Bearloga has uploaded a new change for review.
https://gerrit.wikimedia.org/r/251174
Change subject: Add time frame selection (preset + custom) > Added an option
to view last 7/30/90 days or have a custom range > Uses global setting by
default with ability to override for "local" plot > This is independent of
the KPI date range stuff because that g
......................................................................
Add time frame selection (preset + custom)
> Added an option to view last 7/30/90 days or have a custom range
> Uses global setting by default with ability to override for "local" plot
> This is independent of the KPI date range stuff because that gets 2x range
in order to compute a % change, so letting the user specify a "custom" range
would take a lot of work and would be wonky. So the new selector (specific
to KPI tabset) only has presets: Yesterday and Last 7/30/90 days.
+ Moved smoothing into a 'Global Settings' menu where time frame also lives
+ Fixes the KPI date range selection
> It relied on unstable data, so that got fixed.
> It used "safe_tail" which is bad because it relies on n rather than dates
> Switched over to subsetting by dates
! Note: This relies on polloi 0.0.5 to be CR'd and merged.
Bug: T116782
Change-Id: I72a90a00235c536da35a893e03a68f9a23caa62d
---
M CHANGELOG.md
M server.R
M tab_documentation/kpis_summary.md
M ui.R
M utils.R
5 files changed, 314 insertions(+), 202 deletions(-)
git pull ssh://gerrit.wikimedia.org:29418/wikimedia/discovery/rainbow
refs/changes/74/251174/1
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3628ba9..12412a7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,14 @@
# Change Log (Patch Notes)
All notable changes to this project will be documented in this file.
+## 2015/11/05
+- Added time frame selection
+- Fixed KPI date range selection (more robust, correct)
+
+## 2015/11/04
+- Began the move to 'date'-named datasets (as opposed to 'timestamp'-named
datasets)
+- Removed moving average as a smoothing option
+
## 2015/09/30
- Added a change log
- Added a contributor code of conduct
diff --git a/server.R b/server.R
index a17775e..287487c 100644
--- a/server.R
+++ b/server.R
@@ -1,4 +1,4 @@
-## Version 0.2.0
+## Version 0.3.0
source("utils.R")
existing_date <- Sys.Date() - 1
@@ -14,6 +14,24 @@
read_augmented_clickthrough()
read_lethal_dose()
existing_date <<- Sys.Date()
+ }
+
+ # This is used to subset for getting the bounds for
polloi::subset_by_date_range():
+ time_frame_range <- function(tf_selector) {
+ tf_setting <- input[[tf_selector]]
+ if ( tf_setting == 'global' ) {
+ if ( input$timeframe_global == 'custom' ) {
+ return(input$daterange_global)
+ } else {
+ tf_setting <- input$timeframe_global
+ }
+ }
+ return(switch(tf_setting,
+ all = c(as.Date("2015-04-14"), Sys.Date()-1),
+ week = c(Sys.Date()-8, Sys.Date()-1),
+ month = c(Sys.Date()-31, Sys.Date()-1),
+ quarter = c(Sys.Date()-91, Sys.Date()-1),
+ custom = input[[paste(tf_selector, "daterange", sep =
"_")]]))
}
## Desktop value boxes
@@ -46,18 +64,17 @@
## The dynamic graphs of events on desktop
output$desktop_event_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(desktop_dygraph_set,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_desktop_event)),
- xlab = "Date", ylab = "Events", title = "Desktop
search events, by day")
+ desktop_dygraph_set %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_desktop_event))
%>%
+
polloi::subset_by_date_range(time_frame_range("desktop_event_timeframe")) %>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Events", title = "Desktop
search events, by day")
})
output$desktop_load_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(desktop_load_data,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_desktop_load)),
- xlab = "Date", ylab = "Load time (ms)", title =
"Desktop load times, by day",
- use_si = FALSE)
+ desktop_load_data %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_desktop_load)) %>%
+ polloi::subset_by_date_range(time_frame_range("desktop_load_timeframe"))
%>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Load time (ms)", title =
"Desktop load times, by day", use_si = FALSE)
})
## Mobile value boxes
@@ -90,18 +107,17 @@
## Mobile plots
output$mobile_event_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(mobile_dygraph_set,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_mobile_event)),
- xlab = "Date", ylab = "Events", title = "Mobile
search events, by day")
+ mobile_dygraph_set %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_mobile_event)) %>%
+ polloi::subset_by_date_range(time_frame_range("mobile_event_timeframe"))
%>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Events", title = "Mobile
search events, by day")
})
output$mobile_load_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(mobile_load_data,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_mobile_load)),
- xlab = "Date", ylab = "Load time (ms)", "Mobile
result load times, by day",
- use_si = FALSE)
+ mobile_load_data %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_mobile_load)) %>%
+ polloi::subset_by_date_range(time_frame_range("mobile_load_timeframe"))
%>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Load time (ms)", title =
"Mobile search events, by day", use_si = FALSE)
})
## App value boxes
@@ -134,112 +150,103 @@
## App plots
output$android_event_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(android_dygraph_set,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_app_event)),
- xlab = "Date", ylab = "Events", "Android mobile app
search events, by day")
+ android_dygraph_set %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_app_event)) %>%
+ polloi::subset_by_date_range(time_frame_range("app_event_timeframe")) %>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Events", title = "Android
mobile app search events, by day")
})
output$android_load_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(android_load_data,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_app_load)),
- xlab = "Date", ylab = "Load time (ms)", "Android
result load times, by day",
- use_si = FALSE)
+ android_load_data %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_app_load)) %>%
+ polloi::subset_by_date_range(time_frame_range("app_load_timeframe")) %>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Load time (ms)", title =
"Android result load times, by day", use_si = FALSE)
})
output$ios_event_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(ios_dygraph_set,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_app_event)),
- xlab = "Date", ylab = "Events", "iOS mobile app
search events, by day")
+ ios_dygraph_set %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_app_event)) %>%
+ polloi::subset_by_date_range(time_frame_range("app_event_timeframe")) %>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Events", title = "iOS mobile
app search events, by day")
})
output$ios_load_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(ios_load_data,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_app_load)),
