analysis/prep_juv_winter_fish.R
In KevinSee/QRFcapacity: Carrying Capacity with QRF
# Author: Kevin See
# Purpose: Prep the data collected by various outfits
# Created: 5/16/2019
# Last Modified: 10/17/2019
# Notes:
#-----------------------------------------------------------------
# load needed libraries
library(lubridate)
library(readxl)
library(stringr)
library(tidyverse)
library(magrittr)
library(measurements)
#-------------------------
# set NAS prefix, depending on operating system
#-------------------------
if(.Platform$OS.type != 'unix') {
nas_prefix = "S:"
} else if(.Platform$OS.type == 'unix') {
nas_prefix = "~/../../Volumes/ABS/"
}
#-----------------------------------------------------------------
# Bring in some CHaMP habitat data at the channel unit scale, to fill in blank Tier 1 classifications.
data(champ_cu)
data(champ_site_2011_17)
cu_data = champ_cu %>%
select(VisitID, SiteName = Site, ChUnitNumber, Tier1) %>%
mutate(Tier1 = recode(Tier1,
'Fast-NonTurbulent/Glide' = 'Run',
'Fast-Turbulent' = 'Riffle',
'Slow/Pool' = 'Pool',
'Small Side Channel' = 'SC')) %>%
left_join(champ_site_2011_17 %>%
select(SiteName = Site,
VisitID,
Watershed) %>%
distinct()) %>%
select(VisitID, SiteName, Watershed, ChUnitNumber, Tier1)
#-----------------------------------------------------------------
# Read in fish data from ODFW, QCI and WDFW.
#-----------------------------------------------------------------
# ODFW
#-----------------------------------------------------------------
# snorkel
odfw_snork = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/ODFW - Basin Data for Kevin.xlsx'),
'SnorkelCount') %>%
rename(SiteUnit = `Site_ID_+Unit`,
SiteName = `Site ID`,
ChUnitNumber = `Unit No.`,
Tier1 = `Unit Type`,
Temp = `H20 Temp`) %>%
mutate(Tier1 = recode(Tier1,
'FNT' = 'Run')) %>%
mutate(Date = if_else(!is.na(Time),
ymd_hms(paste(date(Date), hour(Time), minute(Time), second(Time))),
Date)) %>%
select(-(Tot_Count:ncol(.)), -Time) %>%
gather(species, count, `BT counted`:`CH counted`) %>%
mutate(species = str_replace(species, ' counted', '')) %>%
mutate_at(vars(SiteUnit, SiteName),
list(str_to_upper))
# mark recapture
odfw_mr = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/ODFW - Basin Data for Kevin.xlsx'),
'MarkRecap') %>%
rename(SiteName = `Site ID`,
ChUnitNumber = `Unit No.`,
Tier1 = `Unit Type`,
SampleMethod = Method) %>%
mutate(Tier1 = recode(Tier1,
'FNT' = 'Run')) %>%
select(-`N-hat(chapman)`, -Time) %>%
mutate_at(vars(SiteName),
list(str_to_upper)) %>%
gather(sample, value, `BT Marked`:`CH New`) %>%
mutate(SiteUnit = paste(SiteName, ChUnitNumber, sep = '_'),
species = str_sub(sample, 1, 2),
sample = str_sub(sample, 4),
sample = factor(sample,
levels = c('Marked', 'New', 'Recap'))) %>%
mutate(SampleMethod = str_replace(SampleMethod, 'Efish', 'efish'),
Method = 'MarkRecap') %>%
select(-SampleMethod) %>%
spread(sample, value) %>%
mutate(Capture = New + Recap) %>%
select(SiteName:Method,
Pass1_M = Marked,
Pass2_C = Capture,
Pass3_R = Recap)
# depletions
odfw_depl_org = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/ODFW - Basin Data for Kevin.xlsx'),
'Depletions') %>%
rename(SiteName = `Site ID`,
ChUnitNumber = `Unit No.`,
Tier1 = `Unit Type`) %>%
mutate(Tier1 = recode(Tier1,
'FNT' = 'Run')) %>%
mutate_at(vars(SiteName),
list(str_to_upper)) %>%
mutate(Date = if_else(!is.na(Time),
ymd_hms(paste(date(Date), hour(Time), minute(Time), second(Time))),
Date)) %>%
mutate(Method = 'Depletion',
SiteUnit = paste(SiteName, ChUnitNumber, sep = '_')) %>%
select(-matches('tot$'), -matches('Nhat'), -X, -Y, -Time) %>%
gather(variable, value, matches('Pass')) %>%
mutate(Pass = str_sub(variable, 1, 5),
Pass = str_replace(Pass, 'Pass', '')) %>%
mutate(variable = ifelse(grepl('Effort', variable),
'Effort',
variable),
species = ifelse(variable != 'Effort',
str_sub(variable, -2),
NA)) %>%
mutate(value = as.numeric(value))
# don't worry about effort, not enough data to incorporate that as variable
odfw_depl = odfw_depl_org %>%
filter(variable != 'Effort') %>%
select(-variable) %>%
spread(Pass, value) %>%
rename(Pass1_M = `1`,
Pass2_C = `2`,
Pass3_R = `3`)
# snorkel counts
odfw_snork_only = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/qry_Count_Salmonids_By_Unit_winter.xlsx'),
'Combined_Kevins_Format') %>%
select(Stream:pass3_R) %>%
rename(Pass1_M = pass1_M,
Pass2_C = pass2_C,
Pass3_R = pass3_R,
Tier1 = Tier_1) %>%
mutate(Tier1 = recode(Tier1,
'FNT' = 'Run')) %>%
# use scaled count (the raw fish count divided by % of channel unit sampled. Units were sometimes partially sampled due to ice or extremely shallow water.)
