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# The following script was written by Benjamin Lind (@benjaminlind)
# It implements methods, described in the paper:
# Neal, Zachary. "The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors." Social Networks 39 (2014): 84-97.
# https://www.sciencedirect.com/science/article/abs/pii/S0378873314000343
# 
# The Marvel Universe Social Network
# Dataset created by Cesc Rosselló, Ricardo Alberich, and Joe Miro
# The network data is bipartite. There are characters and the books they appear within.
# Information on the data: http://bioinfo.uib.es/~joemiro/marvel.html
# Primary source: http://www.chronologyproject.com/

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library(rvest)
library(tidyverse)
library(lubridate)
library(scales)
library(Cairo)
library(lubridate)

url <- "https://liquipedia.net/dota2/Team_Secret"
page <- read_html(url)

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library(rvest)
library(tidyverse)
library(googlesheets)

# Getting results table from fantasy prizetrac
overview <- read_html("http://fantasy.prizetrac.kr/views/international2018/overview.php")
fpoints <- html_table(overview)[[1]]

# Team names for cleaning players' names
teams <- c("FnaticFnatic", "VGJ.StormVGJS", "OGOG", "Evil GeniusesEG",

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#' @title Assocation Rules Visualization Shiny App
#' @description Launches a Shiny App that provides an interactive interface to the visualizations of the \code{arulesViz} package.
#' The app allows users to mine rules based on all or just subsets of features, sort by criteria (lift, support, confidence) and visualize
#' using network graph, grouped bubble and scatter plots. \cr
#' Users filter rules to target only those with a certain variable on the RHS or LHS of the rule.
#' Rule mining is computed using the \link{apriori} algorithm from \code{arules}.
#' 
#' @param dataset data.frame, this is the dataset that association rules will be mined from.  Each row is treated as a transaction.  Seems to work 
#' OK when a the S4 transactions class from \code{arules} is used, however this is not thoroughly tested.
#' @param bin logical, \code{TRUE} will automatically discretize/bin numerical data into categorical features that can be used for association analysis.

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Teams

Team	Avarege	Shortest	Longest	Total	Radiant	Dire
Immortals	35:37	27:20	50:46	6 / 11 (54.5%)	3 / 5 (60.0%)	3 / 6 (50.0%)
Mineski.YDB	38:42	25:15	74:16	8 / 10 (80.0%)	3 / 3 (100.0%)	5 / 7 (71.4%)
Evil Geniuses	42:51	21:58	82:16	4 / 9 (44.4%)	3 / 5 (60.0%)	1 / 4 (25.0%)
LGD-GAMING	33:54	28:55	38:15	6 / 9 (66.7%)	3 / 5 (60.0%)	3 / 4 (75.0%)
VGJ Thunder	43:06	21:58	82:16	3 / 8 (37.5%)	1 / 3 (33.3%)	2 / 5 (40.0%)
Team Secret	41:45	29:58	54:39	3 / 7 (42.9%)	3 / 5 (60.0%)	0 / 2 (0.0%)

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library(tidyverse)
library(jsonlite)
library(lubridate)
library(ggridges)
opendota_query <- function(sqlQuery) {
    requestUrl = paste("https://api.opendota.com/api/explorer?sql=",
                       URLencode(sqlQuery), sep="")
    result <- fromJSON(requestUrl)
    return(result$rows)
}

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library(jsonlite)
library(data.table)

opendota.query <- function(sqlQuery) {
  requestUrl = paste("https://api.opendota.com/api/explorer?sql=", URLencode(sqlQuery), sep="")
  result <- fromJSON(requestUrl)
  return(result$rows)
}

cis_mid_agg <- '

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q_miders <- "
SELECT 
pm2.leagueid,
    pm2.start_time,
    pm2.match_id,
    pm2.account_id,
    pm2.is_radiant,
    pm2.win,
    pm2.player_slot,
    pm2.hero_id,