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112 lines
3.5 KiB
R
112 lines
3.5 KiB
R
library(foreach)
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library(iterators)
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library(ggplot2)
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library(dplyr)
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#------
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messages <- read.csv2("transcript/minecraft_data-1659808309634.csv", sep = ",")
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messages <- messages %>% mutate(
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start_time = as.POSIXct(strptime(start_time, "%F %T")),
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end_time = as.POSIXct(strptime(end_time, "%F %T"))
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)
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n_m <- nrow(messages)
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#--------
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ggplot(messages, aes(x=start_time)) + theme_minimal() + geom_histogram(binwidth = 3600)
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ggplot(messages %>% transmute(tdiff = difftime(start_time, lag(start_time), units = "secs")), aes(x=tdiff)) + theme_minimal() +
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stat_ecdf() +
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stat_ecdf(data=data.frame(tdiff=rlomax(n_m*10, 1/(b_p + len),
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(a_p + n_m))), color="red")
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ggplot(messages %>% transmute(tdiff = as.numeric(difftime(start_time, lag(start_time)), units = "secs")), aes(x=tdiff)) + theme_minimal() +
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geom_histogram(binwidth = 10) +
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geom_vline(xintercept = qdiff)
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ggplot(messages, aes(x=start_time)) + theme_minimal() + geom_vline(aes(xintercept=start_time)) + geom_histogram(binwidth = 3600)
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ggplot(messages %>% mutate(tdiff = difftime(start_time, lag(start_time), units = "secs")), aes(x=start_time, y=tdiff)) + theme_minimal() +
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geom_point() +
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geom_hline(yintercept = qdiff)
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#-------
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# Print messages
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for(i in 1:nrow(messages)) {
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line = messages[i,]
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cat(sprintf("[%s] <%s> %s\n", line$start_time, line$names, line$transcription_line))
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if(difftime(messages[i+1,"start_time"], line$start_time, units = "secs") > qdiff + 20)
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cat("----------------------\n")
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}
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# Show where messages were selected
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ggplot(messages, aes(x=pub_time)) + theme_minimal() +
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geom_histogram(binwidth = 600) +
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geom_histogram(data=fmess, binwidth = 600, fill="green", alpha=0.7)
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#-------------
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# Bayesian
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# p(d < 3/60) = 0.01
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# d ~ exp(l)
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# l ~ gamma(a_p, b_p)
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# l = argmax_l dgamma(a_p, 60)
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a_p <- 13
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b_p <- 60
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# Total length of messages
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# len <- as.numeric(difftime(messages[n_m,]$pub_time, messages[1,]$pub_time, units = "secs"))
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len <- 697666.923
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# Poisson lambda for messages per second
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library(extraDistr)
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gen_diffs <- rlomax(
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n_m,
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1/(b_p + len),
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(a_p + n_m)
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)
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ggplot() + geom_histogram(mapping = aes(x=cumsum(gen_diffs)), binwidth = 3600)
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# Plot with overlaid posterior
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ggplot(messages, aes(x=start_time)) + theme_minimal() +
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geom_histogram(binwidth = 600) +
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geom_histogram(mapping = aes(x=cumsum(rexp(n_m, lambda)) + messages[1,"start_time"]), binwidth = 600, fill="red", alpha=0.3)
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#---------------
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# Diff with 0.01 quantile
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qdiff <- qlomax(0.02, 1/(b_p + len), (a_p + n_m))
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fmess <- messages %>% filter(
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as.numeric(difftime(start_time, lag(start_time), units = "secs")) < qdiff |
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as.numeric(difftime(lead(start_time), start_time, units = "secs")) < qdiff
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)
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#----------------
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ggplot(messages %>% mutate(len = as.double(difftime(end_time, start_time, unit="secs"))), aes(x=len)) + geom_histogram(binwidth = 0.5)
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ggplot(messages[1:50,], aes(xmin=start_time, xmax=end_time, ymin=-1, ymax = 1)) + theme_minimal() + geom_rect()
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#---------
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last <- 1
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for (i in 2:nrow(messages)) {
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if (messages[i,]$start_time == messages[last,]$end_time) {
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messages[last,]$transcription_line <- paste(messages[last,]$transcription_line, messages[i,]$transcription_line)
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messages[last,]$end_time <- messages[i,]$end_time
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messages[i,]$transcription_line <- NA
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} else {
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last <- i
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}
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}
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messages <- messages %>% filter(!is.na(transcription_line))
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messages %>% transmute(touch = start_time == lag(end_time))
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