| // Global variables | |
| var BASEURI = 'https://app.firmafon.dk/api/v1'; | |
| var OPENTIME, CLOSETIME; | |
| var SS = SpreadsheetApp.getActiveSpreadsheet(); | |
| function readConfig() { | |
| OPENTIME = new Date(); | |
| OPENTIME.setHours(SS.getRangeByName("OpenHour").getValue()); | |
| OPENTIME.setMinutes(SS.getRangeByName("OpenMinute").getValue()); | |
| OPENTIME.setSeconds(0); |
| require(RMySQL) | |
| require(ggplot2) | |
| require(scales) | |
| myusername = "peter" | |
| mypassword = "sekret" | |
| system('ssh -f [email protected] -L 3306:localhost:3306 -N -o ExitOnForwardFailure=yes') | |
| con <- dbConnect(MySQL(), | |
| user=myusername, password=mypassword, |
| library(rvest) | |
| library(ggvis) | |
| trim <- function (x) gsub("^\\s+|\\s+$", "", x) | |
| to_number <- function(string) { | |
| string <- trim(string) | |
| len <- nchar(string) | |
| last_char <-substr(string, len, len) | |
| suppressWarnings( |
| library(RPostgreSQL) | |
| library(dplyr) | |
| library(whisker) | |
| source("config.R") | |
| redshift <<- src_postgres( | |
| dbname = redshift_database, | |
| host = redshift_server, | |
| port = redshift_port, | |
| user = redshift_username, |
| select | |
| product_segment as Segment, | |
| round(avg(revenue_then)) as `Average Revenue`, | |
| count(distinct company_id) as n, | |
| round(power(power(sum(revenue_now) / sum(revenue_then), 1/period_diff(date_format(month_now, "%Y%m"), date_format(month_then, "%Y%m"))), 12), 2) as `Annual Revenue Retention`, | |
| round(1 - power(1 - sum(churned)/count(*), 1/period_diff(date_format(month_now, "%Y%m"), date_format(month_then, "%Y%m"))), 3) as `Monthly Churn` | |
| from ( | |
| select old.company_id, product_segment, size_segment, channel_segment, old.revenue as revenue_then, new.revenue as revenue_now, new.revenue is null as churned, new.revenue_month as month_now, old.revenue_month as month_then | |
| from ( | |
| select |
| library(lubridate) # needs >= v1.6.0.9 for correct handling of lubridate.week.start with floor_date | |
| options(lubridate.week.start = 1) # Weeks start on Mondays, use 7 for Sundays | |
| library(dplyr) | |
| library(ggplot2) | |
| library(scales) | |
| library(glue) | |
| source("../analytics/utils/db.R", local = FALSE) | |
| query <- function(sql, db = mainframe_db) {return(collect(tbl(db, sql(sql))))} | |
| period_diff <- function(start, end, unit = "month") { |
| var MAX_AGENTS_TO_TEST = 200; | |
| function determineAgents(callDuration, calls, periodLength, answerTarget, serviceLevelTarget) { | |
| if(calls === 0) return(0); | |
| for (var agents = 1; agents <= MAX_AGENTS_TO_TEST; agents++) { | |
| if(serviceLevel(callDuration, calls, periodLength, agents, answerTarget) >= serviceLevelTarget) break; | |
| } | |
| return(agents); | |
| } |
| import numpy as np | |
| from tensorflow import keras | |
| from tensorflow.keras import layers | |
| import time | |
| start_time = time.time() | |
| # Model / data parameters | |
| num_classes = 10 | |
| input_shape = (28, 28, 1) |
| description |
|---|
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