[MediaWiki-commits] [Gerrit] wikimedia...golden[master]: Expand "other" countries data
Bearloga has submitted this change and it was merged.
Change subject: Expand "other" countries data
..
Expand "other" countries data
- Traffic, clickthrough rate, number of visits, ctr_visit (proportion of visits
that have at least one clickthrough), number of sessions, ctr_session
(proportion of sessions that have at least one clickthrough), by all countries
and US regions
- Last action by all countries and US regions
- Most common section clicked per visit by all countries and US regions
- First visit clickthrough by all countries and US regions
Also fixed two bugs in:
- Most common section clicked
- Generate click breakdown (last action): 1) add sort by timestamp 2) delete
lines which modify raw data
Bug: T138107
Change-Id: I51c8ca4222175d0ba5c1c48d187c9b0bbe8f7a3d
---
M portal/portal.R
1 file changed, 85 insertions(+), 9 deletions(-)
Approvals:
Bearloga: Verified; Looks good to me, approved
diff --git a/portal/portal.R b/portal/portal.R
index bf8d3b6..5b77aed 100644
--- a/portal/portal.R
+++ b/portal/portal.R
@@ -46,7 +46,6 @@
dplyr::arrange(session, ts) %>%
dplyr::group_by(session) %>%
dplyr::mutate(visit = cumsum(type == "landing")) %>%
-dplyr::filter(visit == 1) %>%
dplyr::filter(type == "clickthrough") %>%
dplyr::group_by(date, session, visit, section_used) %>%
dplyr::tally() %>%
@@ -75,19 +74,25 @@
dplyr::mutate(date = data$date[1]) %>%
dplyr::select(c(date, `no action`, `primary links`, `search`, `secondary
links`, `other languages`, `other projects`))
- # Generate click breakdown
- data <- data[order(data$type, decreasing = FALSE), ]
- data <- data[!duplicated(data$session), ]
- breakdown_data <- data[, j = list(events = .N), by = c("date",
"section_used")]
+ # Generate click breakdown (last action)
+ breakdown_data <- data %>%
+dplyr::arrange(ts) %>%
+dplyr::filter(!duplicated(session, fromLast = TRUE)) %>%
+dplyr::group_by(date, section_used) %>%
+dplyr::summarize(events = n()) %>%
+data.table::as.data.table()
# Generate by-country breakdown with regional data for US
- regions <- data.frame(abb = paste0("US:", c(as.character(state.abb), "DC")),
+ data("ISO_3166_1", package = "ISOcodes")
+ us_other_abb <- c("AS", "GU", "MP", "PR", "VI")
+ us_other_mask <- match(us_other_abb, ISO_3166_1$Alpha_2)
+ regions <- data.frame(abb = c(paste0("US:", c(as.character(state.abb),
"DC")), us_other_abb),
# ^ need to verify that District of Columbia shows up
as DC and not another abbreviation
-region = paste0("U.S. (",
c(as.character(state.region), "South"), ")"),
-state = c(state.name, "District of Columbia"),
+region = paste0("U.S. (",
c(as.character(state.region), "South", rep("Other",5)), ")"),
+state = c(state.name, "District of Columbia",
ISO_3166_1$Name[us_other_mask]),
stringsAsFactors = FALSE)
regions$region[regions$region == "U.S. (North Central)"] <- "U.S. (Midwest)"
- regions$region[state.division == "Pacific"] <- "U.S. (Pacific)" # see
https://phabricator.wikimedia.org/T136257#2399411
+ regions$region[c(state.division == "Pacific", rep(FALSE, 5))] <- "U.S.
(Pacific)" # see https://phabricator.wikimedia.org/T136257#2399411
countries <- data.frame(abb = c(regions$abb, "GB", "CA",
"DE", "IN", "AU", "CN",
"RU", "PH", "FR"),
@@ -110,6 +115,71 @@
dplyr::select(c(date, country, events)) %>%
dplyr::arrange(desc(country))
+ # Experimental: Generate all countries breakdown
+ all_countries <- data.frame(abb = c(regions$abb,
ISO_3166_1$Alpha_2[-us_other_mask]),
+ name = c(regions$region,
ISO_3166_1$Name[-us_other_mask]),
+ stringsAsFactors = FALSE)
+ data_w_countryname <- as.data.frame(data) %>%
+dplyr::mutate(country = ifelse(country %in% all_countries$abb, country,
"Other")) %>%
+dplyr::left_join(all_countries, by = c("country" = "abb")) %>%
+dplyr::mutate(name = ifelse(is.na(name), "Other", name)) %>%
+dplyr::select(-country) %>% dplyr::rename(country = name)
+
+ ctr_visit <- data_w_countryname %>%
+dplyr::arrange(session, ts) %>%
+dplyr::group_by(session) %>%
+dplyr::mutate(visit = cumsum(type == "landing")) %>%
+dplyr::group_by(date, country, session, visit) %>%
+dplyr::summarize(dummy_clt = sum(type=="clickthrough")>1) %>%
+dplyr::group_by(country) %>%
+dplyr::summarize(n_visit = n(), ctr_visit = sum(dummy_clt)/n())
+ ctr_session <- data_w_countryname %>%
+dplyr::group_by(date, country, session) %>%
+dplyr::summarize(dummy_clt = sum(type=="clickthrough")>1) %>%
+dplyr::group_by(country) %>%
+dplyr::summarize(n_session = n(), ctr_session = sum(dummy_clt)/n())
+ all_country_data <-
[MediaWiki-commits] [Gerrit] wikimedia...golden[master]: Expand "other" countries data
Chelsyx has uploaded a new change for review.
https://gerrit.wikimedia.org/r/310473
Change subject: Expand "other" countries data
..
