :List of 25
#> $ query : chr "https://api.sleeper.app/v1/players/nfl/trending/add/"
#> $ response:List of 10
#> ..- 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 debug, 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 4994 157599
#> 2 5163 94475
#> 3 1535 91276
#> 4 6012 75473
#> 5 6931 67759
#> 6 4150 58560
This isn’t very helpful without knowing who these players are - let’s pull the players endpoint in as well.
players <- sleeper_players() %>%
select(player_id, player_name, pos, team, age)
trending <- df_trending %>%
left_join(players, by = "player_id")
trending
#> # A tibble: 25 x 6
#> player_id count player_name pos team age
#> <chr> <int> <chr> <chr> <chr> <dbl>
#> 1 4994 157599 Kalen Ballage RB LAC 24.9
#> 2 5163 94475 Ryan Nall RB CHI 24.9
#> 3 1535 91276 Cordarrelle Patterson WR CHI 29.7
#> 4 6012 75473 Travis Homer RB SEA 22.3
#> 5 6931 67759 DeeJay Dallas RB SEA 22.2
#> 6 4150 58560 Wayne Gallman RB NYG 26.1
#> 7 6918 49667 Salvon Ahmed RB MIA 21.9
#> 8 3934 40049 Troymaine Pope RB LAC 27
#> 9 1048 39989 Lamar Miller RB CHI 29.6
#> 10 MIN 39109 <NA> DEF MIN NA
#> # ... with 15 more rows
There - this means something to us now! As of this writing, Kalen Ballage is the most added player. Haven’t we been bitten by this before?