All spec scaling

library(reshape2)
library(ggplot2)
library(dplyr)
library(broom)
data <- read.csv("csv/all.csv")

Filtering and plotting

Then we define a function that can be used to filter the dataset to a particular encounter and difficulty. It also performs outlier filtering - for each 5-ilvl bucket, for each spec, we compute the 95th percentile of DPS, then remove any parses from the dataset for that ilvl/spec above that cutoff.

d <- data
m <- d %>% group_by(class, spec, encounter) %>% do(tidy(lm(dps ~ ilvl, data=.)))

slopes <- m[m$term == "ilvl",]

d <- data
m <- d %>% group_by(class, spec) %>% do(tidy(lm(dps ~ ilvl, data=.)))
all_slopes <- m[m$term == "ilvl",]

scale_encounter <- function(s, title) {
  s$class = with(s, factor(class, levels = rev(levels(class))))
  s$spec = with(s, factor(spec, levels = rev(levels(spec))))
  ggplot(data=s, aes(x=paste(class, spec), y=estimate, group=interaction(class, spec), reorder=estimate, fill=paste(class, spec))) +
    geom_bar(position=position_dodge(width = 0.9), stat="identity", colour="black") +
    labs(y="DPS gain/ilvl", x="Encounter", title=paste("DPS scaling per ilvl:", title)) +
    coord_flip() +
    guides(fill=FALSE)
}
scale_encounter(slopes[slopes$encounter == "Nythendra",], title="Nythendra")

scale_encounter(slopes[slopes$encounter == "Ursoc",], title="Ursoc")

scale_encounter(slopes[slopes$encounter == "Dragons_of_Nightmare",], title="Dragons_of_Nightmare")

scale_encounter(slopes[slopes$encounter == "Elerethe_Renferal",], title="Elerethe_Renferal")

scale_encounter(slopes[slopes$encounter == "Il_gynoth__Heart_of_Corruption",], title="Il_gynoth__Heart_of_Corruption")

scale_encounter(slopes[slopes$encounter == "Cenarius",], title="Cenarius")

scale_encounter(slopes[slopes$encounter == "Xavius",], title="Xavius")

scale_encounter(all_slopes, title="Overall")