request_streaming_samples.r

library(tidyverse)

# sample, treatment, chunk size, timestamp
chunks <- read.csv("results.csv")

sorted_and_summed <- chunks %>%
    group_by(sample) %>%
    arrange(timestamp, .by_group = T) %>%
    mutate(
        bytes_received = cumsum(bytes),
        progress = bytes_received / max(bytes_received)
    ) %>%
    ungroup()

sample_summary <- sorted_and_summed %>%
    group_by(treatment, sample) %>%
    summarize(
        time_moment100 = max(timestamp),
        time_moment75 = min(timestamp[progress > 0.75]),
        time_moment50 = min(timestamp[progress > 0.5]),
    ) %>%
    ungroup() %>%
    group_by(treatment) %>%
    mutate(
        # map time_moment to its density
        dens100 = sapply(time_moment100, approxfun(density(time_moment100))),
        dens75 = sapply(time_moment75, approxfun(density(time_moment75))),
        dens50 = sapply(time_moment50, approxfun(density(time_moment50))),
        # normalize
        normalized_density = dens100 * dens75 * dens50,
        normalized_density = normalized_density / max(normalized_density)
    )

joined <- sorted_and_summed %>%
    left_join(sample_summary) %>%
    mutate(
        # when lines are painted less dense will paint first
        sample = factor(sample),
        sample = fct_reorder(sample, normalized_density)
    )

ggplot(joined, aes(
    x = timestamp,
    y = bytes_received,
    group = sample,
    alpha = normalized_density,
    color = normalized_density,
    size = normalized_density
)) +
    geom_step() +
    scale_color_continuous(type = "viridis") +
    scale_alpha(range = c(0, 1), guide = F) +
    scale_size(range = c(5, 2), guide = F) +
    facet_grid(rows = vars(treatment)) +
    theme(strip.text.y = element_text(angle = 0)) +
    labs(
        title = "Expirement Title",
        subtitle = "Content Download",
        color = "Normalized\nDensity",
        x = "Milliseconds Elapsed",
        y = "Bytes Received"
    )