Count customers by group

Count number of customers by group. This function will collect data from the database if using SQL backend.

countCustomers(df, groupVars = NULL)

Arguments

df	A data frame with a column named customerUID
groupVars	A character vector of variable names to group by

Value

A data frame with columns for grouping variables and a column named customers for number of customers Data frame is passed through prettyData function.

See also

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

Examples

# Demo data: Count number of customers each year purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "csv",
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017))
) %>%
  countCustomers(c("itemYear", "itemType"))
#> # A tibble: 8 x 3
#>   itemYear itemType customers
#>   <fct>    <fct>        <int>
#> 1 2010     Fish          1628
#> 2 2011     Fish          1608
#> 3 2012     Fish          1873
#> 4 2013     Fish          1755
#> 5 2014     Fish          1786
#> 6 2015     Fish          1793
#> 7 2016     Fish          1868
#> 8 2017     Fish          1738
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: Count number of customers each year purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "sql",
  conn = myConn,
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017))
) %>%
  countCustomers(c("itemYear", "itemType"))
}