In southwick-associates/lictemplate: Create template files for license data processing

Notes

Include some notes here about your assessment of the initial validation.

knitr::opts_chunk$set(comment = NA)
library(tidyverse)
library(lubridate)
library(DBI)
library(salic)
library(salicprep)
source("../params.R")

Load Data

lic <- read_csv("../../data/lic-clean.csv") %>%
    filter(type %in% c("fish", "hunt", "combo", "trap"))

con <- dbConnect(RSQLite::SQLite(), db_standard)
sale <- tbl(con, "sale") %>% 
    select(lic_id, cust_id, year, dot, start_date, end_date) %>%
    collect() %>%
    semi_join(lic, by = "lic_id")
cust <- tbl(con, "cust") %>% 
    select(cust_id, sex, dob, cust_res) %>% 
    collect() %>%
    semi_join(sale, by = "cust_id")
dbDisconnect(con)

cust <- df_factor_sex(cust, "sex") %>% 
    df_factor_res("cust_res") %>%
    mutate(dob = ymd(dob), birth_year = year(dob))

All Customers

Overall

Initial summary of customers per year:

• Year-to-year variation in churn and customers is typically small.
• If there are data issues, it may show up in strange patterns here.
• The USFWS Historical License Data may be useful for ground-truthing customer counts.

summary_initial(sale)

Customers

filter(cust, birth_year >= 1900) %>% 
    plot_demo(c("sex", "birth_year", "cust_res"))

Hunters

Overall

hunt <- sale %>%
    semi_join(filter(lic, type %in% c("hunt", "combo")), by = "lic_id")
summary_initial(hunt)

Customers

filter(cust, birth_year >= 1900) %>% 
    semi_join(hunt, by = "cust_id") %>%
    plot_demo(c("sex", "birth_year", "cust_res"))

Customer Trends

hunt <- left_join(hunt, cust, by = "cust_id") %>%
    recode_agecat() %>%
    df_factor_age()
plot_demo(hunt, c("sex", "age", "cust_res"), trend = TRUE)

Anglers

Overall

fish <- sale %>%
    semi_join(filter(lic, type %in% c("fish", "combo")), by = "lic_id")
summary_initial(fish)

Customers

filter(cust, birth_year >= 1900) %>% 
    semi_join(fish, by = "cust_id") %>%
    plot_demo(c("sex", "birth_year", "cust_res"))

Customer Trends

fish <- left_join(fish, cust, by = "cust_id") %>%
    recode_agecat() %>%
    df_factor_age()
plot_demo(fish, c("sex", "age", "cust_res"), trend = TRUE)

Dates

Year vs Transaction Date

The year variable provided by the state doesn't always line up with transaction dates. This typically has to do with a "license year" not directly corresponding to calendar year (at least for certain license types).

sale <- mutate_at(sale, vars(dot, start_date, end_date), "ymd")
summary_year_dot(sale, lastyr) %>% knitr::kable()

Transaction Date Ranges

Getting a sense of what the transaction date looks like.

summary_date(sale, "dot", yrs)

License Durations

Looking at the difference between start and end date to get a sense of what that looks like.

summary_duration(sale, yrs)

License Types

Gaps in license types for certain years can sometimes (though not always) indicate missing data. This is typically alot of data to scan, included mainly in case it's needed for future reference.

lic <- mutate(lic, description = paste(type, lic_id, description, sep = "-"))
sale <- left_join(sale, lic, by = "lic_id")
x <- summary_lic_types(sale)
spread(x, year, n, fill = "") %>% 
    knitr::kable(caption = "Customer counts by year for license types")

plot_lic_types(x)

southwick-associates/lictemplate documentation built on Jan. 10, 2022, 6:44 p.m.