Expand "other" countries data
- Traffic and clickthrough rate by all countries and US regions
- Last action by all countries and US regions
- Most common section clicked per visit by all countries and US regions
- First visit clickthrough by all countries and US regions
Also fixed two bugs in:
- Most common section clicked
- Generate click breakdown (last action): 1) add sort by timestamp 2) delete
lines which modify raw data
Bug: T138107
Change-Id: I51c8ca4222175d0ba5c1c48d187c9b0bbe8f7a3d
---
M portal/portal.R
1 file changed, 74 insertions(+), 5 deletions(-)
git pull ssh://gerrit.wikimedia.org:29418/wikimedia/discovery/golden
refs/changes/73/310473/1
diff --git a/portal/portal.R b/portal/portal.R
index bf8d3b6..475aa00 100644
--- a/portal/portal.R
+++ b/portal/portal.R
@@ -46,7 +46,6 @@
dplyr::arrange(session, ts) %>%
dplyr::group_by(session) %>%
dplyr::mutate(visit = cumsum(type == "landing")) %>%
-dplyr::filter(visit == 1) %>%
dplyr::filter(type == "clickthrough") %>%
dplyr::group_by(date, session, visit, section_used) %>%
dplyr::tally() %>%
@@ -75,10 +74,13 @@
dplyr::mutate(date = data$date[1]) %>%
dplyr::select(c(date, `no action`, `primary links`, `search`, `secondary
links`, `other languages`, `other projects`))
- # Generate click breakdown
- data <- data[order(data$type, decreasing = FALSE), ]
- data <- data[!duplicated(data$session), ]
- breakdown_data <- data[, j = list(events = .N), by = c("date",
"section_used")]
+ # Generate click breakdown (last action)
+ breakdown_data <- data %>%
+dplyr::arrange(ts) %>%
+ dplyr::filter(!duplicated(session, fromLast = TRUE)) %>%
+ dplyr::group_by(date, section_used) %>%
+ dplyr::summarize(events = n()) %>%
+ data.table::as.data.table()
# Generate by-country breakdown with regional data for US
regions <- data.frame(abb = paste0("US:", c(as.character(state.abb), "DC")),
@@ -110,6 +112,67 @@
dplyr::select(c(date, country, events)) %>%
dplyr::arrange(desc(country))
+ # Experimental: Generate all countries breakdown
+ data("ISO_3166_1", package = "ISOcodes")
+ all_countries <- data.frame(abb = c(regions$abb, ISO_3166_1$Alpha_2),
+ name = c(regions$region, ISO_3166_1$Name),
+ stringsAsFactors = FALSE)
+ all_country_data <- as.data.frame(data) %>%
+dplyr::mutate(country = ifelse(country %in% all_countries$abb, country,
"Other")) %>%
+dplyr::left_join(all_countries, by = c("country" = "abb")) %>%
+dplyr::mutate(name = ifelse(is.na(name), "Other", name)) %>%
+dplyr::select(-country) %>% dplyr::rename(country = name) %>%
+dplyr::group_by(country) %>%
+dplyr::summarize(events = n(), ctr = sum(type=="clickthrough")/n()) %>%
+dplyr::mutate(date = date) %>%
+dplyr::select(c(date, country, events, ctr)) %>%
+dplyr::arrange(desc(country))
+
+ # Last action by country
+ last_action_country <- as.data.frame(data) %>%
+dplyr::mutate(country = ifelse(country %in% all_countries$abb, country,
"Other")) %>%
+dplyr::left_join(all_countries, by = c("country" = "abb")) %>%
+dplyr::mutate(name = ifelse(is.na(name), "Other", name)) %>%
+dplyr::select(-country) %>% dplyr::rename(country = name) %>%
+dplyr::arrange(ts) %>%
+ dplyr::filter(!duplicated(session, fromLast = TRUE)) %>%
+ dplyr::group_by(date, country, section_used) %>%
+ dplyr::summarize(events = n()) %>%
+ dplyr::mutate(prop = events/sum(events))
+
+ # Most common section clicked by country
+ most_common_country <- as.data.frame(data) %>%
+dplyr::mutate(country = ifelse(country %in% all_countries$abb, country,
"Other")) %>%
+dplyr::left_join(all_countries, by = c("country" = "abb")) %>%
+dplyr::mutate(name = ifelse(is.na(name), "Other", name)) %>%
+dplyr::select(-country) %>% dplyr::rename(country = name) %>%
+dplyr::arrange(session, ts) %>%
+dplyr::group_by(session) %>%
+dplyr::mutate(visit = cumsum(type == "landing")) %>%
+dplyr::filter(type == "clickthrough") %>%
+dplyr::group_by(date, country, session, visit, section_used) %>%
+dplyr::tally() %>%
+dplyr::top_n(1, n) %>%
+dplyr::ungroup() %>%
+dplyr::group_by(date, country, section_used) %>%
+dplyr::summarize(visits = n()) %>%
+ dplyr::mutate(prop = visits/sum(visits)) %>%
+dplyr::ungroup()
+
+ # First visit clickthrough rates by country
+ first_visits_country <- as.data.frame(data) %>%
+dplyr::mutate(country = ifelse(country %in% all_countries$abb, country,
"Other")) %>%
+dplyr::left_join(all_countries, by = c("country" = "abb")) %>%
+dplyr::mutate(name = ifelse(is.na(name), "Other", name)) %>%
+dplyr::selec
