commit e82de493279a0e74b55e5fd66a4056a1cecf19c5 Author: Karsten Loesing karsten.loesing@gmx.net Date: Fri Jan 11 11:39:12 2019 +0100
Simplify Rserve setup. --- src/main/R/rserver/Rserv.conf | 2 - src/main/R/rserver/graphs.R | 1539 ------------------------------------ src/main/R/rserver/rserve-init.R | 1609 +++++++++++++++++++++++++++++++++++++- src/main/R/rserver/tables.R | 58 -- 4 files changed, 1600 insertions(+), 1608 deletions(-)
diff --git a/src/main/R/rserver/Rserv.conf b/src/main/R/rserver/Rserv.conf deleted file mode 100644 index 1fb3039..0000000 --- a/src/main/R/rserver/Rserv.conf +++ /dev/null @@ -1,2 +0,0 @@ -workdir /srv/metrics.torproject.org/metrics/website/rserve/workdir -source rserve-init.R diff --git a/src/main/R/rserver/graphs.R b/src/main/R/rserver/graphs.R deleted file mode 100644 index 0d7a90c..0000000 --- a/src/main/R/rserver/graphs.R +++ /dev/null @@ -1,1539 +0,0 @@ -countrylist <- list( - "ad" = "Andorra", - "ae" = "the United Arab Emirates", - "af" = "Afghanistan", - "ag" = "Antigua and Barbuda", - "ai" = "Anguilla", - "al" = "Albania", - "am" = "Armenia", - "an" = "the Netherlands Antilles", - "ao" = "Angola", - "aq" = "Antarctica", - "ar" = "Argentina", - "as" = "American Samoa", - "at" = "Austria", - "au" = "Australia", - "aw" = "Aruba", - "ax" = "the Aland Islands", - "az" = "Azerbaijan", - "ba" = "Bosnia and Herzegovina", - "bb" = "Barbados", - "bd" = "Bangladesh", - "be" = "Belgium", - "bf" = "Burkina Faso", - "bg" = "Bulgaria", - "bh" = "Bahrain", - "bi" = "Burundi", - "bj" = "Benin", - "bl" = "Saint Bartelemey", - "bm" = "Bermuda", - "bn" = "Brunei", - "bo" = "Bolivia", - "bq" = "Bonaire, Sint Eustatius and Saba", - "br" = "Brazil", - "bs" = "the Bahamas", - "bt" = "Bhutan", - "bv" = "the Bouvet Island", - "bw" = "Botswana", - "by" = "Belarus", - "bz" = "Belize", - "ca" = "Canada", - "cc" = "the Cocos (Keeling) Islands", - "cd" = "the Democratic Republic of the Congo", - "cf" = "Central African Republic", - "cg" = "Congo", - "ch" = "Switzerland", - "ci" = "Côte d'Ivoire", - "ck" = "the Cook Islands", - "cl" = "Chile", - "cm" = "Cameroon", - "cn" = "China", - "co" = "Colombia", - "cr" = "Costa Rica", - "cu" = "Cuba", - "cv" = "Cape Verde", - "cw" = "Curaçao", - "cx" = "the Christmas Island", - "cy" = "Cyprus", - "cz" = "the Czech Republic", - "de" = "Germany", - "dj" = "Djibouti", - "dk" = "Denmark", - "dm" = "Dominica", - "do" = "the Dominican Republic", - "dz" = "Algeria", - "ec" = "Ecuador", - "ee" = "Estonia", - "eg" = "Egypt", - "eh" = "the Western Sahara", - "er" = "Eritrea", - "es" = "Spain", - "et" = "Ethiopia", - "fi" = "Finland", - "fj" = "Fiji", - "fk" = "the Falkland Islands (Malvinas)", - "fm" = "the Federated States of Micronesia", - "fo" = "the Faroe Islands", - "fr" = "France", - "ga" = "Gabon", - "gb" = "the United Kingdom", - "gd" = "Grenada", - "ge" = "Georgia", - "gf" = "French Guiana", - "gg" = "Guernsey", - "gh" = "Ghana", - "gi" = "Gibraltar", - "gl" = "Greenland", - "gm" = "Gambia", - "gn" = "Guinea", - "gp" = "Guadeloupe", - "gq" = "Equatorial Guinea", - "gr" = "Greece", - "gs" = "South Georgia and the South Sandwich Islands", - "gt" = "Guatemala", - "gu" = "Guam", - "gw" = "Guinea-Bissau", - "gy" = "Guyana", - "hk" = "Hong Kong", - "hm" = "Heard Island and McDonald Islands", - "hn" = "Honduras", - "hr" = "Croatia", - "ht" = "Haiti", - "hu" = "Hungary", - "id" = "Indonesia", - "ie" = "Ireland", - "il" = "Israel", - "im" = "the Isle of Man", - "in" = "India", - "io" = "the British Indian Ocean Territory", - "iq" = "Iraq", - "ir" = "Iran", - "is" = "Iceland", - "it" = "Italy", - "je" = "Jersey", - "jm" = "Jamaica", - "jo" = "Jordan", - "jp" = "Japan", - "ke" = "Kenya", - "kg" = "Kyrgyzstan", - "kh" = "Cambodia", - "ki" = "Kiribati", - "km" = "Comoros", - "kn" = "Saint Kitts and Nevis", - "kp" = "North Korea", - "kr" = "the Republic of Korea", - "kw" = "Kuwait", - "ky" = "the Cayman Islands", - "kz" = "Kazakhstan", - "la" = "Laos", - "lb" = "Lebanon", - "lc" = "Saint Lucia", - "li" = "Liechtenstein", - "lk" = "Sri Lanka", - "lr" = "Liberia", - "ls" = "Lesotho", - "lt" = "Lithuania", - "lu" = "Luxembourg", - "lv" = "Latvia", - "ly" = "Libya", - "ma" = "Morocco", - "mc" = "Monaco", - "md" = "the Republic of Moldova", - "me" = "Montenegro", - "mf" = "Saint Martin", - "mg" = "Madagascar", - "mh" = "the Marshall Islands", - "mk" = "Macedonia", - "ml" = "Mali", - "mm" = "Burma", - "mn" = "Mongolia", - "mo" = "Macau", - "mp" = "the Northern Mariana Islands", - "mq" = "Martinique", - "mr" = "Mauritania", - "ms" = "Montserrat", - "mt" = "Malta", - "mu" = "Mauritius", - "mv" = "the Maldives", - "mw" = "Malawi", - "mx" = "Mexico", - "my" = "Malaysia", - "mz" = "Mozambique", - "na" = "Namibia", - "nc" = "New Caledonia", - "ne" = "Niger", - "nf" = "Norfolk Island", - "ng" = "Nigeria", - "ni" = "Nicaragua", - "nl" = "the Netherlands", - "no" = "Norway", - "np" = "Nepal", - "nr" = "Nauru", - "nu" = "Niue", - "nz" = "New Zealand", - "om" = "Oman", - "pa" = "Panama", - "pe" = "Peru", - "pf" = "French Polynesia", - "pg" = "Papua New Guinea", - "ph" = "the Philippines", - "pk" = "Pakistan", - "pl" = "Poland", - "pm" = "Saint Pierre and Miquelon", - "pn" = "the Pitcairn Islands", - "pr" = "Puerto Rico", - "ps" = "the Palestinian Territory", - "pt" = "Portugal", - "pw" = "Palau", - "py" = "Paraguay", - "qa" = "Qatar", - "re" = "Reunion", - "ro" = "Romania", - "rs" = "Serbia", - "ru" = "Russia", - "rw" = "Rwanda", - "sa" = "Saudi Arabia", - "sb" = "the Solomon Islands", - "sc" = "the Seychelles", - "sd" = "Sudan", - "se" = "Sweden", - "sg" = "Singapore", - "sh" = "Saint Helena", - "si" = "Slovenia", - "sj" = "Svalbard and Jan Mayen", - "sk" = "Slovakia", - "sl" = "Sierra Leone", - "sm" = "San Marino", - "sn" = "Senegal", - "so" = "Somalia", - "sr" = "Suriname", - "ss" = "South Sudan", - "st" = "São Tomé and Príncipe", - "sv" = "El Salvador", - "sx" = "Sint Maarten", - "sy" = "the Syrian Arab Republic", - "sz" = "Swaziland", - "tc" = "Turks and Caicos Islands", - "td" = "Chad", - "tf" = "the French Southern Territories", - "tg" = "Togo", - "th" = "Thailand", - "tj" = "Tajikistan", - "tk" = "Tokelau", - "tl" = "East Timor", - "tm" = "Turkmenistan", - "tn" = "Tunisia", - "to" = "Tonga", - "tr" = "Turkey", - "tt" = "Trinidad and Tobago", - "tv" = "Tuvalu", - "tw" = "Taiwan", - "tz" = "the United Republic of Tanzania", - "ua" = "Ukraine", - "ug" = "Uganda", - "um" = "the United States Minor Outlying Islands", - "us" = "the United States", - "uy" = "Uruguay", - "uz" = "Uzbekistan", - "va" = "Vatican City", - "vc" = "Saint Vincent and the Grenadines", - "ve" = "Venezuela", - "vg" = "the British Virgin Islands", - "vi" = "the United States Virgin Islands", - "vn" = "Vietnam", - "vu" = "Vanuatu", - "wf" = "Wallis and Futuna", - "ws" = "Samoa", - "xk" = "Kosovo", - "ye" = "Yemen", - "yt" = "Mayotte", - "za" = "South Africa", - "zm" = "Zambia", - "zw" = "Zimbabwe") - -countryname <- function(country) { - res <- countrylist[[country]] - if (is.null(res)) - res <- "no-man's-land" - res -} - -# Helper function that takes date limits as input and returns major breaks as -# output. The main difference to the built-in major breaks is that we're trying -# harder to align major breaks with first days of weeks (Sundays), months, -# quarters, or years. -custom_breaks <- function(input) { - scales_index <- cut(as.numeric(max(input) - min(input)), - c(-1, 7, 12, 56, 180, 600, 2000, Inf), labels = FALSE) - from_print_format <- c("%F", "%F", "%Y-W%U-7", "%Y-%m-01", "%Y-01-01", - "%Y-01-01", "%Y-01-01")[scales_index] - from_parse_format <- ifelse(scales_index == 3, "%Y-W%U-%u", "%F") - by <- c("1 day", "2 days", "1 week", "1 month", "3 months", "1 year", - "2 years")[scales_index] - seq(as.Date(as.character(min(input), from_print_format), - format = from_parse_format), max(input), by = by) -} - -# Helper function that takes date limits as input and returns minor breaks as -# output. As opposed to the built-in minor breaks, we're not just adding one -# minor break half way through between two major breaks. Instead, we're plotting -# a minor break for every day, week, month, or quarter between two major breaks. -custom_minor_breaks <- function(input) { - scales_index <- cut(as.numeric(max(input) - min(input)), - c(-1, 7, 12, 56, 180, 600, 2000, Inf), labels = FALSE) - from_print_format <- c("%F", "%F", "%F", "%Y-W%U-7", "%Y-%m-01", "%Y-01-01", - "%Y-01-01")[scales_index] - from_parse_format <- ifelse(scales_index == 4, "%Y-W%U-%u", "%F") - by <- c("1 day", "1 day", "1 day", "1 week", "1 month", "3 months", - "1 year")[scales_index] - seq(as.Date(as.character(min(input), from_print_format), - format = from_parse_format), max(input), by = by) -} - -# Helper function that takes breaks as input and returns labels as output. We're -# going all ISO-8601 here, though we're not just writing %Y-%m-%d everywhere, -# but %Y-%m or %Y if all breaks are on the first of a month or even year. -custom_labels <- function(breaks) { - if (all(format(breaks, format = "%m-%d") == "01-01", na.rm = TRUE)) { - format(breaks, format = "%Y") - } else { - if (all(format(breaks, format = "%d") == "01", na.rm = TRUE)) { - format(breaks, format = "%Y-%m") - } else { - format(breaks, format = "%F") - } - } -} - -# Helper function to format numbers in non-scientific notation with spaces as -# thousands separator. -formatter <- function(x, ...) { - format(x, ..., scientific = FALSE, big.mark = " ") -} - -theme_update( - # Make plot title centered, and leave some room to the plot. - plot.title = element_text(hjust = 0.5, margin = margin(b = 11)), - - # Leave a little more room to the right for long x axis labels. - plot.margin = margin(5.5, 11, 5.5, 5.5) -) - -# Set the default line size of geom_line() to 1. -update_geom_defaults("line", list(size = 1)) - -copyright_notice <- "The Tor Project - https://metrics.torproject.org/" - -stats_dir <- "/srv/metrics.torproject.org/metrics/shared/stats/" - -rdata_dir <- "/srv/metrics.torproject.org/metrics/shared/RData/" - -# Helper function that copies the appropriate no data object to filename. -copy_no_data <- function(filename) { - len <- nchar(filename) - extension <- substr(filename, len - 3, len) - if (".csv" == extension) { - write("# No data available for the given parameters.", file=filename) - } else { - file.copy(paste(rdata_dir, "no-data-available", extension, sep = ""), - filename) - } -} - -# Helper function wrapping calls into error handling. -robust_call <- function(wrappee, filename) { - tryCatch(eval(wrappee), error = function(e) copy_no_data(filename), - finally = if (!file.exists(filename) || file.size(filename) == 0) { - copy_no_data(filename) - }) -} - -# Write the result of the given FUN, typically a prepare_ function, as .csv file -# to the given path_p. -write_data <- function(FUN, ..., path_p) { - FUN(...) %>% - write.csv(path_p, quote = FALSE, row.names = FALSE, na = "") -} - -# Disable readr's automatic progress bar. -options(readr.show_progress = FALSE) - -prepare_networksize <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "networksize.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - relays = col_double(), - bridges = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) -} - -plot_networksize <- function(start_p, end_p, path_p) { - prepare_networksize(start_p, end_p) %>% - gather(variable, value, -date) %>% - complete(date = full_seq(date, period = 1), - variable = c("relays", "bridges")) %>% - ggplot(aes(x = date, y = value, colour = variable)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - scale_colour_hue("", breaks = c("relays", "bridges"), - labels = c("Relays", "Bridges")) + - ggtitle("Number of relays") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_versions <- function(start_p = NULL, end_p = NULL) { - read_csv(paste(stats_dir, "versions.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - version = col_character(), - relays = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) -} - -plot_versions <- function(start_p, end_p, path_p) { - s <- prepare_versions(start_p, end_p) - known_versions <- unique(s$version) - getPalette <- colorRampPalette(brewer.pal(12, "Paired")) - colours <- data.frame(breaks = known_versions, - values = rep(brewer.pal(min(12, length(known_versions)), "Paired"), - len = length(known_versions)), - stringsAsFactors = FALSE) - versions <- s[s$version %in% known_versions, ] - visible_versions <- sort(unique(versions$version)) - versions <- versions %>% - complete(date = full_seq(date, period = 1), nesting(version)) %>% - ggplot(aes(x = date, y = relays, colour = version)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - scale_colour_manual(name = "Tor version", - values = colours[colours$breaks %in% visible_versions, 2], - breaks = visible_versions) + - ggtitle("Relay versions") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_platforms <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "platforms.