#' Process dummy variables
#' Function to calculate revenues and distances
#' @param xx Index value
#' @param td2 The tow dates
#' @param dat1 The Data
#' @param hab_dist Distance used to filter for habits and revenues
#' @param net_cost Way of netting out costs for revenue calclations
#' @export
process_dummys2 <- function(xx, td2 = td1, dat1 = dat, hab_dist = 5, n_cost){
temp_dat <- td2[xx, ]
fltz <- strsplit(temp_dat$fleet_name, "_")[[1]]
#-----------------------------------------------------------------------------------------------
#Did vessel fish in past 30 days?
dum30 <- dat1 %>% ungroup %>% filter(haul_id != temp_dat$haul_id,
set_date %within% temp_dat$days_inter,
depth_bin == temp_dat$depth_bin, drvid == temp_dat$fished_drvid,
fleet_name %in% fltz)
dum30 <- dum30 %>% distinct(haul_id, .keep_all = T)
#calculate distances
dum30$dist <- gcd_slc(long1 = temp_dat$set_long, lat1 = temp_dat$set_lat,
long2 = deg2rad(dum30$set_long), deg2rad(dum30$set_lat))
dum30 <- dum30 %>% filter(dist <= hab_dist)
#Add dummy coefficient
dum30_val <- nrow(dum30)
#-----------------------------------------------------------------------------------------------
# Vessel fish in the past 30 days of last year?
dum30y <- dat1 %>% ungroup %>% filter(haul_id != temp_dat$haul_id, set_date %within% temp_dat$prev_year_days_inter,
depth_bin == temp_dat$depth_bin, drvid == temp_dat$fished_drvid,
fleet_name %in% fltz)
dum30y <- dum30y %>% distinct(haul_id, .keep_all = T)
#calculate distances
dum30y$dist <- gcd_slc(long1 = temp_dat$set_long, lat1 = temp_dat$set_lat,
long2 = deg2rad(dum30y$set_long), deg2rad(dum30y$set_lat))
dum30y <- dum30y %>% filter(dist <= hab_dist)
#Add dummy coefficient
dum30y_val <- nrow(dum30y)
# if(nrow(dum30y) > 0) dum30y_val <- 1
#-----------------------------------------------------------------------------------------------
#Calculate the revenues within a finer radius and from the whole fleet, rather than individual vessel
# browser()
dum_rev <- dat1 %>% ungroup %>% filter(haul_id != temp_dat$haul_id, set_date %within% temp_dat$days_inter,
depth_bin == temp_dat$depth_bin, fleet_name %in% fltz)
dum_rev <- dum_rev %>% distinct(haul_id, .keep_all = T)
#Calculate distance
dum_rev$dist <- gcd_slc(long1 = temp_dat$set_long, lat1 = temp_dat$set_lat,
long2 = deg2rad(dum_rev$set_long), lat2 = deg2rad(dum_rev$set_lat))
dum_rev <- dum_rev %>% filter(dist <= hab_dist)
#Calculate revenue in a faster way with fewer if statements
dum_rev_val <- nrow(dum_rev)
#####Here control whether you use value of all species or just target and groundfish species
#Right now including all species "tgow_rev"
# dum_rev[is.na(dum_rev$weak_quota_value), 'weak_quota_value'] <- 0
# browser()
mean_rev <- mean(dum_rev$tgow_rev)
mean_rev <- replace(mean_rev, is.na(mean_rev), 0)
dum_rev_dollars <- mean_rev
#Deprecated features
# mean_weak <- mean(dum_rev$weak_quota_value, na.rm = T)
# mean_weak <- replace(mean_weak, is.na(mean_weak), 0)
# dum_rev$quota_cost <- dum_rev$avg_quota_price * dum_rev$apounds
# mean_qc <- mean(dum_rev$quota_cost, na.rm = T)
# mean_qc <- replace(mean_qc, is.na(mean_qc), 0)
# #Calculate different values based on arguments
# if(n_cost == "trev"){
# dum_rev_dollars <- mean_rev
# }
# if(n_cost == 'cons'){
# dum_rev_dollars <- mean_rev - mean_weak
# }
# #quota costs for all species included
# if(n_cost == "qcos"){
# dum_rev_dollars <- mean_rev - mean_qc
# }
# dum_rev_dollars <- mean_rev - mean_weak
temp_dat$dummy_prev_days <- dum30_val
temp_dat$dummy_prev_year_days <- dum30y_val
temp_dat$dummy_miss <- dum_rev_val
temp_dat$miss_rev <- dum_rev_dollars
return(temp_dat)
}
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