Convenience helpers to munge hoopR's structure-faithful named-list result sets
into a single tibble. hoopR's nba_*() wrappers return the endpoint's
resultSets verbatim as a named list of tibbles (lossless, no munging); these
reduce that list on demand, making explicit – and composable – the three
reductions older scrapers tended to hard-code inside each task function.
Usage
nba_bind_sets(result_sets, tag_column = "set_name")
nba_join_sets(result_sets, join_key)
nba_nest_sets(result_sets, keep_cols, nest_col = ".data")Arguments
- result_sets
A named list of tibbles (an
nba_*()return).- tag_column
Name of the origin column
nba_bind_sets()adds (holds the set names). Defaults to"set_name".- join_key
Single shared column name
nba_join_sets()joins the sets on.- keep_cols
Grouping key column(s)
nba_nest_sets()keeps wide; every other column is nested.- nest_col
Name of the list-column
nba_nest_sets()creates. Defaults to".data".
Details
nba_bind_sets() row-binds the sets into one long tibble, tagging each row
with its origin (tactic B). Use when the sets share a schema, or you simply
want them stacked with an origin column (e.g. draft rounds, per-mode stat
blocks). Columns absent from a given set fill with NA.
nba_join_sets() successively left-joins the sets into one wide tibble on a
shared key (tactic C). Use when the sets are different facets of the same
entity (e.g. player stats + team stats keyed on a game or player id).
nba_nest_sets() keeps keep_cols (plus the set name) wide and nests every
other column into a list-column, preserving all data when no single flat
schema can hold it (tactic D). Use for incompatible sets (e.g. a player
profile's season totals + career totals + awards). Recover a set with
tidyr::unnest().
All three accept a named list of tibbles – an nba_*() return, or the output
of nba_stats_map_result_sets() – and return a single tibble.
Examples
sets <- list(
PlayerStats = data.frame(GAME_ID = "1", PERSON_ID = c(1, 2), PTS = c(10, 20)),
TeamStats = data.frame(GAME_ID = "1", TEAM_ID = 99, PTS = 30)
)
nba_bind_sets(sets, tag_column = "set") # B: row-bind + tag
#> # A tibble: 3 × 5
#> set GAME_ID PERSON_ID PTS TEAM_ID
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 PlayerStats 1 1 10 NA
#> 2 PlayerStats 1 2 20 NA
#> 3 TeamStats 1 NA 30 99
nba_join_sets(sets, join_key = "GAME_ID") # C: join/widen on a key
#> # A tibble: 2 × 5
#> GAME_ID PERSON_ID PTS.x TEAM_ID PTS.y
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 10 99 30
#> 2 1 2 20 99 30
nba_nest_sets(sets, keep_cols = "GAME_ID") # D: nest into a list-column
#> # A tibble: 2 × 3
#> set_name GAME_ID .data
#> <chr> <chr> <list>
#> 1 PlayerStats 1 <tibble [2 × 3]>
#> 2 TeamStats 1 <tibble [1 × 3]>