select(-count) %>%
rename(count = `scaled_count*`) %>%
mutate(SurveyType = recode(SurveyType,
'Snorkel' = 'Snorkeling')) %>%
mutate_at(vars(matches('^Pass')),
list(as.numeric))
odfw_data = odfw_snork %>%
select(-Method) %>%
full_join(odfw_mr %>%
bind_rows(odfw_depl) %>%
select(-Date, -Temp)) %>%
mutate(Discharge = NA) %>%
mutate(SurveyType = 'Calibration') %>%
rename(DCEtype = Method) %>%
select(SiteUnit:Date,
Crew, DCEtype, SurveyType, Temp, Discharge,
everything()) %>%
bind_rows(odfw_snork_only) %>%
mutate(Tier1 = recode(Tier1,
'Fast-NonTurbulent' = 'Run',
'Fast-Turbulent' = 'Riffle',
'Slow/Pool' = 'Pool',
'SmSideChnnl' = 'Small Side Channel'))
# what's missing?
odfw_data %>%
summarise_at(vars(everything()),
list(~sum(is.na(.)) / length(.))) %>%
gather(variable, percNA)
#-----------------------------------------------------------------
# QCI
#-----------------------------------------------------------------
qci_2018 = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/QCI_winterQRF_capture.xlsx')) %>%
rename(ChUnitNumber = ChUnitNum,
Stream = StreamName,
Date = SurveyDateTime,
Temp = `Temp(°C)`,
Discharge = `Discharge(m3/s)`,
species = Species,
count = SnorkelCount,
SurveyType = Method,
Pass2_C = C,
Pass3_R = R) %>%
mutate(SiteUnit = paste(SiteName, ChUnitNumber, sep = '_'),
Discharge = as.numeric(Discharge),
Crew = 'QCI',
DCEtype = recode(DCEtype,
'MR' = 'MarkRecap'),
species = recode(species,
'Chinook' = 'CH',
'Steelhead' = 'OM'),
Tier1 = ifelse(Tier2 == 'Fast Non-turbulent',
'Run',
ifelse(Tier2 == 'Riffle',
'Riffle',
ifelse(Tier2 %in% c('Pool/Off Channel', 'Side Channel'),
'Small Side Channel',
'Pool')))) %>%
select(one_of(names(odfw_data)), PercentIceCover, Tier2) %>%
select(SiteUnit:Discharge, PercentIceCover, Tier2, everything())
# for surveys with missing discharge data, use discharge from other surveys at the same site
qci_discharge = qci_2018 %>%
select(Date, Stream, SiteName, SurveyType, Discharge) %>%
mutate(Date = floor_date(Date, unit = 'day')) %>%
distinct()
qci_2018 %<>%
left_join(qci_discharge %>%
group_by(Stream, SiteName) %>%
summarise(nTot = n_distinct(Date),
nNonNA = n_distinct(Date[!is.na(Discharge)])) %>%
left_join(qci_discharge %>%
filter(!is.na(Discharge)) %>%
group_by(SiteName) %>%
summarise_at(vars(Discharge),
list(min = min,
median = median,
mean = mean,
max = max))) %>%
ungroup() %>%
filter(nTot > nNonNA,
nNonNA > 0) %>%
select(SiteName, meanDis = mean)) %>%
mutate(Discharge = if_else(is.na(Discharge),
meanDis,
Discharge)) %>%
select(-meanDis)
