Feedback should be send to goran.milovanovic_ext@wikimedia.de.

The campaign is run from 2019/01/02 to 2019/01/16.

CURRENT UPDATE: Complete dataset as of 2019/01/16.

0. Data Acquisiton

NOTE: the Data Acquisition code chunk is not fully reproducible from this Report. The data are collected by running an R script on stat1007.eqiad.wmnet, collecting the data as .tsv and .csv files, copying manually, and processing locally.

0.1 Daily Update

### --- Data Acquisition for the Thank You 2019 Campaign
### --- run from stat1007
### --- /home/goransm/Analytics/NewEditors/Campaigns/2019_ThankYou

### --- to data directory
dataDir <- '/home/goransm/Analytics/NewEditors/Campaigns/2019_ThankYou/data/'
setwd(dataDir)

### --- determine cetDay
library(lubridate)
cetDay <- Sys.time()
cetDay
attr(cetDay, "tzone") <- "Europe/Berlin"
# - one day behind for crontab
# - (i.e. waiting for wmf.webrequest to complete is data acquisition)
cetDay <- ymd(
  strsplit(as.character(cetDay), 
           split = " ", 
           fixed = T)[[1]][1]
  ) - 1

### -----------------------------------------------------------------------
### --- Collect Banner Impression Data
### -----------------------------------------------------------------------

# - function: wmde_collect_banner_impressions
wmde_collect_banner_impressions <- function(uri_host, 
                                            uri_path, 
                                            uri_query, 
                                            cetDay,
                                            queryFile,
                                            fileName,
                                            dataDir) {
  
  # - NOTE:
  # - expected format for cetDay is: YYYY-MM-DD
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(stringr)
  
  # - WHERE condition: create datetime_condition
  cet_condition <- seq(
    from = as.POSIXct(paste0(cetDay," 0:00"), tz = "Europe/Berlin"),
    to = as.POSIXct(paste0(cetDay," 23:00"), tz = "Europe/Berlin"),
    by = "hour"
  ) 
  attr(cet_condition, "tzone") <- "UTC"
  cet_condition <- as.character(cet_condition)
  cet_condition <- unlist(str_extract_all(cet_condition, "^([[:digit:]]|\\s|-)*"))
  cet_years <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][1]
    })
  cet_months <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][2]
    })
  cet_months <- gsub("^0", "", cet_months)
  cet_days <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][3]
    })
  cet_days <- gsub("^0", "", cet_days)
  cet_hours <- sapply(strsplit(cet_condition, split = " ", fixed = T), 
                      function(x) {
                        x[2]
                      })
  cet_hours <- gsub("^0", "", cet_hours)
  datetimeCondition <- paste0(
    "year = ", cet_years, " AND ",
    "month = ", cet_months, " AND ",
    "day = ", cet_days, " AND ", 
    "hour = ", cet_hours
  )
  datetimeCondition <- paste("(", 
                             datetimeCondition, 
                             ")",
                             collapse = " OR ", 
                             sep = "")
  
  # - WHERE condition: create uri_path_condition
  if (length(uri_path) > 1) {
    uri_path_condition <- paste0("(",
                                 paste(
                                   paste0("uri_path = '", uri_path, "'"),
                                   collapse = " OR ", sep = " "),
                                 ")"
                                 )
  } else {
    uri_path_condition = paste0("uri_path = '", uri_path, "'")
  }
  
  # - WHERE condition: create uri_host_condition
  if (length(uri_host) > 1) {
    uri_host_condition <- paste0("(",
                                 paste(
                                   paste0("uri_host = '", uri_host, "'"),
                                   collapse = " OR ", sep = " "),
                                 ")"
    )
  } else {
    uri_host_condition = paste0("uri_host = '", uri_host, "'")
  }
  
  # - WHERE condition: create uri_query_condition
  if (length(uri_query) > 1) {
    uri_query_condition <- paste0("(",
                                 paste(
                                   paste0("uri_query LIKE '%", uri_query, "%'"),
                                   collapse = " OR ", sep = " "),
                                 ")"
    )
  } else {
    uri_query_condition = paste0("uri_query LIKE '%", uri_query, "%'")
  }
  
  # - compose HiveQL query
  hiveQuery <- paste0(
    "USE wmf;
    SELECT uri_query FROM webrequest
    WHERE (",
    uri_host_condition, " AND ",
    uri_path_condition, " AND ",
    uri_query_condition, " AND ",
    "(", datetimeCondition, ")",
    ");"
  )
  
  # - write hql
  write(hiveQuery, queryFile)
  # - execute hql script:
  hiveArgs <- '/usr/local/bin/beeline -f'
  hiveInput <- paste0(queryFile, ' > ', paste0(dataDir, fileName))
  # - command:
  hiveCommand <- paste(hiveArgs, hiveInput)
  return(
    system(command = hiveCommand, wait = TRUE))
}

# - set params to wmde_collect_banner_impressions
# - for the Thank You 2919
uri_host <- c('de.wikipedia.org', 'de.m.wikipedia.org')
uri_path  <- '/beacon/impression'
uri_query <- c('WMDE_2019_thx'
               )
queryFile <- 'thankyou2019_BannerImpressions.hql'
fileName <- paste0("bannerImpressions_", cetDay, ".tsv")

# - collect Banner Impression data
wmde_collect_banner_impressions(uri_host,
                                uri_path,
                                uri_query,
                                cetDay,
                                queryFile,
                                fileName,
                                dataDir)

### -----------------------------------------------------------------------
### --- Wrangle Banner Impression Data
### -----------------------------------------------------------------------

# - function: wmde_process_banner_impressions
wmde_process_banner_impressions <- function(fileName,
                                            dataDir, 
                                            cetDay, 
                                            campaignName) {
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(stringr)
  library(dplyr)
  
  # - load
  bannerData <- read.delim(fileName, 
                           stringsAsFactors = F, 
                           sep = "\t")
  colnames(bannerData) <- 'uri_query'
  
  # - clean
  wInfo <- which(grepl("campaign=", bannerData$uri_query))
  bannerData <- bannerData[wInfo, ]
  
  # - split
  bannerData <- strsplit(bannerData, split = "&", fixed = T)
  # - extract relevant fields
  # - banner:
  banner <- sapply(bannerData, function(x) {
    x[which(grepl("^banner=", x))]
  })
  banner <- gsub("^banner=", "", banner)
  # - recordImpressionSampleRate:
  recordImpressionSampleRate <- sapply(bannerData, function(x) {
    x[which(grepl("^recordImpressionSampleRate=", x))]
  })
  recordImpressionSampleRate <- as.numeric(
    gsub("^recordImpressionSampleRate=", "", recordImpressionSampleRate)
    )
  # - result:
  result <- sapply(bannerData, function(x) {
    x[which(grepl("^result=", x))]
  })
  result <- gsub("^result=", "", result)
  
  # - compose table:
  bannerObservations <- data.frame(banner = banner, 
                                   recordImpressionSampleRate = recordImpressionSampleRate, 
                                   result = result, 
                                   stringsAsFactors = F)
  
  # - filter for result=show
  bannerObservations <- dplyr::filter(bannerObservations,
                                      result == "show")
  
  # - correction for recordImpressionSampleRate
  bannerObservations$recordImpressionSampleRate <- 
    1/bannerObservations$recordImpressionSampleRate
  
  # - aggregate:
  bannerObservations <- bannerObservations %>% 
    dplyr::select(banner, recordImpressionSampleRate) %>% 
    dplyr::group_by(banner) %>% 
    dplyr::summarise(impressions = sum(recordImpressionSampleRate))
  
  # - add cetDay, me
  bannerObservations$date <- cetDay
  bannerObservations$campaign <- campaignName
  
  # - store:
  write.csv(bannerObservations, 
            paste0("bannerImpressionsAggregated_",
                   strsplit(
                     strsplit(fileName, split = "_", fixed = T)[[1]][2],
                     split = ".", 
                     fixed = T)[[1]][1],
                   ".csv"
                   )
            )
  
}

# - wrangle Banner Impression data
campaignName <- "thankyou2019"
wmde_process_banner_impressions(fileName = fileName, 
                                dataDir = dataDir, 
                                cetDay = cetDay,
                                campaignName = campaignName)

### -----------------------------------------------------------------------
### --- Collect Pageviews
### -----------------------------------------------------------------------

# - function: wmde_collect_pageviews
wmde_collect_pageviews <- function(uri_host,
                                   uri_path,
                                   cetDay,
                                   queryFile,
                                   fileName,
                                   dataDir) {
  
  # - NOTE:
  # - expected format for cetDay is: YYYY-MM-DD
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(stringr)
  
  # - WHERE condition: create datetime_condition
  cet_condition <- seq(
    from = as.POSIXct(paste0(cetDay," 0:00"), tz = "Europe/Berlin"),
    to = as.POSIXct(paste0(cetDay," 23:00"), tz = "Europe/Berlin"),
    by = "hour"
  ) 
  attr(cet_condition, "tzone") <- "UTC"
  cet_condition <- as.character(cet_condition)
  cet_condition <- unlist(str_extract_all(cet_condition, "^([[:digit:]]|\\s|-)*"))
  cet_years <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][1]
    })
  cet_months <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][2]
    })
  cet_months <- gsub("^0", "", cet_months)
  cet_days <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][3]
    })
  cet_days <- gsub("^0", "", cet_days)
  cet_hours <- sapply(strsplit(cet_condition, split = " ", fixed = T), 
                      function(x) {
                        x[2]
                      })
  cet_hours <- gsub("^0", "", cet_hours)
  datetimeCondition <- paste0(
    "year = ", cet_years, " AND ",
    "month = ", cet_months, " AND ",
    "day = ", cet_days, " AND ", 
    "hour = ", cet_hours
  )
  datetimeCondition <- paste("(", 
                             datetimeCondition, 
                             ")",
                             collapse = " OR ", 
                             sep = "")
  
  # - WHERE condition: create uri_path_condition
  if (length(uri_path) > 1) {
    uri_path_condition <- paste0("(",
                                 paste(
                                   paste0("uri_path = '", uri_path, "'"),
                                   collapse = " OR ", sep = " "),
                                 ")"
    )
  } else {
    uri_path_condition = paste0("uri_path = '", uri_path, "'")
  }
  
  # - WHERE condition: create uri_host_condition
  if (length(uri_host) > 1) {
    uri_host_condition <- paste0("(",
                                 paste(
                                   paste0("uri_host = '", uri_host, "'"),
                                   collapse = " OR ", sep = " "),
                                 ")"
    )
  } else {
    uri_host_condition = paste0("uri_host = '", uri_host, "'")
  }
  
  # - compose HiveQL query
  hiveQuery <- paste0(
    "USE wmf;
    SELECT uri_path, uri_query, referer FROM webrequest
    WHERE (",
    uri_host_condition, " AND ",
    uri_path_condition, " AND ",
    "(", datetimeCondition, ")",
    ");"
    )
  
  # - write hql
  write(hiveQuery, queryFile)
  # - execute hql script:
  hiveArgs <- '/usr/local/bin/beeline -f'
  hiveInput <- paste0(queryFile, ' > ', fileName)
  # - command:
  hiveCommand <- paste(hiveArgs, hiveInput)
  return(
    system(command = hiveCommand, wait = TRUE))
}

# - set params to wmde_collect_pageviews
# - for the Thank You 2919
uri_host <- c('de.wikipedia.org', 'de.m.wikipedia.org')
uri_path  <- c(
  '/wiki/Wikipedia:Wikimedia_Deutschland/LerneWikipedia')
queryFile <- 'thankyou2019_Pageviews.hql'
fileName <- paste0("pageviews_", cetDay, ".tsv")

# - collect Pageviews data
wmde_collect_pageviews(uri_host,
                       uri_path,
                       cetDay,
                       queryFile,
                       fileName,
                       dataDir)

### -----------------------------------------------------------------------
### --- Wrangle Pageviews
### -----------------------------------------------------------------------

# - function: wmde_process_pageviews
wmde_process_pageviews <- function(fileName,
                                   dataDir, 
                                   uri_query_filter, 
                                   cetDay = cetDay,
                                   campaignName = campaignName) {
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(stringr)
  library(dplyr)
  library(tidyr)
  library(data.table)

