Calculate participation rate

Calculate participation rate by group. Participation rate is the number of customers divided by the population size.

calcParticipation(df, groupVars, pop)

Arguments

df	A data frame
groupVars	A character vector of variable names to group by
pop	A dataframe of population by county, year, gender, and age group.

See also

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

Examples

# Get list of valid counties (used to filter out data entry errors)
validCounties <- countyMaps %>%
  dplyr::filter(region == "nebraska") %>%
  dplyr::distinct(county) %>%
  dplyr::pull(county)

# Demo data: Participation rate for customers purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "csv",
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017), state = "NE")
) %>%
  calcParticipation(c("itemType", "county"),
    pop = statePop %>% filter(state == "NE")
  ) %>%
  dplyr::filter(county %in% validCounties)
#> # A tibble: 91 x 5
#>    itemType county    customers   pop participationRate
#>    <fct>    <chr>         <int> <dbl>             <dbl>
#>  1 Fish     adams           104 31564          0.00329 
#>  2 Fish     antelope         31  6406          0.00484 
#>  3 Fish     arthur            1   421          0.00238 
#>  4 Fish     blaine            4   502          0.00797 
#>  5 Fish     boone            14  5357          0.00261 
#>  6 Fish     box butte        59 11200          0.00527 
#>  7 Fish     boyd              2  2049          0.000976
#>  8 Fish     brown            11  3058          0.00360 
#>  9 Fish     buffalo         169 48834          0.00346 
#> 10 Fish     burt             18  6546          0.00275 
#> # … with 81 more rows
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

# Get list of valid counties (used to filter out data entry errors)
validCounties <- countyMaps %>%
  dplyr::filter(region == "nebraska") %>%
  dplyr::distinct(county) %>%
  dplyr::pull(county)

# SQL Backend: Participation rate for customers purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "sql",
  conn = myConn,
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017), state = "NE")
) %>%
  calcParticipation(c("itemType", "county"),
    pop = statePop %>% filter(state == "NE")
  ) %>%
  dplyr::filter(county %in% validCounties)
}