forked from commfish/seak_sablefish
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmr_pj_diagnostic_scrap.R
275 lines (198 loc) · 7.95 KB
/
mr_pj_diagnostic_scrap.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
ugh<-marks[1:20,]
dys<-unique(ugh$date)
unique(ugh$year)
ugh$whole_kg
ugh$total_obs
ugh$marked
ugh$tags_from_fishery
ugh$mean_weight
ugh$WPUE
ugh$interp_mean =
ugh$mean_npue <- ugh$WPUE/ugh$mean_weight
cumsum(ugh$tags_from_fishery)
for (d in dys){
Dat<-Ugh[Ugh$date == d,]
Dat$whole_kg = sum(whole_kg),
total_obs = sum(total_obs),
total_marked = sum(marked),
tags_from_fishery = sum(tags_from_fishery),
mean_weight = mean(mean_weight),
mean_wpue = mean(WPUE)
}
fshbio<-read.csv(paste0("data/fishery/fishery_bio_2000_", YEAR,".csv"))
str(fshbio)
read_csv(paste0("data/fishery/fishery_bio_2000_", YEAR,".csv"),
guess_max = 50000) %>%
filter(!is.na(weight)) %>%
mutate(date = ymd(as.Date(date, "%m/%d/%Y"))) %>%
select(date, trip_no, weight, Stat) %>%
group_by(date, trip_no) %>%
dplyr::summarize(mean_weight_bios = mean(weight)) -> fsh_bio2
view(fsh_bio2)
read_csv(paste0("data/fishery/fishery_bio_2000_", YEAR,".csv"),
guess_max = 50000) %>%
#filter(!is.na(weight)) %>%
mutate(date = ymd(as.Date(date, "%m/%d/%Y"))) %>%
select(date, trip_no, Stat) %>%
group_by(date, trip_no) -> fsh_bio3 #%>%
# dplyr::summarize(mean_weight_bios = mean(weight))
view(fsh_bio3)
unique(fsh_bio3$Stat)
left_join(marks, fsh_bio3, by = c("date", "trip_no"))-> marks2
view(marks2)
left_join(marks, fsh_bio3, by = c("date", "trip_no")) %>%
#mutate(mean_weight = ifelse(!is.na(mean_weight_bios), mean_weight_bios, mean_weight)) %>%
select(-mean_weight_bios) -> marks2
read_csv(paste0("data/fishery/nsei_daily_tag_accounting_2004_", YEAR-1, ".csv")) -> marks3
marks3 %>%
filter(year >= FIRST_YEAR &
!year %in% NO_MARK_SRV) %>%
mutate(all_observed = ifelse(
!grepl(c("Missing|missing|Missed|missed|eastern|Eastern|not counted|
Did not observe|did not observe|dressed|Dressed"), comments) &
observed_flag == "Yes", "Yes", "No"),
mean_weight = ifelse(all_observed == "Yes", whole_kg/total_obs, NA),
year_trip = paste0(year, "_", trip_no)) -> marks3
#left_join(marks3, fsh_tx, by = c("date", "trip_no"))-> marks2
left_join(marks3, fsh_tx, by = c("date", "year_trip"))-> marks2
view(marks2)
ex5<-marks3[marks3$year == 2005,]
tx5<-fsh_tx[fsh_tx$year == 2005,]
view(ex5)
unique(ex5$year_trip)
unique(tx5$year_trip)
unique(tx5$Stat)
left_join(ex5, tx5 %>%
select(year_trip, Stat),
by = c("year_trip"))-> ex5.2
ex5.2<-distinct(ex5.2)
view(ex5.2)
nrow(ex5.2)
nrow(distinct(ex5.2))
tx5$Stat[tx5$year_trip == "2005_9501"]
ex5.2[ex5.2$year_trip == "2005_9501",]
fsh_tx[fsh_tx$year_trip=="2005_9501",]
fsh_tx[fsh_tx$trip_no =="9501",]
nostat<-marks3[is.na(marks3$Stat),]
view(nostat)
with(nostat, table(year))
nrow(marks3)
fsh_tx[fsh_tx$trip_no == 9301,]
rawtx<-read.csv(paste0("data/fishery/nseiharvest_ifdb_1985_", YEAR,".csv"))
rawtx[rawtx$trip_no == 9301,]
rawtx[rawtx$date == "2020-09-19",]
unique(rawtx$trip_no[rawtx$year == 2020])
#check fishery CPUE for missing trip numbers...