- xlab = "Date", ylab = "Load time (ms)", "iOS result
load times, by day",
- use_si = FALSE)
+ ios_load_data %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_app_load)) %>%
+ polloi::subset_by_date_range(time_frame_range("app_load_timeframe")) %>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Load time (ms)", title =
"iOS result load times, by day", use_si = FALSE)
})
## API plots
output$cirrus_aggregate <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(split_dataset$cirrus[,c(1,3)],
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_fulltext_search)),
- xlab = "Date", ylab = "Searches", "Full-text via API
usage by day",
- legend_name = "Searches")
+ split_dataset$cirrus[, c(1, 3)] %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_fulltext_search))
%>%
+
polloi::subset_by_date_range(time_frame_range("fulltext_search_timeframe")) %>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Searches", title =
"Full-text via API usage by day", legend_name = "Searches")
})
output$open_aggregate <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(split_dataset$open[,c(1,3)],
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_open_search)),
- xlab = "Date", ylab = "Searches", "OpenSearch API
usage by day",
- legend_name = "Searches")
+ split_dataset$open[, c(1, 3)] %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_open_search)) %>%
+ polloi::subset_by_date_range(time_frame_range("open_search_timeframe"))
%>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Searches", title =
"OpenSearch API usage by day", legend_name = "Searches")
})
output$geo_aggregate <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(split_dataset$geo[,c(1,3)],
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_geo_search)),
- xlab = "Date", ylab = "Searches", "Geo Search API
usage by day",
- legend_name = "Searches")
+ split_dataset$geo[, c(1, 3)] %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_geo_search)) %>%
+ polloi::subset_by_date_range(time_frame_range("geo_search_timeframe"))
%>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Searches", title = "Geo
Search API usage by day", legend_name = "Searches")
})
output$language_aggregate <- renderDygraph({
- polloi::make_dygraph(data =
polloi::smoother(split_dataset$language[,c(1,3)],
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_language_search)),
- xlab = "Date", ylab = "Searches", "Language Search
API usage by day",
- legend_name = "Searches")
+ split_dataset$language[, c(1, 3)] %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_language_search))
%>%
+
polloi::subset_by_date_range(time_frame_range("language_search_timeframe")) %>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Searches", title = "Language
Search API usage by day", legend_name = "Searches")
})
output$prefix_aggregate <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(split_dataset$prefix[,c(1,3)],
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_prefix_search)),
- xlab = "Date", ylab = "Searches", "Prefix Search API
usage by day",
- legend_name = "Searches")
+ split_dataset$prefix[, c(1, 3)] %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_prefix_search))
%>%
+
polloi::subset_by_date_range(time_frame_range("prefix_search_timeframe")) %>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Searches", title = "Prefix
Search API usage by day", legend_name = "Searches")
})
# Failure plots
output$failure_rate_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(failure_dygraph_set,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_failure_rate)),
- xlab = "Date", ylab = "Queries", "Search Queries with
Zero Results, by day")
+ failure_dygraph_set %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_failure_rate)) %>%
+ polloi::subset_by_date_range(time_frame_range("failure_rate_timeframe"))
%>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Queries", title = "Search
Queries with Zero Results, by day")
})
output$failure_rate_change_plot <- renderDygraph({
- polloi::make_dygraph(data =
polloi::smoother(failure_roc_dygraph_set[,c(1,3)],
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_failure_rate)),
- xlab = "Date", ylab = "Change (%)", "Zero Results
rate change, by day",
- legend_name = "Change")
+ failure_roc_dygraph_set[, c(1, 3)] %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global, input$smoothing_failure_rate)) %>%
+ polloi::subset_by_date_range(time_frame_range("failure_rate_timeframe"))
%>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Change (%)", title = "Zero
Results rate change, by day", legend_name = "Change")
})
output$failure_breakdown_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(failure_breakdown_dygraph_set,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_failure_breakdown)),
- xlab = "Date", ylab = "Zero Results Rate (%)", "Zero
result rate by search type")
+ failure_breakdown_dygraph_set %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global,
input$smoothing_failure_breakdown)) %>%
+
polloi::subset_by_date_range(time_frame_range("failure_breakdown_timeframe"))
%>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Zero Results Rate (%)",
title = "Zero result rate by search type")
})
output$suggestion_dygraph_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(suggestion_dygraph_set,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_failure_suggestions)),
- xlab = "Date", ylab = "Zero Results Rate (%)", "Zero
Result Rates with Search Suggestions")
+ suggestion_dygraph_set %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global,
input$smoothing_failure_suggestions)) %>%
+
polloi::subset_by_date_range(time_frame_range("failure_suggestions_timeframe"))
%>%
+ polloi::make_dygraph(xlab = "Date", ylab = "Zero Results Rate (%)",
title = "Zero Result Rates with Search Suggestions")
})
output$lethal_dose_plot <- renderDygraph({
- polloi::make_dygraph(data = polloi::smoother(user_page_visit_dataset,
- smooth_level =
polloi::smooth_switch(input$smoothing_global,
-
input$smoothing_lethal_dose_plot)),
- xlab = "", ylab = "Time (s)",
- title = "Time at which we have lost N% of the users")
%>%
+ user_page_visit_dataset %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global,
input$smoothing_lethal_dose_plot)) %>%
+ polloi::subset_by_date_range(time_frame_range("lethal_dose_timeframe"))
%>%
+ polloi::make_dygraph(xlab = "", ylab = "Time (s)", title = "Time at
which we have lost N% of the users") %>%
dyAxis("x", ticker = "Dygraph.dateTicker", axisLabelFormatter =
CustomAxisFormatter,
axisLabelWidth = 100, pixelsPerLabel = 80) %>%
dyLegend(labelsDiv = "lethal_dose_plot_legend")
@@ -250,65 +257,74 @@
date_range <- input$kpi_summary_date_range_selector
switch(date_range,
daily = {
- temp <- polloi::safe_tail(desktop_load_data, 2)$date %>% {
- paste0(as.character(., "%A, %b "),
- sub("([a-z]{2})", "<sup>\\1</sup>",
- sapply(as.numeric(as.character(., "%e")),
toOrdinal)))
- }
+ dates <- Sys.Date() - c(1, 2)
+ temp <- dates %>%
+ as.character("%e") %>%
+ as.numeric %>%
+ sapply(toOrdinal) %>%
+ sub("([a-z]{2})", "<sup>\\1</sup>", .) %>%
+ paste0(as.character(dates, "%A, %b "), .)