  # - load
  pageviewsData <- readLines(fileName)
  wStart <- which(grepl("^uri_path", pageviewsData))
  pageviewsData <- pageviewsData[(wStart + 2):(length(pageviewsData) - 2)]
  pageviewsData <- data.frame(dat = pageviewsData, 
                              stringsAsFactors = F)
  pageviewsData <- separate(pageviewsData,
                            dat,
                            into = c('uri_path', 'uri_query', 'referer'),
                            sep = "\t")
  # - apply uri_query_filter
  # - NOTE: Autumn 2018, looking in both: uri_query, referer
  w_uri_query <- which(grepl(uri_query_filter, pageviewsData$uri_query))
  w_uri_query_referer <- which(grepl(uri_query_filter, pageviewsData$referer))
  w_uri_query <- unique(c(w_uri_query, w_uri_query_referer))
  pageviewsData <- pageviewsData[w_uri_query, ]
  w_uri_query_referer <- which(grepl(uri_query_filter, pageviewsData$referer))
  w_uri_query_referer_delete <- setdiff(1:dim(pageviewsData)[1], w_uri_query_referer)
  pageviewsData$referer[w_uri_query_referer_delete] <- ''
  # - when there is no uri_query, use the query from the referer field if present there
  pageviewsData$referer <- str_extract(pageviewsData$referer, "\\?campaign=.*$")
  pageviewsData$referer[is.na(pageviewsData$referer)] <- ""
  pageviewsData$referer <- gsub("?campaign=", "", pageviewsData$referer, fixed = T)
  pageviewsData$uri_query <- gsub("?campaign=", "", pageviewsData$uri_query, fixed = T)
  pageviewsData$uri_query[pageviewsData$uri_query == ""] <- 
    pageviewsData$referer[pageviewsData$uri_query == ""]
  pageviewsData <- dplyr::filter(pageviewsData, 
                          uri_query != "")
  pageviewsData$referer <- NULL
  # - clean up a bit:
  pageviewsData$uri_query <- gsub("/.*$", "", pageviewsData$uri_query)
  
  # - aggregate:
  pageviewsData <- pageviewsData %>% 
    dplyr::group_by(uri_query, uri_path) %>% 
    dplyr::summarise(pageviews = n())
  colnames(pageviewsData) <- c('Tag', 'Page', 'Pageviews')
  
  # - add cetDay, campaignName
  pageviewsData$date <- cetDay
  pageviewsData$campaign <- campaignName

  # - store:
  write.csv(pageviewsData, 
            paste0("pageviewsAggregated_",
                   strsplit(
                     strsplit(fileName, split = "_", fixed = T)[[1]][2],
                     split = ".", 
                     fixed = T)[[1]][1],
                   ".csv"
            )
  )
  
}

# - set params to wmde_process_pageviews
# - for the Thank You 2919
uri_query_filter <- 'WMDE_2019_thx'

# - wrangle pageviews
wmde_process_pageviews(fileName = fileName,
                       dataDir = dataDir,
                       uri_query_filter = uri_query_filter, 
                       cetDay = cetDay,
                       campaignName = campaignName) 


### -----------------------------------------------------------------------
### --- Collect User Registrations
### -----------------------------------------------------------------------

# - function: wmde_collect_registrations
wmde_collect_registrations <- function(logSchema, 
                                       web_host,
                                       event_campaign, 
                                       cetDay,
                                       dataDir, 
                                       fileName, 
                                       campaignName) {
  
  # - WHERE condition: create start_timestamp, stop_timestamp
  cet_condition <- seq(
    from = as.POSIXct(paste0(cetDay," 0:00"), tz = "Europe/Berlin"),
    to = as.POSIXct(paste0(cetDay," 24:00"), tz = "Europe/Berlin"),
    by = "hour"
  ) 
  attr(cet_condition, "tzone") <- "UTC"
  cet_condition <- as.character(cet_condition)
  start_timestamp <- paste(
    unlist(str_extract_all(cet_condition[1],
                    "[[:digit:]]")),
    collapse = "")
  stop_timestamp <- paste(
    unlist(str_extract_all(tail(cet_condition, 1),
                           "[[:digit:]]")),
    collapse = "")
  
  # - WHERE condition: create event_campaign_condition
  if (length(event_campaign) > 1) {
    event_campaign_condition <- paste0("(",
                                       paste(
                                         paste0("event_campaign LIKE '%", event_campaign, "%'"),
                                         collapse = " OR ", sep = " "),
                                       ")"
    )
  } else {
    event_campaign_condition = paste0("event_campaign LIKE '%", event_campaign, "%'")
  }
  

  # - compose SQL query:
  sqlParams <- 'mysql --defaults-file=/etc/mysql/conf.d/analytics-research-client.cnf -h analytics-slave.eqiad.wmnet -A -e'
  query <- paste0(
    "\"SELECT * FROM ", 
    paste0("log.", logSchema), 
    " WHERE ((webHost = '", 
    web_host, 
    "') AND (timestamp > ", 
    start_timestamp, 
    ") AND (timestamp <= ", 
    stop_timestamp, 
    ") AND (", 
    event_campaign_condition,
    "));\"")
  sqlOutput <- paste0("> ", paste0(dataDir, "/", fileName))
    
  # - run command
  qCommand <- paste(sqlParams, query, sqlOutput, sep = " ")
  system(command = qCommand, wait = TRUE)
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(stringr)
  library(dplyr)
  library(tidyr)
  library(data.table)
  
  # - load
  userReg <- fread(fileName, sep = "\t")
  
  # - filter bots
  wBot <- which(grepl("\"is_bot\": true", userReg$userAgent))
  if (length(wBot) > 0) {
    userReg <- userReg[-wBot, ]
  }
  
  # - select fields
  userReg <- userReg %>% 
    dplyr::select(event_userId, 
                  event_userName, 
                  event_isSelfMade, 
                  event_campaign, 
                  timestamp)
  
  # - add cetDay, campaignName
  userReg$date <- cetDay
  userReg$campaign <- campaignName
  
  # - store:
  write.csv(userReg, 
            paste0(
              strsplit(fileName, split = ".", fixed = T)[[1]][1],
            ".csv")
  )
  
  # - remove temp .tsv file
  file.remove(fileName)
  
}

# - set params for: wmde_collect_registrations
logSchema <- 'ServerSideAccountCreation_17719237' 
web_host <- 'de.wikipedia.org'
event_campaign <- 'WMDE_2019_thx'
fileName <- paste0("userRegistrations_", cetDay, ".tsv")

# - collect user registrations
wmde_collect_registrations(logSchema = logSchema,
                           web_host = web_host,
                           event_campaign = event_campaign,
                           cetDay = cetDay,
                           dataDir = dataDir,
                           fileName = fileName, 
                           campaignName = campaignName)

### -----------------------------------------------------------------------
### --- Wrangle User Registrations
### -----------------------------------------------------------------------

# - function: wmde_process_registrations
wmde_process_registrations <- function(fileName,
                                       dataDir, 
                                       cetDay, 
                                       campaignName) {
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(dplyr)
  library(data.table)

  # - load
  userReg <- fread(fileName)
  
  # - agregate
  userReg <- userReg %>% 
    dplyr::select(event_campaign) %>% 
    dplyr::group_by(event_campaign) %>% 
    dplyr::summarise(Registrations = n())

  # - add cetDay, campaignName
  userReg$date <- cetDay
  userReg$campaign <- campaignName
  
  # - store:
  write.csv(userReg, 
            paste0('userRegistrationsAggreagted_', cetDay, ".csv")
  )
  
}

# - set params for: wmde_process_registrations
fileName <- paste0("userRegistrations_", cetDay, ".csv")

# - wrangle user registrations:
wmde_process_registrations(fileName,
                           dataDir,
                           cetDay,
                           campaignName)

### --- Collect Newsletter registrations - for 2018_AuBC excl.
# - ServerSideAccountCreation_17719237 schema
qCommand <- "mysql --defaults-file=/etc/mysql/conf.d/analytics-research-client.cnf -h analytics-slave.eqiad.wmnet -A -e \"select * from log.ServerSideAccountCreation_17719237 where ((webHost = 'de.wikipedia.org') and (timestamp >= 20181011000000) and (event_campaign like '%WMDE_neweditors_autumn_2018_lpn%'));\" > /home/goransm/RScripts/NewEditors/2018_AutumnBannerCampaign/_data/Newsletter_userRegistrations.tsv"
system(command = qCommand, wait = TRUE)
### --- Wrangle Newsletter registrations - for 2018_AuBC excl.
# - function: wmde_process_registrations_general
wmde_process_registrations_general <- function(fileName,
                                       dataDir, 
                                       cetDay, 
                                       campaignName, 
                                       outFileName) {
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(dplyr)
  library(data.table)
  
  # - load
  userReg <- fread(fileName)
  
  # - agregate
  userReg <- userReg %>% 
    dplyr::select(event_campaign) %>% 
    dplyr::group_by(event_campaign) %>% 
    dplyr::summarise(Registrations = n())
  
  # - add cetDay, campaignName
  userReg$date <- cetDay
  userReg$campaign <- campaignName
  
  # - store:
  write.csv(userReg, 
            outFileName
  )
  
}

0.2 Data Aggregation

NOTE: Not run from this report; the data were already pre-processed and aggregated by the following R script before being submitted to analytical procedures:

### --- Report Generation for the Thank You 2919
### --- run locally

### --- to data directory
dataDir <- 
  '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_data/'
analyticsDir <- 
  '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_analytics/'
setwd(analyticsDir)

### --- Report Banner Impression Data

# - function: wmde_report_banner_impressions
wmde_report_banner_impressions <- function(dataDir) {
  
  # - Setup
  library(data.table)
  library(dplyr)

  # - list files:
  lF <- list.files(dataDir)
  
  # - filter aggregated banner impression data
  lF <- lF[grepl("bannerImpressionsAggregated_", lF, fixed = T)]
  
  # - load files and merge
  bannerData <- vector(mode = "list", length = length(lF))
  for (i in 1:length(lF)) {
    if (grepl("csv$|tsv$", lF[i])) {
      bannerData[[i]] <- fread(paste0(dataDir, lF[i]))
    } else {
      bannerData[[i]] <- NULL
    }
  }
  bannerData <- rbindlist(bannerData)
  bannerData$V1 <- NULL
  
  # - aggregates
  perBannerTotals <- bannerData %>% 
    select(banner, impressions) %>% 
    group_by(banner) %>% 
    summarise(totalImpressions = sum(impressions))
  perDayTotals <- bannerData %>% 
    select(date, impressions) %>% 
    group_by(date) %>% 
    summarise(totalImpressions = sum(impressions))
  
  # - output
  return(
    list(bannerImpressionsReport = bannerData, 
         perBannerTotals = perBannerTotals, 
         perDayTotals = perDayTotals)
  )
    
}

# - Report banner impressions
bannerImpressionsData <- wmde_report_banner_impressions(dataDir)
bannerImpressionsFile <- bannerImpressionsData$bannerImpressionsReport
write.csv(bannerImpressionsFile, "bannerImpressionsFile.csv")
bannerTotals <- bannerImpressionsData$perBannerTotals
write.csv(bannerTotals, "bannerTotals.csv")
bannerDayTotals <- bannerImpressionsData$perDayTotals
write.csv(bannerDayTotals, "bannerDayTotals.csv")


### --- Report Pageviews Data

# - function: wmde_report_pageviews
wmde_report_pageviews <- function(dataDir) {
  
  # - Setup
  library(data.table)
  library(dplyr)
  
  # - list files:
  lF <- list.files(dataDir)
  
  # - filter aggregated banner impression data
  lF <- lF[grepl("pageviewsAggregated_", lF, fixed = T)]
  
  # - load files and merge
  pageviewsData <- vector(mode = "list", length = length(lF))
  for (i in 1:length(lF)) {
    if (grepl("csv$|tsv$", lF[i])) {
      pageviewsData[[i]] <- fread(paste0(dataDir, lF[i]))
    } else {
      pageviewsData[[i]] <- NULL
    }
  }
  pageviewsData <- rbindlist(pageviewsData)
  pageviewsData$V1 <- NULL
  
  # - aggregates
  perDayTotals <- pageviewsData %>% 
    select(date, Pageviews) %>% 
    group_by(date) %>% 
    summarise(totalPageviews = sum(Pageviews))
  perTagTotals <- pageviewsData %>% 
    select(Tag, Pageviews) %>% 
    group_by(Tag) %>% 
    summarise(totalPageviews = sum(Pageviews))
  perPageTotals <- pageviewsData %>% 
    select(Page, Pageviews) %>% 
    group_by(Page) %>% 
    summarise(totalPageviews = sum(Pageviews))
  perPageDayTotals <- pageviewsData %>% 
    select(Page, date, Pageviews) %>%
    group_by(Page, date) %>% 
    summarise(totalPageviews = sum(Pageviews))
  perTagDayTotals <- pageviewsData %>% 
    select(Tag, date, Pageviews) %>%
    group_by(Tag, date) %>% 
    summarise(totalPageviews = sum(Pageviews))
  perTagPageTotals <- pageviewsData %>% 
    select(Tag, Page, Pageviews) %>%
    group_by(Tag, Page) %>% 
    summarise(totalPageviews = sum(Pageviews))
    
