projects.mako.cc - gmail-maildir-counter/blob - analysis.R

   1 library(data.table)
   2 library(ggplot2)
   3 library(reshape)
   4
   5 theme_set(theme_bw())
   6
   7 d <- read.delim("mail_metadata.tsv", header=FALSE,
   8     col.names=c("flags", "timestamp", "precedence", "google"))
   9
  10 d$timestamp <- as.POSIXct(d$timestamp, tz="UTC",
  11                           origin=as.POSIXct("1970-01-01 00:00:00"))
  12
  13 # limit the dataset to emails sent post timestamp
  14 d <- d[d$timestamp > as.POSIXct("2004-04-01 00:00:00"),]
  15
  16 d$week <- cut(d$timestamp, breaks="weeks")
  17
  18 # list and then drop list mail
  19 table(d$precedence)
  20 d <- d[is.na(d$precedence),]
  21 d$precedence <- NULL
  22
  23 d$replied <- grepl('R', d$flags)
  24
  25 google.by.week <- function (d) {
  26     setDT(d)
  27
  28     weeks <- d[,list(total=length(google), google=table(google)["TRUE"]), by=week]
  29
  30     # drop things
  31     weeks <- weeks[weeks$total > 1,]
  32     weeks$google.prop <- weeks$google / weeks$total
  33
  34     weeks$week <- as.Date(as.character(weeks$week))
  35
  36     return(weeks)
  37 }
  38
  39 # find proportions per year
  40 replied <- google.by.week(d[d$replied,])
  41 replied <- replied[complete.cases(replied),]
  42
  43 replied.tbl <- as.data.frame(
  44     tapply(replied$google, substr(as.character(replied$week), 1, 4), sum) /
  45     tapply(replied$total, substr(as.character(replied$week), 1, 4), sum))
  46
  47 colnames(replied.tbl) <- "prop.google"
  48 replied.tbl$year <- row.names(replied.tbl)
  49 row.names(replied.tbl) <- NULL
  50
  51 ggplot(data=replied.tbl) + aes(x=year, y=prop.google) +
  52     geom_bar(stat="identity")
  53
  54 replied.tbl
  55
  56 # Graph #1: Emails from Google Over Time
  57 #######################################################
  58
  59 raw.data <- google.by.week(d)
  60 raw.data$google.prop <- NULL
  61
  62 raw.data <- melt(raw.data, id.var="week")
  63
  64
  65 pdf(file="emails_gmail_over_time.pdf", width=10, height=6)
  66
  67 ggplot(data=raw.data) + aes(x=week, y=value, color=variable, group=variable) +
  68     geom_point() +
  69     stat_smooth(method="loess", show_guide=FALSE) +
  70     scale_color_discrete("", breaks=c("total", "google"),
  71                          labels=c("All Emails", "From Google")) +
  72     scale_x_date("Date") +
  73     scale_y_continuous("Number of Emails")
  74
  75 dev.off()
  76
  77 # Graph #2: Proportions of Email from Google
  78 #######################################################
  79
  80 prop.data <- rbind(cbind(google.by.week(d), subset="All Email"),
  81                    cbind(google.by.week(d[d$replied]), subset="Email with Replies"))
  82
  83
  84 pdf(file="emails_gmail_prop_over_time.pdf", width=10, height=8)
  85
  86 ggplot(data=prop.data) + aes(x=week, y=google.prop, size=total, group=subset) +
  87     geom_point() + facet_grid(subset~.) +
  88     scale_y_continuous("Proportion from Google", limits=c(0,1)) +
  89     scale_x_date("Date") +
  90     scale_size("Emails") +
  91     stat_smooth(method="loess", show_guide=FALSE)
  92
  93 dev.off()