},
weekly = {
- date_range_index <- c(1, 7, 8, 14)
- temp <- polloi::safe_tail(desktop_load_data,
date_range_index[4])$date %>% {
- paste0(as.character(.[date_range_index], "%b "),
- sub("([a-z]{2})", "<sup>\\1</sup>",
- sapply(as.numeric(as.character(.[date_range_index],
"%e")), toOrdinal)))
- } %>% {
- c(paste(.[1:2], collapse = "-"), paste(.[3:4], collapse = "-"))
- }
+ dates <- Sys.Date() - c(1, 8, 9, 15)
+ temp <- dates %>%
+ as.character("%e") %>%
+ as.numeric %>%
+ sapply(toOrdinal) %>%
+ sub("([a-z]{2})", "<sup>\\1</sup>", .) %>%
+ paste0(as.character(dates, "%b "), .) %>%
+ {
+ c(paste(.[1:2], collapse = "-"), paste(.[3:4], collapse =
"-"))
+ }
},
monthly = {
- date_range_index <- c(1, 31, 31, 60)
- temp <- polloi::safe_tail(desktop_load_data,
date_range_index[4])$date %>% {
- paste0(as.character(.[date_range_index], "%b "),
- sub("([a-z]{2})", "<sup>\\1</sup>",
- sapply(as.numeric(as.character(.[date_range_index],
"%e")), toOrdinal)))
- } %>% {
- c(paste(.[1:2], collapse = "-"), paste(.[3:4], collapse = "-"))
- }
+ dates <- Sys.Date() - c(1, 31, 32, 61)
+ temp <- dates %>%
+ as.character("%e") %>%
+ as.numeric %>%
+ sapply(toOrdinal) %>%
+ sub("([a-z]{2})", "<sup>\\1</sup>", .) %>%
+ paste0(as.character(dates, "%b "), .) %>%
+ {
+ c(paste(.[1:2], collapse = "-"), paste(.[3:4], collapse =
"-"))
+ }
},
quarterly = {
- date_range_index <- c(1, 90)
- temp <- polloi::safe_tail(desktop_load_data,
date_range_index[2])$date %>% {
- paste0(as.character(.[date_range_index], "%B "),
- sub("([a-z]{2})", "<sup>\\1</sup>",
- sapply(as.numeric(as.character(.[date_range_index],
"%e")), toOrdinal)))
- } %>% paste0(collapse = "-")
- return(HTML("<h3 class='kpi_date'>KPI summary for ", temp,
":</h3>"))
+ dates <- Sys.Date() - c(1, 91)
+ return(dates %>%
+ as.character("%e") %>%
+ as.numeric %>%
+ sapply(toOrdinal) %>%
+ sub("([a-z]{2})", "<sup>\\1</sup>", .) %>%
+ paste0(as.character(dates, "%B "), .) %>%
+ paste0(collapse = "-") %>%
+ HTML("<h3 class='kpi_date'>KPI summary for ", .,
":</h3>"))
})
return(HTML("<h3 class='kpi_date'>KPI summary for ", temp[2], ", and %
change from ", temp[1], ":</h3>"))
})
+
output$kpi_summary_box_load_time <- renderValueBox({
date_range <- input$kpi_summary_date_range_selector
- x <- lapply(list(desktop_load_data, mobile_load_data,
- android_load_data, ios_load_data),
- polloi::safe_tail, n = date_range_switch(date_range)) %>%
+ x <- list(desktop_load_data, mobile_load_data, android_load_data,
ios_load_data) %>%
+ lapply(polloi::subset_by_date_range, from = start_date(date_range), to =
Sys.Date() - 1) %>%
lapply(function(data_tail) return(data_tail$Median))
if ( date_range == "quarterly" ) {
- y <- median(apply(do.call(cbind, x), 1, median))
+ y <- median(apply(do.call(cbind_fill, x), 1, median, na.rm = TRUE))
return(valueBox(subtitle = "Load time", value = sprintf("%.0fms", y),
color = "orange"))
}
- x %<>% do.call(cbind, .) %>% apply(MARGIN = 1, FUN = median)
+ missing_values <- any(is.na(do.call(cbind_fill, x)))
+ x %<>% do.call(cbind_fill, .) %>% apply(MARGIN = 1, FUN = median, na.rm =
TRUE)
y1 <- median(polloi::half(x)); y2 <- median(polloi::half(x, FALSE)); z <-
100 * (y2 - y1) / y1
if (abs(z) > 0) {
- return(valueBox(subtitle = sprintf("Load time (%.1f%%)", z),
- value = sprintf("%.0fms", y2),
+ return(valueBox(subtitle = sprintf("Load time (%s%.1f%%)",
ifelse(missing_values, "~", ""), z),
+ value = sprintf("%s%.0fms", ifelse(missing_values, "~",
""), y2),
color = polloi::cond_color(z > 0, "red"), icon =
polloi::cond_icon(z > 0)))
}
return(valueBox(subtitle = "Load time (no change)", value =
sprintf("%.0fms", y2), color = "orange"))
})
output$kpi_summary_box_zero_results <- renderValueBox({
date_range <- input$kpi_summary_date_range_selector
- x <- polloi::safe_tail(failure_dygraph_set, date_range_switch(date_range))
+ x <- polloi::subset_by_date_range(failure_dygraph_set, from =