str(fsh_cpue)
read_csv(paste0("data/fishery/fishery_cpue_2022reboot_1997_", YEAR,".csv"),
guess_max = 50000) %>%
filter(Spp_cde == "710") %>%
mutate(sable_kg_set = sable_lbs_set * 0.45359237, # conversion lb to kg
std_hooks = 2.2 * no_hooks * (1 - exp(-0.57 * (0.0254 * hook_space))), #standardize hook spacing (Sigler & Lunsford 2001, CJFAS)
# kg sablefish/1000 hooks, following Mueter 2007
WPUE = sable_kg_set / (std_hooks / 1000)) %>%
filter(!is.na(date) &
!is.na(sable_lbs_set) &
# omit special projects before/after fishery
julian_day > 226 & julian_day < 322) %>%
group_by(year, trip_no) %>%
dplyr::summarize(WPUE = mean(WPUE)) -> fsh_cpue2
str(fsh_cpue2)
fsh_cpue2<-read.csv(paste0("data/fishery/fishery_cpue_2022reboot_1997_", YEAR,".csv"))
fsh_cpue2$year_trip = paste0(fsh_cpue2$year, "_", fsh_cpue2$trip_no)
fsh_cpue2[fsh_cpue2$year_trip == "2020_9301",]
fsh_cpue2[fsh_cpue2$trip_no == 9302,]
marks3[marks3$trip_no == 9302,]
fsh_cpue2[fsh_cpue2$year_trip == "2020_101",]
rawtx[rawtx$year == 2020 & is.na(rawtx$trip_no),]
#===============================================================================
read_csv(paste0("data/fishery/nsei_daily_tag_accounting_2004_", YEAR-1, ".csv")) -> marks4
marks4 %>%
filter(year >= FIRST_YEAR &
!year %in% NO_MARK_SRV) %>%
mutate(all_observed = ifelse(
!grepl(c("Missing|missing|Missed|missed|eastern|Eastern|not counted|
Did not observe|did not observe|dressed|Dressed"), comments) &
observed_flag == "Yes", "Yes", "No"),
mean_weight = ifelse(all_observed == "Yes", whole_kg/total_obs, NA),
year_trip = paste0(year, "_", trip_no)) -> marks4
nrow(marks4)
left_join(marks4, fsh_cpue2 %>%
select(year_trip, Stat),
by = c("year_trip"))-> marks4
view(marks4)
marks4<-distinct(marks4)
nostat4<-marks4[is.na(marks4$Stat),]
view(nostat4) #some missing trip numbers not present in fishery cpue or fish_tx data!!!
with(nostat4, table(year))
nrow(marks4)
3327/1423
head(mtry,20)
view(mtry[1:6,])
head(marks3)
view(mtry[mtry$year_trip == "2005_106",])
view(fsh_tx[fsh_tx$year_trip == "2005_106",])
view(marks[marks$year_trip == "2005_106",])
1397.5+4125
marks[marks$trip_no == 106 & marks$year == 2005,]
str(fsh_tx)
view(fsh_tx[fsh_tx$year_trip == "2005_106",])
view(mtry)
test<-mtry[mtry$year == 2005,][1:10,]
test %>%
# padr::pad fills in missing dates with NAs, grouping by years.
pad(group = "year") %>%
group_by(year, date) %>%
dplyr::summarize(whole_kg = sum(whole_kg.y),
total_obs = sum(total_obs),
total_marked = sum(marked),
tags_from_fishery = sum(tags_from_fishery),
mean_weight = mean(mean_weight),
mean_wpue = mean(WPUE)) %>%
# interpolate mean_weight column to get npue from wpue (some trips have wpue
# data but no bio data)
mutate(interp_mean = zoo::na.approx(mean_weight, maxgap = 20, rule = 2),
mean_npue = mean_wpue / interp_mean) %>% #<-weight to n
# padr::fill_ replaces NAs with 0 for specified cols
fill_by_value(whole_kg, total_obs, total_marked, tags_from_fishery, value = 0) %>%
group_by(year) %>%
mutate(cum_whole_kg = cumsum(whole_kg), #cumsum makes vector
cum_obs = cumsum(total_obs),
cum_marks = cumsum(total_marked),
julian_day = yday(date)) -> t3
daily_marks3[daily_marks3$year_trip == "2005_106",]
view(test)
view(t3)
mksub<-marks3[marks3$year_trip == "2005_2003" |
marks3$year_trip == "2005_2006" |
marks3$year_trip == "2005_58" |
marks3$year_trip == "2005_59",]
view(mksub)
philcpue<-read.csv(paste0("data/fishery/fishery_cpue_2022reboot_1997_", YEAR,".csv"))
view(philcpue)
view(philcpue[philcpue$year == 2005 & philcpue$trip_no == 58,])
tcpue<-philcpue[philcpue$year == 2005 & philcpue$trip_no == 58,]
tcpue %>% filter(Spp_cde == "710") %>%
mutate(sable_kg_set = sable_lbs_set * 0.45359237, # conversion lb to kg
std_hooks = 2.2 * no_hooks * (1 - exp(-0.57 * (0.0254 * hook_space))), #standardize hook spacing (Sigler & Lunsford 2001, CJFAS)
# kg sablefish/1000 hooks, following Mueter 2007
WPUE = sable_kg_set / (std_hooks / 1000)) %>%
filter(!is.na(date) &
!is.na(sable_lbs_set) &
# omit special projects before/after fishery
julian_day > 226 & julian_day < 322) %>%
group_by(year, trip_no, Stat) %>%
dplyr::summarize(WPUE = mean(WPUE)) -> pcpue2
view(pcpue1)
view(pcpue2)
view(fsh_cpue_stat[fsh_cpue_stat$year == 2005 & fsh_cpue_stat$trip_no == 58,])
view(marks4[marks4$year_trip == "2005_58",])
view(marks5[marks5$year == 2005 & marks5$trip_no == 58,])
view(marks3[marks3$year_trip == "2005_58",])
view(fsh_cpue[fsh_cpue$year == 2005 & fsh_cpue$trip_no == 58,])
view(fsh_cpue_stat)
fsh_cpue[fsh_cpue$year == 2005 & fsh_cpue$trip_no == 58,]
fsh_cpue_stat[fsh_cpue_stat$year == 2005 & fsh_cpue_stat$trip_no == 58,]
view(marks)
view(marks2)
view(marks5)