  # - output
  return(
    list(pageviewsDataReport = pageviewsData, 
         perDayTotals = perDayTotals, 
         perTagTotals = perTagTotals, 
         perPageTotals = perPageTotals, 
         perPageDayTotals = perPageDayTotals, 
         perTagDayTotals = perTagDayTotals,
         perTagPageTotals = perTagPageTotals)
  )
  
}

# - Report pageviews:
pageviewsData <- wmde_report_pageviews(dataDir)
pageviewsReportFile <- pageviewsData$pageviewsDataReport
write.csv(pageviewsReportFile, "pageviewsReportFile.csv")
pageviews_perDayTotals <- pageviewsData$perDayTotals
write.csv(pageviews_perDayTotals, "pageviews_perDayTotals.csv")
pageviews_perTagTotals <- pageviewsData$perTagTotals
write.csv(pageviews_perTagTotals, "pageviews_perTagTotals.csv")
pageviews_perPageTotals <- pageviewsData$perPageTotals
write.csv(pageviews_perPageTotals, "pageviews_perPageTotals.csv")
pageviews_perPageDayTotals <- pageviewsData$perPageDayTotals
write.csv(pageviews_perPageDayTotals, "pageviews_perPageDayTotals.csv")
pageviews_perTagDayTotals <- pageviewsData$perTagDayTotals
write.csv(pageviews_perTagDayTotals, "pageviews_perTagDayTotals.csv")
pageviews_perTagPageTotals <- pageviewsData$perTagPageTotals
write.csv(pageviews_perTagPageTotals, "perTagPageTotals.csv")

### --- Report User Registrations

# - function: wmde_report_registrations
wmde_report_registrations <- function(dataDir) {
  
  # - Setup
  library(data.table)
  library(dplyr)
  
  # - list files:
  lF <- list.files(dataDir)
  
  # - filter aggregated user registration data
  lF <- lF[grepl("userRegistrationsAggreagted_", lF, fixed = T)]
  
  # - load files and merge
  registrationData <- vector(mode = "list", length = length(lF))
  for (i in 1:length(lF)) {
    if (grepl("csv$|tsv$", lF[i])) {
      registrationData[[i]] <- fread(paste0(dataDir, lF[i]))
    } else {
      registrationData[[i]] <- NULL
    }
  }
  registrationData <- rbindlist(registrationData)
  registrationData$V1 <- NULL
  
  # - aggregates
  perDayTotals <- registrationData %>% 
    select(date, Registrations) %>% 
    group_by(date) %>% 
    summarise(totalRegistrations = sum(Registrations))
  perTagTotals <- registrationData %>% 
    select(event_campaign, Registrations) %>% 
    group_by(event_campaign) %>% 
    summarise(totalRegistrations = sum(Registrations))
  perTagDayTotals <- registrationData %>% 
    select(event_campaign, date, Registrations) %>% 
    group_by(event_campaign, date) %>% 
    summarise(totalRegistrations = sum(Registrations))
  
  # - output
  return(
    list(registrationsDataReport = registrationData, 
         perDayTotals = perDayTotals, 
         perTagTotals = perTagTotals, 
         perTagDayTotals = perTagDayTotals)
  )
  
}

# - Report upon user registrations
userRegData <- wmde_report_registrations(dataDir)
userRegistrationsReportFile <- userRegData$registrationsDataReport
write.csv(userRegistrationsReportFile, "userRegistrationsReportFile.csv")
userRegistrations_perDayTotals <- userRegData$perDayTotals
write.csv(userRegistrations_perDayTotals, "userRegistrations_perDayTotals.csv")
userRegistrations_perTagTotals <- userRegData$perTagTotals
write.csv(userRegistrations_perTagTotals, "userRegistrations_perTagTotals.csv")
userRegistrations_perTagDayTotals <- userRegData$perTagDayTotals
write.csv(userRegistrations_perTagDayTotals, "userRegistrations_perTagDayTotals.csv")

1. Campaign Banners and Pages

This section presents all data and statistics on the campaign banners and pages.

1.2 Pageviews

1.2.1 Pageviews Overview

Chart 1.2.1. Pageviews Overview.

dataSet <- read.csv(
  '_analytics/pageviews_perPageDayTotals.csv',
                    header = T,
                    row.names = 1,
                    check.names = F,
                    stringsAsFactors = F)
dataSet$Page <- gsub("/wiki/Wikipedia:Wikipedia_|/wiki/Wikipedia:Wikimedia_", "", dataSet$Page)
# - Visualize w. {ggplot2}
ggplot(dataSet, aes(x = date,
                    y = totalPageviews,
                    group = Page,
                    color = Page,
                    fill = Page,
                    label = totalPageviews,
                    )) + 
  geom_path(size = .25) + 
  geom_point(size = 1.5) +
  geom_point(size = 1, color = "white") +
  scale_y_continuous(labels = comma) +
  ggtitle('Thank You 2919: Pageviews') +
  theme_minimal() + 
  geom_text_repel(size = 3.5, show.legend = FALSE) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "top")

1.2.1 Pageviews Overview: Table

Table 1.2.1. Pageviews Overview

### --- Full Dataset (Table Report)
datatable(dataSet %>% arrange(desc(totalPageviews)))

2. User Registrations

All data on user registrations are presented in this section.

2.1 Registrations per tag and day

Chart 2.1. Registrations per tag and day. Please note: points with no data labels signify 0 user registrations.

# - Standard registrations
dataSet <- read.csv(
  '_analytics/userRegistrationsReportFile.csv',
                    header = T,
                    row.names = 1,
                    check.names = F,
                    stringsAsFactors = F)
dataSet <- dataSet %>% 
  filter(!(event_campaign %in% 'WMDE_neweditors_autumn_2018_lpn'))
dataSet$campaign <- NULL
ggplot(dataSet, aes(x = date,
                    y = Registrations,
                    group = event_campaign,
                    color = event_campaign,
                    fill = event_campaign,
                    label = Registrations,
                    )) + 
  geom_path(size = .25) + 
  geom_point(size = 1.5) +
  geom_point(size = 1, color = "white") +
  scale_y_continuous(labels = comma) +
  ggtitle('Thank You 2919: Registrations per Tag') +
  theme_minimal() + 
  geom_text_repel(size = 3.5, show.legend = F) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "top")

### --- Full Dataset (Table Report)
colnames(dataSet)[1] <- 'Tag'
datatable(dataSet %>% arrange(Tag, date, desc(Registrations)))

3. User Edits

3.1 Collect user edits

This data acquisition chank is not reproducible from this Report; runs in production on stat1007:

### ------------------------------------------------------
### --- Collect all user edits per day from dewiki.revision
### --- from 2. January 2019, w. event_campaign
### --- update 1: one week after then end: 23th January
### --- update 2: two weeks after then end: 30th January
### ------------------------------------------------------
localDir <- '/home/goransm/Analytics/NewEditors/Campaigns/2019_ThankYou/data/'
startTimestamp <- '20190102000000' # - campaign begins
# - SQL query
sqlQuery <- paste("\"SELECT rev_user, SUBSTR(rev_timestamp, 1, 8) 
                  FROM dewiki.revision WHERE rev_timestamp >= ", startTimestamp, ";\"", sep = "")

### --- output filename
filename <- paste(localDir,'ReferenceEdits_dewiki_20190123', ".tsv", sep = "")

### --- execute sql script:
sqlArgs <- 'mysql --defaults-file=/etc/mysql/conf.d/analytics-research-client.cnf -h analytics-store.eqiad.wmnet -A -e'
sqlInput <- paste(sqlQuery,
                  " > ",
                  filename,
                  sep = "")
# - command:
sqlCommand <- paste(sqlArgs, sqlInput)
system(command = sqlCommand, wait = TRUE)

This analysis is scheduled for January 24th and 30th Phab

3.1 User edits: daily

Wrangle the dataset; visualize campaign edits per day.

allEdits <- 
  fread(
    '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_analytics/ReferenceEdits_dewiki_20190131.tsv'
    )
userRegs <- read.csv(
  '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_analytics/fullRegistrationDataset.csv',
                    header = T,
                    row.names = 1,
                    check.names = F,
                    stringsAsFactors = F)
# - keep only campaign registered users:
userEdits <- allEdits[allEdits$rev_user %in% unique(userRegs$event_userId), ]
rm(userRegs); rm(allEdits); gc()
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  993551 53.1    1770749 94.6  1770749 94.6
Vcells 2671840 20.4    9089513 69.4  9089499 69.4
# - wrangle timestamp
colnames(userEdits) <- c('user_id', 'date')
userEdits$date <- sapply(userEdits$date, function(x) {
  paste(
    substr(x, 1, 4),
    substr(x, 5, 6), 
    substr(x, 7, 8),
    sep = "-"
  )
})
# - aggregate per day
userEditsDay <- userEdits %>% 
  group_by(date) %>% 
  summarise(edits = n()) %>% 
  arrange(date)
# - input zero edits at missing dates
userEditsDayCopy <- userEditsDay
editDateRange <- userEditsDayCopy$date
editDateRange <- as.POSIXct(editDateRange)
editDateRange <- seq(min(editDateRange), max(editDateRange), by = "day")
userEditsDayCopy <- data.frame(date = as.character(editDateRange), edits = numeric(length(editDateRange)), 
                               stringsAsFactors = F)
userEditsDayCopy <- left_join(userEditsDayCopy, userEditsDay, by = "date")
userEditsDayCopy$edits.x <- NULL
colnames(userEditsDayCopy) <- c('date', 'edits')
userEditsDayCopy$edits[is.na(userEditsDayCopy$edits)] <- 0
userEditsDay <-userEditsDayCopy
# - visualize daily edits
# - Visualize w. {ggplot2}
ggplot(userEditsDay, aes(x = date,
                         y = edits,
                         label = edits
                    )) + 
  geom_path(size = .5, color = 'blue', group = 1) + 
  geom_point(size = 1.5, color = 'blue') + 
  geom_point(size = 1.5, color = 'white') +
  geom_text_repel(size = 3.5) + 
  ggtitle('Thank You 2919: Edits per day') +
  theme_minimal() + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "top")

3.1 User edits: edit classes

# - Edit | 1 | 2-4 | 5-9 | 10-49 | >50
editBoundaries <- list(
  c(0, 1), 
  c(2, 4),
  c(5, 9),
  c(10, 49)
)
userEdits_user <- userEdits %>% 
  group_by(user_id) %>% 
  summarise(edits = n())
userEdits_user$editClass <- sapply(userEdits_user$edits, function(x) {
  wEC <- sapply(editBoundaries, function(y) {
    x >= y[1] & x <= y[2]
  })
  if (sum(wEC) == 0) {
    return(">= 50")
  } else {
    return(paste0("(",
                  editBoundaries[[which(wEC)]][1],
                  " - ",
                  editBoundaries[[which(wEC)]][2], 
                  ")"
                  )
    )
  }
})
editClass <- as.data.frame(table(userEdits_user$editClass), 
                           stringsAsFactors = F)
colnames(editClass) <- c('Edit Class', 'Num.Users')
editClass$order <- as.numeric(sapply(editClass$`Edit Class`, function(x) {
  lower <- str_extract(x, '[[:digit:]]+')
}))
editClass <- arrange(editClass, order)
editClass$order <- NULL
datatable(editClass)

3.2 User edits: when do the edits happen?

Chart 3.2.1

Q. Also it would be interesting to know when the new users edit: is there a possibility to calculate in which time interval new users are active, e.g. directly after registration, or some days later etc.? On the x-axis would be time after registration in days, with 0 being point of registration and on the y-axis number of edits. The diagram would then show average edits per user x-days after registration. Could this work? Phab

Note. The chart presents the mean number of edits calculated from the total number of edits per day divided by the number of active users (i.e. by how many users edited) on that day.