start_date(date_range), to = Sys.Date() - 1)
x <- transform(x, Rate = `Zero Result Queries` / `Search Queries`)$Rate
if (date_range == "quarterly") {
return(valueBox(subtitle = "Zero results rate", color = "orange",
@@ -327,9 +343,10 @@
})
output$kpi_summary_box_api_usage <- renderValueBox({
date_range <- input$kpi_summary_date_range_selector
- x <- lapply(split_dataset, function(x) {
- polloi::safe_tail(x, date_range_switch(date_range))$events
- }) %>% do.call(cbind, .) %>%
+ x <- split_dataset %>%
+ lapply(polloi::subset_by_date_range, from = start_date(date_range), to =
Sys.Date() - 1) %>%
+ lapply(function(x) return(x$events)) %>%
+ do.call(cbind, .) %>%
transform(total = cirrus + geo + language + open + prefix) %>%
{ .$total }
if (date_range == "quarterly") {
@@ -351,7 +368,7 @@
return(valueBox(subtitle = "User engagement", color = "black", value =
"NA"))
}
#=========================================================================
- x <- polloi::safe_tail(augmented_clickthroughs,
date_range_switch(date_range))
+ x <- polloi::subset_by_date_range(augmented_clickthroughs, from =
start_date(date_range), to = Sys.Date() - 1)
if (date_range == "quarterly") {
return(valueBox(subtitle = "User engagement", color = "orange",
value = sprintf("%.1f%%", median(x$user_engagement))))
@@ -370,12 +387,13 @@
value = sprintf("%.1f%%", y2), color = "orange"))
})
output$kpi_summary_api_usage_proportions <- renderPlot({
- n <- date_range_switch(input$kpi_summary_date_range_selector, 1, 7, 30, 90)
- api_latest <- cbind("Full-text via API" =
polloi::safe_tail(split_dataset$cirrus, n)$events,
- "Geo Search" = polloi::safe_tail(split_dataset$geo,
n)$events,
- "OpenSearch" = polloi::safe_tail(split_dataset$open,
n)$events,
- "Language" = polloi::safe_tail(split_dataset$language,
n)$events,
- "Prefix" = polloi::safe_tail(split_dataset$prefix,
n)$events) %>%
+ start_date <- Sys.Date() - switch(input$kpi_summary_date_range_selector,
+ daily = 1, weekly = 8, monthly = 31,
quarterly = 91)
+ api_latest <- cbind("Full-text via API" =
polloi::subset_by_date_range(split_dataset$cirrus, from = start_date, to =
Sys.Date() - 1)$events,
+ "Geo Search" =
polloi::subset_by_date_range(split_dataset$geo, from = start_date, to =
Sys.Date() - 1)$events,
+ "OpenSearch" =
polloi::subset_by_date_range(split_dataset$open, from = start_date, to =
Sys.Date() - 1)$events,
+ "Language" =
polloi::subset_by_date_range(split_dataset$language, from = start_date, to =
Sys.Date() - 1)$events,
+ "Prefix" =
polloi::subset_by_date_range(split_dataset$prefix, from = start_date, to =
Sys.Date() - 1)$events) %>%
apply(2, median) %>% round
api_latest <- data.frame(API = names(api_latest),
Events = api_latest,
@@ -392,27 +410,28 @@
## KPI Modules
output$kpi_load_time_series <- renderDygraph({
smooth_level <- input$smoothing_kpi_load_time
- num_of_days_in_common <- min(sapply(list(desktop_load_data$Median,
mobile_load_data$Median, android_load_data$Median, ios_load_data$Median),
length))
+ date_range <- input$kpi_summary_date_range_selector
+ start_date <- Sys.Date() - switch(input$kpi_summary_date_range_selector,
+ daily = 1, weekly = 8, monthly = 31,
quarterly = 91)
load_times <- list(desktop_load_data, mobile_load_data, android_load_data,
ios_load_data) %>%
- lapply(polloi::safe_tail, num_of_days_in_common) %>%
- lapply(function(data_tail) return(data_tail$Median)) %>%
- as.data.frame %>%
- { colnames(.) <- c("Desktop", "Mobile Web", "Android", "iOS"); . } %>%
+ lapply(polloi::subset_by_date_range, from = start_date, to = Sys.Date()
- 1) %>%
+ lapply(function(data_tail) return(data_tail[, c('date', 'Median')])) %>%
+ { names(.) <- c("Desktop", "Mobile Web", "Android", "iOS"); . } %>%
+ dplyr::bind_rows(.id = "Platform") %>%
+ unique %>%
+ tidyr::spread('Platform', 'Median')
+ missing_values <- any(is.na(load_times))
+ load_times %<>%
{
- Median = apply(., 1, median)
- cbind(Median = Median, .)
