eoppe1022

library(rtweet)
library(dplyr)
library(ggplot2)
library(extrafont)

theme_awesome <- function(font) {
  
  theme_minimal() +
    theme(plot.title = element_text(hjust = 0, face = "bold", family = font, size = 45, margin = margin(1, 0, 20, 0)),

eoppe1022

library(tidyverse) #install.packages("tidyverse")
library(rvest) #install.packages("rvest")
library(RSelenium) #install.packages("RSelenium")
library(progress) #install.packages("progress")

## 2 functions: get_schedule() and get_box_score()
## Some things are a bit iffy but it should work

## Gets schedule. You need this
## Example: get_schedule("WHL", "2018-19")

eoppe1022

## Let goal_data be a data frame with (at least) columns "goal", "primary_assist", "secondary_assist", and "team"

library(tidyverse)
library(ggfittext)
library(ggalluvial)
library(extrafont)

## The theme for the chart
theme_awesome <- function(font) {
  

eoppe1022

library(tidyverse)
library(extrafont)
library(ggalluvial)

## Custom Font
theme_awesome <- function(font) {
  
  theme_minimal(base_family = font) +
    theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 50, margin = margin(1, 0, 20, 0)),

eoppe1022

## calculates betweenness (with igraph::betweenness()) for each player on each team for each season and each league in only the games that each player played in

## mydata$game_info is a data frame, where each row is a different game
## the columns are my_season (different seasons), my_league (NHL or CHL or whatever), my_team (NYR or whatever), home_team, away_team, ...
## ... home_skater_names (every skater on the home team separated by a "|"), away_skater_names, ...
## ... and goals (a nested data frame that has a column for team, season, league, game strength, goal scorer, primary assister, and secondary assister
## i also have a data frame mydata$goals that has columns for team, season, league, game strength, goal scorer, primary assister, and secondary assister

library(tidyverse)
library(igraph)

eoppe1022

song_data %>% 
  unnest() %>% 
  unnest_tokens(word, lyric) %>%
  anti_join(stop_words, by = "word") %>%
  left_join(angry_words, by = "word") %>%
  group_by(track_name, energy, album_name, duration_ms, valence) %>% 
  summarize(angry_words = sum(anger, na.rm = TRUE)) %>%
  ungroup() %>%
  select(track_name, album_name, angry_words) %>% 
  arrange(desc(angry_words))

eoppe1022

library(scales)

song_data %>% 
  unnest() %>% 
  unnest_tokens(word, lyric) %>%
  anti_join(stop_words, by = "word") %>%
  left_join(angry_words, by = "word") %>%
  group_by(track_name, energy, album_name, duration_ms, valence) %>% 
  summarize(percent_angry = sum(anger, na.rm = TRUE) / n(), word_count = n()) %>% 
  ungroup() %>%

eoppe1022

library(scales)

song_data %>% 
  unnest() %>% 
  unnest_tokens(word, lyric) %>%
  anti_join(stop_words, by = "word") %>%
  left_join(angry_words, by = "word") %>%
  group_by(track_name, energy, album_name, duration_ms, valence) %>% 
  summarize(percent_angry = sum(anger, na.rm = TRUE) / n(), word_count = n()) %>% 
  ungroup() %>%

eoppe1022

song_data %>% 
  unnest() %>% 
  unnest_tokens(word, lyric) %>%
  anti_join(stop_words, by = "word") %>%
  left_join(angry_words, by = "word") %>%
  group_by(track_name, energy, album_name, duration_ms, valence) %>% 
  summarize(percent_angry = sum(anger, na.rm = TRUE) / n(), word_count = n()) %>% 
  ungroup() %>%
  select(track_name, album_name, percent_angry) %>%
  arrange(desc(percent_angry))

eoppe1022

song_data %>% 
  unnest() %>% 
  unnest_tokens(word, lyric) %>%
  anti_join(stop_words, by = "word")