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - platform = col_factor(levels = NULL), - relays = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - mutate(platform = tolower(platform)) %>% - spread(platform, relays) -} - -plot_platforms <- function(start_p, end_p, path_p) { - prepare_platforms(start_p, end_p) %>% - gather(platform, relays, -date) %>% - complete(date = full_seq(date, period = 1), nesting(platform)) %>% - ggplot(aes(x = date, y = relays, colour = platform)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - scale_colour_manual(name = "Platform", - breaks = c("linux", "macos", "bsd", "windows", "other"), - labels = c("Linux", "macOS", "BSD", "Windows", "Other"), - values = c("linux" = "#56B4E9", "macos" = "#333333", "bsd" = "#E69F00", - "windows" = "#0072B2", "other" = "#009E73")) + - ggtitle("Relay platforms") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_dirbytes <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "bandwidth.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - isexit = col_logical(), - isguard = col_logical(), - bwread = col_skip(), - bwwrite = col_skip(), - dirread = col_double(), - dirwrite = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(is.na(isexit)) %>% - filter(is.na(isguard)) %>% - mutate(dirread = dirread * 8 / 1e9, - dirwrite = dirwrite * 8 / 1e9) %>% - select(date, dirread, dirwrite) -} - -plot_dirbytes <- function(start_p, end_p, path_p) { - prepare_dirbytes(start_p, end_p) %>% - gather(variable, value, -date) %>% - complete(date = full_seq(date, period = 1), nesting(variable)) %>% - ggplot(aes(x = date, y = value, colour = variable)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), - limits = c(0, NA)) + - scale_colour_hue(name = "", - breaks = c("dirwrite", "dirread"), - labels = c("Written dir bytes", "Read dir bytes")) + - ggtitle("Number of bytes spent on answering directory requests") + - labs(caption = copyright_notice) + - theme(legend.position = "top") - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_relayflags <- function(start_p = NULL, end_p = NULL, flag_p = NULL) { - read_csv(file = paste(stats_dir, "relayflags.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - flag = col_factor(levels = NULL), - relays = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(if (!is.null(flag_p)) flag %in% flag_p else TRUE) -} - -plot_relayflags <- function(start_p, end_p, flag_p, path_p) { - prepare_relayflags(start_p, end_p, flag_p) %>% - complete(date = full_seq(date, period = 1), flag = unique(flag)) %>% - ggplot(aes(x = date, y = relays, colour = flag)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - scale_colour_manual(name = "Relay flags", values = c("#E69F00", - "#56B4E9", "#009E73", "#EE6A50", "#000000", "#0072B2"), - breaks = flag_p, labels = flag_p) + - ggtitle("Number of relays with relay flags assigned") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_torperf <- function(start_p = NULL, end_p = NULL, server_p = NULL, - filesize_p = NULL) { - read_csv(file = paste(stats_dir, "torperf-1.1.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - filesize = col_double(), - source = col_character(), - server = col_character(), - q1 = col_double(), - md = col_double(), - q3 = col_double(), - timeouts = col_skip(), - failures = col_skip(), - requests = col_skip())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(if (!is.null(server_p)) server == server_p else TRUE) %>% - filter(if (!is.null(filesize_p)) - filesize == ifelse(filesize_p == "50kb", 50 * 1024, - ifelse(filesize_p == "1mb", 1024 * 1024, 5 * 1024 * 1024)) else - TRUE) %>% - transmute(date, filesize, source, server, q1 = q1 / 1e3, md = md / 1e3, - q3 = q3 / 1e3) -} - -plot_torperf <- function(start_p, end_p, server_p, filesize_p, path_p) { - prepare_torperf(start_p, end_p, server_p, filesize_p) %>% - filter(source != "") %>% - complete(date = full_seq(date, period = 1), nesting(source)) %>% - ggplot(aes(x = date, y = md, ymin = q1, ymax = q3, fill = source)) + - geom_ribbon(alpha = 0.5) + - geom_line(aes(colour = source), size = 0.75) + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = unit_format(unit = "s"), - limits = c(0, NA)) + - scale_fill_hue(name = "Source") + - scale_colour_hue(name = "Source") + - ggtitle(paste("Time to complete", - ifelse(filesize_p == "50kb", "50 KiB", - ifelse(filesize_p == "1mb", "1 MiB", "5 MiB")), - "request to", server_p, "server")) + - labs(caption = copyright_notice) + - theme(legend.position = "top") - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_torperf_failures <- function(start_p = NULL, end_p = NULL, - server_p = NULL, filesize_p = NULL) { - read_csv(file = paste(stats_dir, "torperf-1.1.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - filesize = col_double(), - source = col_character(), - server = col_character(), - q1 = col_skip(), - md = col_skip(), - q3 = col_skip(), - timeouts = col_double(), - failures = col_double(), - requests = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(if (!is.null(filesize_p)) - filesize == ifelse(filesize_p == "50kb", 50 * 1024, - ifelse(filesize_p == "1mb", 1024 * 1024, 5 * 1024 * 1024)) else - TRUE) %>% - filter(if (!is.null(server_p)) server == server_p else TRUE) %>% - filter(requests > 0) %>% - transmute(date, filesize, source, server, timeouts = timeouts / requests, - failures = failures / requests) -} - -plot_torperf_failures <- function(start_p, end_p, server_p, filesize_p, - path_p) { - prepare_torperf_failures(start_p, end_p, server_p, filesize_p) %>% - filter(source != "") %>% - gather(variable, value, -c(date, filesize, source, server)) %>% - mutate(variable = factor(variable, levels = c("timeouts", "failures"), - labels = c("Timeouts", "Failures"))) %>% - ggplot(aes(x = date, y = value, colour = source)) + - geom_point(size = 2, alpha = 0.5) + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = percent, limits = c(0, NA)) + - scale_colour_hue(name = "Source") + - facet_grid(variable ~ .) + - ggtitle(paste("Timeouts and failures of", - ifelse(filesize_p == "50kb", "50 KiB", - ifelse(filesize_p == "1mb", "1 MiB", "5 MiB")), - "requests to", server_p, "server")) + - labs(caption = copyright_notice) + - theme(legend.position = "top") - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_onionperf_buildtimes <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "buildtimes.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - source = col_character(), - position = col_double(), - q1 = col_double(), - md = col_double(), - q3 = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) -} - -plot_onionperf_buildtimes <- function(start_p, end_p, path_p) { - prepare_onionperf_buildtimes(start_p, end_p) %>% - filter(source != "") %>% - mutate(date = as.Date(date), - position = factor(position, levels = seq(1, 3, 1), - labels = c("1st hop", "2nd hop", "3rd hop"))) %>% - complete(date = full_seq(date, period = 1), nesting(source, position)) %>% - ggplot(aes(x = date, y = md, ymin = q1, ymax = q3, fill = source)) + - geom_ribbon(alpha = 0.5) + - geom_line(aes(colour = source), size = 0.75) + - facet_grid(position ~ .) + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = unit_format(unit = "ms"), - limits = c(0, NA)) + - scale_fill_hue(name = "Source") + - scale_colour_hue(name = "Source") + - ggtitle("Circuit build times") + - labs(caption = copyright_notice) + - theme(legend.position = "top") - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_onionperf_latencies <- function(start_p = NULL, end_p = NULL, - server_p = NULL) { - read_csv(file = paste(stats_dir, "latencies.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - source = col_character(), - server = col_character(), - q1 = col_double(), - md = col_double(), - q3 = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(if (!is.null(server_p)) server == server_p else TRUE) -} - -plot_onionperf_latencies <- function(start_p, end_p, server_p, path_p) { - prepare_onionperf_latencies(start_p, end_p, server_p) %>% - filter(source != "") %>% - complete(date = full_seq(date, period = 1), nesting(source)) %>% - ggplot(aes(x = date, y = md, ymin = q1, ymax = q3, fill = source)) + - geom_ribbon(alpha = 0.5) + - geom_line(aes(colour = source), size = 0.75) + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = unit_format(unit = "ms"), - limits = c(0, NA)) + - scale_fill_hue(name = "Source") + - scale_colour_hue(name = "Source") + - ggtitle(paste("Circuit round-trip latencies to", server_p, "server")) + - labs(caption = copyright_notice) + - theme(legend.position = "top") - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_connbidirect <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "connbidirect2.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - direction = col_factor(levels = NULL), - quantile = col_double(), - fraction = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - mutate(quantile = paste("X", quantile, sep = ""), - fraction = fraction / 100) %>% - spread(quantile, fraction) %>% - rename(q1 = X0.25, md = X0.5, q3 = X0.75) -} - -plot_connbidirect <- function(start_p, end_p, path_p) { - prepare_connbidirect(start_p, end_p) %>% - complete(date = full_seq(date, period = 1), nesting(direction)) %>% - ggplot(aes(x = date, y = md, ymin = q1, ymax = q3, fill = direction)) + - geom_ribbon(alpha = 0.5) + - geom_line(aes(colour = direction), size = 0.75) + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = percent, limits = c(0, NA)) + - scale_colour_hue(name = "Medians and interquartile ranges", - breaks = c("both", "write", "read"), - labels = c("Both reading and writing", "Mostly writing", - "Mostly reading")) + - scale_fill_hue(name = "Medians and interquartile ranges", - breaks = c("both", "write", "read"), - labels = c("Both reading and writing", "Mostly writing", - "Mostly reading")) + - ggtitle("Fraction of connections used uni-/bidirectionally") + - labs(caption = copyright_notice) + - theme(legend.position = "top") - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_bandwidth_flags <- function(start_p = NULL, end_p = NULL) { - advbw <- read_csv(file = paste(stats_dir, "advbw.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - isexit = col_logical(), - isguard = col_logical(), - advbw = col_double())) %>% - transmute(date, have_guard_flag = isguard, have_exit_flag = isexit, - variable = "advbw", value = advbw * 8 / 1e9) - bwhist <- read_csv(file = paste(stats_dir, "bandwidth.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - isexit = col_logical(), - isguard = col_logical(), - bwread = col_double(), - bwwrite = col_double(), - dirread = col_double(), - dirwrite = col_double())) %>% - transmute(date, have_guard_flag = isguard, have_exit_flag = isexit, - variable = "bwhist", value = (bwread + bwwrite) * 8 / 2e9) - rbind(advbw, bwhist) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(!is.na(have_exit_flag)) %>% - filter(!is.na(have_guard_flag)) %>% - spread(variable, value) -} - -plot_bandwidth_flags <- function(start_p, end_p, path_p) { - prepare_bandwidth_flags(start_p, end_p) %>% - gather(variable, value, c(advbw, bwhist)) %>% - unite(flags, have_guard_flag, have_exit_flag) %>% - mutate(flags = factor(flags, - levels = c("FALSE_TRUE", "TRUE_TRUE", "TRUE_FALSE", "FALSE_FALSE"), - labels = c("Exit only", "Guard and Exit", "Guard only", - "Neither Guard nor Exit"))) %>% - mutate(variable = ifelse(variable == "advbw", - "Advertised bandwidth", "Consumed bandwidth")) %>% - ggplot(aes(x = date, y = value, fill = flags)) + - geom_area() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), - limits = c(0, NA)) + - scale_fill_manual(name = "", - values = c("#03B3FF", "#39FF02", "#FFFF00", "#AAAA99")) + - facet_grid(variable ~ .) + - ggtitle("Advertised and consumed bandwidth by relay flags") + - labs(caption = copyright_notice) + - theme(legend.position = "top") - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_userstats_relay_country <- function(start_p = NULL, end_p = NULL, - country_p = NULL, events_p = NULL) { - read_csv(file = paste(stats_dir, "clients.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - node = col_character(), - country = col_character(), - transport = col_character(), - version = col_character(), - lower = col_double(), - upper = col_double(), - clients = col_double(), - frac = col_double()), - na = character()) %>% - filter(node == "relay") %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(if (!is.null(country_p)) - country == ifelse(country_p == "all", "", country_p) else TRUE) %>% - filter(transport == "") %>% - filter(version == "") %>% - select(date, country, clients, lower, upper, frac) %>% - rename(users = clients) -} - -plot_userstats_relay_country <- function(start_p, end_p, country_p, events_p, - path_p) { - u <- prepare_userstats_relay_country(start_p, end_p, country_p, events_p) %>% - complete(date = full_seq(date, period = 1)) - plot <- ggplot(u, aes(x = date, y = users)) - if (length(na.omit(u$users)) > 0 & events_p != "off" & - country_p != "all") { - upturns <- u[u$users > u$upper, c("date", "users")] - downturns <- u[u$users < u$lower, c("date", "users")] - if (events_p == "on") { - u[!is.na(u$lower) & u$lower < 0, "lower"] <- 0 - plot <- plot + - geom_ribbon(data = u, aes(ymin = lower, ymax = upper), fill = "gray") - } - if (length(upturns$date) > 0) - plot <- plot + - geom_point(data = upturns, aes(x = date, y = users), size = 5, - colour = "dodgerblue2") - if (length(downturns$date) > 0) - plot <- plot + - geom_point(data = downturns, aes(x = date, y = users), size = 5, - colour = "firebrick2") - } - plot <- plot + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - ggtitle(paste("Directly connecting users", - ifelse(country_p == "all", "", - paste(" from", countryname(country_p))), sep = "")) + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_userstats_bridge_country <- function(start_p = NULL, end_p = NULL, - country_p = NULL) { - read_csv(file = paste(stats_dir, "clients.