+ Median = apply(.[, -1], 1, median, na.rm = TRUE)
+ cbind(., Median = Median)
} %>%
- cbind(date = polloi::safe_tail(desktop_load_data,
num_of_days_in_common)$date, .) %>%
polloi::smoother(smooth_level = ifelse(smooth_level == "global",
input$smoothing_global, smooth_level), rename = FALSE) %>%
{ xts::xts(.[, -1], order.by = .[, 1]) }
- return(dygraph(load_times,
- main = "Load times over time",
- xlab = "Date",
- ylab = "Load time (ms)") %>%
- dySeries("Median", axis = 'y2', strokeWidth = 4, label =
"Cross-platform Median") %>%
- dyAxis("y2", label = "Day-to-day % change in median load time",
- independentTicks = TRUE, drawGrid = FALSE) %>%
- dyLegend(width = 500, show = "always") %>%
+ return(dygraph(load_times, xlab = "Date", ylab = "Load time (ms)",
+ main = ifelse(missing_values, "Approximate load times over
time", "Load times over time")) %>%
+ dySeries("Median", axis = 'y', strokeWidth = 4, label =
"Cross-platform Median") %>%
+ dyLegend(width = 500, show = "always", labelsDiv =
"kpi_load_time_series_legend") %>%
dyOptions(strokeWidth = 2, colors = RColorBrewer::brewer.pal(5,
"Set2")[5:1],
drawPoints = FALSE, pointSize = 3, labelsKMB = TRUE,
includeZero = TRUE) %>%
@@ -420,20 +439,21 @@
})
output$kpi_zero_results_series <- renderDygraph({
smooth_level <- input$smoothing_kpi_zero_results
- zrr <- 100 * failure_dygraph_set$`Zero Result Queries` /
failure_dygraph_set$`Search Queries`
- zrr_change <- 100 * (zrr[2:length(zrr)] -
zrr[1:(length(zrr)-1)])/zrr[1:(length(zrr)-1)]
- zrr <- data.frame(date = failure_dygraph_set$date[-1], rate = zrr[-1],
change = zrr_change)
- zrr %<>% polloi::smoother(ifelse(smooth_level == "global",
input$smoothing_global, smooth_level), rename = FALSE)
+ start_date <- Sys.Date() - switch(input$kpi_summary_date_range_selector,
daily = 1, weekly = 8, monthly = 31, quarterly = 91)
+ zrr <- failure_dygraph_set %>%
+ polloi::subset_by_date_range(from = start_date, to = Sys.Date()) %>%
+ transform(`Rate` = 100 * `Zero Result Queries` / `Search Queries`)
+ zrr_change <- 100 * (zrr$Rate[2:nrow(zrr)] -
zrr$Rate[1:(nrow(zrr)-1)])/zrr$Rate[1:(nrow(zrr)-1)]
+ zrr <- cbind(zrr[, c('date', 'Rate')], Change = c(NA, zrr_change)) %>%
+ polloi::smoother(ifelse(smooth_level == "global",
input$smoothing_global, smooth_level), rename = FALSE)
zrr <- xts::xts(zrr[, -1], zrr[, 1])
- return(dygraph(zrr,
- main = "Zero results rate over time",
- xlab = "Date",
+ return(dygraph(zrr, main = "Zero results rate over time", xlab = "Date",
ylab = "% of search queries that yield zero results") %>%
- dySeries("change", axis = 'y2', label = "day-to-day % change",
strokeWidth = 1) %>%
+ dySeries("Change", axis = 'y2', label = "Day-to-day % change",
strokeWidth = 1) %>%
dyLimit(limit = 12.50, label = "Goal: 12.50% zero results rate",
color = RColorBrewer::brewer.pal(3, "Set2")[3]) %>%
dyAxis("y2", label = "Day-to-day % change",
- valueRange = c(-1, 1) *
max(max(abs(as.numeric(zrr$change))), 10),
+ valueRange = c(-1, 1) *
max(max(abs(as.numeric(zrr$Change)), na.rm = TRUE), 10),
axisLineColor = RColorBrewer::brewer.pal(3, "Set2")[2],
axisLabelColor = RColorBrewer::brewer.pal(3, "Set2")[2],
independentTicks = TRUE, drawGrid = FALSE) %>%
@@ -449,7 +469,12 @@
})
output$kpi_api_usage_series <- renderDygraph({
smooth_level <- input$smoothing_kpi_api_usage
- api_usage <- cbind(date = split_dataset$cirrus$date,
as.data.frame(lapply(split_dataset, function(x) x$events)))
+ start_date <- Sys.Date() - switch(input$kpi_summary_date_range_selector,
daily = 1, weekly = 8, monthly = 31, quarterly = 91)
+ api_usage <- split_dataset %>%
+ lapply(polloi::subset_by_date_range, from = start_date, to = Sys.Date()
- 1) %>%
+ dplyr::bind_rows() %>%
+ tidyr::spread("event_type", "events") %>%
+ as.data.frame
if ( input$kpi_api_usage_series_include_open ) {
api_usage <- transform(api_usage, all = cirrus + geo + language + open +
prefix)
} else {
@@ -493,8 +518,10 @@
dyCSS(css = system.file("custom.css", package = "polloi")))
})
output$kpi_augmented_clickthroughs_series <- renderDygraph({
- smoothed_data <- polloi::smoother(augmented_clickthroughs,
- smooth_level = polloi::smooth_switch(input$smoothing_global,
input$smoothing_augmented_clickthroughs))
+ start_date <- Sys.Date() - switch(input$kpi_summary_date_range_selector,
daily = 1, weekly = 8, monthly = 31, quarterly = 91)
+ smoothed_data <- augmented_clickthroughs %>%
+ polloi::subset_by_date_range(from = start_date, to = Sys.Date() - 1) %>%
+ polloi::smoother(smooth_level =
polloi::smooth_switch(input$smoothing_global,
input$smoothing_augmented_clickthroughs))
polloi::make_dygraph(data = smoothed_data, xlab = "Date", ylab = "Rates",
"User engagement (augmented clickthroughs) by day") %>%
dySeries(name = colnames(smoothed_data)[2], strokeWidth = 1.5,
strokePattern = "dashed") %>%
dySeries(name = colnames(smoothed_data)[3], strokeWidth = 1.5,
strokePattern = "dashed") %>%
diff --git a/tab_documentation/kpis_summary.md
b/tab_documentation/kpis_summary.md
index 24dada2..4bec1fd 100644
--- a/tab_documentation/kpis_summary.md
+++ b/tab_documentation/kpis_summary.md
@@ -6,10 +6,10 @@
- **API usage** We want people, both within our movement and outside it, to be
able to easily access our information.
