calcChurn: Calculate churn

In chrischizinski/huntfishapp: Exploratory Data Analysis Application for Hunting, Fishing, and Outdoor Recreation

Description

Calculate churn using variable window size

Usage

calcChurn(df, groupVars = NULL, deathThreshold = 5)

Arguments

df	A data frame
groupVars	A character vector of variable names to group by
deathThreshold	A numeric value indicating how many years must elapse before an individual is considered churned

Value

A dataframe with columns for grouping variables and columns churnedCustomers, retainedCustomers, totalCustomers, churnRate, and retentionRate

See Also

Other analysis functions: calcGenderProportion, calcParticipation, calcRecruitment, countCustomers, countItems, itemGroupCount, sumRevenue

Examples

# Demo data: Churn for customers purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "csv",
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017))
) %>%
  calcChurn(c("itemType"))
## Not run: 
# Database connection. Suggest using keyring package to avoid hardcoding
# passwords
myConn <- DBI::dbConnect(odbc::odbc(),
  dsn = "HuntFishApp", # Your datasource name
  uid = keyring::key_get("HuntFishAppUID"), # Your username
  pwd = keyring::key_get("HuntFishAppPWD")
) # Your password

# SQL Backend: Churn for customers purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "sql",
  conn = myConn,
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017))
) %>%
  calcChurn(c("itemType"))

## End(Not run)

chrischizinski/huntfishapp documentation built on Sept. 7, 2020, 12:14 p.m.