# - load user registrations per day, per user
userRegistrationsDaily <- fread("_analytics/fullRegistrationDataset.csv")
userRegistrationsDaily <- select(userRegistrationsDaily, 
                                 event_userId, date)
editsSinceReg <- userRegistrationsDaily 
editsSinceReg <- left_join(editsSinceReg, 
                           userEdits, 
                           by = c("event_userId" = "user_id")) %>% 
  arrange(event_userId)
colnames(editsSinceReg) <- c("user_id", "registration", "edit")
editsSinceReg <- filter(editsSinceReg, !is.na(edit))
editsSinceReg$registration <- as.POSIXct(editsSinceReg$registration)
editsSinceReg$edit <- as.POSIXct(editsSinceReg$edit)
editsSinceReg$diff <- (editsSinceReg$edit - editsSinceReg$registration)/(60^2*24)
avgEditsSinceReg <- editsSinceReg %>% 
  select(diff, user_id) %>% 
  group_by(diff, user_id) %>% 
  summarise(edits = n()) %>% 
  select(diff, edits) %>% 
  group_by(diff) %>% 
  summarise(meanEdits = mean(edits))
avgEditsSinceReg$diff <- as.numeric(avgEditsSinceReg$diff)
avgEditsFrame <- data.frame(diff = seq(min(avgEditsSinceReg$diff), max(avgEditsSinceReg$diff)))
avgEditsFrame <- left_join(avgEditsFrame, avgEditsSinceReg, by = "diff")
avgEditsFrame$meanEdits[is.na(avgEditsFrame$meanEdits)] <- 0
avgEditsFrame$meanEdits <- round(avgEditsFrame$meanEdits, 2)
ggplot(avgEditsFrame, aes(x = diff, 
                    y = meanEdits, 
                    label = meanEdits)) + 
  geom_path(color = "blue", size = .25) + 
  geom_point(color = "blue", size = 1.5) + 
  geom_point(color = "white", size = 1) + 
  ggtitle('Thank You 2919: Average number of edits N days after registration') + 
  xlab("Days after registration") + ylab("Mean edits per user") + 
  theme_minimal() + 
  geom_text_repel(size = 3.5, show.legend = FALSE) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 0, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "right")

Chart 3.2.2

Note. The chart presents the mean number of edits calculated from the total number of edits per day divided by the number ofusers registered until that day (i.e. by how many campaign registered users could have edited on that day) see Phab.

# - load user registrations per day, per user
userRegistrationsDaily <- fread("_analytics/fullRegistrationDataset.csv")
userRegistrationsDaily <- select(userRegistrationsDaily, 
                                 event_userId, date)
cumulativeRegistrations <- userRegistrationsDaily %>% 
  select(date) %>% 
  group_by(date) %>% 
  summarise(registrations = n())
cumulativeRegistrations$registrations <- cumsum(cumulativeRegistrations$registrations)
userEditsPerDay <- userEdits %>% 
  select(date) %>%
  group_by(date) %>% 
  summarise(edits = n())
userEditsPerDay <- left_join(userEditsPerDay, cumulativeRegistrations,
                             by = "date")
userEditsPerDay$registrations[is.na(userEditsPerDay$registrations)] <- 
  max(userEditsPerDay$registrations, na.rm = T)
userEditsPerDay$meanEdits = round(userEditsPerDay$edits/userEditsPerDay$registrations, 3)
dateRangeFrame <- data.frame(date = 
                               as.character(
                                 seq(min(as.POSIXct(userEditsPerDay$date)), 
                                     max(as.POSIXct(userEditsPerDay$date)), 
                                     by = "day")),
                             stringsAsFactors = F)
userEditsPerDay <- left_join(dateRangeFrame, userEditsPerDay, by = "date")
userEditsPerDay$meanEdits[is.na(userEditsPerDay$meanEdits)] <- 0
ggplot(userEditsPerDay, aes(x = date,
                            y = meanEdits,
                            label = meanEdits)) + 
  geom_path(color = "blue", size = .25, group = 1) + 
  geom_point(color = "blue", size = 1.5) + 
  geom_point(color = "white", size = 1) + 
  ggtitle('Thank You 2919: Average number of edits N days after registration') + 
  xlab("Days after registration") + ylab("Mean edits per user") + 
  theme_minimal() + 
  geom_text_repel(size = 3.5, show.legend = FALSE) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "right")

Chart 3.2.3

Note. The chart presents the mean number of edits calculated from the total number of edits per day divided by the number of active users (i.e. by how many users edited) until that day." see Phab.

# - load user registrations per day, per user
userEditsRev <- userEdits %>% 
  arrange(date)
uniqueDates <- unique(userEditsRev$date)
editors <- list()
editors <- lapply(uniqueDates, function(x) {
  unique(userEditsRev$user_id[userEditsRev$date %in% x])
})
names(editors) <- uniqueDates
editorsDaily <- list()
for (i in 1:length(editors)) {
  editorsDaily[[i]] <- c(unique(unname(unlist(editors[1:i]))))
}
editorsDaily <- lapply(editorsDaily, length)
editorsDaily <- data.frame(editors = unname(unlist(editorsDaily)), 
                           date = uniqueDates, 
                           stringsAsFactors = F)
userEditsPerDay <- userEdits %>% 
  select(date) %>%
  group_by(date) %>% 
  summarise(edits = n())
userEditsPerDay <- left_join(userEditsPerDay, editorsDaily,
                             by = "date")
userEditsPerDay$meanEdits = round(userEditsPerDay$edits/userEditsPerDay$editors, 3)
dateRangeFrame <- data.frame(date = 
                               as.character(
                                 seq(min(as.POSIXct(userEditsPerDay$date)), 
                                     max(as.POSIXct(userEditsPerDay$date)), 
                                     by = "day")),
                             stringsAsFactors = F)
userEditsPerDay <- left_join(dateRangeFrame, userEditsPerDay, by = "date")
userEditsPerDay$meanEdits[is.na(userEditsPerDay$meanEdits)] <- 0
ggplot(userEditsPerDay, aes(x = date,
                            y = meanEdits,
                            label = meanEdits)) + 
  geom_path(color = "blue", size = .25, group = 1) + 
  geom_point(color = "blue", size = 1.5) + 
  geom_point(color = "white", size = 1) + 
  ggtitle('Thank You 2919: Average number of edits N days after registration') + 
  xlab("Days after registration") + ylab("Mean edits per user") + 
  theme_minimal() + 
  geom_text_repel(size = 3.5, show.legend = FALSE) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "right")

4. Training Modules

4.1 Collect training modules data

The dataset is obtained directly from the maintainer.

# - training modules data
trainModules <- fread(
    '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_analytics/wmde_training_data_2019-01.csv')
# - get user registrations w. user names for joins
locDir <- 
'/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_data/'
lF <- list.files(locDir)
lF <- lF[grepl("userRegistrations_", lF)]
userReg <- lapply(lF, function(x) {
  fread(paste0(locDir, x))
})
userReg <- rbindlist(userReg)
userReg$V1 <- NULL
trainModules <- left_join(trainModules,
                          select(userReg,  event_userId, event_userName),
                          by = c("username" = "event_userName"))
trainModules <- trainModules[!is.na(trainModules$event_userId), ]
trainModules$username <- NULL

4.2 Training modules: overview

49 new editors have started at least one training module; 32 have completed at least one the training and 19 did not.

The following table 4.2.1 gives and overview of how many users have completed a particular training module.

tmUsers <- trainModules %>% 
  select(event_userId, training_module, module_completion_date) %>% 
  mutate(module_completion_date = ifelse(module_completion_date == '', F, T)) %>% 
  group_by(event_userId, training_module, module_completion_date) %>% 
  summarise(n = n()) %>% 
  filter(module_completion_date == T) %>% 
  select(training_module) %>% 
  group_by(training_module) %>% 
  summarise(editors = n())
datatable(tmUsers)

Table 4.2.2 gives and overview of how many users have started a particular training module.

tmUsers <- trainModules %>% 
  select(training_module) %>% 
  group_by(training_module) %>% 
  summarise(editors = n())
datatable(tmUsers)

Table 4.2.3 The last slide from a training module completed by new editors; the editors column shows the number of editors who have abandoned (or completed) their traning at the respective slide.

tmLastSlide <- trainModules %>%
  group_by(training_module, last_slide_completed) %>% 
  summarise(editors = n())
datatable(tmLastSlide)

Table 4.2.4 User edits: training modules completed vs not completed.

tmEdits <- left_join(userEdits, trainModules, by = c('user_id' = 'event_userId'))
tmEdits <- tmEdits %>% 
  select(training_module, module_completion_date)
tmEdits$completed <- sapply(tmEdits$module_completion_date, function(x) {
  if(is.na(x) | x == '') {return(FALSE)} else {return(TRUE)}
})
tmEdits$started <- sapply(tmEdits$training_module, function(x) {
  if(is.na(x) | x == '') {return(FALSE)} else {return(TRUE)}
})
tmEdits$module_completion_date <- NULL
tmEdits$training_module <- NULL
tmEdits$training_outcome <- ifelse(tmEdits$completed, 'Completed', NA)
tmEdits$training_outcome <- ifelse(tmEdits$started & is.na(tmEdits$training_outcome), 
                                   'Started', tmEdits$training_outcome)
tmEdits$training_outcome[is.na(tmEdits$training_outcome)] <- 'No training' 
tmEdits <- tmEdits %>% 
  group_by(training_outcome) %>% 
  summarise(edits = n())
datatable(tmEdits)

Table 4.2.5 User edits: average number of edits per user, training modules completed vs not completed.

tmEdits <- left_join(userEdits, trainModules, by = c('user_id' = 'event_userId'))
tmEdits <- tmEdits %>% 
  select(user_id, training_module, module_completion_date)
tmEdits$completed <- sapply(tmEdits$module_completion_date, function(x) {
  if(is.na(x) | x == '') {return(FALSE)} else {return(TRUE)}
})
tmEdits$started <- sapply(tmEdits$training_module, function(x) {
  if(is.na(x) | x == '') {return(FALSE)} else {return(TRUE)}
})
tmEdits$module_completion_date <- NULL
tmEdits$training_module <- NULL
tmEdits$training_outcome <- ifelse(tmEdits$completed, 'Completed', NA)
tmEdits$training_outcome <- ifelse(tmEdits$started & is.na(tmEdits$training_outcome), 
                                   'Started', tmEdits$training_outcome)
tmEdits$training_outcome[is.na(tmEdits$training_outcome)] <- 'No training' 
tmEdits <- tmEdits %>% 
  group_by(user_id, training_outcome) %>% 
  summarise(edits = n()) %>% 
  group_by(training_outcome) %>% 
  summarise(`mean(Edits)` = round(mean(edits), 2))
datatable(tmEdits)

Do you see in the tables from Ragesoss how many people did or begin the modules in general - regardless of the fact they register or not. Question behind the question: I would like to know how many people potentially just did the module, but didn’t register beforhand. (see: Phab)

# - training modules data
trainModules <- fread(
    '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_analytics/wmde_training_data_2019-01.csv')
# - Unique number of trainModules$username -2 (Stefan and Sage)
length(unique(trainModules$username)) - 2
[1] 387

387 people took the training modules irrespective of registration.

---
title: 'Thank You Campaign 2019: Report'
author: "Goran S. Milovanovic, Data Scientist, WMDE"
date: "January 21, 2019"
output:
  html_notebook:
    code_folding: hide
    theme: simplex
    toc: yes
    toc_float: yes
    toc_depth: 5
  html_document:
    toc: yes
    toc_depth: 5
---

**Feedback** should be send to `goran.milovanovic_ext@wikimedia.de`. 

The campaign is run from 2019/01/02 to 2019/01/16.

**CURRENT UPDATE:** Complete dataset as of 2019/01/16.

```{r, echo = F, warning = 'hide', message = F, results = 'hide'}
# !diagnostics off
### --- Setup
knitr::opts_chunk$set(fig.width = 15, fig.height = 8) 
rm(list = ls())
library(stringr)
library(dplyr)
library(tidyr)
library(data.table)
library(ggplot2)
library(ggrepel)
library(scales)
library(RColorBrewer)
library(kableExtra)
library(rmarkdown)
library(knitr)
library(DT)
library(reshape2)
library(purrr)
```

## 0. Data Acquisiton

**NOTE:** the Data Acquisition code chunk is not fully reproducible from this Report. The data are collected by running an R script on `stat1007.eqiad.wmnet`, collecting the data as `.tsv` and `.csv` files, copying manually, and processing locally. 