- **User Engagement** (not quite **[User
Satisfaction](https://meta.wikimedia.org/wiki/Research:Measuring_User_Search_Satisfaction)**)
This is an augmented version of clickthrough rate. In it we are including the
proportion of users' sessions exceeding a pre-specified threshold. **Note**
that we deployed v2.0 of the satisfaction schema on 9/2/2015. You may see
**NA** if we do not have enough data available at the time.
-Outages and inaccuracies
+Additional information
------
-* None so far!
+In the case of a data outage, the medians will be computed from non-missing
data. When this is the case, the value displayed will be approximate and will
have a '~'.
Questions, bug reports, and feature suggestions
------
diff --git a/ui.R b/ui.R
index 2ee9956..6071b7d 100644
--- a/ui.R
+++ b/ui.R
@@ -6,6 +6,24 @@
#Header elements for the visualisation
header <- dashboardHeader(title = "Search Metrics", disable = FALSE)
+# Standardised input selector for smoothing
+smooth_select <- function(input_id, label = "Smoothing") {
+ return(selectInput(inputId = input_id, label = label, selectize = TRUE,
+ selected = "global", choices = c("Use Global Setting" =
"global",
+ "No Smoothing" = "day",
"Weekly Median" = "week", "Monthly Median" = "month")))
+}
+# Standardized selectors for time frame
+timeframe_select <- function(input_id, label = "Time Frame") {
+ return(selectInput(inputId = input_id, label = label, selectize = TRUE,
selected = "global",
+ choices = c("Use Global Setting" = "global", "Last 7
days" = "week",
+ "Last 30 days" = "month", "Last 90 days" =
"quarter", "Custom" = "custom")))
+}
+timeframe_daterange <- function(select_input_id, label = "Custom Date Range") {
+ return(conditionalPanel(paste0("input.", select_input_id," == 'custom'"),
+ dateRangeInput(paste(select_input_id, "daterange",
sep = "_"), label = label,
+ start = Sys.Date()-11, end =
Sys.Date()-1, min = "2015-04-14")))
+}
+
#Sidebar elements for the search visualisations.
sidebar <- dashboardSidebar(
tags$head(
@@ -14,11 +32,17 @@
),
sidebarMenu(
menuItem(text = "KPIs",
+ div(selectInput("kpi_summary_date_range_selector",
+ label = "KPI data range", multiple = FALSE,
selected = "weekly",
+ choices = list("Yesterday" = "daily", "Last 7
days" = "weekly",
+ "Last 30 days" = "monthly", "Last
90 days" = "quarterly")),
+ style = "margin-bottom:-10px;"),
menuSubItem(text = "Summary", tabName = "kpis_summary"),
menuSubItem(text = "Load times", tabName = "kpi_load_time"),
menuSubItem(text = "Zero results", tabName = "kpi_zero_results"),
menuSubItem(text = "API usage", tabName = "kpi_api_usage"),
- menuSubItem(text = "Augmented Clickthrough", tabName =
"kpi_augmented_clickthroughs")),
+ menuSubItem(text = "Augmented Clickthrough", tabName =
"kpi_augmented_clickthroughs"),
+ icon = icon("star", lib = "glyphicon")),
menuItem(text = "Desktop",
menuSubItem(text = "Events", tabName = "desktop_events"),
menuSubItem(text = "Load times", tabName = "desktop_load")),
@@ -38,37 +62,36 @@
menuSubItem(text = "Summary", tabName = "failure_rate"),
menuSubItem(text = "Search Type Breakdown", tabName =
"failure_breakdown"),
menuSubItem(text = "Search Suggestions", tabName =
"failure_suggestions")),
- menuItem(text = "Page Visit Times", tabName = "survival",
- badgeLabel = "new", badgeColor = "fuchsia"),
- selectInput(inputId = "smoothing_global", label = "Smoothing (Global
Setting)", selectize = TRUE, selected = "day",
- choices = c("No Smoothing" = "day", "Weekly Median" = "week",
"Monthly Median" = "month"))
+ menuItem(text = "Page Visit Times", tabName = "survival"),
+ menuItem(text = "Global Settings",
+ selectInput(inputId = "smoothing_global", label = "Smoothing",
selectize = TRUE, selected = "day",
+ choices = c("No Smoothing" = "day", "Weekly Median" =
"week", "Monthly Median" = "month")),
+ selectInput(inputId = "timeframe_global", label = "Time Frame",
selectize = TRUE, selected = "",
+ choices = c("All available data" = "all", "Last 7
days" = "week", "Last 30 days" = "month",
+ "Last 90 days" = "quarter", "Custom" =
"custom")),
+ conditionalPanel("input.timeframe_global == 'custom'",
+ dateRangeInput("daterange_global", label =
"Custom Date Range",
+ start = Sys.Date()-11, end =
Sys.Date()-1, min = "2015-04-14")),
+ icon = icon("cog", lib = "glyphicon"))
)
)
-
-# Standardised input selector for smoothing
-smooth_select <- function(input_id, label = "Smoothing") {
- return(selectInput(inputId = input_id, label = label, selectize = TRUE,
- selected = "global", choices = c("Use Global Setting" =
"global",
- "No Smoothing" = "day", "Weekly Median" = "week",
"Monthly Median" = "month")))
-}
#Body elements for the search visualisations.
