nba_team_stats: Generate summary stats for NBA players based on ESPN NBA data

In UBC-MDS/rsketball: Scrape data from ESPN NBA and show summary statistics/visualisations

Description

The function provides descriptive (mean, median, 25 statistic of interest (3PA, 3PM, etc) along with teams of interest (GS, HOU, etc). If positions of interest (C, PG, etc) are specified, the returned tibble depicts relevant descriptive statistics for the relevant positions in the relevant teams.

For reference on the scraped data columns information, please refer to the dataset description: https://github.com/UBC-MDS/rsketball/blob/master/dataset_description.md

For detailed use cases, please refer to the vignette: https://ubc-mds.github.io/rsketball/articles/rsketball-vignette.html

Usage

nba_team_stats(
  nba_data,
  stats_filter = c(),
  teams_filter = c(),
  positions_filter = c()
)

Arguments

nba_data	tibble of scraped ESPN NBA data
stats_filter	character vector
teams_filter	character vector
positions_filter	character vector

Value

A tibble

Examples

nba_data <- tibble::tibble(NAME = c("James", "Steph", "Bosh", "Klay", "Kobe"),
                          TEAM = c("MIA","MIA","MIA","GS","GS"),
                          POS = c("SF", "PG", "C", "C", "PG"),
                          PTS = c(5,4,3,2,10),
                          TO = c(1,2,3,4,3))

# Find descriptive stats for all teams without position
nba_team_stats(nba_data)

# Find specific stats (PTS, TO) for specific teams (GS, MIA) for specific positions (PG, C)
nba_team_stats(nba_data, stats_filter = c("PTS","TO"),
               teams_filter = c("GS","MIA"), positions_filter = c("C","PG"))

# Find specific stats (PTS, TO) for specific teams (GS) without positions_filter inputs
nba_team_stats(nba_data, stats_filter = c("PTS","TO"), teams_filter = c("GS"))

# Find specific stats (PTS, TO) for all individual teams (unspecified)
# for specific positions (PG, C)
nba_team_stats(nba_data, stats_filter = c("PTS","TO"), positions_filter = c("PG"))

UBC-MDS/rsketball documentation built on April 1, 2020, 4:37 a.m.