In epijim/FootballData: Easy access to Football API data and quick toy summaries

To rebuild run make build.

knitr::opts_chunk$set(echo = TRUE)

library(glue)
library(kableExtra)
library(tidyverse)
library(FootballData)

select_country <- "England"
select_league <- "Championship"
select_team_interest <- "Nottingham Forest FC"

Competitions

Get all competitions.

competitions <- football_get_competition()

glimpse(competitions)

Filter to the ones on the free plan.

competitions_curated <- competitions %>%
  filter(
    plan == "TIER_ONE" 
  )

Show them.

competitions_curated %>%
  select(country,league,flag) %>%
  knitr::kable(escape = F)

League info

Look at r select_country - r select_league.

league <- competitions_curated %>%
  filter(country == select_country & league == select_league) %>%
  pull(competition_id) %>%
  as.character() %>%
  football_get_teams()

league %>%
  select(team,crest) %>%
  knitr::kable(escape = F)

Standings

What's the current league standings.

standings <- competitions_curated %>%
  filter(country == select_country & league == select_league) %>%
  pull(competition_id) %>%
  as.character() %>%
  football_get_standing()

glimpse(standings)

standings %>%
  select(position,points,team,form,crest) %>%
  knitr::kable(escape = F)

Scheduled games

upcoming_foe_data <- standings %>%
  filter(
      team == select_team_interest
    ) %>%
  pull(team_id) %>%
  as.character() %>%
  football_get_upcoming() %>%
  arrange(utcDate) 

glimpse(upcoming_foe_data)

Metrics - League

metrics <- football_calculate_competition_metrics(standings)

# table
metrics %>%
  mutate(Team = paste(crest,team)) %>%
  select(Team, Mood = metric_mood)  %>%
  mutate(
    Mood = as.character(Mood),
    Mood = case_when(
      str_detect(Mood,"Jubilant") ~ cell_spec(
        Mood, color = "black", background = "#98FB98"
      ),
      str_detect(Mood,"Optimistic") ~ cell_spec(
        Mood, color = "black", background = "#e5f5f9"
      ),
      str_detect(Mood,"Ambivalent") ~ cell_spec(
        Mood, color = "black", background = "#f7fcb9"
      ),
      str_detect(Mood,"Doldrums") ~ cell_spec(
        Mood, color = "black", background = "#ffc4c4"
      ),
      TRUE ~ Mood
    )
  ) %>%
  knitr::kable(escape = F)

Metrics - Team

metrics_team <- metrics %>%
  filter(team == select_team_interest)

metrics_team_summary <- metrics_team %>% select(team,crest,metric_mood)

metrics_team %>%
  select(-c(metric_mood_numeric,metric_mood)) %>%
  pivot_longer(starts_with("metric"),
               names_to = "Variable",
               values_to = "Modifier"
               ) %>%
  mutate(
    Effect = case_when(
      str_detect(Modifier, "\\+") ~ "Positive",
      str_detect(Modifier, "\\-") ~ "Negative",
      TRUE ~ "Neutral"
    )
  ) %>%
  select(
    Effect,Modifier
  ) %>% arrange(Effect) %>%
  mutate(
    Effect = case_when(
      Effect == "Negative" ~ cell_spec(
        Effect, color = "white", bold = TRUE, background = "#F66E5CFF"
        ),
      Effect == "Positive" ~ cell_spec(
        Effect, color = "white", bold = TRUE, background = "#228B22"
      ),
      TRUE ~ Effect
    )
  ) %>%
  knitr::kable(escape = F)