library(RPostgreSQL)
# Parse arguments
optionList <- list(
optparse::make_option(
opt_str = '--host'
, type = 'character'
, default = 'localhost'
, help = 'Hostname for database connection'
) ,
optparse::make_option(
opt_str = '--port'
, type = 'character'
, default = '5441'
, help = 'Port for database connection'
) ,
optparse::make_option(
opt_str = '--user'
, type = 'character'
, default = NULL
, help = 'User name for database connection'
) ,
optparse::make_option(
opt_str = '--pass'
, type = 'character'
, default = NULL
, help = 'Password for database connection'
) ,
optparse::make_option(
opt_str = '--rundate'
, type = 'character'
, default = as.character(Sys.Date())
, help = 'The most recent date to include in the analysis.
Must be entered in the form yyyy-mm-dd. Defaults to current date.'
) ,
optparse::make_option(
opt_str = '--mindate'
, type = 'character'
, default = '2016-01-01'
, help = 'The earliest date to occur in the analysis.'
) ,
optparse::make_option(
opt_str = '--yearbeginning'
, type = 'character'
, default = '2016-01-01'
, help = 'The date at which active users start getting counted. Anyone who did not have a session before this date is excluded from the analysis.'
) ,
optparse::make_option(
opt_str = '--outloc'
, type = 'character'
, default = NULL
, help = 'Location to save the output.
Enter as /path/to/output not /path/to/output/'
) ,
optparse::make_option(
opt_str = '--outname'
, type = 'character'
, default = NULL
, help = 'Name of output csv file. Enter as name_of_output not name_of_output.csv'
)
)
opt_parser <- optparse::OptionParser(option_list = optionList)
opt <- optparse::parse_args(opt_parser)
# Connect to redshift
driver <- DBI::dbDriver("PostgreSQL")
connection <- RPostgreSQL::dbConnect(
driver
, dbname = 'insightsbeta'
, host = opt$host
, port = opt$port
, user = opt$user
, password = opt$pass
)
assign("redshift_connection"
, list(drv = driver, con = connection)
, envir = .GlobalEnv)
# Define temporary tables that future queries will use.
dbSendQuery(redshift_connection$con,
flashreport::query_user_flash_cat
)
dbSendQuery(redshift_connection$con,
flashreport::query_pa_flash_cat
)
# Define date ranges and query types to get results for.
run_date <- as.Date(opt$rundate)
min_date <- as.Date(opt$mindate)
days_between <- as.numeric(run_date - min_date)
min_week <- ceiling(days_between/7)
weeks_back <- min_week:1
start_dates <- run_date - 7*weeks_back
end_dates <- start_dates + 6
year_beginning <- as.Date(opt$yearbeginning)
date_ranges <- data.frame(
range_types =
c(
rep('week', times = length(weeks_back))
, rep('ytd', times = length(weeks_back))
)
, max_dates = rep(end_dates, times = 2)
, stringsAsFactors = F
)
query_types <- paste0(c('au', 'pa', 'notifications'), 'Query')
# Run queries and put results into a long data frame.
long_flash_report <- flashreport::get_results(date_ranges, query_types)
# Postprocess results.
long_flash_report_dates_formatted <-
flashreport::format_LFR_dates(long_flash_report )
long_flash_report_2 <-
flashreport::curate_user_groups(long_flash_report_dates_formatted)
long_flash_report_subaggregate <-
flashreport::summarise_by_subaggregate(long_flash_report_2)
long_flash_report_aggregate <-
flashreport::summarise_in_aggregate(long_flash_report_2)
long_flash_report_3 <- rbind(long_flash_report_2
, long_flash_report_subaggregate
, long_flash_report_aggregate)
# Calculate WAU percentage for each user group, subaggregate, and aggregate,
# and for each date range.
long_flash_report_WAU_pct <-
flashreport::calculate_WAU_percentage(long_flash_report_3)
# Calculate total actions for each user group, subaggregate, and aggregate,
# and for each date range.
long_flash_report_total_actions <-
flashreport::calculate_total_actions(long_flash_report_3)
# Calculate average actions per WAU for each user group, subaggregate, and aggregate,
# and for each date range.
long_flash_report_actions_per_AU <-
flashreport::calculate_actions_per_AU(
long_flash_report_3
, long_flash_report_total_actions
)
# Calculate notifications_response_rate for each user group, subaggregate, and aggregate,
# and for each date range.
long_flash_report_NRR <-
flashreport::calculate_NRR(long_flash_report_3)
long_flash_report_final <- rbind(long_flash_report_3
, long_flash_report_WAU_pct
, long_flash_report_total_actions
, long_flash_report_actions_per_AU
, long_flash_report_NRR)
write.csv(long_flash_report_final
, file = paste0(opt$outloc, "/", opt$outname, ".csv")
, row.names = F)
dbDisconnect(redshift_connection$con)
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