sandbox.Rmd

```{r}
wave_seasons <- data.frame(seas = seq(60,300,15),
                           w2 = c(rep(0,9),seq(15,60,15),rep(60,4)),
                           w3 = c(rep(0,1),seq(15,60,15),rep(60,12)),
                           w4 = rep(60,17),
                           w5 = c(rep(0,5),seq(15,60,15),rep(60,8)),
                           w6 = c(rep(0,13),seq(15,60,15)))
tail(wave_seasons)

get_days <- function(seas,w2,w3,w4,w5,w6) {
days <- tibble(day = 1:365,
               open = c(rep(0,65),rep(0,60-w2),rep(1,w2),
                        rep(0,60-w3),rep(1,w3),
                        rep(NA,60),
                        #rep(0,60-w3),rep(1,w3),
                        rep(1,w5),rep(0,60-w5),
                        rep(1,w6),rep(0,60-w6)))
return(days)
}
#purrr::pmap(tail(wave_seasons,1),list(w2=w2,w3=w3,w4=w4,w5=w5,w6=w6),get_days)
new_regs <- c(5,21,150)
x <- filter(wave_seasons, seas == new_regs[3])
out <- get_days(w2=x$w2,w3=x$w3,w4=x$w4,w5=x$w5,w6=x$w6) %>% 
  mutate(bag = ifelse(open==1, new_regs[1], 0),
         minlen = ifelse(open==1, new_regs[2], 50)) %>% 
  I()
states <- c("MA", "RI", "CT", "NY", "NJ", "DE", "MD", "VA", "NC")
output_regs <- tibble(states=states,
       out = rep(list(out),length(states))) %>% 
  unnest(cols = c(out)) %>% 
  I()
output_regs
```

```{r}
library(lubridate)
#read clipboard
#reg_table <- clipr::read_clip_tbl()
#saveRDS(reg_table,file="fluke-regs-2019.rds")
regs2019 <- readRDS("fluke-regs-2019.rds") %>% 
  mutate(year = rep(2019,nrow(.))) %>%  
  separate(Dates, sep ="-", into = c("start","end")) %>% 
  unite(col = "start", year, start, sep = " ", remove = FALSE) %>% 
  unite(col = "end", year, end, sep = " ") %>% 
  mutate(start = yday(parse_date_time(start, "%y%m%d")),
         end = yday(parse_date_time(end, "%y%m%d")),
         day = map2(start,end,function(x,y){seq(x,y,by=1)}),
         states = State
         ) %>% 
  select(states,Period,day) %>% 
  unnest(cols = c(day)) %>% 
  I()
regs2019
#as_date(reg_table$Dates)
```

```{r}
lou_regs <- output_regs %>% 
  left_join(regs2019) %>% 
  na.omit() %>% 
  group_by(states, Period) %>% 
  summarize(prop = round(sum(open, na.rm=TRUE)/length(open), digits = 3), .groups = "drop") %>% 
  mutate(bag = ifelse(prop>0, new_regs[1], 0),
         minlen = ifelse(prop>0, new_regs[2], 50)) %>% 
  I()
lou_regs
write_csv(lou_regs,"mgmt-period-regs.csv")
```