Calculate gender proportion

Calculate gender proportion by group. This function collects data from the database.

calcGenderProportion(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 dataframe with columns for grouping variables and a column named genderProportion for gender proportion. Data frame is passed through prettyData function.

See also

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

Examples

# Demo data: Gender proportions for customers purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "csv",
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017))
) %>%
  calcGenderProportion(c("itemYear", "itemType"))
#> # A tibble: 16 x 5
#>    gender itemYear itemType customers genderProportion
#>    <fct>  <fct>    <fct>        <int>            <dbl>
#>  1 Female 2010     Fish           385            0.236
#>  2 Female 2011     Fish           389            0.242
#>  3 Female 2012     Fish           455            0.243
#>  4 Female 2013     Fish           474            0.270
#>  5 Female 2014     Fish           494            0.277
#>  6 Female 2015     Fish           491            0.274
#>  7 Female 2016     Fish           522            0.279
#>  8 Female 2017     Fish           495            0.285
#>  9 Male   2010     Fish          1243            0.764
#> 10 Male   2011     Fish          1219            0.758
#> 11 Male   2012     Fish          1418            0.757
#> 12 Male   2013     Fish          1281            0.730
#> 13 Male   2014     Fish          1292            0.723
#> 14 Male   2015     Fish          1302            0.726
#> 15 Male   2016     Fish          1346            0.721
#> 16 Male   2017     Fish          1243            0.715
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: Gender proportions for customers purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "sql",
  conn = myConn,
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017))
) %>%
  calcGenderProportion(c("itemYear", "itemType"))
}