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - node = col_character(), - country = col_character(), - transport = col_character(), - version = col_character(), - lower = col_double(), - upper = col_double(), - clients = col_double(), - frac = col_double()), - na = character()) %>% - filter(node == "bridge") %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(if (!is.null(country_p)) - country == ifelse(country_p == "all", "", country_p) else TRUE) %>% - filter(transport == "") %>% - filter(version == "") %>% - select(date, country, clients, frac) %>% - rename(users = clients) -} - -plot_userstats_bridge_country <- function(start_p, end_p, country_p, path_p) { - prepare_userstats_bridge_country(start_p, end_p, country_p) %>% - complete(date = full_seq(date, period = 1)) %>% - ggplot(aes(x = date, y = users)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - ggtitle(paste("Bridge users", - ifelse(country_p == "all", "", - paste(" from", countryname(country_p))), sep = "")) + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_userstats_bridge_transport <- function(start_p = NULL, end_p = NULL, - transport_p = NULL) { - u <- read_csv(file = paste(stats_dir, "clients.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - node = col_character(), - country = col_character(), - transport = col_character(), - version = col_character(), - lower = col_double(), - upper = col_double(), - clients = col_double(), - frac = col_double())) %>% - filter(node == "bridge") %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(is.na(country)) %>% - filter(is.na(version)) %>% - filter(!is.na(transport)) %>% - select(date, transport, clients, frac) - if (is.null(transport_p) || "!<OR>" %in% transport_p) { - n <- u %>% - filter(transport != "<OR>") %>% - group_by(date, frac) %>% - summarize(clients = sum(clients)) - u <- rbind(u, data.frame(date = n$date, transport = "!<OR>", - clients = n$clients, frac = n$frac)) - } - u %>% - filter(if (!is.null(transport_p)) transport %in% transport_p else TRUE) %>% - select(date, transport, clients, frac) %>% - rename(users = clients) %>% - arrange(date, transport) -} - -plot_userstats_bridge_transport <- function(start_p, end_p, transport_p, - path_p) { - if (length(transport_p) > 1) { - title <- paste("Bridge users by transport") - } else { - title <- paste("Bridge users using", - ifelse(transport_p == "<??>", "unknown pluggable transport(s)", - ifelse(transport_p == "<OR>", "default OR protocol", - ifelse(transport_p == "!<OR>", "any pluggable transport", - ifelse(transport_p == "fte", "FTE", - ifelse(transport_p == "websocket", "Flash proxy/websocket", - paste("transport", transport_p))))))) - } - u <- prepare_userstats_bridge_transport(start_p, end_p, transport_p) %>% - complete(date = full_seq(date, period = 1), nesting(transport)) - if (length(transport_p) > 1) { - plot <- ggplot(u, aes(x = date, y = users, colour = transport)) - } else { - plot <- ggplot(u, aes(x = date, y = users)) - } - plot <- plot + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - ggtitle(title) + - labs(caption = copyright_notice) - if (length(transport_p) > 1) { - plot <- plot + - scale_colour_hue(name = "", breaks = transport_p, - labels = ifelse(transport_p == "<??>", "Unknown PT", - ifelse(transport_p == "<OR>", "Default OR protocol", - ifelse(transport_p == "!<OR>", "Any PT", - ifelse(transport_p == "fte", "FTE", - ifelse(transport_p == "websocket", "Flash proxy/websocket", - transport_p)))))) - } - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_userstats_bridge_version <- function(start_p = NULL, end_p = NULL, - version_p = NULL) { - read_csv(file = paste(stats_dir, "clients.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - node = col_character(), - country = col_character(), - transport = col_character(), - version = col_character(), - lower = col_double(), - upper = col_double(), - clients = col_double(), - frac = col_double())) %>% - filter(node == "bridge") %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(is.na(country)) %>% - filter(is.na(transport)) %>% - filter(if (!is.null(version_p)) version == version_p else TRUE) %>% - select(date, version, clients, frac) %>% - rename(users = clients) -} - -plot_userstats_bridge_version <- function(start_p, end_p, version_p, path_p) { - prepare_userstats_bridge_version(start_p, end_p, version_p) %>% - complete(date = full_seq(date, period = 1)) %>% - ggplot(aes(x = date, y = users)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - ggtitle(paste("Bridge users using IP", version_p, sep = "")) + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_userstats_bridge_combined <- function(start_p = NULL, end_p = NULL, - country_p = NULL) { - if (!is.null(country_p) && country_p == "all") { - prepare_userstats_bridge_country(start_p, end_p, country_p) - } else { - read_csv(file = paste(stats_dir, "userstats-combined.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - node = col_skip(), - country = col_character(), - transport = col_character(), - version = col_skip(), - frac = col_double(), - low = col_double(), - high = col_double()), - na = character()) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(if (!is.null(country_p)) country == country_p else TRUE) %>% - select(date, country, transport, low, high, frac) %>% - arrange(date, country, transport) - } -} - -plot_userstats_bridge_combined <- function(start_p, end_p, country_p, path_p) { - if (country_p == "all") { - plot_userstats_bridge_country(start_p, end_p, country_p, path_p) - } else { - top <- 3 - u <- prepare_userstats_bridge_combined(start_p, end_p, country_p) - a <- aggregate(list(mid = (u$high + u$low) / 2), - by = list(transport = u$transport), FUN = sum) - a <- a[order(a$mid, decreasing = TRUE)[1:top], ] - u <- u[u$transport %in% a$transport, ] %>% - complete(date = full_seq(date, period = 1), nesting(country, transport)) - title <- paste("Bridge users by transport from ", - countryname(country_p), sep = "") - ggplot(u, aes(x = as.Date(date), ymin = low, ymax = high, - fill = transport)) + - geom_ribbon(alpha = 0.5, size = 0.5) + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", limits = c(0, NA), labels = formatter) + - scale_colour_hue("Top-3 transports") + - scale_fill_hue("Top-3 transports") + - ggtitle(title) + - labs(caption = copyright_notice) + - theme(legend.position = "top") - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) - } -} - -prepare_advbwdist_perc <- function(start_p = NULL, end_p = NULL, p_p = NULL) { - read_csv(file = paste(stats_dir, "advbwdist.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - isexit = col_logical(), - relay = col_skip(), - percentile = col_integer(), - advbw = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(if (!is.null(p_p)) percentile %in% as.numeric(p_p) else - percentile != "") %>% - transmute(date, percentile = as.factor(percentile), - variable = ifelse(is.na(isexit), "all", "exits"), - advbw = advbw * 8 / 1e9) %>% - spread(variable, advbw) %>% - rename(p = percentile) -} - -plot_advbwdist_perc <- function(start_p, end_p, p_p, path_p) { - prepare_advbwdist_perc(start_p, end_p, p_p) %>% - gather(variable, advbw, -c(date, p)) %>% - mutate(variable = ifelse(variable == "all", "All relays", - "Exits only")) %>% - complete(date = full_seq(date, period = 1), nesting(p, variable)) %>% - ggplot(aes(x = date, y = advbw, colour = p)) + - facet_grid(variable ~ .) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), - limits = c(0, NA)) + - scale_colour_hue(name = "Percentile") + - ggtitle("Advertised bandwidth distribution") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_advbwdist_relay <- function(start_p = NULL, end_p = NULL, n_p = NULL) { - read_csv(file = paste(stats_dir, "advbwdist.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - isexit = col_logical(), - relay = col_integer(), - percentile = col_skip(), - advbw = col_double())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(if (!is.null(n_p)) relay %in% as.numeric(n_p) else - relay != "") %>% - transmute(date, relay = as.factor(relay), - variable = ifelse(is.na(isexit), "all", "exits"), - advbw = advbw * 8 / 1e9) %>% - spread(variable, advbw) %>% - rename(n = relay) -} - -plot_advbwdist_relay <- function(start_p, end_p, n_p, path_p) { - prepare_advbwdist_relay(start_p, end_p, n_p) %>% - gather(variable, advbw, -c(date, n)) %>% - mutate(variable = ifelse(variable == "all", "All relays", - "Exits only")) %>% - complete(date = full_seq(date, period = 1), nesting(n, variable)) %>% - ggplot(aes(x = date, y = advbw, colour = n)) + - facet_grid(variable ~ .) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), - limits = c(0, NA)) + - scale_colour_hue(name = "n") + - ggtitle("Advertised bandwidth of n-th fastest relays") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_hidserv_dir_onions_seen <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "hidserv.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - type = col_factor(levels = NULL), - wmean = col_skip(), - wmedian = col_skip(), - wiqm = col_double(), - frac = col_double(), - stats = col_skip())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(type == "dir-onions-seen") %>% - transmute(date, onions = ifelse(frac >= 0.01, wiqm, NA), frac) -} - -plot_hidserv_dir_onions_seen <- function(start_p, end_p, path_p) { - prepare_hidserv_dir_onions_seen(start_p, end_p) %>% - complete(date = full_seq(date, period = 1)) %>% - ggplot(aes(x = date, y = onions)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", limits = c(0, NA), labels = formatter) + - ggtitle("Unique .onion addresses") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_hidserv_rend_relayed_cells <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "hidserv.csv", sep = ""), - col_types = cols( - date = col_date(format = ""), - type = col_factor(levels = NULL), - wmean = col_skip(), - wmedian = col_skip(), - wiqm = col_double(), - frac = col_double(), - stats = col_skip())) %>% - filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% - filter(type == "rend-relayed-cells") %>% - transmute(date, - relayed = ifelse(frac >= 0.01, wiqm * 8 * 512 / (86400 * 1e9), NA), frac) -} - -plot_hidserv_rend_relayed_cells <- function(start_p, end_p, path_p) { - prepare_hidserv_rend_relayed_cells(start_p, end_p) %>% - complete(date = full_seq(date, period = 1)) %>% - ggplot(aes(x = date, y = relayed)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), - limits = c(0, NA)) + - ggtitle("Onion-service traffic") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_webstats_tb <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "webstats.csv", sep = ""), - col_types = cols( - log_date = col_date(format = ""), - request_type = col_factor(levels = NULL), - platform = col_skip(), - channel = col_skip(), - locale = col_skip(), - incremental = col_skip(), - count = col_double())) %>% - filter(if (!is.null(start_p)) log_date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) log_date <= as.Date(end_p) else TRUE) %>% - filter(request_type %in% c("tbid", "tbsd", "tbup", "tbur")) %>% - group_by(log_date, request_type) %>% - summarize(count = sum(count)) %>% - spread(request_type, count) %>% - rename(date = log_date, initial_downloads = tbid, - signature_downloads = tbsd, update_pings = tbup, - update_requests = tbur) -} - -plot_webstats_tb <- function(start_p, end_p, path_p) { - prepare_webstats_tb(start_p, end_p) %>% - gather(request_type, count, -date) %>% - mutate(request_type = factor(request_type, - levels = c("initial_downloads", "signature_downloads", "update_pings", - "update_requests"), - labels = c("Initial downloads", "Signature downloads", "Update pings", - "Update requests"))) %>% - ungroup() %>% - complete(date = full_seq(date, period = 1), nesting(request_type)) %>% - ggplot(aes(x = date, y = count)) + - geom_point() + - geom_line() + - facet_grid(request_type ~ ., scales = "free_y") + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - theme(strip.text.y = element_text(angle = 0, hjust = 0, size = rel(1.5)), - strip.background = element_rect(fill = NA)) + - ggtitle("Tor Browser downloads and updates") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_webstats_tb_platform <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "webstats.csv", sep = ""), - col_types = cols( - log_date = col_date(format = ""), - request_type = col_factor(levels = NULL), - platform = col_factor(levels = NULL), - channel = col_skip(), - locale = col_skip(), - incremental = col_skip(), - count = col_double())) %>% - filter(if (!is.null(start_p)) log_date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) log_date <= as.Date(end_p) else TRUE) %>% - filter(request_type %in% c("tbid", "tbup")) %>% - group_by(log_date, platform, request_type) %>% - summarize(count = sum(count)) %>% - spread(request_type, count, fill = 0) %>% - rename(date = log_date, initial_downloads = tbid, update_pings = tbup) -} - -plot_webstats_tb_platform <- function(start_p, end_p, path_p) { - prepare_webstats_tb_platform(start_p, end_p) %>% - gather(request_type, count, -c(date, platform)) %>% - mutate(request_type = factor(request_type, - levels = c("initial_downloads", "update_pings"), - labels = c("Initial downloads", "Update pings"))) %>% - ungroup() %>% - complete(date = full_seq(date, period = 1), - nesting(platform, request_type)) %>% - ggplot(aes(x = date, y = count, colour = platform)) + - geom_point() + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - scale_colour_hue(name = "Platform", - breaks = c("w", "m", "l", "o", ""), - labels = c("Windows", "macOS", "Linux", "Other", "Unknown")) + - facet_grid(request_type ~ ., scales = "free_y") + - theme(strip.text.y = element_text(angle = 0, hjust = 0, size = rel(1.5)), - strip.background = element_rect(fill = NA), - legend.position = "top") + - ggtitle("Tor Browser downloads and updates by platform") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_webstats_tb_locale <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "webstats.csv", sep = ""), - col_types = cols( - log_date = col_date(format = ""), - request_type = col_factor(levels = NULL), - platform = col_skip(), - channel = col_skip(), - locale = col_factor(levels = NULL), - incremental = col_skip(), - count = col_double())) %>% - filter(if (!is.null(start_p)) log_date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) log_date <= as.Date(end_p) else TRUE) %>% - filter(request_type %in% c("tbid", "tbup")) %>% - rename(date = log_date) %>% - group_by(date, locale, request_type) %>% - summarize(count = sum(count)) %>% - mutate(request_type = factor(request_type, levels = c("tbid", "tbup"))) %>% - spread(request_type, count, fill = 0) %>% - rename(initial_downloads = tbid, update_pings = tbup) -} - -plot_webstats_tb_locale <- function(start_p, end_p, path_p) { - d <- prepare_webstats_tb_locale(start_p, end_p) %>% - gather(request_type, count, -c(date, locale)) %>% - mutate(request_type = factor(request_type, - levels = c("initial_downloads", "update_pings"), - labels = c("Initial downloads", "Update pings"))) - e <- d - e <- aggregate(list(count = e$count), by = list(locale = e$locale), FUN = sum) - e <- e[order(e$count, decreasing = TRUE), ] - e <- e[1:5, ] - d <- aggregate(list(count = d$count), by = list(date = d$date, - request_type = d$request_type, - locale = ifelse(d$locale %in% e$locale, d$locale, "(other)")), FUN = sum) - d %>% - complete(date = full_seq(date, period = 1), - nesting(locale, request_type)) %>% - ggplot(aes(x = date, y = count, colour = locale)) + - geom_point() + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - scale_colour_hue(name = "Locale", - breaks = c(e$locale, "(other)"), - labels = c(as.character(e$locale), "Other")) + - facet_grid(request_type ~ ., scales = "free_y") + - theme(strip.text.y = element_text(angle = 0, hjust = 0, size = rel(1.5)), - strip.background = element_rect(fill = NA), - legend.position = "top") + - guides(col = guide_legend(nrow = 1)) + - ggtitle("Tor Browser downloads and updates by locale") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_webstats_tm <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "webstats.csv", sep = ""), - col_types = cols( - log_date = col_date(format = ""), - request_type = col_factor(levels = NULL), - platform = col_skip(), - channel = col_skip(), - locale = col_skip(), - incremental = col_skip(), - count = col_double())) %>% - filter(if (!is.null(start_p)) log_date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) log_date <= as.Date(end_p) else TRUE) %>% - filter(request_type %in% c("tmid", "tmup")) %>% - group_by(log_date, request_type) %>% - summarize(count = sum(count)) %>% - mutate(request_type = factor(request_type, levels = c("tmid", "tmup"))) %>% - spread(request_type, count, drop = FALSE, fill = 0) %>% - rename(date = log_date, initial_downloads = tmid, update_pings = tmup) -} - -plot_webstats_tm <- function(start_p, end_p, path_p) { - prepare_webstats_tm(start_p, end_p) %>% - gather(request_type, count, -date) %>% - mutate(request_type = factor(request_type, - levels = c("initial_downloads", "update_pings"), - labels = c("Initial downloads", "Update pings"))) %>% - ungroup() %>% - complete(date = full_seq(date, period = 1), nesting(request_type)) %>% - ggplot(aes(x = date, y = count)) + - geom_point() + - geom_line() + - facet_grid(request_type ~ ., scales = "free_y") + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - theme(strip.text.y = element_text(angle = 0, hjust = 0, size = rel(1.5)), - strip.background = element_rect(fill = NA)) + - ggtitle("Tor Messenger downloads and updates") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_relays_ipv6 <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "ipv6servers.csv", sep = ""), - col_types = cols( - valid_after_date = col_date(format = ""), - server = col_factor(levels = NULL), - guard_relay = col_skip(), - exit_relay = col_skip(), - announced_ipv6 = col_logical(), - exiting_ipv6_relay = col_logical(), - reachable_ipv6_relay = col_logical(), - server_count_sum_avg = col_double(), - advertised_bandwidth_bytes_sum_avg = col_skip())) %>% - filter(if (!is.null(start_p)) - valid_after_date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) - valid_after_date <= as.Date(end_p) else TRUE) %>% - filter(server == "relay") %>% - group_by(valid_after_date) %>% - summarize(total = sum(server_count_sum_avg), - announced = sum(server_count_sum_avg[announced_ipv6]), - reachable = sum(server_count_sum_avg[reachable_ipv6_relay]), - exiting = sum(server_count_sum_avg[exiting_ipv6_relay])) %>% - rename(date = valid_after_date) -} - -plot_relays_ipv6 <- function(start_p, end_p, path_p) { - prepare_relays_ipv6(start_p, end_p) %>% - complete(date = full_seq(date, period = 1)) %>% - gather(category, count, -date) %>% - ggplot(aes(x = date, y = count, colour = category)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - scale_colour_hue(name = "", h.start = 90, - breaks = c("total", "announced", "reachable", "exiting"), - labels = c("Total (IPv4) OR", "IPv6 announced OR", "IPv6 reachable OR", - "IPv6 exiting")) + - ggtitle("Relays by IP version") + - labs(caption = copyright_notice) + - theme(legend.position = "top") - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_bridges_ipv6 <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "ipv6servers.csv", sep = ""), - col_types = cols( - valid_after_date = col_date(format = ""), - server = col_factor(levels = NULL), - guard_relay = col_skip(), - exit_relay = col_skip(), - announced_ipv6 = col_logical(), - exiting_ipv6_relay = col_skip(), - reachable_ipv6_relay = col_skip(), - server_count_sum_avg = col_double(), - advertised_bandwidth_bytes_sum_avg = col_skip())) %>% - filter(if (!is.null(start_p)) - valid_after_date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) - valid_after_date <= as.Date(end_p) else TRUE) %>% - filter(server == "bridge") %>% - group_by(valid_after_date) %>% - summarize(total = sum(server_count_sum_avg), - announced = sum(server_count_sum_avg[announced_ipv6])) %>% - rename(date = valid_after_date) -} - -plot_bridges_ipv6 <- function(start_p, end_p, path_p) { - prepare_bridges_ipv6(start_p, end_p) %>% - complete(date = full_seq(date, period = 1)) %>% - gather(category, count, -date) %>% - ggplot(aes(x = date, y = count, colour = category)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - scale_colour_hue(name = "", h.start = 90, - breaks = c("total", "announced"), - labels = c("Total (IPv4) OR", "IPv6 announced OR")) + - ggtitle("Bridges by IP version") + - labs(caption = copyright_notice) + - theme(legend.position = "top") - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_advbw_ipv6 <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "ipv6servers.csv", sep = ""), - col_types = cols( - valid_after_date = col_date(format = ""), - server = col_factor(levels = NULL), - guard_relay = col_logical(), - exit_relay = col_logical(), - announced_ipv6 = col_logical(), - exiting_ipv6_relay = col_logical(), - reachable_ipv6_relay = col_logical(), - server_count_sum_avg = col_skip(), - advertised_bandwidth_bytes_sum_avg = col_double())) %>% - filter(if (!is.null(start_p)) - valid_after_date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) - valid_after_date <= as.Date(end_p) else TRUE) %>% - filter(server == "relay") %>% - mutate(advertised_bandwidth_bytes_sum_avg = - advertised_bandwidth_bytes_sum_avg * 8 / 1e9) %>% - group_by(valid_after_date) %>% - summarize(total = sum(advertised_bandwidth_bytes_sum_avg), - total_guard = sum(advertised_bandwidth_bytes_sum_avg[guard_relay]), - total_exit = sum(advertised_bandwidth_bytes_sum_avg[exit_relay]), - reachable_guard = sum(advertised_bandwidth_bytes_sum_avg[ - reachable_ipv6_relay & guard_relay]), - reachable_exit = sum(advertised_bandwidth_bytes_sum_avg[ - reachable_ipv6_relay & exit_relay]), - exiting = sum(advertised_bandwidth_bytes_sum_avg[ - exiting_ipv6_relay])) %>% - rename(date = valid_after_date) -} - -plot_advbw_ipv6 <- function(start_p, end_p, path_p) { - prepare_advbw_ipv6(start_p, end_p) %>% - complete(date = full_seq(date, period = 1)) %>% - gather(category, advbw, -date) %>% - ggplot(aes(x = date, y = advbw, colour = category)) + - geom_line() + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), - limits = c(0, NA)) + - scale_colour_hue(name = "", h.start = 90, - breaks = c("total", "total_guard", "total_exit", "reachable_guard", - "reachable_exit", "exiting"), - labels = c("Total (IPv4) OR", "Guard total (IPv4)", "Exit total (IPv4)", - "Reachable guard IPv6 OR", "Reachable exit IPv6 OR", "IPv6 exiting")) + - ggtitle("Advertised bandwidth by IP version") + - labs(caption = copyright_notice) + - theme(legend.position = "top") + - guides(colour = guide_legend(nrow = 2, byrow = TRUE)) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - -prepare_totalcw <- function(start_p = NULL, end_p = NULL) { - read_csv(file = paste(stats_dir, "totalcw.csv", sep = ""), - col_types = cols( - valid_after_date = col_date(format = ""), - nickname = col_character(), - have_guard_flag = col_logical(), - have_exit_flag = col_logical(), - measured_sum_avg = col_double())) %>% - filter(if (!is.null(start_p)) - valid_after_date >= as.Date(start_p) else TRUE) %>% - filter(if (!is.null(end_p)) - valid_after_date <= as.Date(end_p) else TRUE) %>% - group_by(valid_after_date, nickname) %>% - summarize(measured_sum_avg = sum(measured_sum_avg)) %>% - rename(date = valid_after_date, totalcw = measured_sum_avg) %>% - arrange(date, nickname) -} - -plot_totalcw <- function(start_p, end_p, path_p) { - prepare_totalcw(start_p, end_p) %>% - mutate(nickname = ifelse(is.na(nickname), "consensus", nickname)) %>% - mutate(nickname = factor(nickname, - levels = c("consensus", unique(nickname[nickname != "consensus"])))) %>% - ungroup() %>% - complete(date = full_seq(date, period = 1), nesting(nickname)) %>% - ggplot(aes(x = date, y = totalcw, colour = nickname)) + - geom_line(na.rm = TRUE) + - scale_x_date(name = "", breaks = custom_breaks, - labels = custom_labels, minor_breaks = custom_minor_breaks) + - scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + - scale_colour_hue(name = "") + - ggtitle("Total consensus weights across bandwidth authorities") + - labs(caption = copyright_notice) - ggsave(filename = path_p, width = 8, height = 5, dpi = 150) -} - - diff --git a/src/main/R/rserver/rserve-init.R b/src/main/R/rserver/rserve-init.R index f160698..57e14f5 100644 --- a/src/main/R/rserver/rserve-init.R +++ b/src/main/R/rserver/rserve-init.R @@ -1,12 +1,1603 @@ -##Pre-loaded libraries and graphing functions to speed things up +require(ggplot2) +require(RColorBrewer) +require(scales) +require(dplyr) +require(tidyr) +require(readr)