### 0.1 Daily Update

```{r, echo = T, eval = F}

### --- Data Acquisition for the Thank You 2019 Campaign
### --- run from stat1007
### --- /home/goransm/Analytics/NewEditors/Campaigns/2019_ThankYou

### --- to data directory
dataDir <- '/home/goransm/Analytics/NewEditors/Campaigns/2019_ThankYou/data/'
setwd(dataDir)

### --- determine cetDay
library(lubridate)
cetDay <- Sys.time()
cetDay
attr(cetDay, "tzone") <- "Europe/Berlin"
# - one day behind for crontab
# - (i.e. waiting for wmf.webrequest to complete is data acquisition)
cetDay <- ymd(
  strsplit(as.character(cetDay), 
           split = " ", 
           fixed = T)[[1]][1]
  ) - 1

### -----------------------------------------------------------------------
### --- Collect Banner Impression Data
### -----------------------------------------------------------------------

# - function: wmde_collect_banner_impressions
wmde_collect_banner_impressions <- function(uri_host, 
                                            uri_path, 
                                            uri_query, 
                                            cetDay,
                                            queryFile,
                                            fileName,
                                            dataDir) {
  
  # - NOTE:
  # - expected format for cetDay is: YYYY-MM-DD
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(stringr)
  
  # - WHERE condition: create datetime_condition
  cet_condition <- seq(
    from = as.POSIXct(paste0(cetDay," 0:00"), tz = "Europe/Berlin"),
    to = as.POSIXct(paste0(cetDay," 23:00"), tz = "Europe/Berlin"),
    by = "hour"
  ) 
  attr(cet_condition, "tzone") <- "UTC"
  cet_condition <- as.character(cet_condition)
  cet_condition <- unlist(str_extract_all(cet_condition, "^([[:digit:]]|\\s|-)*"))
  cet_years <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][1]
    })
  cet_months <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][2]
    })
  cet_months <- gsub("^0", "", cet_months)
  cet_days <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][3]
    })
  cet_days <- gsub("^0", "", cet_days)
  cet_hours <- sapply(strsplit(cet_condition, split = " ", fixed = T), 
                      function(x) {
                        x[2]
                      })
  cet_hours <- gsub("^0", "", cet_hours)
  datetimeCondition <- paste0(
    "year = ", cet_years, " AND ",
    "month = ", cet_months, " AND ",
    "day = ", cet_days, " AND ", 
    "hour = ", cet_hours
  )
  datetimeCondition <- paste("(", 
                             datetimeCondition, 
                             ")",
                             collapse = " OR ", 
                             sep = "")
  
  # - WHERE condition: create uri_path_condition
  if (length(uri_path) > 1) {
    uri_path_condition <- paste0("(",
                                 paste(
                                   paste0("uri_path = '", uri_path, "'"),
                                   collapse = " OR ", sep = " "),
                                 ")"
                                 )
  } else {
    uri_path_condition = paste0("uri_path = '", uri_path, "'")
  }
  
  # - WHERE condition: create uri_host_condition
  if (length(uri_host) > 1) {
    uri_host_condition <- paste0("(",
                                 paste(
                                   paste0("uri_host = '", uri_host, "'"),
                                   collapse = " OR ", sep = " "),
                                 ")"
    )
  } else {
    uri_host_condition = paste0("uri_host = '", uri_host, "'")
  }
  
  # - WHERE condition: create uri_query_condition
  if (length(uri_query) > 1) {
    uri_query_condition <- paste0("(",
                                 paste(
                                   paste0("uri_query LIKE '%", uri_query, "%'"),
                                   collapse = " OR ", sep = " "),
                                 ")"
    )
  } else {
    uri_query_condition = paste0("uri_query LIKE '%", uri_query, "%'")
  }
  
  # - compose HiveQL query
  hiveQuery <- paste0(
    "USE wmf;
    SELECT uri_query FROM webrequest
    WHERE (",
    uri_host_condition, " AND ",
    uri_path_condition, " AND ",
    uri_query_condition, " AND ",
    "(", datetimeCondition, ")",
    ");"
  )
  
  # - write hql
  write(hiveQuery, queryFile)
  # - execute hql script:
  hiveArgs <- '/usr/local/bin/beeline -f'
  hiveInput <- paste0(queryFile, ' > ', paste0(dataDir, fileName))
  # - command:
  hiveCommand <- paste(hiveArgs, hiveInput)
  return(
    system(command = hiveCommand, wait = TRUE))
}

# - set params to wmde_collect_banner_impressions
# - for the Thank You 2919
uri_host <- c('de.wikipedia.org', 'de.m.wikipedia.org')
uri_path  <- '/beacon/impression'
uri_query <- c('WMDE_2019_thx'
               )
queryFile <- 'thankyou2019_BannerImpressions.hql'
fileName <- paste0("bannerImpressions_", cetDay, ".tsv")

# - collect Banner Impression data
wmde_collect_banner_impressions(uri_host,
                                uri_path,
                                uri_query,
                                cetDay,
                                queryFile,
                                fileName,
                                dataDir)

### -----------------------------------------------------------------------
### --- Wrangle Banner Impression Data
### -----------------------------------------------------------------------

# - function: wmde_process_banner_impressions
wmde_process_banner_impressions <- function(fileName,
                                            dataDir, 
                                            cetDay, 
                                            campaignName) {
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(stringr)
  library(dplyr)
  
  # - load
  bannerData <- read.delim(fileName, 
                           stringsAsFactors = F, 
                           sep = "\t")
  colnames(bannerData) <- 'uri_query'
  
  # - clean
  wInfo <- which(grepl("campaign=", bannerData$uri_query))
  bannerData <- bannerData[wInfo, ]
  
  # - split
  bannerData <- strsplit(bannerData, split = "&", fixed = T)
  # - extract relevant fields
  # - banner:
  banner <- sapply(bannerData, function(x) {
    x[which(grepl("^banner=", x))]
  })
  banner <- gsub("^banner=", "", banner)
  # - recordImpressionSampleRate:
  recordImpressionSampleRate <- sapply(bannerData, function(x) {
    x[which(grepl("^recordImpressionSampleRate=", x))]
  })
  recordImpressionSampleRate <- as.numeric(
    gsub("^recordImpressionSampleRate=", "", recordImpressionSampleRate)
    )
  # - result:
  result <- sapply(bannerData, function(x) {
    x[which(grepl("^result=", x))]
  })
  result <- gsub("^result=", "", result)
  
  # - compose table:
  bannerObservations <- data.frame(banner = banner, 
                                   recordImpressionSampleRate = recordImpressionSampleRate, 
                                   result = result, 
                                   stringsAsFactors = F)
  
  # - filter for result=show
  bannerObservations <- dplyr::filter(bannerObservations,
                                      result == "show")
  
  # - correction for recordImpressionSampleRate
  bannerObservations$recordImpressionSampleRate <- 
    1/bannerObservations$recordImpressionSampleRate
  
  # - aggregate:
  bannerObservations <- bannerObservations %>% 
    dplyr::select(banner, recordImpressionSampleRate) %>% 
    dplyr::group_by(banner) %>% 
    dplyr::summarise(impressions = sum(recordImpressionSampleRate))
  
  # - add cetDay, me
  bannerObservations$date <- cetDay
  bannerObservations$campaign <- campaignName
  
  # - store:
  write.csv(bannerObservations, 
            paste0("bannerImpressionsAggregated_",
                   strsplit(
                     strsplit(fileName, split = "_", fixed = T)[[1]][2],
                     split = ".", 
                     fixed = T)[[1]][1],
                   ".csv"
                   )
            )
  
}

# - wrangle Banner Impression data
campaignName <- "thankyou2019"
wmde_process_banner_impressions(fileName = fileName, 
                                dataDir = dataDir, 
                                cetDay = cetDay,
                                campaignName = campaignName)

### -----------------------------------------------------------------------
### --- Collect Pageviews
### -----------------------------------------------------------------------

# - function: wmde_collect_pageviews
wmde_collect_pageviews <- function(uri_host,
                                   uri_path,
                                   cetDay,
                                   queryFile,
                                   fileName,
                                   dataDir) {
  
  # - NOTE:
  # - expected format for cetDay is: YYYY-MM-DD
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(stringr)
  
  # - WHERE condition: create datetime_condition
  cet_condition <- seq(
    from = as.POSIXct(paste0(cetDay," 0:00"), tz = "Europe/Berlin"),
    to = as.POSIXct(paste0(cetDay," 23:00"), tz = "Europe/Berlin"),
    by = "hour"
  ) 
  attr(cet_condition, "tzone") <- "UTC"
  cet_condition <- as.character(cet_condition)
  cet_condition <- unlist(str_extract_all(cet_condition, "^([[:digit:]]|\\s|-)*"))
  cet_years <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][1]
    })
  cet_months <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][2]
    })
  cet_months <- gsub("^0", "", cet_months)
  cet_days <- sapply(
    strsplit(cet_condition, split = " ", fixed = T), function(x) {
      strsplit(x, split = "-")[[1]][3]
    })
  cet_days <- gsub("^0", "", cet_days)
  cet_hours <- sapply(strsplit(cet_condition, split = " ", fixed = T), 
                      function(x) {
                        x[2]
                      })
  cet_hours <- gsub("^0", "", cet_hours)
  datetimeCondition <- paste0(
    "year = ", cet_years, " AND ",
    "month = ", cet_months, " AND ",
    "day = ", cet_days, " AND ", 
    "hour = ", cet_hours
  )
  datetimeCondition <- paste("(", 
                             datetimeCondition, 
                             ")",
                             collapse = " OR ", 
                             sep = "")
  
  # - WHERE condition: create uri_path_condition
  if (length(uri_path) > 1) {
    uri_path_condition <- paste0("(",
                                 paste(
                                   paste0("uri_path = '", uri_path, "'"),
                                   collapse = " OR ", sep = " "),
                                 ")"
    )
  } else {
    uri_path_condition = paste0("uri_path = '", uri_path, "'")
  }
  
  # - WHERE condition: create uri_host_condition
  if (length(uri_host) > 1) {
    uri_host_condition <- paste0("(",
                                 paste(
                                   paste0("uri_host = '", uri_host, "'"),
                                   collapse = " OR ", sep = " "),
                                 ")"
    )
  } else {
    uri_host_condition = paste0("uri_host = '", uri_host, "'")
  }
  
  # - compose HiveQL query
  hiveQuery <- paste0(
    "USE wmf;
    SELECT uri_path, uri_query, referer FROM webrequest
    WHERE (",
    uri_host_condition, " AND ",
    uri_path_condition, " AND ",
    "(", datetimeCondition, ")",
    ");"
    )
  
  # - write hql
  write(hiveQuery, queryFile)
  # - execute hql script:
  hiveArgs <- '/usr/local/bin/beeline -f'
  hiveInput <- paste0(queryFile, ' > ', fileName)
  # - command:
  hiveCommand <- paste(hiveArgs, hiveInput)
  return(
    system(command = hiveCommand, wait = TRUE))
}

# - set params to wmde_collect_pageviews
# - for the Thank You 2919
uri_host <- c('de.wikipedia.org', 'de.m.wikipedia.org')
uri_path  <- c(
  '/wiki/Wikipedia:Wikimedia_Deutschland/LerneWikipedia')
queryFile <- 'thankyou2019_Pageviews.hql'
fileName <- paste0("pageviews_", cetDay, ".tsv")

# - collect Pageviews data
wmde_collect_pageviews(uri_host,
                       uri_path,
                       cetDay,
                       queryFile,
                       fileName,
                       dataDir)

### -----------------------------------------------------------------------
### --- Wrangle Pageviews
### -----------------------------------------------------------------------

# - function: wmde_process_pageviews
wmde_process_pageviews <- function(fileName,
                                   dataDir, 
                                   uri_query_filter, 
                                   cetDay = cetDay,
                                   campaignName = campaignName) {
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(stringr)
  library(dplyr)
  library(tidyr)
  library(data.table)

  # - load
  pageviewsData <- readLines(fileName)
  wStart <- which(grepl("^uri_path", pageviewsData))
  pageviewsData <- pageviewsData[(wStart + 2):(length(pageviewsData) - 2)]
  pageviewsData <- data.frame(dat = pageviewsData, 
                              stringsAsFactors = F)
  pageviewsData <- separate(pageviewsData,
                            dat,
                            into = c('uri_path', 'uri_query', 'referer'),
                            sep = "\t")
  # - apply uri_query_filter
  # - NOTE: Autumn 2018, looking in both: uri_query, referer
  w_uri_query <- which(grepl(uri_query_filter, pageviewsData$uri_query))
  w_uri_query_referer <- which(grepl(uri_query_filter, pageviewsData$referer))
  w_uri_query <- unique(c(w_uri_query, w_uri_query_referer))
  pageviewsData <- pageviewsData[w_uri_query, ]
  w_uri_query_referer <- which(grepl(uri_query_filter, pageviewsData$referer))
  w_uri_query_referer_delete <- setdiff(1:dim(pageviewsData)[1], w_uri_query_referer)
  pageviewsData$referer[w_uri_query_referer_delete] <- ''
  # - when there is no uri_query, use the query from the referer field if present there
  pageviewsData$referer <- str_extract(pageviewsData$referer, "\\?campaign=.*$")
  pageviewsData$referer[is.na(pageviewsData$referer)] <- ""
  pageviewsData$referer <- gsub("?campaign=", "", pageviewsData$referer, fixed = T)
  pageviewsData$uri_query <- gsub("?campaign=", "", pageviewsData$uri_query, fixed = T)
  pageviewsData$uri_query[pageviewsData$uri_query == ""] <- 
    pageviewsData$referer[pageviewsData$uri_query == ""]
  pageviewsData <- dplyr::filter(pageviewsData, 
                          uri_query != "")
  pageviewsData$referer <- NULL
  # - clean up a bit:
  pageviewsData$uri_query <- gsub("/.*$", "", pageviewsData$uri_query)
  
  # - aggregate:
  pageviewsData <- pageviewsData %>% 
    dplyr::group_by(uri_query, uri_path) %>% 
    dplyr::summarise(pageviews = n())
  colnames(pageviewsData) <- c('Tag', 'Page', 'Pageviews')
  
  # - add cetDay, campaignName
  pageviewsData$date <- cetDay
  pageviewsData$campaign <- campaignName

  # - store:
  write.csv(pageviewsData, 
            paste0("pageviewsAggregated_",
                   strsplit(
                     strsplit(fileName, split = "_", fixed = T)[[1]][2],
                     split = ".", 
                     fixed = T)[[1]][1],
                   ".csv"
            )
  )
  
}

# - set params to wmde_process_pageviews
# - for the Thank You 2919
uri_query_filter <- 'WMDE_2019_thx'

# - wrangle pageviews
wmde_process_pageviews(fileName = fileName,
                       dataDir = dataDir,
                       uri_query_filter = uri_query_filter, 
                       cetDay = cetDay,
                       campaignName = campaignName) 


### -----------------------------------------------------------------------
### --- Collect User Registrations
### -----------------------------------------------------------------------

# - function: wmde_collect_registrations
wmde_collect_registrations <- function(logSchema, 
                                       web_host,
                                       event_campaign, 
                                       cetDay,
                                       dataDir, 
                                       fileName, 
                                       campaignName) {
  
  # - WHERE condition: create start_timestamp, stop_timestamp
  cet_condition <- seq(
    from = as.POSIXct(paste0(cetDay," 0:00"), tz = "Europe/Berlin"),
    to = as.POSIXct(paste0(cetDay," 24:00"), tz = "Europe/Berlin"),
    by = "hour"
  ) 
  attr(cet_condition, "tzone") <- "UTC"
  cet_condition <- as.character(cet_condition)
  start_timestamp <- paste(
    unlist(str_extract_all(cet_condition[1],
                    "[[:digit:]]")),
    collapse = "")
  stop_timestamp <- paste(
    unlist(str_extract_all(tail(cet_condition, 1),
                           "[[:digit:]]")),
    collapse = "")
  
  # - WHERE condition: create event_campaign_condition
  if (length(event_campaign) > 1) {
    event_campaign_condition <- paste0("(",
                                       paste(
                                         paste0("event_campaign LIKE '%", event_campaign, "%'"),
                                         collapse = " OR ", sep = " "),
                                       ")"
    )
  } else {
    event_campaign_condition = paste0("event_campaign LIKE '%", event_campaign, "%'")
  }
  

  # - compose SQL query:
  sqlParams <- 'mysql --defaults-file=/etc/mysql/conf.d/analytics-research-client.cnf -h analytics-slave.eqiad.wmnet -A -e'
  query <- paste0(
    "\"SELECT * FROM ", 
    paste0("log.", logSchema), 
    " WHERE ((webHost = '", 
    web_host, 
    "') AND (timestamp > ", 
    start_timestamp, 
    ") AND (timestamp <= ", 
    stop_timestamp, 
    ") AND (", 
    event_campaign_condition,
    "));\"")
  sqlOutput <- paste0("> ", paste0(dataDir, "/", fileName))
    
  # - run command
  qCommand <- paste(sqlParams, query, sqlOutput, sep = " ")
  system(command = qCommand, wait = TRUE)
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(stringr)
  library(dplyr)
  library(tidyr)
  library(data.table)
  
  # - load
  userReg <- fread(fileName, sep = "\t")
  
  # - filter bots
  wBot <- which(grepl("\"is_bot\": true", userReg$userAgent))
  if (length(wBot) > 0) {
    userReg <- userReg[-wBot, ]
  }
  
  # - select fields
  userReg <- userReg %>% 
    dplyr::select(event_userId, 
                  event_userName, 
                  event_isSelfMade, 
                  event_campaign, 
                  timestamp)
  
  # - add cetDay, campaignName
  userReg$date <- cetDay
  userReg$campaign <- campaignName
  
  # - store:
  write.csv(userReg, 
            paste0(
              strsplit(fileName, split = ".", fixed = T)[[1]][1],
            ".csv")
  )
  
  # - remove temp .tsv file
  file.remove(fileName)
  
}

# - set params for: wmde_collect_registrations
logSchema <- 'ServerSideAccountCreation_17719237' 
web_host <- 'de.wikipedia.org'
event_campaign <- 'WMDE_2019_thx'
fileName <- paste0("userRegistrations_", cetDay, ".tsv")

# - collect user registrations
wmde_collect_registrations(logSchema = logSchema,
                           web_host = web_host,
                           event_campaign = event_campaign,
                           cetDay = cetDay,
                           dataDir = dataDir,
                           fileName = fileName, 
                           campaignName = campaignName)

### -----------------------------------------------------------------------
### --- Wrangle User Registrations
### -----------------------------------------------------------------------

# - function: wmde_process_registrations
wmde_process_registrations <- function(fileName,
                                       dataDir, 
                                       cetDay, 
                                       campaignName) {
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(dplyr)
  library(data.table)

  # - load
  userReg <- fread(fileName)
  
  # - agregate
  userReg <- userReg %>% 
    dplyr::select(event_campaign) %>% 
    dplyr::group_by(event_campaign) %>% 
    dplyr::summarise(Registrations = n())

  # - add cetDay, campaignName
  userReg$date <- cetDay
  userReg$campaign <- campaignName
  
  # - store:
  write.csv(userReg, 
            paste0('userRegistrationsAggreagted_', cetDay, ".csv")
  )
  
}

# - set params for: wmde_process_registrations
fileName <- paste0("userRegistrations_", cetDay, ".csv")

# - wrangle user registrations:
wmde_process_registrations(fileName,
                           dataDir,
                           cetDay,
                           campaignName)

### --- Collect Newsletter registrations - for 2018_AuBC excl.
# - ServerSideAccountCreation_17719237 schema
qCommand <- "mysql --defaults-file=/etc/mysql/conf.d/analytics-research-client.cnf -h analytics-slave.eqiad.wmnet -A -e \"select * from log.ServerSideAccountCreation_17719237 where ((webHost = 'de.wikipedia.org') and (timestamp >= 20181011000000) and (event_campaign like '%WMDE_neweditors_autumn_2018_lpn%'));\" > /home/goransm/RScripts/NewEditors/2018_AutumnBannerCampaign/_data/Newsletter_userRegistrations.tsv"
system(command = qCommand, wait = TRUE)
### --- Wrangle Newsletter registrations - for 2018_AuBC excl.
# - function: wmde_process_registrations_general
wmde_process_registrations_general <- function(fileName,
                                       dataDir, 
                                       cetDay, 
                                       campaignName, 
                                       outFileName) {
  
  # - to dataDir
  setwd(dataDir)
  
  # - libraries
  library(dplyr)
  library(data.table)
  
  # - load
  userReg <- fread(fileName)
  
  # - agregate
  userReg <- userReg %>% 
    dplyr::select(event_campaign) %>% 
    dplyr::group_by(event_campaign) %>% 
    dplyr::summarise(Registrations = n())
  
  # - add cetDay, campaignName
  userReg$date <- cetDay
  userReg$campaign <- campaignName
  
  # - store:
  write.csv(userReg, 
            outFileName
  )
  
}