# # what's missing?
# qci_2018 %>%
# summarise_at(vars(everything()),
# list(~sum(is.na(.)) / length(.))) %>%
# gather(variable, percNA)
#
# xtabs(~ DCEtype + SurveyType + is.na(count), qci_2018)
# get QCI/ABS data from 2018-2019
qci_2019 = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/winterQRF_18to19_capture.xlsx')) %>%
rename(ChUnitNumber = HabitatUnit,
Stream = StreamName,
Date = SampledDate,
Tier2 = HabitatType,
species = Species,
SurveyType = CaptureMethod,
Pass1_M = M,
Pass2_C = C,
Pass3_R = R) %>%
mutate_at(vars(ChUnitNumber, starts_with('Pass')),
list(as.numeric)) %>%
mutate(SiteName = str_remove(SiteName, '_QRF2018$'),
SiteName = str_remove(SiteName, '_QRF2019$'),
SiteName = str_replace(SiteName,
'^LEM-',
'CBW05583-'),
SiteName = str_replace(SiteName,
'^SFS-',
'CBW05583-'),
SiteName = if_else(SiteName == 'LittleSprings1',
paste0('LEM00002-', SiteName),
SiteName),
SiteName = if_else(grepl('Big0Springs', SiteName),
paste0('LEM00001-', SiteName),
SiteName),
SiteName = if_else(grepl('CBW05583-181535', SiteName),
'CBW05583-181535',
SiteName)) %>%
mutate(SiteUnit = paste(SiteName, ChUnitNumber, sep = '_'),
Crew = 'QCI',
DCEtype = if_else(is.na(Pass2_C),
'Count',
'MarkRecap'),
species = recode(species,
'Chinook' = 'CH',
'Steelhead' = 'OM'),
Tier1 = ifelse(Tier2 %in% c('Fast Non-turbulent', 'FNT', 'Run'),
'Run',
ifelse(Tier2 %in% c('Riffle', 'LSC Riffle'),
'Riffle',
ifelse(Tier2 == 'Rapid',
'Rapid',
ifelse(Tier2 %in% c('OCA', 'SC', 'SSC'),
'Small Side Channel',
'Pool'))))) %>%
select(one_of(names(odfw_data)), Tier2) %>%
select(SiteUnit:Discharge, Tier2, everything())
qci_2019 %>%
select(Site = SiteName, Date) %>%
distinct() %>%
anti_join(champ_site_2011_17 %>%
select(Site) %>%
distinct())
qci_data = qci_2018 %>%
bind_rows(qci_2019)
#-----------------------------------------------------------------
# WDFW
#-----------------------------------------------------------------
# Flow data
wdfw_flow = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/WDFW Night Snorkel Data.xlsx'),
'Flow',
range = 'O4:Q39') %>%
rename(Stream = 1,
VisitID = `Site ID`,
FlowCFS = `Flow CFS`) %>%
filter(!grepl('_2$', VisitID)) %>%
mutate(Discharge = conv_unit(FlowCFS,
'ft3_per_sec',
'm3_per_sec'),
VisitID = as.integer(str_replace(VisitID, '_1$', ''))) %>%
select(-FlowCFS) %>%
full_join(read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/WDFW Night Snorkel Data.xlsx'),
'Flow',
range = 'A1:D632') %>%
rename(VisitID = `Stream ID`) %>%
distinct() %>%
mutate(Date = if_else(!is.na(Time),
ymd_hms(paste(date(Date), hour(Time), minute(Time), second(Time))),
Date)) %>%
select(-Time) %>%
filter(!grepl('_2$', VisitID)) %>%
mutate(VisitID = as.integer(str_replace(VisitID, '_1$', '')))) %>%
select(FlowDate = Date, everything())
# snorkel
wdfw_snork = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/WDFW Night Snorkel Data.xlsx'),
'Raw Snorkel Data') %>%
select(Date,
Time = `Start Time`,
Temp = `Water Temp C`,
Snorkelers:Stream,
Watershed = Basin,
VisitID = `Visit ID`,
SiteID = `Site ID`,
SiteLength = `Site L (m)`,
NumSnork = `# Snrk`,
ChUnit = `Unit #`,
Tier2 = Type,
PercSnork = `% Snrk`,
Visibility = `Vis (0-3)`,
ChUnitNotes = `Channel Unit Notes`,
SiteNotes = `Site Notes`,
`BT <80`:`Sculpin`) %>%
mutate(Date = if_else(!is.na(Time),
ymd_hms(paste(date(Date), hour(Time), minute(Time), second(Time))),