-library("ggplot2") -library("RColorBrewer") -library("scales") -library(dplyr) -library(tidyr) -library(readr) +countrylist <- list( + "ad" = "Andorra", + "ae" = "the United Arab Emirates", + "af" = "Afghanistan", + "ag" = "Antigua and Barbuda", + "ai" = "Anguilla", + "al" = "Albania", + "am" = "Armenia", + "an" = "the Netherlands Antilles", + "ao" = "Angola", + "aq" = "Antarctica", + "ar" = "Argentina", + "as" = "American Samoa", + "at" = "Austria", + "au" = "Australia", + "aw" = "Aruba", + "ax" = "the Aland Islands", + "az" = "Azerbaijan", + "ba" = "Bosnia and Herzegovina", + "bb" = "Barbados", + "bd" = "Bangladesh", + "be" = "Belgium", + "bf" = "Burkina Faso", + "bg" = "Bulgaria", + "bh" = "Bahrain", + "bi" = "Burundi", + "bj" = "Benin", + "bl" = "Saint Bartelemey", + "bm" = "Bermuda", + "bn" = "Brunei", + "bo" = "Bolivia", + "bq" = "Bonaire, Sint Eustatius and Saba", + "br" = "Brazil", + "bs" = "the Bahamas", + "bt" = "Bhutan", + "bv" = "the Bouvet Island", + "bw" = "Botswana", + "by" = "Belarus", + "bz" = "Belize", + "ca" = "Canada", + "cc" = "the Cocos (Keeling) Islands", + "cd" = "the Democratic Republic of the Congo", + "cf" = "Central African Republic", + "cg" = "Congo", + "ch" = "Switzerland", + "ci" = "Côte d'Ivoire", + "ck" = "the Cook Islands", + "cl" = "Chile", + "cm" = "Cameroon", + "cn" = "China", + "co" = "Colombia", + "cr" = "Costa Rica", + "cu" = "Cuba", + "cv" = "Cape Verde", + "cw" = "Curaçao", + "cx" = "the Christmas Island", + "cy" = "Cyprus", + "cz" = "the Czech Republic", + "de" = "Germany", + "dj" = "Djibouti", + "dk" = "Denmark", + "dm" = "Dominica", + "do" = "the Dominican Republic", + "dz" = "Algeria", + "ec" = "Ecuador", + "ee" = "Estonia", + "eg" = "Egypt", + "eh" = "the Western Sahara", + "er" = "Eritrea", + "es" = "Spain", + "et" = "Ethiopia", + "fi" = "Finland", + "fj" = "Fiji", + "fk" = "the Falkland Islands (Malvinas)", + "fm" = "the Federated States of Micronesia", + "fo" = "the Faroe Islands", + "fr" = "France", + "ga" = "Gabon", + "gb" = "the United Kingdom", + "gd" = "Grenada", + "ge" = "Georgia", + "gf" = "French Guiana", + "gg" = "Guernsey", + "gh" = "Ghana", + "gi" = "Gibraltar", + "gl" = "Greenland", + "gm" = "Gambia", + "gn" = "Guinea", + "gp" = "Guadeloupe", + "gq" = "Equatorial Guinea", + "gr" = "Greece", + "gs" = "South Georgia and the South Sandwich Islands", + "gt" = "Guatemala", + "gu" = "Guam", + "gw" = "Guinea-Bissau", + "gy" = "Guyana", + "hk" = "Hong Kong", + "hm" = "Heard Island and McDonald Islands", + "hn" = "Honduras", + "hr" = "Croatia", + "ht" = "Haiti", + "hu" = "Hungary", + "id" = "Indonesia", + "ie" = "Ireland", + "il" = "Israel", + "im" = "the Isle of Man", + "in" = "India", + "io" = "the British Indian Ocean Territory", + "iq" = "Iraq", + "ir" = "Iran", + "is" = "Iceland", + "it" = "Italy", + "je" = "Jersey", + "jm" = "Jamaica", + "jo" = "Jordan", + "jp" = "Japan", + "ke" = "Kenya", + "kg" = "Kyrgyzstan", + "kh" = "Cambodia", + "ki" = "Kiribati", + "km" = "Comoros", + "kn" = "Saint Kitts and Nevis", + "kp" = "North Korea", + "kr" = "the Republic of Korea", + "kw" = "Kuwait", + "ky" = "the Cayman Islands", + "kz" = "Kazakhstan", + "la" = "Laos", + "lb" = "Lebanon", + "lc" = "Saint Lucia", + "li" = "Liechtenstein", + "lk" = "Sri Lanka", + "lr" = "Liberia", + "ls" = "Lesotho", + "lt" = "Lithuania", + "lu" = "Luxembourg", + "lv" = "Latvia", + "ly" = "Libya", + "ma" = "Morocco", + "mc" = "Monaco", + "md" = "the Republic of Moldova", + "me" = "Montenegro", + "mf" = "Saint Martin", + "mg" = "Madagascar", + "mh" = "the Marshall Islands", + "mk" = "Macedonia", + "ml" = "Mali", + "mm" = "Burma", + "mn" = "Mongolia", + "mo" = "Macau", + "mp" = "the Northern Mariana Islands", + "mq" = "Martinique", + "mr" = "Mauritania", + "ms" = "Montserrat", + "mt" = "Malta", + "mu" = "Mauritius", + "mv" = "the Maldives", + "mw" = "Malawi", + "mx" = "Mexico", + "my" = "Malaysia", + "mz" = "Mozambique", + "na" = "Namibia", + "nc" = "New Caledonia", + "ne" = "Niger", + "nf" = "Norfolk Island", + "ng" = "Nigeria", + "ni" = "Nicaragua", + "nl" = "the Netherlands", + "no" = "Norway", + "np" = "Nepal", + "nr" = "Nauru", + "nu" = "Niue", + "nz" = "New Zealand", + "om" = "Oman", + "pa" = "Panama", + "pe" = "Peru", + "pf" = "French Polynesia", + "pg" = "Papua New Guinea", + "ph" = "the Philippines", + "pk" = "Pakistan", + "pl" = "Poland", + "pm" = "Saint Pierre and Miquelon", + "pn" = "the Pitcairn Islands", + "pr" = "Puerto Rico", + "ps" = "the Palestinian Territory", + "pt" = "Portugal", + "pw" = "Palau", + "py" = "Paraguay", + "qa" = "Qatar", + "re" = "Reunion", + "ro" = "Romania", + "rs" = "Serbia", + "ru" = "Russia", + "rw" = "Rwanda", + "sa" = "Saudi Arabia", + "sb" = "the Solomon Islands", + "sc" = "the Seychelles", + "sd" = "Sudan", + "se" = "Sweden", + "sg" = "Singapore", + "sh" = "Saint Helena", + "si" = "Slovenia", + "sj" = "Svalbard and Jan Mayen", + "sk" = "Slovakia", + "sl" = "Sierra Leone", + "sm" = "San Marino", + "sn" = "Senegal", + "so" = "Somalia", + "sr" = "Suriname", + "ss" = "South Sudan", + "st" = "São Tomé and Príncipe", + "sv" = "El Salvador", + "sx" = "Sint Maarten", + "sy" = "the Syrian Arab Republic", + "sz" = "Swaziland", + "tc" = "Turks and Caicos Islands", + "td" = "Chad", + "tf" = "the French Southern Territories", + "tg" = "Togo", + "th" = "Thailand", + "tj" = "Tajikistan", + "tk" = "Tokelau", + "tl" = "East Timor", + "tm" = "Turkmenistan", + "tn" = "Tunisia", + "to" = "Tonga", + "tr" = "Turkey", + "tt" = "Trinidad and Tobago", + "tv" = "Tuvalu", + "tw" = "Taiwan", + "tz" = "the United Republic of Tanzania", + "ua" = "Ukraine", + "ug" = "Uganda", + "um" = "the United States Minor Outlying Islands", + "us" = "the United States", + "uy" = "Uruguay", + "uz" = "Uzbekistan", + "va" = "Vatican City", + "vc" = "Saint Vincent and the Grenadines", + "ve" = "Venezuela", + "vg" = "the British Virgin Islands", + "vi" = "the United States Virgin Islands", + "vn" = "Vietnam", + "vu" = "Vanuatu", + "wf" = "Wallis and Futuna", + "ws" = "Samoa", + "xk" = "Kosovo", + "ye" = "Yemen", + "yt" = "Mayotte", + "za" = "South Africa", + "zm" = "Zambia", + "zw" = "Zimbabwe")
-source('graphs.R') -source('tables.R') +countryname <- function(country) { + res <- countrylist[[country]] + if (is.null(res)) + res <- "no-man's-land" + res +} + +# Helper function that takes date limits as input and returns major breaks as +# output. The main difference to the built-in major breaks is that we're trying +# harder to align major breaks with first days of weeks (Sundays), months, +# quarters, or years. +custom_breaks <- function(input) { + scales_index <- cut(as.numeric(max(input) - min(input)), + c(-1, 7, 12, 56, 180, 600, 2000, Inf), labels = FALSE) + from_print_format <- c("%F", "%F", "%Y-W%U-7", "%Y-%m-01", "%Y-01-01", + "%Y-01-01", "%Y-01-01")[scales_index] + from_parse_format <- ifelse(scales_index == 3, "%Y-W%U-%u", "%F") + by <- c("1 day", "2 days", "1 week", "1 month", "3 months", "1 year", + "2 years")[scales_index] + seq(as.Date(as.character(min(input), from_print_format), + format = from_parse_format), max(input), by = by) +} + +# Helper function that takes date limits as input and returns minor breaks as +# output. As opposed to the built-in minor breaks, we're not just adding one +# minor break half way through between two major breaks. Instead, we're plotting +# a minor break for every day, week, month, or quarter between two major breaks. +custom_minor_breaks <- function(input) { + scales_index <- cut(as.numeric(max(input) - min(input)), + c(-1, 7, 12, 56, 180, 600, 2000, Inf), labels = FALSE) + from_print_format <- c("%F", "%F", "%F", "%Y-W%U-7", "%Y-%m-01", "%Y-01-01", + "%Y-01-01")[scales_index] + from_parse_format <- ifelse(scales_index == 4, "%Y-W%U-%u", "%F") + by <- c("1 day", "1 day", "1 day", "1 week", "1 month", "3 months", + "1 year")[scales_index] + seq(as.Date(as.character(min(input), from_print_format), + format = from_parse_format), max(input), by = by) +} + +# Helper function that takes breaks as input and returns labels as output. We're +# going all ISO-8601 here, though we're not just writing %Y-%m-%d everywhere, +# but %Y-%m or %Y if all breaks are on the first of a month or even year. +custom_labels <- function(breaks) { + if (all(format(breaks, format = "%m-%d") == "01-01", na.rm = TRUE)) { + format(breaks, format = "%Y") + } else { + if (all(format(breaks, format = "%d") == "01", na.rm = TRUE)) { + format(breaks, format = "%Y-%m") + } else { + format(breaks, format = "%F") + } + } +} + +# Helper function to format numbers in non-scientific notation with spaces as +# thousands separator. +formatter <- function(x, ...) { + format(x, ..., scientific = FALSE, big.mark = " ") +} + +theme_update( + # Make plot title centered, and leave some room to the plot. + plot.title = element_text(hjust = 0.5, margin = margin(b = 11)), + + # Leave a little more room to the right for long x axis labels. + plot.margin = margin(5.5, 11, 5.5, 5.5) +) + +# Set the default line size of geom_line() to 1. +update_geom_defaults("line", list(size = 1)) + +copyright_notice <- "The Tor Project - https://metrics.torproject.org/" + +stats_dir <- "/srv/metrics.torproject.org/metrics/shared/stats/" + +rdata_dir <- "/srv/metrics.torproject.org/metrics/shared/RData/" + +# Helper function that copies the appropriate no data object to filename. +copy_no_data <- function(filename) { + len <- nchar(filename) + extension <- substr(filename, len - 3, len) + if (".csv" == extension) { + write("# No data available for the given parameters.", file=filename) + } else { + file.copy(paste(rdata_dir, "no-data-available", extension, sep = ""), + filename) + } +} + +# Helper function wrapping calls into error handling. +robust_call <- function(wrappee, filename) { + tryCatch(eval(wrappee), error = function(e) copy_no_data(filename), + finally = if (!file.exists(filename) || file.size(filename) == 0) { + copy_no_data(filename) + }) +} + +# Write the result of the given FUN, typically a prepare_ function, as .csv file +# to the given path_p. +write_data <- function(FUN, ..., path_p) { + FUN(...) %>% + write.csv(path_p, quote = FALSE, row.names = FALSE, na = "") +} + +# Disable readr's automatic progress bar. +options(readr.show_progress = FALSE) + +prepare_networksize <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "networksize.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + relays = col_double(), + bridges = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) +} + +plot_networksize <- function(start_p, end_p, path_p) { + prepare_networksize(start_p, end_p) %>% + gather(variable, value, -date) %>% + complete(date = full_seq(date, period = 1), + variable = c("relays", "bridges")) %>% + ggplot(aes(x = date, y = value, colour = variable)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + scale_colour_hue("", breaks = c("relays", "bridges"), + labels = c("Relays", "Bridges")) + + ggtitle("Number of relays") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_versions <- function(start_p = NULL, end_p = NULL) { + read_csv(paste(stats_dir, "versions.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + version = col_character(), + relays = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) +} + +plot_versions <- function(start_p, end_p, path_p) { + s <- prepare_versions(start_p, end_p) + known_versions <- unique(s$version) + getPalette <- colorRampPalette(brewer.pal(12, "Paired")) + colours <- data.frame(breaks = known_versions, + values = rep(brewer.pal(min(12, length(known_versions)), "Paired"), + len = length(known_versions)), + stringsAsFactors = FALSE) + versions <- s[s$version %in% known_versions, ] + visible_versions <- sort(unique(versions$version)) + versions <- versions %>% + complete(date = full_seq(date, period = 1), nesting(version)) %>% + ggplot(aes(x = date, y = relays, colour = version)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + scale_colour_manual(name = "Tor version", + values = colours[colours$breaks %in% visible_versions, 2], + breaks = visible_versions) + + ggtitle("Relay versions") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_platforms <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "platforms.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + platform = col_factor(levels = NULL), + relays = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + mutate(platform = tolower(platform)) %>% + spread(platform, relays) +} + +plot_platforms <- function(start_p, end_p, path_p) { + prepare_platforms(start_p, end_p) %>% + gather(platform, relays, -date) %>% + complete(date = full_seq(date, period = 1), nesting(platform)) %>% + ggplot(aes(x = date, y = relays, colour = platform)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + scale_colour_manual(name = "Platform", + breaks = c("linux", "macos", "bsd", "windows", "other"), + labels = c("Linux", "macOS", "BSD", "Windows", "Other"), + values = c("linux" = "#56B4E9", "macos" = "#333333", "bsd" = "#E69F00", + "windows" = "#0072B2", "other" = "#009E73")) + + ggtitle("Relay platforms") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_dirbytes <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "bandwidth.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + isexit = col_logical(), + isguard = col_logical(), + bwread = col_skip(), + bwwrite = col_skip(), + dirread = col_double(), + dirwrite = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(is.na(isexit)) %>% + filter(is.na(isguard)) %>% + mutate(dirread = dirread * 8 / 1e9, + dirwrite = dirwrite * 8 / 1e9) %>% + select(date, dirread, dirwrite) +} + +plot_dirbytes <- function(start_p, end_p, path_p) { + prepare_dirbytes(start_p, end_p) %>% + gather(variable, value, -date) %>% + complete(date = full_seq(date, period = 1), nesting(variable)) %>% + ggplot(aes(x = date, y = value, colour = variable)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), + limits = c(0, NA)) + + scale_colour_hue(name = "", + breaks = c("dirwrite", "dirread"), + labels = c("Written dir bytes", "Read dir bytes")) + + ggtitle("Number of bytes spent on answering directory requests") + + labs(caption = copyright_notice) + + theme(legend.position = "top") + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_relayflags <- function(start_p = NULL, end_p = NULL, flag_p = NULL) { + read_csv(file = paste(stats_dir, "relayflags.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + flag = col_factor(levels = NULL), + relays = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(if (!is.null(flag_p)) flag %in% flag_p else TRUE) +} + +plot_relayflags <- function(start_p, end_p, flag_p, path_p) { + prepare_relayflags(start_p, end_p, flag_p) %>% + complete(date = full_seq(date, period = 1), flag = unique(flag)) %>% + ggplot(aes(x = date, y = relays, colour = flag)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + scale_colour_manual(name = "Relay flags", values = c("#E69F00", + "#56B4E9", "#009E73", "#EE6A50", "#000000", "#0072B2"), + breaks = flag_p, labels = flag_p) + + ggtitle("Number of relays with relay flags assigned") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_torperf <- function(start_p = NULL, end_p = NULL, server_p = NULL, + filesize_p = NULL) { + read_csv(file = paste(stats_dir, "torperf-1.1.