```

### 0.2 Data Aggregation

**NOTE:** Not run from this report; the data were already pre-processed and aggregated by the following `R` script before being submitted to analytical procedures:

```{r, echo = T, eval = F}
### --- Report Generation for the Thank You 2919
### --- run locally

### --- to data directory
dataDir <- 
  '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_data/'
analyticsDir <- 
  '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_analytics/'
setwd(analyticsDir)

### --- Report Banner Impression Data

# - function: wmde_report_banner_impressions
wmde_report_banner_impressions <- function(dataDir) {
  
  # - Setup
  library(data.table)
  library(dplyr)

  # - list files:
  lF <- list.files(dataDir)
  
  # - filter aggregated banner impression data
  lF <- lF[grepl("bannerImpressionsAggregated_", lF, fixed = T)]
  
  # - load files and merge
  bannerData <- vector(mode = "list", length = length(lF))
  for (i in 1:length(lF)) {
    if (grepl("csv$|tsv$", lF[i])) {
      bannerData[[i]] <- fread(paste0(dataDir, lF[i]))
    } else {
      bannerData[[i]] <- NULL
    }
  }
  bannerData <- rbindlist(bannerData)
  bannerData$V1 <- NULL
  
  # - aggregates
  perBannerTotals <- bannerData %>% 
    select(banner, impressions) %>% 
    group_by(banner) %>% 
    summarise(totalImpressions = sum(impressions))
  perDayTotals <- bannerData %>% 
    select(date, impressions) %>% 
    group_by(date) %>% 
    summarise(totalImpressions = sum(impressions))
  
  # - output
  return(
    list(bannerImpressionsReport = bannerData, 
         perBannerTotals = perBannerTotals, 
         perDayTotals = perDayTotals)
  )
    
}

# - Report banner impressions
bannerImpressionsData <- wmde_report_banner_impressions(dataDir)
bannerImpressionsFile <- bannerImpressionsData$bannerImpressionsReport
write.csv(bannerImpressionsFile, "bannerImpressionsFile.csv")
bannerTotals <- bannerImpressionsData$perBannerTotals
write.csv(bannerTotals, "bannerTotals.csv")
bannerDayTotals <- bannerImpressionsData$perDayTotals
write.csv(bannerDayTotals, "bannerDayTotals.csv")


### --- Report Pageviews Data

# - function: wmde_report_pageviews
wmde_report_pageviews <- function(dataDir) {
  
  # - Setup
  library(data.table)
  library(dplyr)
  
  # - list files:
  lF <- list.files(dataDir)
  
  # - filter aggregated banner impression data
  lF <- lF[grepl("pageviewsAggregated_", lF, fixed = T)]
  
  # - load files and merge
  pageviewsData <- vector(mode = "list", length = length(lF))
  for (i in 1:length(lF)) {
    if (grepl("csv$|tsv$", lF[i])) {
      pageviewsData[[i]] <- fread(paste0(dataDir, lF[i]))
    } else {
      pageviewsData[[i]] <- NULL
    }
  }
  pageviewsData <- rbindlist(pageviewsData)
  pageviewsData$V1 <- NULL
  
  # - aggregates
  perDayTotals <- pageviewsData %>% 
    select(date, Pageviews) %>% 
    group_by(date) %>% 
    summarise(totalPageviews = sum(Pageviews))
  perTagTotals <- pageviewsData %>% 
    select(Tag, Pageviews) %>% 
    group_by(Tag) %>% 
    summarise(totalPageviews = sum(Pageviews))
  perPageTotals <- pageviewsData %>% 
    select(Page, Pageviews) %>% 
    group_by(Page) %>% 
    summarise(totalPageviews = sum(Pageviews))
  perPageDayTotals <- pageviewsData %>% 
    select(Page, date, Pageviews) %>%
    group_by(Page, date) %>% 
    summarise(totalPageviews = sum(Pageviews))
  perTagDayTotals <- pageviewsData %>% 
    select(Tag, date, Pageviews) %>%
    group_by(Tag, date) %>% 
    summarise(totalPageviews = sum(Pageviews))
  perTagPageTotals <- pageviewsData %>% 
    select(Tag, Page, Pageviews) %>%
    group_by(Tag, Page) %>% 
    summarise(totalPageviews = sum(Pageviews))
    
  # - output
  return(
    list(pageviewsDataReport = pageviewsData, 
         perDayTotals = perDayTotals, 
         perTagTotals = perTagTotals, 
         perPageTotals = perPageTotals, 
         perPageDayTotals = perPageDayTotals, 
         perTagDayTotals = perTagDayTotals,
         perTagPageTotals = perTagPageTotals)
  )
  
}

# - Report pageviews:
pageviewsData <- wmde_report_pageviews(dataDir)
pageviewsReportFile <- pageviewsData$pageviewsDataReport
write.csv(pageviewsReportFile, "pageviewsReportFile.csv")
pageviews_perDayTotals <- pageviewsData$perDayTotals
write.csv(pageviews_perDayTotals, "pageviews_perDayTotals.csv")
pageviews_perTagTotals <- pageviewsData$perTagTotals
write.csv(pageviews_perTagTotals, "pageviews_perTagTotals.csv")
pageviews_perPageTotals <- pageviewsData$perPageTotals
write.csv(pageviews_perPageTotals, "pageviews_perPageTotals.csv")
pageviews_perPageDayTotals <- pageviewsData$perPageDayTotals
write.csv(pageviews_perPageDayTotals, "pageviews_perPageDayTotals.csv")
pageviews_perTagDayTotals <- pageviewsData$perTagDayTotals
write.csv(pageviews_perTagDayTotals, "pageviews_perTagDayTotals.csv")
pageviews_perTagPageTotals <- pageviewsData$perTagPageTotals
write.csv(pageviews_perTagPageTotals, "perTagPageTotals.csv")

### --- Report User Registrations

# - function: wmde_report_registrations
wmde_report_registrations <- function(dataDir) {
  
  # - Setup
  library(data.table)
  library(dplyr)
  
  # - list files:
  lF <- list.files(dataDir)
  
  # - filter aggregated user registration data
  lF <- lF[grepl("userRegistrationsAggreagted_", lF, fixed = T)]
  
  # - load files and merge
  registrationData <- vector(mode = "list", length = length(lF))
  for (i in 1:length(lF)) {
    if (grepl("csv$|tsv$", lF[i])) {
      registrationData[[i]] <- fread(paste0(dataDir, lF[i]))
    } else {
      registrationData[[i]] <- NULL
    }
  }
  registrationData <- rbindlist(registrationData)
  registrationData$V1 <- NULL
  
  # - aggregates
  perDayTotals <- registrationData %>% 
    select(date, Registrations) %>% 
    group_by(date) %>% 
    summarise(totalRegistrations = sum(Registrations))
  perTagTotals <- registrationData %>% 
    select(event_campaign, Registrations) %>% 
    group_by(event_campaign) %>% 
    summarise(totalRegistrations = sum(Registrations))
  perTagDayTotals <- registrationData %>% 
    select(event_campaign, date, Registrations) %>% 
    group_by(event_campaign, date) %>% 
    summarise(totalRegistrations = sum(Registrations))
  
  # - output
  return(
    list(registrationsDataReport = registrationData, 
         perDayTotals = perDayTotals, 
         perTagTotals = perTagTotals, 
         perTagDayTotals = perTagDayTotals)
  )
  
}

# - Report upon user registrations
userRegData <- wmde_report_registrations(dataDir)
userRegistrationsReportFile <- userRegData$registrationsDataReport
write.csv(userRegistrationsReportFile, "userRegistrationsReportFile.csv")
userRegistrations_perDayTotals <- userRegData$perDayTotals
write.csv(userRegistrations_perDayTotals, "userRegistrations_perDayTotals.csv")
userRegistrations_perTagTotals <- userRegData$perTagTotals
write.csv(userRegistrations_perTagTotals, "userRegistrations_perTagTotals.csv")
userRegistrations_perTagDayTotals <- userRegData$perTagDayTotals
write.csv(userRegistrations_perTagDayTotals, "userRegistrations_perTagDayTotals.csv")
```

