Calculate recruitment

Calculate recruitment using a variable window size

calcRecruitment(df, groupVars = NULL, birthThreshold = 5)

Arguments

df	A data frame
groupVars	A character vector of variable names to group by
birthThreshold	A numeric value indicating how many years without purchase before an individual is considered a recruit

Value

A dataframe with columns for grouping variables and columns for totalCustomers, retainedCustomers, recruitedCustomers, and recruitementRate

See also

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

Examples

# Demo data: Recruitment for customers purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "csv",
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017))
) %>%
  calcRecruitment(c("itemType"))
#> # A tibble: 3 x 6
#>   itemYear itemType recruitedCustom… retainedCustome… totalCustomers
#>   <fct>    <fct>               <dbl>            <dbl>          <dbl>
#> 1 2015     Fish                  591             1202           1793
#> 2 2016     Fish                  640             1228           1868
#> 3 2017     Fish                  520             1218           1738
#> # … with 1 more variable: recruitmentRate <dbl>
if (FALSE) {
# 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: Recruitment for customers purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "sql",
  conn = myConn,
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017))
) %>%
  calcRecruitment(c("itemType"))
}