body <- dashboardBody(
tabItems(
tabItem(tabName = "kpis_summary",
- selectInput("kpi_summary_date_range_selector", label = "Data
range", multiple = FALSE, selected = "weekly",
- choices = list("Yesterday" = "daily", "Last 7 days" =
"weekly",
- "Last 30 days" = "monthly", "Last 90
days" = "quarterly")),
htmlOutput("kpi_summary_date_range"),
fluidRow(valueBoxOutput("kpi_summary_box_load_time", width = 3),
valueBoxOutput("kpi_summary_box_zero_results", width = 3),
valueBoxOutput("kpi_summary_box_api_usage", width = 3),
valueBoxOutput("kpi_summary_box_augmented_clickthroughs",
width = 3)),
plotOutput("kpi_summary_api_usage_proportions", height = "30px"),
- includeMarkdown("./tab_documentation/kpis_summary.md")
- ),
+ includeMarkdown("./tab_documentation/kpis_summary.md")),
tabItem(tabName = "kpi_load_time",
- smooth_select("smoothing_kpi_load_time"),
+ fluidRow(
+ column(smooth_select("smoothing_kpi_load_time"), width = 4),
+ column(div(id = "kpi_load_time_series_legend"), width = 8)
+ ),
dygraphOutput("kpi_load_time_series"),
includeMarkdown("./tab_documentation/kpi_load_time.md")),
tabItem(tabName = "kpi_zero_results",
@@ -103,27 +126,37 @@
fluidRow(
valueBoxOutput("desktop_event_searches"),
valueBoxOutput("desktop_event_resultsets"),
- valueBoxOutput("desktop_event_clickthroughs")
- ),
- smooth_select("smoothing_desktop_event"),
+ valueBoxOutput("desktop_event_clickthroughs")),
+ fluidRow(
+ column(smooth_select("smoothing_desktop_event"), width = 4),
+ column(timeframe_select("desktop_event_timeframe"), width = 4),
+ column(timeframe_daterange("desktop_event_timeframe"), width =
4)),
dygraphOutput("desktop_event_plot"),
includeMarkdown("./tab_documentation/desktop_events.md")),
tabItem(tabName = "desktop_load",
- smooth_select("smoothing_desktop_load"),
+ fluidRow(
+ column(smooth_select("smoothing_desktop_load"), width = 4),
+ column(timeframe_select("desktop_load_timeframe"), width = 4),
+ column(timeframe_daterange("desktop_load_timeframe"), width =
4)),
dygraphOutput("desktop_load_plot"),
includeMarkdown("./tab_documentation/desktop_load.md")),
tabItem(tabName = "mobile_events",
fluidRow(
valueBoxOutput("mobile_event_searches"),
valueBoxOutput("mobile_event_resultsets"),
- valueBoxOutput("mobile_event_clickthroughs")
- ),
- smooth_select("smoothing_mobile_event"),
+ valueBoxOutput("mobile_event_clickthroughs")),
+ fluidRow(
+ column(smooth_select("smoothing_mobile_event"), width = 4),
+ column(timeframe_select("mobile_event_timeframe"), width = 4),
+ column(timeframe_daterange("mobile_event_timeframe"), width =
4)),
dygraphOutput("mobile_event_plot"),
includeMarkdown("./tab_documentation/mobile_events.md")
),
tabItem(tabName = "mobile_load",
- smooth_select("smoothing_mobile_load"),
+ fluidRow(
+ column(smooth_select("smoothing_mobile_load"), width = 4),
+ column(timeframe_select("mobile_load_timeframe"), width = 4),
+ column(timeframe_daterange("mobile_load_timeframe"), width = 4)),
dygraphOutput("mobile_load_plot"),
includeMarkdown("./tab_documentation/mobile_load.md")
),
@@ -131,65 +164,95 @@
fluidRow(
valueBoxOutput("app_event_searches"),
valueBoxOutput("app_event_resultsets"),
- valueBoxOutput("app_event_clickthroughs")
- ),
- smooth_select("smoothing_app_event"),
+ valueBoxOutput("app_event_clickthroughs")),
+ fluidRow(
+ column(smooth_select("smoothing_app_event"), width = 4),
+ column(timeframe_select("app_event_timeframe"), width = 4),
+ column(timeframe_daterange("app_event_timeframe"), width = 4)),
dygraphOutput("android_event_plot"),
dygraphOutput("ios_event_plot"),
includeMarkdown("./tab_documentation/app_events.md")
),
tabItem(tabName = "app_load",
- smooth_select("smoothing_app_load"),
+ fluidRow(
+ column(smooth_select("smoothing_app_load"), width = 4),
+ column(timeframe_select("app_load_timeframe"), width = 4),
+ column(timeframe_daterange("app_load_timeframe"), width = 4)),
dygraphOutput("android_load_plot"),
dygraphOutput("ios_load_plot"),
includeMarkdown("./tab_documentation/app_load.md")
),
tabItem(tabName = "fulltext_search",
- smooth_select("smoothing_fulltext_search"),
+ fluidRow(
+ column(smooth_select("smoothing_fulltext_search"), width = 4),
+ column(timeframe_select("fulltext_search_timeframe"), width = 4),
+ column(timeframe_daterange("fulltext_search_timeframe"), width =
4)),
dygraphOutput("cirrus_aggregate"),
includeMarkdown("./tab_documentation/fulltext_basic.md")
),
tabItem(tabName = "open_search",