## 1. Campaign Banners and Pages

This section presents all data and statistics on the campaign banners and pages.

### 1.1 Banner Impressions
#### 1.1.1 Banner Impressions Overview

**Chart 1.1.1** Daily Banner Impressions

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
dataSet <- read.csv(
  '_analytics/bannerImpressionsFile.csv',
                    header = T,
                    row.names = 1,
                    check.names = F,
                    stringsAsFactors = F)
# - Visualize w. {ggplot2}
ggplot(dataSet, aes(x = date,
                    y = impressions,
                    label = impressions,
                    group = banner,
                    color = banner,
                    fill = banner
                    )) + 
  geom_path(size = .5) + 
  geom_point(size = 1.5) +
  facet_wrap(~banner, ncol = 2) + 
  ggtitle('Thank You 2919: Banner Impressions') +
  theme_minimal() + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "top")
```

#### 1.1.1 Banner Impressions Overview: Table

**Table 1.1.1.** Daily Banner Impressions

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
### --- Full Dataset (Table Report)
datatable(dataSet)
```

#### 1.1.2 Total Banner Impressions
**Chart 1.1.2.** Total Banner Impressions

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
dataSet <- read.csv(
  '_analytics/bannerTotals.csv',
                    header = T,
                    row.names = 1,
                    check.names = F,
                    stringsAsFactors = F)
ggplot(dataSet, aes(x = banner, 
                    y = totalImpressions, 
                    color = banner, 
                    fill = banner, 
                    label = totalImpressions)) + 
  geom_bar(width = .5, stat = "identity") + 
  scale_y_continuous(labels = comma) +
  ggtitle('Thank You 2919: Banner Impressions') +
  theme_minimal() + 
  geom_label_repel(size = 3.5, color = "white", show.legend = FALSE) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "right") + 
  theme(axis.text.x = element_blank())
```

#### 1.1.3 Banner Impressions per Day

**Chart 1.1.3.** Banner Impressions per Day

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
dataSet <- read.csv(
  '_analytics/bannerDayTotals.csv',
                    header = T,
                    row.names = 1,
                    check.names = F,
                    stringsAsFactors = F)
ggplot(dataSet, aes(x = date, 
                    y = totalImpressions, 
                    label = totalImpressions)) +
  geom_path(size = .25, group = 1, color = "darkblue") +
  geom_point(size = 1.5, color = "darkblue") + 
  geom_point(size = 1, color = "white") + 
  scale_y_continuous(labels = comma) +
  ggtitle('Thank You 2919: Banner Impressions') +
  theme_minimal() + 
  geom_label_repel(size = 3.5, show.legend = FALSE) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "right") + 
  theme(axis.text.x = element_text(angle = 90))
```

### 1.2 Pageviews

#### 1.2.1 Pageviews Overview

**Chart 1.2.1.** Pageviews Overview. 

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
dataSet <- read.csv(
  '_analytics/pageviews_perPageDayTotals.csv',
                    header = T,
                    row.names = 1,
                    check.names = F,
                    stringsAsFactors = F)
dataSet$Page <- gsub("/wiki/Wikipedia:Wikipedia_|/wiki/Wikipedia:Wikimedia_", "", dataSet$Page)
# - Visualize w. {ggplot2}
ggplot(dataSet, aes(x = date,
                    y = totalPageviews,
                    group = Page,
                    color = Page,
                    fill = Page,
                    label = totalPageviews,
                    )) + 
  geom_path(size = .25) + 
  geom_point(size = 1.5) +
  geom_point(size = 1, color = "white") +
  scale_y_continuous(labels = comma) +
  ggtitle('Thank You 2919: Pageviews') +
  theme_minimal() + 
  geom_text_repel(size = 3.5, show.legend = FALSE) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "top")
```


#### 1.2.1 Pageviews Overview: Table

**Table 1.2.1.** Pageviews Overview

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
### --- Full Dataset (Table Report)
datatable(dataSet %>% arrange(desc(totalPageviews)))
```


## 2. User Registrations

All data on user registrations are presented in this section.

### 2.1 Registrations per tag and day

**Chart 2.1.** Registrations per tag and day. Please note: points with no data labels signify 0 user registrations.

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
# - Standard registrations
dataSet <- read.csv(
  '_analytics/userRegistrationsReportFile.csv',
                    header = T,
                    row.names = 1,
                    check.names = F,
                    stringsAsFactors = F)
dataSet <- dataSet %>% 
  filter(!(event_campaign %in% 'WMDE_neweditors_autumn_2018_lpn'))
dataSet$campaign <- NULL
ggplot(dataSet, aes(x = date,
                    y = Registrations,
                    group = event_campaign,
                    color = event_campaign,
                    fill = event_campaign,
                    label = Registrations,
                    )) + 
  geom_path(size = .25) + 
  geom_point(size = 1.5) +
  geom_point(size = 1, color = "white") +
  scale_y_continuous(labels = comma) +
  ggtitle('Thank You 2919: Registrations per Tag') +
  theme_minimal() + 
  geom_text_repel(size = 3.5, show.legend = F) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "top")
```

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
### --- Full Dataset (Table Report)
colnames(dataSet)[1] <- 'Tag'
datatable(dataSet %>% arrange(Tag, date, desc(Registrations)))
```

## 3. User Edits

### 3.1 Collect user edits

This data acquisition chank is not reproducible from this Report; runs in production on `stat1007`:

```{r, echo = T, eval = F}
### ------------------------------------------------------
### --- Collect all user edits per day from dewiki.revision
### --- from 2. January 2019, w. event_campaign
### --- update 1: one week after then end: 23th January
### --- update 2: two weeks after then end: 30th January
### ------------------------------------------------------
localDir <- '/home/goransm/Analytics/NewEditors/Campaigns/2019_ThankYou/data/'
startTimestamp <- '20190102000000' # - campaign begins
# - SQL query
sqlQuery <- paste("\"SELECT rev_user, SUBSTR(rev_timestamp, 1, 8) 
                  FROM dewiki.revision WHERE rev_timestamp >= ", startTimestamp, ";\"", sep = "")

### --- output filename
filename <- paste(localDir,'ReferenceEdits_dewiki_20190123', ".tsv", sep = "")

### --- execute sql script:
sqlArgs <- 'mysql --defaults-file=/etc/mysql/conf.d/analytics-research-client.cnf -h analytics-store.eqiad.wmnet -A -e'
sqlInput <- paste(sqlQuery,
                  " > ",
                  filename,
                  sep = "")
# - command:
sqlCommand <- paste(sqlArgs, sqlInput)
system(command = sqlCommand, wait = TRUE)
```

This analysis is scheduled for January 24th and 30th [Phab](https://phabricator.wikimedia.org/T211690#4891528)

### 3.1 User edits: daily

Wrangle the dataset; visualize campaign edits per day.

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
allEdits <- 
  fread(
    '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_analytics/ReferenceEdits_dewiki_20190131.tsv'
    )
userRegs <- read.csv(
  '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_analytics/fullRegistrationDataset.csv',
                    header = T,
                    row.names = 1,
                    check.names = F,
                    stringsAsFactors = F)
# - keep only campaign registered users:
userEdits <- allEdits[allEdits$rev_user %in% unique(userRegs$event_userId), ]
rm(userRegs); rm(allEdits); gc()
# - wrangle timestamp
colnames(userEdits) <- c('user_id', 'date')
userEdits$date <- sapply(userEdits$date, function(x) {
  paste(
    substr(x, 1, 4),
    substr(x, 5, 6), 
    substr(x, 7, 8),
    sep = "-"
  )
})
# - aggregate per day
userEditsDay <- userEdits %>% 
  group_by(date) %>% 
  summarise(edits = n()) %>% 
  arrange(date)
# - input zero edits at missing dates
userEditsDayCopy <- userEditsDay
editDateRange <- userEditsDayCopy$date
editDateRange <- as.POSIXct(editDateRange)
editDateRange <- seq(min(editDateRange), max(editDateRange), by = "day")
userEditsDayCopy <- data.frame(date = as.character(editDateRange), edits = numeric(length(editDateRange)), 
                               stringsAsFactors = F)
userEditsDayCopy <- left_join(userEditsDayCopy, userEditsDay, by = "date")
userEditsDayCopy$edits.x <- NULL
colnames(userEditsDayCopy) <- c('date', 'edits')
userEditsDayCopy$edits[is.na(userEditsDayCopy$edits)] <- 0
userEditsDay <-userEditsDayCopy
# - visualize daily edits
# - Visualize w. {ggplot2}
ggplot(userEditsDay, aes(x = date,
                         y = edits,
                         label = edits
                    )) + 
  geom_path(size = .5, color = 'blue', group = 1) + 
  geom_point(size = 1.5, color = 'blue') + 
  geom_point(size = 1.5, color = 'white') +
  geom_text_repel(size = 3.5) + 
  ggtitle('Thank You 2919: Edits per day') +
  theme_minimal() + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "top")
```

### 3.1 User edits: edit classes

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
# - Edit | 1 | 2-4 | 5-9 | 10-49 | >50
editBoundaries <- list(
  c(0, 1), 
  c(2, 4),
  c(5, 9),
  c(10, 49)
)
userEdits_user <- userEdits %>% 
  group_by(user_id) %>% 
  summarise(edits = n())
userEdits_user$editClass <- sapply(userEdits_user$edits, function(x) {
  wEC <- sapply(editBoundaries, function(y) {
    x >= y[1] & x <= y[2]
  })
  if (sum(wEC) == 0) {
    return(">= 50")
  } else {
    return(paste0("(",
                  editBoundaries[[which(wEC)]][1],
                  " - ",
                  editBoundaries[[which(wEC)]][2], 
                  ")"
                  )
    )
  }
})
editClass <- as.data.frame(table(userEdits_user$editClass), 
                           stringsAsFactors = F)
colnames(editClass) <- c('Edit Class', 'Num.Users')
editClass$order <- as.numeric(sapply(editClass$`Edit Class`, function(x) {
  lower <- str_extract(x, '[[:digit:]]+')
}))
editClass <- arrange(editClass, order)
editClass$order <- NULL
datatable(editClass)
```

### 3.2 User edits: when do the edits happen? 

**Chart 3.2.1**

**Q.** Also it would be interesting to know when the new users edit: is there a possibility to calculate in which time interval new users are active, e.g. directly after registration, or some days later etc.? On the x-axis would be time after registration in days, with 0 being point of registration and on the y-axis number of edits. The diagram would then show average edits per user x-days after registration. Could this work? [Phab](https://phabricator.wikimedia.org/T211690#4934030)

**Note.** The chart presents the mean number of edits calculated from the total number of edits per day divided by the number of active users (i.e. by how many users edited) on that day.