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + filesize = col_double(), + source = col_character(), + server = col_character(), + q1 = col_double(), + md = col_double(), + q3 = col_double(), + timeouts = col_skip(), + failures = col_skip(), + requests = col_skip())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(if (!is.null(server_p)) server == server_p else TRUE) %>% + filter(if (!is.null(filesize_p)) + filesize == ifelse(filesize_p == "50kb", 50 * 1024, + ifelse(filesize_p == "1mb", 1024 * 1024, 5 * 1024 * 1024)) else + TRUE) %>% + transmute(date, filesize, source, server, q1 = q1 / 1e3, md = md / 1e3, + q3 = q3 / 1e3) +} + +plot_torperf <- function(start_p, end_p, server_p, filesize_p, path_p) { + prepare_torperf(start_p, end_p, server_p, filesize_p) %>% + filter(source != "") %>% + complete(date = full_seq(date, period = 1), nesting(source)) %>% + ggplot(aes(x = date, y = md, ymin = q1, ymax = q3, fill = source)) + + geom_ribbon(alpha = 0.5) + + geom_line(aes(colour = source), size = 0.75) + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = unit_format(unit = "s"), + limits = c(0, NA)) + + scale_fill_hue(name = "Source") + + scale_colour_hue(name = "Source") + + ggtitle(paste("Time to complete", + ifelse(filesize_p == "50kb", "50 KiB", + ifelse(filesize_p == "1mb", "1 MiB", "5 MiB")), + "request to", server_p, "server")) + + labs(caption = copyright_notice) + + theme(legend.position = "top") + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_torperf_failures <- function(start_p = NULL, end_p = NULL, + server_p = NULL, filesize_p = NULL) { + read_csv(file = paste(stats_dir, "torperf-1.1.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + filesize = col_double(), + source = col_character(), + server = col_character(), + q1 = col_skip(), + md = col_skip(), + q3 = col_skip(), + timeouts = col_double(), + failures = col_double(), + requests = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(if (!is.null(filesize_p)) + filesize == ifelse(filesize_p == "50kb", 50 * 1024, + ifelse(filesize_p == "1mb", 1024 * 1024, 5 * 1024 * 1024)) else + TRUE) %>% + filter(if (!is.null(server_p)) server == server_p else TRUE) %>% + filter(requests > 0) %>% + transmute(date, filesize, source, server, timeouts = timeouts / requests, + failures = failures / requests) +} + +plot_torperf_failures <- function(start_p, end_p, server_p, filesize_p, + path_p) { + prepare_torperf_failures(start_p, end_p, server_p, filesize_p) %>% + filter(source != "") %>% + gather(variable, value, -c(date, filesize, source, server)) %>% + mutate(variable = factor(variable, levels = c("timeouts", "failures"), + labels = c("Timeouts", "Failures"))) %>% + ggplot(aes(x = date, y = value, colour = source)) + + geom_point(size = 2, alpha = 0.5) + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = percent, limits = c(0, NA)) + + scale_colour_hue(name = "Source") + + facet_grid(variable ~ .) + + ggtitle(paste("Timeouts and failures of", + ifelse(filesize_p == "50kb", "50 KiB", + ifelse(filesize_p == "1mb", "1 MiB", "5 MiB")), + "requests to", server_p, "server")) + + labs(caption = copyright_notice) + + theme(legend.position = "top") + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_onionperf_buildtimes <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "buildtimes.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + source = col_character(), + position = col_double(), + q1 = col_double(), + md = col_double(), + q3 = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) +} + +plot_onionperf_buildtimes <- function(start_p, end_p, path_p) { + prepare_onionperf_buildtimes(start_p, end_p) %>% + filter(source != "") %>% + mutate(date = as.Date(date), + position = factor(position, levels = seq(1, 3, 1), + labels = c("1st hop", "2nd hop", "3rd hop"))) %>% + complete(date = full_seq(date, period = 1), nesting(source, position)) %>% + ggplot(aes(x = date, y = md, ymin = q1, ymax = q3, fill = source)) + + geom_ribbon(alpha = 0.5) + + geom_line(aes(colour = source), size = 0.75) + + facet_grid(position ~ .) + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = unit_format(unit = "ms"), + limits = c(0, NA)) + + scale_fill_hue(name = "Source") + + scale_colour_hue(name = "Source") + + ggtitle("Circuit build times") + + labs(caption = copyright_notice) + + theme(legend.position = "top") + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_onionperf_latencies <- function(start_p = NULL, end_p = NULL, + server_p = NULL) { + read_csv(file = paste(stats_dir, "latencies.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + source = col_character(), + server = col_character(), + q1 = col_double(), + md = col_double(), + q3 = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(if (!is.null(server_p)) server == server_p else TRUE) +} + +plot_onionperf_latencies <- function(start_p, end_p, server_p, path_p) { + prepare_onionperf_latencies(start_p, end_p, server_p) %>% + filter(source != "") %>% + complete(date = full_seq(date, period = 1), nesting(source)) %>% + ggplot(aes(x = date, y = md, ymin = q1, ymax = q3, fill = source)) + + geom_ribbon(alpha = 0.5) + + geom_line(aes(colour = source), size = 0.75) + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = unit_format(unit = "ms"), + limits = c(0, NA)) + + scale_fill_hue(name = "Source") + + scale_colour_hue(name = "Source") + + ggtitle(paste("Circuit round-trip latencies to", server_p, "server")) + + labs(caption = copyright_notice) + + theme(legend.position = "top") + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_connbidirect <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "connbidirect2.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + direction = col_factor(levels = NULL), + quantile = col_double(), + fraction = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + mutate(quantile = paste("X", quantile, sep = ""), + fraction = fraction / 100) %>% + spread(quantile, fraction) %>% + rename(q1 = X0.25, md = X0.5, q3 = X0.75) +} + +plot_connbidirect <- function(start_p, end_p, path_p) { + prepare_connbidirect(start_p, end_p) %>% + complete(date = full_seq(date, period = 1), nesting(direction)) %>% + ggplot(aes(x = date, y = md, ymin = q1, ymax = q3, fill = direction)) + + geom_ribbon(alpha = 0.5) + + geom_line(aes(colour = direction), size = 0.75) + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = percent, limits = c(0, NA)) + + scale_colour_hue(name = "Medians and interquartile ranges", + breaks = c("both", "write", "read"), + labels = c("Both reading and writing", "Mostly writing", + "Mostly reading")) + + scale_fill_hue(name = "Medians and interquartile ranges", + breaks = c("both", "write", "read"), + labels = c("Both reading and writing", "Mostly writing", + "Mostly reading")) + + ggtitle("Fraction of connections used uni-/bidirectionally") + + labs(caption = copyright_notice) + + theme(legend.position = "top") + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_bandwidth_flags <- function(start_p = NULL, end_p = NULL) { + advbw <- read_csv(file = paste(stats_dir, "advbw.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + isexit = col_logical(), + isguard = col_logical(), + advbw = col_double())) %>% + transmute(date, have_guard_flag = isguard, have_exit_flag = isexit, + variable = "advbw", value = advbw * 8 / 1e9) + bwhist <- read_csv(file = paste(stats_dir, "bandwidth.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + isexit = col_logical(), + isguard = col_logical(), + bwread = col_double(), + bwwrite = col_double(), + dirread = col_double(), + dirwrite = col_double())) %>% + transmute(date, have_guard_flag = isguard, have_exit_flag = isexit, + variable = "bwhist", value = (bwread + bwwrite) * 8 / 2e9) + rbind(advbw, bwhist) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(!is.na(have_exit_flag)) %>% + filter(!is.na(have_guard_flag)) %>% + spread(variable, value) +} + +plot_bandwidth_flags <- function(start_p, end_p, path_p) { + prepare_bandwidth_flags(start_p, end_p) %>% + gather(variable, value, c(advbw, bwhist)) %>% + unite(flags, have_guard_flag, have_exit_flag) %>% + mutate(flags = factor(flags, + levels = c("FALSE_TRUE", "TRUE_TRUE", "TRUE_FALSE", "FALSE_FALSE"), + labels = c("Exit only", "Guard and Exit", "Guard only", + "Neither Guard nor Exit"))) %>% + mutate(variable = ifelse(variable == "advbw", + "Advertised bandwidth", "Consumed bandwidth")) %>% + ggplot(aes(x = date, y = value, fill = flags)) + + geom_area() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), + limits = c(0, NA)) + + scale_fill_manual(name = "", + values = c("#03B3FF", "#39FF02", "#FFFF00", "#AAAA99")) + + facet_grid(variable ~ .) + + ggtitle("Advertised and consumed bandwidth by relay flags") + + labs(caption = copyright_notice) + + theme(legend.position = "top") + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_userstats_relay_country <- function(start_p = NULL, end_p = NULL, + country_p = NULL, events_p = NULL) { + read_csv(file = paste(stats_dir, "clients.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + node = col_character(), + country = col_character(), + transport = col_character(), + version = col_character(), + lower = col_double(), + upper = col_double(), + clients = col_double(), + frac = col_double()), + na = character()) %>% + filter(node == "relay") %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(if (!is.null(country_p)) + country == ifelse(country_p == "all", "", country_p) else TRUE) %>% + filter(transport == "") %>% + filter(version == "") %>% + select(date, country, clients, lower, upper, frac) %>% + rename(users = clients) +} + +plot_userstats_relay_country <- function(start_p, end_p, country_p, events_p, + path_p) { + u <- prepare_userstats_relay_country(start_p, end_p, country_p, events_p) %>% + complete(date = full_seq(date, period = 1)) + plot <- ggplot(u, aes(x = date, y = users)) + if (length(na.omit(u$users)) > 0 & events_p != "off" & + country_p != "all") { + upturns <- u[u$users > u$upper, c("date", "users")] + downturns <- u[u$users < u$lower, c("date", "users")] + if (events_p == "on") { + u[!is.na(u$lower) & u$lower < 0, "lower"] <- 0 + plot <- plot + + geom_ribbon(data = u, aes(ymin = lower, ymax = upper), fill = "gray") + } + if (length(upturns$date) > 0) + plot <- plot + + geom_point(data = upturns, aes(x = date, y = users), size = 5, + colour = "dodgerblue2") + if (length(downturns$date) > 0) + plot <- plot + + geom_point(data = downturns, aes(x = date, y = users), size = 5, + colour = "firebrick2") + } + plot <- plot + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + ggtitle(paste("Directly connecting users", + ifelse(country_p == "all", "", + paste(" from", countryname(country_p))), sep = "")) + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_userstats_bridge_country <- function(start_p = NULL, end_p = NULL, + country_p = NULL) { + read_csv(file = paste(stats_dir, "clients.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + node = col_character(), + country = col_character(), + transport = col_character(), + version = col_character(), + lower = col_double(), + upper = col_double(), + clients = col_double(), + frac = col_double()), + na = character()) %>% + filter(node == "bridge") %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(if (!is.null(country_p)) + country == ifelse(country_p == "all", "", country_p) else TRUE) %>% + filter(transport == "") %>% + filter(version == "") %>% + select(date, country, clients, frac) %>% + rename(users = clients) +} + +plot_userstats_bridge_country <- function(start_p, end_p, country_p, path_p) { + prepare_userstats_bridge_country(start_p, end_p, country_p) %>% + complete(date = full_seq(date, period = 1)) %>% + ggplot(aes(x = date, y = users)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + ggtitle(paste("Bridge users", + ifelse(country_p == "all", "", + paste(" from", countryname(country_p))), sep = "")) + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_userstats_bridge_transport <- function(start_p = NULL, end_p = NULL, + transport_p = NULL) { + u <- read_csv(file = paste(stats_dir, "clients.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + node = col_character(), + country = col_character(), + transport = col_character(), + version = col_character(), + lower = col_double(), + upper = col_double(), + clients = col_double(), + frac = col_double())) %>% + filter(node == "bridge") %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(is.na(country)) %>% + filter(is.na(version)) %>% + filter(!is.na(transport)) %>% + select(date, transport, clients, frac) + if (is.null(transport_p) || "!<OR>" %in% transport_p) { + n <- u %>% + filter(transport != "<OR>") %>% + group_by(date, frac) %>% + summarize(clients = sum(clients)) + u <- rbind(u, data.frame(date = n$date, transport = "!<OR>", + clients = n$clients, frac = n$frac)) + } + u %>% + filter(if (!is.null(transport_p)) transport %in% transport_p else TRUE) %>% + select(date, transport, clients, frac) %>% + rename(users = clients) %>% + arrange(date, transport) +} + +plot_userstats_bridge_transport <- function(start_p, end_p, transport_p, + path_p) { + if (length(transport_p) > 1) { + title <- paste("Bridge users by transport") + } else { + title <- paste("Bridge users using", + ifelse(transport_p == "<??