- smooth_select("smoothing_open_search"),
+ fluidRow(
+ column(smooth_select("smoothing_open_search"), width = 4),
+ column(timeframe_select("open_search_timeframe"), width = 4),
+ column(timeframe_daterange("open_search_timeframe"), width = 4)),
dygraphOutput("open_aggregate"),
includeMarkdown("./tab_documentation/open_basic.md")
),
tabItem(tabName = "geo_search",
- smooth_select("smoothing_geo_search"),
+ fluidRow(
+ column(smooth_select("smoothing_geo_search"), width = 4),
+ column(timeframe_select("geo_search_timeframe"), width = 4),
+ column(timeframe_daterange("geo_search_timeframe"), width = 4)),
dygraphOutput("geo_aggregate"),
includeMarkdown("./tab_documentation/geo_basic.md")
),
tabItem(tabName = "prefix_search",
- smooth_select("smoothing_prefix_search"),
+ fluidRow(
+ column(smooth_select("smoothing_prefix_search"), width = 4),
+ column(timeframe_select("prefix_search_timeframe"), width = 4),
+ column(timeframe_daterange("prefix_search_timeframe"), width =
4)),
dygraphOutput("prefix_aggregate"),
includeMarkdown("./tab_documentation/prefix_basic.md")
),
tabItem(tabName = "language_search",
- smooth_select("smoothing_language_search"),
+ fluidRow(
+ column(smooth_select("smoothing_language_search"), width = 4),
+ column(timeframe_select("language_search_timeframe"), width = 4),
+ column(timeframe_daterange("language_search_timeframe"), width =
4)),
dygraphOutput("language_aggregate"),
includeMarkdown("./tab_documentation/language_basic.md")
),
tabItem(tabName = "failure_rate",
- smooth_select("smoothing_failure_rate"),
+ fluidRow(
+ column(smooth_select("smoothing_failure_rate"), width = 4),
+ column(timeframe_select("failure_rate_timeframe"), width = 4),
+ column(timeframe_daterange("failure_rate_timeframe"), width =
4)),
dygraphOutput("failure_rate_plot"),
dygraphOutput("failure_rate_change_plot"),
includeMarkdown("./tab_documentation/failure_rate.md")
),
tabItem(tabName = "failure_breakdown",
- smooth_select("smoothing_failure_breakdown"),
+ fluidRow(
+ column(smooth_select("smoothing_failure_breakdown"), width = 4),
+ column(timeframe_select("failure_breakdown_timeframe"), width =
4),
+ column(timeframe_daterange("failure_breakdown_timeframe"), width
= 4)),
dygraphOutput("failure_breakdown_plot"),
includeMarkdown("./tab_documentation/failure_breakdown.md")
),
tabItem(tabName = "failure_suggestions",
- smooth_select("smoothing_failure_suggestions"),
+ fluidRow(
+ column(smooth_select("smoothing_failure_suggestions"), width =
4),
+ column(timeframe_select("failure_suggestions_timeframe"), width
= 4),
+ column(timeframe_daterange("failure_suggestions_timeframe"),
width = 4)),
dygraphOutput("suggestion_dygraph_plot"),
includeMarkdown("./tab_documentation/failure_suggests.md")
),
tabItem(tabName = "survival",
fluidRow(
column(smooth_select("smoothing_lethal_dose_plot"), width = 4),
- column(div(id = "lethal_dose_plot_legend"), width = 8)
- ),
+ column(timeframe_select("lethal_dose_timeframe"), width = 4),
+ column(timeframe_daterange("lethal_dose_timeframe"), width = 4)),
+ div(id = "lethal_dose_plot_legend"),
dygraphOutput("lethal_dose_plot"),
includeMarkdown("./tab_documentation/survival.md")
)
diff --git a/utils.R b/utils.R
index f6535f0..4393a69 100644
--- a/utils.R
+++ b/utils.R
@@ -128,8 +128,22 @@
x = 1))
}
-date_range_switch <- function(date_range, daily = 2, weekly = 14, monthly =
60, quarterly = 90) {
- return(switch(date_range, daily = daily, weekly = weekly, monthly = monthly,
quarterly = quarterly))
+# This is used in subsetting data for KPIs.
+start_date <- function(date_range) {
+ return(Sys.Date() - (switch(date_range, daily = 2, weekly = 14, monthly =
60, quarterly = 90) + 1))
+}
+
+# Usage: date_range %>% name(c('start', 'end'))
+name <- function(x, labels) {
+ names(x) <- labels
+ return(x)
+}
+
+# From:
http://r.789695.n4.nabble.com/How-to-join-matrices-of-different-row-length-from-a-list-td3177212.html
+cbind_fill <- function(...) {
+ nm <- lapply(list(...), as.matrix)
+ n <- max(sapply(nm, nrow))
+ do.call(cbind, lapply(nm, function (x) rbind(x, matrix(, n-nrow(x),
ncol(x)))))
}
CustomAxisFormatter <- 'function (d, gran) {
--
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Gerrit-MessageType: newchange
Gerrit-Change-Id: I72a90a00235c536da35a893e03a68f9a23caa62d
Gerrit-PatchSet: 1
Gerrit-Project: wikimedia/discovery/rainbow
Gerrit-Branch: master
Gerrit-Owner: Bearloga <[email protected]>
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