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
# - load user registrations per day, per user
userRegistrationsDaily <- fread("_analytics/fullRegistrationDataset.csv")
userRegistrationsDaily <- select(userRegistrationsDaily, 
                                 event_userId, date)
editsSinceReg <- userRegistrationsDaily 
editsSinceReg <- left_join(editsSinceReg, 
                           userEdits, 
                           by = c("event_userId" = "user_id")) %>% 
  arrange(event_userId)
colnames(editsSinceReg) <- c("user_id", "registration", "edit")
editsSinceReg <- filter(editsSinceReg, !is.na(edit))
editsSinceReg$registration <- as.POSIXct(editsSinceReg$registration)
editsSinceReg$edit <- as.POSIXct(editsSinceReg$edit)
editsSinceReg$diff <- (editsSinceReg$edit - editsSinceReg$registration)/(60^2*24)
avgEditsSinceReg <- editsSinceReg %>% 
  select(diff, user_id) %>% 
  group_by(diff, user_id) %>% 
  summarise(edits = n()) %>% 
  select(diff, edits) %>% 
  group_by(diff) %>% 
  summarise(meanEdits = mean(edits))
avgEditsSinceReg$diff <- as.numeric(avgEditsSinceReg$diff)
avgEditsFrame <- data.frame(diff = seq(min(avgEditsSinceReg$diff), max(avgEditsSinceReg$diff)))
avgEditsFrame <- left_join(avgEditsFrame, avgEditsSinceReg, by = "diff")
avgEditsFrame$meanEdits[is.na(avgEditsFrame$meanEdits)] <- 0
avgEditsFrame$meanEdits <- round(avgEditsFrame$meanEdits, 2)
ggplot(avgEditsFrame, aes(x = diff, 
                    y = meanEdits, 
                    label = meanEdits)) + 
  geom_path(color = "blue", size = .25) + 
  geom_point(color = "blue", size = 1.5) + 
  geom_point(color = "white", size = 1) + 
  ggtitle('Thank You 2919: Average number of edits N days after registration') + 
  xlab("Days after registration") + ylab("Mean edits per user") + 
  theme_minimal() + 
  geom_text_repel(size = 3.5, show.legend = FALSE) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 0, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "right")
```

**Chart 3.2.2**

**Note.** The chart presents the mean number of edits calculated from the total number of edits per day divided by the number ofusers registered until that day (i.e. by how many campaign registered users could have edited on that day) [see Phab](https://phabricator.wikimedia.org/T211690#4967757).

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
# - load user registrations per day, per user
userRegistrationsDaily <- fread("_analytics/fullRegistrationDataset.csv")
userRegistrationsDaily <- select(userRegistrationsDaily, 
                                 event_userId, date)
cumulativeRegistrations <- userRegistrationsDaily %>% 
  select(date) %>% 
  group_by(date) %>% 
  summarise(registrations = n())
cumulativeRegistrations$registrations <- cumsum(cumulativeRegistrations$registrations)
userEditsPerDay <- userEdits %>% 
  select(date) %>%
  group_by(date) %>% 
  summarise(edits = n())
userEditsPerDay <- left_join(userEditsPerDay, cumulativeRegistrations,
                             by = "date")
userEditsPerDay$registrations[is.na(userEditsPerDay$registrations)] <- 
  max(userEditsPerDay$registrations, na.rm = T)
userEditsPerDay$meanEdits = round(userEditsPerDay$edits/userEditsPerDay$registrations, 3)
dateRangeFrame <- data.frame(date = 
                               as.character(
                                 seq(min(as.POSIXct(userEditsPerDay$date)), 
                                     max(as.POSIXct(userEditsPerDay$date)), 
                                     by = "day")),
                             stringsAsFactors = F)
userEditsPerDay <- left_join(dateRangeFrame, userEditsPerDay, by = "date")
userEditsPerDay$meanEdits[is.na(userEditsPerDay$meanEdits)] <- 0
ggplot(userEditsPerDay, aes(x = date,
                            y = meanEdits,
                            label = meanEdits)) + 
  geom_path(color = "blue", size = .25, group = 1) + 
  geom_point(color = "blue", size = 1.5) + 
  geom_point(color = "white", size = 1) + 
  ggtitle('Thank You 2919: Average number of edits N days after registration') + 
  xlab("Days after registration") + ylab("Mean edits per user") + 
  theme_minimal() + 
  geom_text_repel(size = 3.5, show.legend = FALSE) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "right")
```

**Chart 3.2.3**

**Note.** The chart presents the mean number of edits calculated from the total number of edits per day divided by the number of active users (i.e. by how many users edited) until that day." [see Phab](https://phabricator.wikimedia.org/T211690#4968552).

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
# - load user registrations per day, per user
userEditsRev <- userEdits %>% 
  arrange(date)
uniqueDates <- unique(userEditsRev$date)
editors <- list()
editors <- lapply(uniqueDates, function(x) {
  unique(userEditsRev$user_id[userEditsRev$date %in% x])
})
names(editors) <- uniqueDates
editorsDaily <- list()
for (i in 1:length(editors)) {
  editorsDaily[[i]] <- c(unique(unname(unlist(editors[1:i]))))
}
editorsDaily <- lapply(editorsDaily, length)
editorsDaily <- data.frame(editors = unname(unlist(editorsDaily)), 
                           date = uniqueDates, 
                           stringsAsFactors = F)
userEditsPerDay <- userEdits %>% 
  select(date) %>%
  group_by(date) %>% 
  summarise(edits = n())
userEditsPerDay <- left_join(userEditsPerDay, editorsDaily,
                             by = "date")
userEditsPerDay$meanEdits = round(userEditsPerDay$edits/userEditsPerDay$editors, 3)
dateRangeFrame <- data.frame(date = 
                               as.character(
                                 seq(min(as.POSIXct(userEditsPerDay$date)), 
                                     max(as.POSIXct(userEditsPerDay$date)), 
                                     by = "day")),
                             stringsAsFactors = F)
userEditsPerDay <- left_join(dateRangeFrame, userEditsPerDay, by = "date")
userEditsPerDay$meanEdits[is.na(userEditsPerDay$meanEdits)] <- 0
ggplot(userEditsPerDay, aes(x = date,
                            y = meanEdits,
                            label = meanEdits)) + 
  geom_path(color = "blue", size = .25, group = 1) + 
  geom_point(color = "blue", size = 1.5) + 
  geom_point(color = "white", size = 1) + 
  ggtitle('Thank You 2919: Average number of edits N days after registration') + 
  xlab("Days after registration") + ylab("Mean edits per user") + 
  theme_minimal() + 
  geom_text_repel(size = 3.5, show.legend = FALSE) + 
  scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "right")
```

## 4. Training Modules

### 4.1 Collect training modules data

The dataset is obtained directly from the maintainer.

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
# - training modules data
trainModules <- fread(
    '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_analytics/wmde_training_data_2019-01.csv')
# - get user registrations w. user names for joins
locDir <- 
'/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_data/'
lF <- list.files(locDir)
lF <- lF[grepl("userRegistrations_", lF)]
userReg <- lapply(lF, function(x) {
  fread(paste0(locDir, x))
})
userReg <- rbindlist(userReg)
userReg$V1 <- NULL
trainModules <- left_join(trainModules,
                          select(userReg,  event_userId, event_userName),
                          by = c("username" = "event_userName"))
trainModules <- trainModules[!is.na(trainModules$event_userId), ]
trainModules$username <- NULL
```

### 4.2 Training modules: overview

**49 new editors** have started at least one training module; **32** have completed at least one the training and **19** did not.

The following table 4.2.1 gives and overview of how many users have **completed** a particular training module.

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
tmUsers <- trainModules %>% 
  select(event_userId, training_module, module_completion_date) %>% 
  mutate(module_completion_date = ifelse(module_completion_date == '', F, T)) %>% 
  group_by(event_userId, training_module, module_completion_date) %>% 
  summarise(n = n()) %>% 
  filter(module_completion_date == T) %>% 
  select(training_module) %>% 
  group_by(training_module) %>% 
  summarise(editors = n())
datatable(tmUsers)
``` 

Table 4.2.2 gives and overview of how many users have **started** a particular training module.

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
tmUsers <- trainModules %>% 
  select(training_module) %>% 
  group_by(training_module) %>% 
  summarise(editors = n())
datatable(tmUsers)
```

Table 4.2.3 The **last slide** from a training module completed by new editors; the `editors` column shows the number of editors who have abandoned (or completed) their traning at the respective slide.

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
tmLastSlide <- trainModules %>%
  group_by(training_module, last_slide_completed) %>% 
  summarise(editors = n())
datatable(tmLastSlide)
```

Table 4.2.4 User edits: training modules completed vs not completed.

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
tmEdits <- left_join(userEdits, trainModules, by = c('user_id' = 'event_userId'))
tmEdits <- tmEdits %>% 
  select(training_module, module_completion_date)
tmEdits$completed <- sapply(tmEdits$module_completion_date, function(x) {
  if(is.na(x) | x == '') {return(FALSE)} else {return(TRUE)}
})
tmEdits$started <- sapply(tmEdits$training_module, function(x) {
  if(is.na(x) | x == '') {return(FALSE)} else {return(TRUE)}
})
tmEdits$module_completion_date <- NULL
tmEdits$training_module <- NULL
tmEdits$training_outcome <- ifelse(tmEdits$completed, 'Completed', NA)
tmEdits$training_outcome <- ifelse(tmEdits$started & is.na(tmEdits$training_outcome), 
                                   'Started', tmEdits$training_outcome)
tmEdits$training_outcome[is.na(tmEdits$training_outcome)] <- 'No training' 
tmEdits <- tmEdits %>% 
  group_by(training_outcome) %>% 
  summarise(edits = n())
datatable(tmEdits)
```

Table 4.2.5 User edits: average number of edits per user, training modules completed vs not completed.

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
tmEdits <- left_join(userEdits, trainModules, by = c('user_id' = 'event_userId'))
tmEdits <- tmEdits %>% 
  select(user_id, training_module, module_completion_date)
tmEdits$completed <- sapply(tmEdits$module_completion_date, function(x) {
  if(is.na(x) | x == '') {return(FALSE)} else {return(TRUE)}
})
tmEdits$started <- sapply(tmEdits$training_module, function(x) {
  if(is.na(x) | x == '') {return(FALSE)} else {return(TRUE)}
})
tmEdits$module_completion_date <- NULL
tmEdits$training_module <- NULL
tmEdits$training_outcome <- ifelse(tmEdits$completed, 'Completed', NA)
tmEdits$training_outcome <- ifelse(tmEdits$started & is.na(tmEdits$training_outcome), 
                                   'Started', tmEdits$training_outcome)
tmEdits$training_outcome[is.na(tmEdits$training_outcome)] <- 'No training' 
tmEdits <- tmEdits %>% 
  group_by(user_id, training_outcome) %>% 
  summarise(edits = n()) %>% 
  group_by(training_outcome) %>% 
  summarise(`mean(Edits)` = round(mean(edits), 2))
datatable(tmEdits)
```

Do you see in the tables from Ragesoss how many people did or begin the modules in general - regardless of the fact they register or not. Question behind the question: I would like to know how many people potentially just did the module, but didn't register beforhand. (see:  [Phab](https://phabricator.wikimedia.org/T211690#4951032))

```{r echo = T, eval = T, warning = 'hide', message = FALSE}
# - training modules data
trainModules <- fread(
    '/home/goransm/Work/___DataKolektiv/Projects/WikimediaDEU/_WMDE_Projects/_misc/NewEditors_Team/2019_ThankYou/_analytics/wmde_training_data_2019-01.csv')
# - Unique number of trainModules$username -2 (Stefan and Sage)
length(unique(trainModules$username)) - 2
```

387 people took the training modules irrespective of registration.