>", "unknown pluggable transport(s)", + ifelse(transport_p == "<OR>", "default OR protocol", + ifelse(transport_p == "!<OR>", "any pluggable transport", + ifelse(transport_p == "fte", "FTE", + ifelse(transport_p == "websocket", "Flash proxy/websocket", + paste("transport", transport_p))))))) + } + u <- prepare_userstats_bridge_transport(start_p, end_p, transport_p) %>% + complete(date = full_seq(date, period = 1), nesting(transport)) + if (length(transport_p) > 1) { + plot <- ggplot(u, aes(x = date, y = users, colour = transport)) + } else { + plot <- ggplot(u, aes(x = date, y = users)) + } + plot <- plot + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + ggtitle(title) + + labs(caption = copyright_notice) + if (length(transport_p) > 1) { + plot <- plot + + scale_colour_hue(name = "", breaks = transport_p, + labels = ifelse(transport_p == "<??>", "Unknown PT", + ifelse(transport_p == "<OR>", "Default OR protocol", + ifelse(transport_p == "!<OR>", "Any PT", + ifelse(transport_p == "fte", "FTE", + ifelse(transport_p == "websocket", "Flash proxy/websocket", + transport_p)))))) + } + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_userstats_bridge_version <- function(start_p = NULL, end_p = NULL, + version_p = NULL) { + read_csv(file = paste(stats_dir, "clients.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + node = col_character(), + country = col_character(), + transport = col_character(), + version = col_character(), + lower = col_double(), + upper = col_double(), + clients = col_double(), + frac = col_double())) %>% + filter(node == "bridge") %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(is.na(country)) %>% + filter(is.na(transport)) %>% + filter(if (!is.null(version_p)) version == version_p else TRUE) %>% + select(date, version, clients, frac) %>% + rename(users = clients) +} + +plot_userstats_bridge_version <- function(start_p, end_p, version_p, path_p) { + prepare_userstats_bridge_version(start_p, end_p, version_p) %>% + complete(date = full_seq(date, period = 1)) %>% + ggplot(aes(x = date, y = users)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + ggtitle(paste("Bridge users using IP", version_p, sep = "")) + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_userstats_bridge_combined <- function(start_p = NULL, end_p = NULL, + country_p = NULL) { + if (!is.null(country_p) && country_p == "all") { + prepare_userstats_bridge_country(start_p, end_p, country_p) + } else { + read_csv(file = paste(stats_dir, "userstats-combined.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + node = col_skip(), + country = col_character(), + transport = col_character(), + version = col_skip(), + frac = col_double(), + low = col_double(), + high = col_double()), + na = character()) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(if (!is.null(country_p)) country == country_p else TRUE) %>% + select(date, country, transport, low, high, frac) %>% + arrange(date, country, transport) + } +} + +plot_userstats_bridge_combined <- function(start_p, end_p, country_p, path_p) { + if (country_p == "all") { + plot_userstats_bridge_country(start_p, end_p, country_p, path_p) + } else { + top <- 3 + u <- prepare_userstats_bridge_combined(start_p, end_p, country_p) + a <- aggregate(list(mid = (u$high + u$low) / 2), + by = list(transport = u$transport), FUN = sum) + a <- a[order(a$mid, decreasing = TRUE)[1:top], ] + u <- u[u$transport %in% a$transport, ] %>% + complete(date = full_seq(date, period = 1), nesting(country, transport)) + title <- paste("Bridge users by transport from ", + countryname(country_p), sep = "") + ggplot(u, aes(x = as.Date(date), ymin = low, ymax = high, + fill = transport)) + + geom_ribbon(alpha = 0.5, size = 0.5) + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", limits = c(0, NA), labels = formatter) + + scale_colour_hue("Top-3 transports") + + scale_fill_hue("Top-3 transports") + + ggtitle(title) + + labs(caption = copyright_notice) + + theme(legend.position = "top") + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) + } +} + +prepare_advbwdist_perc <- function(start_p = NULL, end_p = NULL, p_p = NULL) { + read_csv(file = paste(stats_dir, "advbwdist.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + isexit = col_logical(), + relay = col_skip(), + percentile = col_integer(), + advbw = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(if (!is.null(p_p)) percentile %in% as.numeric(p_p) else + percentile != "") %>% + transmute(date, percentile = as.factor(percentile), + variable = ifelse(is.na(isexit), "all", "exits"), + advbw = advbw * 8 / 1e9) %>% + spread(variable, advbw) %>% + rename(p = percentile) +} + +plot_advbwdist_perc <- function(start_p, end_p, p_p, path_p) { + prepare_advbwdist_perc(start_p, end_p, p_p) %>% + gather(variable, advbw, -c(date, p)) %>% + mutate(variable = ifelse(variable == "all", "All relays", + "Exits only")) %>% + complete(date = full_seq(date, period = 1), nesting(p, variable)) %>% + ggplot(aes(x = date, y = advbw, colour = p)) + + facet_grid(variable ~ .) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), + limits = c(0, NA)) + + scale_colour_hue(name = "Percentile") + + ggtitle("Advertised bandwidth distribution") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_advbwdist_relay <- function(start_p = NULL, end_p = NULL, n_p = NULL) { + read_csv(file = paste(stats_dir, "advbwdist.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + isexit = col_logical(), + relay = col_integer(), + percentile = col_skip(), + advbw = col_double())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(if (!is.null(n_p)) relay %in% as.numeric(n_p) else + relay != "") %>% + transmute(date, relay = as.factor(relay), + variable = ifelse(is.na(isexit), "all", "exits"), + advbw = advbw * 8 / 1e9) %>% + spread(variable, advbw) %>% + rename(n = relay) +} + +plot_advbwdist_relay <- function(start_p, end_p, n_p, path_p) { + prepare_advbwdist_relay(start_p, end_p, n_p) %>% + gather(variable, advbw, -c(date, n)) %>% + mutate(variable = ifelse(variable == "all", "All relays", + "Exits only")) %>% + complete(date = full_seq(date, period = 1), nesting(n, variable)) %>% + ggplot(aes(x = date, y = advbw, colour = n)) + + facet_grid(variable ~ .) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), + limits = c(0, NA)) + + scale_colour_hue(name = "n") + + ggtitle("Advertised bandwidth of n-th fastest relays") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_hidserv_dir_onions_seen <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "hidserv.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + type = col_factor(levels = NULL), + wmean = col_skip(), + wmedian = col_skip(), + wiqm = col_double(), + frac = col_double(), + stats = col_skip())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(type == "dir-onions-seen") %>% + transmute(date, onions = ifelse(frac >= 0.01, wiqm, NA), frac) +} + +plot_hidserv_dir_onions_seen <- function(start_p, end_p, path_p) { + prepare_hidserv_dir_onions_seen(start_p, end_p) %>% + complete(date = full_seq(date, period = 1)) %>% + ggplot(aes(x = date, y = onions)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", limits = c(0, NA), labels = formatter) + + ggtitle("Unique .onion addresses") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_hidserv_rend_relayed_cells <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "hidserv.csv", sep = ""), + col_types = cols( + date = col_date(format = ""), + type = col_factor(levels = NULL), + wmean = col_skip(), + wmedian = col_skip(), + wiqm = col_double(), + frac = col_double(), + stats = col_skip())) %>% + filter(if (!is.null(start_p)) date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) date <= as.Date(end_p) else TRUE) %>% + filter(type == "rend-relayed-cells") %>% + transmute(date, + relayed = ifelse(frac >= 0.01, wiqm * 8 * 512 / (86400 * 1e9), NA), frac) +} + +plot_hidserv_rend_relayed_cells <- function(start_p, end_p, path_p) { + prepare_hidserv_rend_relayed_cells(start_p, end_p) %>% + complete(date = full_seq(date, period = 1)) %>% + ggplot(aes(x = date, y = relayed)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), + limits = c(0, NA)) + + ggtitle("Onion-service traffic") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_webstats_tb <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "webstats.csv", sep = ""), + col_types = cols( + log_date = col_date(format = ""), + request_type = col_factor(levels = NULL), + platform = col_skip(), + channel = col_skip(), + locale = col_skip(), + incremental = col_skip(), + count = col_double())) %>% + filter(if (!is.null(start_p)) log_date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) log_date <= as.Date(end_p) else TRUE) %>% + filter(request_type %in% c("tbid", "tbsd", "tbup", "tbur")) %>% + group_by(log_date, request_type) %>% + summarize(count = sum(count)) %>% + spread(request_type, count) %>% + rename(date = log_date, initial_downloads = tbid, + signature_downloads = tbsd, update_pings = tbup, + update_requests = tbur) +} + +plot_webstats_tb <- function(start_p, end_p, path_p) { + prepare_webstats_tb(start_p, end_p) %>% + gather(request_type, count, -date) %>% + mutate(request_type = factor(request_type, + levels = c("initial_downloads", "signature_downloads", "update_pings", + "update_requests"), + labels = c("Initial downloads", "Signature downloads", "Update pings", + "Update requests"))) %>% + ungroup() %>% + complete(date = full_seq(date, period = 1), nesting(request_type)) %>% + ggplot(aes(x = date, y = count)) + + geom_point() + + geom_line() + + facet_grid(request_type ~ ., scales = "free_y") + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + theme(strip.text.y = element_text(angle = 0, hjust = 0, size = rel(1.5)), + strip.background = element_rect(fill = NA)) + + ggtitle("Tor Browser downloads and updates") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_webstats_tb_platform <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "webstats.csv", sep = ""), + col_types = cols( + log_date = col_date(format = ""), + request_type = col_factor(levels = NULL), + platform = col_factor(levels = NULL), + channel = col_skip(), + locale = col_skip(), + incremental = col_skip(), + count = col_double())) %>% + filter(if (!is.null(start_p)) log_date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) log_date <= as.Date(end_p) else TRUE) %>% + filter(request_type %in% c("tbid", "tbup")) %>% + group_by(log_date, platform, request_type) %>% + summarize(count = sum(count)) %>% + spread(request_type, count, fill = 0) %>% + rename(date = log_date, initial_downloads = tbid, update_pings = tbup) +} + +plot_webstats_tb_platform <- function(start_p, end_p, path_p) { + prepare_webstats_tb_platform(start_p, end_p) %>% + gather(request_type, count, -c(date, platform)) %>% + mutate(request_type = factor(request_type, + levels = c("initial_downloads", "update_pings"), + labels = c("Initial downloads", "Update pings"))) %>% + ungroup() %>% + complete(date = full_seq(date, period = 1), + nesting(platform, request_type)) %>% + ggplot(aes(x = date, y = count, colour = platform)) + + geom_point() + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + scale_colour_hue(name = "Platform", + breaks = c("w", "m", "l", "o", ""), + labels = c("Windows", "macOS", "Linux", "Other", "Unknown")) + + facet_grid(request_type ~ ., scales = "free_y") + + theme(strip.text.y = element_text(angle = 0, hjust = 0, size = rel(1.5)), + strip.background = element_rect(fill = NA), + legend.position = "top") + + ggtitle("Tor Browser downloads and updates by platform") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_webstats_tb_locale <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "webstats.csv", sep = ""), + col_types = cols( + log_date = col_date(format = ""), + request_type = col_factor(levels = NULL), + platform = col_skip(), + channel = col_skip(), + locale = col_factor(levels = NULL), + incremental = col_skip(), + count = col_double())) %>% + filter(if (!is.null(start_p)) log_date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) log_date <= as.Date(end_p) else TRUE) %>% + filter(request_type %in% c("tbid", "tbup")) %>% + rename(date = log_date) %>% + group_by(date, locale, request_type) %>% + summarize(count = sum(count)) %>% + mutate(request_type = factor(request_type, levels = c("tbid", "tbup"))) %>% + spread(request_type, count, fill = 0) %>% + rename(initial_downloads = tbid, update_pings = tbup) +} + +plot_webstats_tb_locale <- function(start_p, end_p, path_p) { + d <- prepare_webstats_tb_locale(start_p, end_p) %>% + gather(request_type, count, -c(date, locale)) %>% + mutate(request_type = factor(request_type, + levels = c("initial_downloads", "update_pings"), + labels = c("Initial downloads", "Update pings"))) + e <- d + e <- aggregate(list(count = e$count), by = list(locale = e$locale), FUN = sum) + e <- e[order(e$count, decreasing = TRUE), ] + e <- e[1:5, ] + d <- aggregate(list(count = d$count), by = list(date = d$date, + request_type = d$request_type, + locale = ifelse(d$locale %in% e$locale, d$locale, "(other)")), FUN = sum) + d %>% + complete(date = full_seq(date, period = 1), + nesting(locale, request_type)) %>% + ggplot(aes(x = date, y = count, colour = locale)) + + geom_point() + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + scale_colour_hue(name = "Locale", + breaks = c(e$locale, "(other)"), + labels = c(as.character(e$locale), "Other")) + + facet_grid(request_type ~ ., scales = "free_y") + + theme(strip.text.y = element_text(angle = 0, hjust = 0, size = rel(1.5)), + strip.background = element_rect(fill = NA), + legend.position = "top") + + guides(col = guide_legend(nrow = 1)) + + ggtitle("Tor Browser downloads and updates by locale") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_webstats_tm <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "webstats.csv", sep = ""), + col_types = cols( + log_date = col_date(format = ""), + request_type = col_factor(levels = NULL), + platform = col_skip(), + channel = col_skip(), + locale = col_skip(), + incremental = col_skip(), + count = col_double())) %>% + filter(if (!is.null(start_p)) log_date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) log_date <= as.Date(end_p) else TRUE) %>% + filter(request_type %in% c("tmid", "tmup")) %>% + group_by(log_date, request_type) %>% + summarize(count = sum(count)) %>% + mutate(request_type = factor(request_type, levels = c("tmid", "tmup"))) %>% + spread(request_type, count, drop = FALSE, fill = 0) %>% + rename(date = log_date, initial_downloads = tmid, update_pings = tmup) +} + +plot_webstats_tm <- function(start_p, end_p, path_p) { + prepare_webstats_tm(start_p, end_p) %>% + gather(request_type, count, -date) %>% + mutate(request_type = factor(request_type, + levels = c("initial_downloads", "update_pings"), + labels = c("Initial downloads", "Update pings"))) %>% + ungroup() %>% + complete(date = full_seq(date, period = 1), nesting(request_type)) %>% + ggplot(aes(x = date, y = count)) + + geom_point() + + geom_line() + + facet_grid(request_type ~ ., scales = "free_y") + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + theme(strip.text.y = element_text(angle = 0, hjust = 0, size = rel(1.5)), + strip.background = element_rect(fill = NA)) + + ggtitle("Tor Messenger downloads and updates") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_relays_ipv6 <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "ipv6servers.csv", sep = ""), + col_types = cols( + valid_after_date = col_date(format = ""), + server = col_factor(levels = NULL), + guard_relay = col_skip(), + exit_relay = col_skip(), + announced_ipv6 = col_logical(), + exiting_ipv6_relay = col_logical(), + reachable_ipv6_relay = col_logical(), + server_count_sum_avg = col_double(), + advertised_bandwidth_bytes_sum_avg = col_skip())) %>% + filter(if (!is.null(start_p)) + valid_after_date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) + valid_after_date <= as.Date(end_p) else TRUE) %>% + filter(server == "relay") %>% + group_by(valid_after_date) %>% + summarize(total = sum(server_count_sum_avg), + announced = sum(server_count_sum_avg[announced_ipv6]), + reachable = sum(server_count_sum_avg[reachable_ipv6_relay]), + exiting = sum(server_count_sum_avg[exiting_ipv6_relay])) %>% + rename(date = valid_after_date) +} + +plot_relays_ipv6 <- function(start_p, end_p, path_p) { + prepare_relays_ipv6(start_p, end_p) %>% + complete(date = full_seq(date, period = 1)) %>% + gather(category, count, -date) %>% + ggplot(aes(x = date, y = count, colour = category)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + scale_colour_hue(name = "", h.start = 90, + breaks = c("total", "announced", "reachable", "exiting"), + labels = c("Total (IPv4) OR", "IPv6 announced OR", "IPv6 reachable OR", + "IPv6 exiting")) + + ggtitle("Relays by IP version") + + labs(caption = copyright_notice) + + theme(legend.position = "top") + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_bridges_ipv6 <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "ipv6servers.csv", sep = ""), + col_types = cols( + valid_after_date = col_date(format = ""), + server = col_factor(levels = NULL), + guard_relay = col_skip(), + exit_relay = col_skip(), + announced_ipv6 = col_logical(), + exiting_ipv6_relay = col_skip(), + reachable_ipv6_relay = col_skip(), + server_count_sum_avg = col_double(), + advertised_bandwidth_bytes_sum_avg = col_skip())) %>% + filter(if (!is.null(start_p)) + valid_after_date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) + valid_after_date <= as.Date(end_p) else TRUE) %>% + filter(server == "bridge") %>% + group_by(valid_after_date) %>% + summarize(total = sum(server_count_sum_avg), + announced = sum(server_count_sum_avg[announced_ipv6])) %>% + rename(date = valid_after_date) +} + +plot_bridges_ipv6 <- function(start_p, end_p, path_p) { + prepare_bridges_ipv6(start_p, end_p) %>% + complete(date = full_seq(date, period = 1)) %>% + gather(category, count, -date) %>% + ggplot(aes(x = date, y = count, colour = category)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + scale_colour_hue(name = "", h.start = 90, + breaks = c("total", "announced"), + labels = c("Total (IPv4) OR", "IPv6 announced OR")) + + ggtitle("Bridges by IP version") + + labs(caption = copyright_notice) + + theme(legend.position = "top") + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_advbw_ipv6 <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "ipv6servers.csv", sep = ""), + col_types = cols( + valid_after_date = col_date(format = ""), + server = col_factor(levels = NULL), + guard_relay = col_logical(), + exit_relay = col_logical(), + announced_ipv6 = col_logical(), + exiting_ipv6_relay = col_logical(), + reachable_ipv6_relay = col_logical(), + server_count_sum_avg = col_skip(), + advertised_bandwidth_bytes_sum_avg = col_double())) %>% + filter(if (!is.null(start_p)) + valid_after_date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) + valid_after_date <= as.Date(end_p) else TRUE) %>% + filter(server == "relay") %>% + mutate(advertised_bandwidth_bytes_sum_avg = + advertised_bandwidth_bytes_sum_avg * 8 / 1e9) %>% + group_by(valid_after_date) %>% + summarize(total = sum(advertised_bandwidth_bytes_sum_avg), + total_guard = sum(advertised_bandwidth_bytes_sum_avg[guard_relay]), + total_exit = sum(advertised_bandwidth_bytes_sum_avg[exit_relay]), + reachable_guard = sum(advertised_bandwidth_bytes_sum_avg[ + reachable_ipv6_relay & guard_relay]), + reachable_exit = sum(advertised_bandwidth_bytes_sum_avg[ + reachable_ipv6_relay & exit_relay]), + exiting = sum(advertised_bandwidth_bytes_sum_avg[ + exiting_ipv6_relay])) %>% + rename(date = valid_after_date) +} + +plot_advbw_ipv6 <- function(start_p, end_p, path_p) { + prepare_advbw_ipv6(start_p, end_p) %>% + complete(date = full_seq(date, period = 1)) %>% + gather(category, advbw, -date) %>% + ggplot(aes(x = date, y = advbw, colour = category)) + + geom_line() + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = unit_format(unit = "Gbit/s"), + limits = c(0, NA)) + + scale_colour_hue(name = "", h.start = 90, + breaks = c("total", "total_guard", "total_exit", "reachable_guard", + "reachable_exit", "exiting"), + labels = c("Total (IPv4) OR", "Guard total (IPv4)", "Exit total (IPv4)", + "Reachable guard IPv6 OR", "Reachable exit IPv6 OR", "IPv6 exiting")) + + ggtitle("Advertised bandwidth by IP version") + + labs(caption = copyright_notice) + + theme(legend.position = "top") + + guides(colour = guide_legend(nrow = 2, byrow = TRUE)) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +prepare_totalcw <- function(start_p = NULL, end_p = NULL) { + read_csv(file = paste(stats_dir, "totalcw.csv", sep = ""), + col_types = cols( + valid_after_date = col_date(format = ""), + nickname = col_character(), + have_guard_flag = col_logical(), + have_exit_flag = col_logical(), + measured_sum_avg = col_double())) %>% + filter(if (!is.null(start_p)) + valid_after_date >= as.Date(start_p) else TRUE) %>% + filter(if (!is.null(end_p)) + valid_after_date <= as.Date(end_p) else TRUE) %>% + group_by(valid_after_date, nickname) %>% + summarize(measured_sum_avg = sum(measured_sum_avg)) %>% + rename(date = valid_after_date, totalcw = measured_sum_avg) %>% + arrange(date, nickname) +} + +plot_totalcw <- function(start_p, end_p, path_p) { + prepare_totalcw(start_p, end_p) %>% + mutate(nickname = ifelse(is.na(nickname), "consensus", nickname)) %>% + mutate(nickname = factor(nickname, + levels = c("consensus", unique(nickname[nickname != "consensus"])))) %>% + ungroup() %>% + complete(date = full_seq(date, period = 1), nesting(nickname)) %>% + ggplot(aes(x = date, y = totalcw, colour = nickname)) + + geom_line(na.rm = TRUE) + + scale_x_date(name = "", breaks = custom_breaks, + labels = custom_labels, minor_breaks = custom_minor_breaks) + + scale_y_continuous(name = "", labels = formatter, limits = c(0, NA)) + + scale_colour_hue(name = "") + + ggtitle("Total consensus weights across bandwidth authorities") + + labs(caption = copyright_notice) + ggsave(filename = path_p, width = 8, height = 5, dpi = 150) +} + +countrynames <- function(countries) { + sapply(countries, countryname) +} + +write_userstats <- function(start, end, node, path) { + end <- min(end, as.character(Sys.Date())) + c <- read.csv(paste("/srv/metrics.torproject.org/metrics/shared/stats/", + "clients.csv", sep = ""), stringsAsFactors = FALSE) + c <- c[c$date >= start & c$date <= end & c$country != '' & + c$transport == '' & c$version == '' & c$node == node, ] + u <- data.frame(country = c$country, users = c$clients, + stringsAsFactors = FALSE) + u <- u[!is.na(u$users), ] + u <- aggregate(list(users = u$users), by = list(country = u$country), + mean) + total <- sum(u$users) + u <- u[!(u$country %in% c("zy", "??", "a1", "a2", "o1", "ap", "eu")), ] + u <- u[order(u$users, decreasing = TRUE), ] + u <- u[1:10, ] + u <- data.frame( + cc = as.character(u$country), + country = sub('the ', '', countrynames(as.character(u$country))), + abs = round(u$users), + rel = sprintf("%.2f", round(100 * u$users / total, 2))) + write.csv(u, path, quote = FALSE, row.names = FALSE) +} + +write_userstats_relay <- function(start, end, path) { + write_userstats(start, end, 'relay', path) +} + +write_userstats_bridge <- function(start, end, path) { + write_userstats(start, end, 'bridge', path) +} + +write_userstats_censorship_events <- function(start, end, path) { + end <- min(end, as.character(Sys.Date())) + c <- read.csv(paste("/srv/metrics.torproject.org/metrics/shared/stats/", + "clients.csv", sep = ""), stringsAsFactors = FALSE) + c <- c[c$date >= start & c$date <= end & c$country != '' & + c$transport == '' & c$version == '' & c$node == 'relay', ] + r <- data.frame(date = c$date, country = c$country, + upturn = ifelse(!is.na(c$upper) & + c$clients > c$upper, 1, 0), + downturn = ifelse(!is.na(c$lower) & + c$clients < c$lower, 1, 0)) + r <- aggregate(r[, c("upturn", "downturn")], + by = list(country = r$country), sum) + r <- r[(r$country %in% names(countrylist)), ] + r <- r[order(r$downturn, r$upturn, decreasing = TRUE), ] + r <- r[1:10, ] + r <- data.frame(cc = r$country, + country = sub('the ', '', countrynames(as.character(r$country))), + downturns = r$downturn, + upturns = r$upturn) + write.csv(r, path, quote = FALSE, row.names = FALSE) +}
diff --git a/src/main/R/rserver/tables.R b/src/main/R/rserver/tables.R deleted file mode 100644 index 28bd3d5..0000000 --- a/src/main/R/rserver/tables.R +++ /dev/null @@ -1,58 +0,0 @@ -countrynames <- function(countries) { - sapply(countries, countryname) -} - -write_userstats <- function(start, end, node, path) { - end <- min(end, as.character(Sys.Date())) - c <- read.csv(paste("/srv/metrics.torproject.org/metrics/shared/stats/", - "clients.csv", sep = ""), stringsAsFactors = FALSE) - c <- c[c$date >= start & c$date <= end & c$country != '' & - c$transport == '' & c$version == '' & c$node == node, ] - u <- data.frame(country = c$country, users = c$clients, - stringsAsFactors = FALSE) - u <- u[!is.na(u$users), ] - u <- aggregate(list(users = u$users), by = list(country = u$country), - mean) - total <- sum(u$users) - u <- u[!(u$country %in% c("zy", "??", "a1", "a2", "o1", "ap", "eu")), ] - u <- u[order(u$users, decreasing = TRUE), ] - u <- u[1:10, ] - u <- data.frame( - cc = as.character(u$country), - country = sub('the ', '', countrynames(as.character(u$country))), - abs = round(u$users), - rel = sprintf("%.2f", round(100 * u$users / total, 2))) - write.csv(u, path, quote = FALSE, row.names = FALSE) -} - -write_userstats_relay <- function(start, end, path) { - write_userstats(start, end, 'relay', path) -} - -write_userstats_bridge <- function(start, end, path) { - write_userstats(start, end, 'bridge', path) -} - -write_userstats_censorship_events <- function(start, end, path) { - end <- min(end, as.character(Sys.Date())) - c <- read.csv(paste("/srv/metrics.torproject.org/metrics/shared/stats/", - "clients.csv", sep = ""), stringsAsFactors = FALSE) - c <- c[c$date >= start & c$date <= end & c$country != '' & - c$transport == '' & c$version == '' & c$node == 'relay', ] - r <- data.frame(date = c$date, country = c$country, - upturn = ifelse(!is.na(c$upper) & - c$clients > c$upper, 1, 0), - downturn = ifelse(!is.na(c$lower) & - c$clients < c$lower, 1, 0)) - r <- aggregate(r[, c("upturn", "downturn")], - by = list(country = r$country), sum) - r <- r[(r$country %in% names(countrylist)), ] - r <- r[order(r$downturn, r$upturn, decreasing = TRUE), ] - r <- r[1:10, ] - r <- data.frame(cc = r$country, - country = sub('the ', '', countrynames(as.character(r$country))), - downturns = r$downturn, - upturns = r$upturn) - write.csv(r, path, quote = FALSE, row.names = FALSE) -} -