Plotting UFC Data Using Highcharter

LOADING IN THE NECESSARY PACKAGES

#Check is the packages are already installed and load the library
pkgTest <- function(...) {
  pkg <- list(...)
  for (i in 1:length(pkg)) {
    if (!require(pkg[[i]], character.only = TRUE)) {
      install.packages(pkg[[i]], dep = TRUE)
      if(!require(pkg[[i]], character.only = TRUE)) stop("Package not found")
    } 
  }
}

pkgTest("purrr")
pkgTest("highcharter")
pkgTest("dplyr")
pkgTest("stringr")

PLOTTING FINISHING METHODS
This plot provides the ability to drill-down into each of the methods by clicking on the bar

#================================================================================================#
#Merge data to get weightclasses
#================================================================================================#
career_fighter_details = merge(fighter_details_data2, data_clean2[, c("NAME", "WeightClass")],
                               by.x = "fighter_one", by.y = "NAME", all.x = TRUE)

career_fighter_details$Round = as.numeric(career_fighter_details$Round)


career_fighter_details = career_fighter_details[, -c(11, 13:21)]

#================================================================================================#
#Finding the general method for how fights ended
#================================================================================================#
# unique(career_fighter_details$Method)

pattern = paste(c("DEC", "KO/TKO", "SUB", "Overturned", "DQ", "Decision", 'CNC'), collapse = '|')


career_fighter_details$general_method = str_extract(as.character(career_fighter_details$Method), pattern)


overall_stats = as.data.frame(round(prop.table(
                  table(career_fighter_details$general_method))*100,2))

#Finding the percentages of each
specific_stats = as.data.frame(round(prop.table(table(career_fighter_details$general_method,
                                                      career_fighter_details$Method))*100,2))


#Trim whitespace from multiple columns
specific_stats[, 1:2] = lapply(specific_stats[, 1:2], function(x) trimws(x))
specific_stats[, 1:2] = lapply(specific_stats[, 1:2], function(x) str_remove_all(x, "\n"))
specific_stats[, 1:2] = lapply(specific_stats[, 1:2], function(x) str_remove_all(x, " "))
 
 
#Re-aggregating the data
specific_stats = as.data.frame(specific_stats %>%
                  group_by(Var1, Var2) %>%
                  summarise_all(sum))



df <- data_frame(
  name = overall_stats[overall_stats$Var1 %in% c("DEC", "KO/TKO", "SUB", "DQ", "CNC", "Overturned"), 1],
  y = overall_stats[overall_stats$Var1 %in% c("DEC", "KO/TKO", "SUB", "DQ", "CNC", "Overturned"), 2],
  drilldown = tolower(name)
)


hc <- highchart() %>%
  hc_chart(type = "column") %>%
  hc_title(text = "Finishing Methods") %>%
  hc_xAxis(type = "category") %>%
  hc_legend(enabled = TRUE) %>%
  hc_plotOptions(
    series = list(
      boderWidth = 0,
      dataLabels = list(enabled = TRUE)
    )
  ) %>%
  hc_add_series(
    data = df,
    name = "General Method",
    colorByPoint = TRUE
  )


#================================================================================================#
#CREATE THE DATAFRAMES THAT ARE GOING TO BE DRILLED DOWN TO
#================================================================================================#
#Finding the subset of data relating to submissions
sub <- data_frame(
  method = specific_stats[specific_stats$Var1 == "SUB" & 
                            str_detect(specific_stats$Var2, "SUB"), 2],
  value = specific_stats[specific_stats$Var1 == "SUB" & 
                            str_detect(specific_stats$Var2, "SUB"), 3]
)

#Finding the subset of data relating to kos/tkos
ko <- data_frame(
  method = specific_stats[specific_stats$Var1 == "KO/TKO" & 
                            str_detect(specific_stats$Var2, "KO/TKO"), 2],
  value = specific_stats[specific_stats$Var1 == "KO/TKO" & 
                            str_detect(specific_stats$Var2, "KO/TKO"), 3]
)

#Finding the subset of data relating to decisions
dec <- data_frame(
  method = specific_stats[specific_stats$Var1 == "DEC" & 
                            str_detect(specific_stats$Var2, "DEC"), 2],
  value = specific_stats[specific_stats$Var1 == "DEC" & 
                            str_detect(specific_stats$Var2, "DEC"), 3]
)

#Finding the subset of data relating to dqs
dq <- data_frame(
  method = specific_stats[specific_stats$Var1 == "DQ" & 
                            str_detect(specific_stats$Var2, "DQ"), 2],
  value = specific_stats[specific_stats$Var1 == "DQ" & 
                            str_detect(specific_stats$Var2, "DQ"), 3]
)

#Finding the subset of data relating to overturned
overturned <- data_frame(
  method = specific_stats[specific_stats$Var1 == "Overturned" & 
                            str_detect(specific_stats$Var2, "Overturned"), 2],
  value = specific_stats[specific_stats$Var1 == "Overturned" & 
                            str_detect(specific_stats$Var2, "Overturned"), 3]
)

#Finding the subset of data relating to cnc
cnc <- data_frame(
  method = specific_stats[specific_stats$Var1 == "CNC" & 
                            str_detect(specific_stats$Var2, "CNC"), 2],
  value = specific_stats[specific_stats$Var1 == "CNC" & 
                            str_detect(specific_stats$Var2, "CNC"), 3]
)

#================================================================================================#
#SPECIFY WHERE IN THE INITIAL PLOT THE SUBSETS SHOULD BE REFERRED TO
#================================================================================================#

hc <- hc %>%
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series = list(
      list(
        id = "sub",
        data = list_parse2(sub)
      ),
      list(
        id = "ko/tko",
        data = list_parse2(ko)
      ),
      list(
        id = "dec",
        data = list_parse2(dec)
      ),
      list(
        id = "dq",
        data = list_parse2(dq)
      ),
      list(
        id = "overturned",
        data = list_parse2(overturned)
      ),
      list(
        id = "cnc",
        data = list_parse2(cnc)
      )
    )
  )

hc

# rmarkdown::render(paste0(
#       "C:/Users/rjobrien/Desktop/Personal Projects/Personal Website/",
#       "2020-03-24-Interactive Highchart.Rmd"
#     ))