Date)) %>%
select(-matches('Total'), -matches('^WH'), -Time) %>%
mutate(ChUnitNumber = str_replace(ChUnit, '[[:alpha:]]', '')) %>%
mutate_at(vars(Temp),
list(as.numeric)) %>%
filter(PercSnork > 0,
NumSnork > 0) %>%
# correct a few dates
mutate(Date = if_else(VisitID == 4806,
min(Date[VisitID == 4806]),
Date)) %>%
left_join(wdfw_flow) %>%
mutate(diff = abs(as.numeric(difftime(Date, FlowDate, units = 'days'))),
Discharge = ifelse(diff > 7,
NA,
Discharge)) %>%
select(-diff, -`Lamprey J`, -Sculpin) %>%
gather(spp_size, count, `BT <80`:`CH >100`) %>%
mutate(species = str_extract(spp_size, '[:alpha:]+'),
species = factor(species,
levels = c('CH', 'OM', 'BT')),
sizeCls = str_replace(spp_size, '[:alpha:]+', ''),
sizeCls = str_trim(sizeCls),
sizeCls = factor(sizeCls,
levels = c('<100', '100+', '<80', '80-129', '130-199', '200+'))) %>%
select(-spp_size) %>%
mutate(count = count / (PercSnork / 100)) %>%
select(Date:SiteNotes, ChUnitNumber, Discharge, species, sizeCls, count) %>%
mutate_at(vars(count),
list(~ as.integer(round(.)))) %>%
mutate(count = ifelse(is.na(count),
0, count))
# WDFW mark-recapture / snorkel calibration surveys
# by size class
wdfw_mr_size_cls = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/WDFW Night Snorkel Data.xlsx'),
'Correction Factor',
range = 'S40:BP60') %>%
rename(Stream = 1,
VisitID = 2,
Tier2 = 3) %>%
select(-starts_with('Total'), -15, -27, -39) %>%
mutate(id = 1:n()) %>%
gather(variable, value, -(Stream:Tier2), -id) %>%
mutate(sizeCls = str_remove(variable, '\\.\\.[:digit:]+'),
index = str_split(variable, '\\.\\.', simplify = T)[,2],
index = as.integer(index),
spp = ifelse(sizeCls %in% c('<100', '100+'),
'CH',
ifelse(index %in% c(6:9, 18:21, 30:33, 42:45),
'OM',
'BT')),
pass = ifelse(spp == 'CH',
ifelse(index %in% c(4,5),
'count',
ifelse(index %in% c(16, 17),
'Pass1_M',
ifelse(index %in% c(28,29),
'Pass3_R',
ifelse(index %in% c(40,41),
'Pass2_unmarked',
NA)))),
NA),
pass = ifelse(spp == 'OM',
ifelse(index %in% c(6:9),
'count',
ifelse(index %in% c(18:21),
'Pass1_M',
ifelse(index %in% c(30:33),
'Pass3_R',
ifelse(index %in% c(42:45),
'Pass2_unmarked',
pass)))),
pass),
pass = ifelse(spp == 'BT',
ifelse(index %in% c(10:13),
'count',
ifelse(index %in% c(22:25),
'Pass1_M',
ifelse(index %in% c(34:37),
'Pass3_R',
ifelse(index %in% c(46:49),
'Pass2_unmarked',
pass)))),
pass)) %>%
mutate(pass = factor(pass),
pass = fct_relevel(pass, 'count'),
spp = factor(spp,
levels = c('CH', 'OM', 'BT')),
sizeCls = factor(sizeCls,
levels = c('<100', '100+', '<80', '80-129', '130-199', '200+'))) %>%
select(-variable, -index) %>%
arrange(id, Stream, VisitID, spp, pass, sizeCls) %>%
spread(pass, value) %>%
mutate(Pass2_C = Pass2_unmarked + Pass3_R) %>%
select(Stream:Tier2, id, species = spp, sizeCls, count, Pass1_M, Pass2_C, Pass3_R) %>%
arrange(id, VisitID, species, sizeCls)
# lump across size classes
wdfw_mr = wdfw_mr_size_cls %>%
group_by(Stream, VisitID, Tier2, id, species) %>%
summarise_at(vars(count:Pass3_R),
list(sum)) %>%
ungroup() %>%
mutate(VisitID = as.integer(str_sub(VisitID, 1, 4))) %>%
mutate(Tier2 = recode(Tier2,
'Fast Non Turb' = 'Run')) %>%
select(-id) %>%
left_join(read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/WDFW Night Snorkel Data.xlsx'),
'Correction Factor',
range = 'A1:Q392') %>%
select(Date,
Time = `Start Time`,
Temp = `Water Temp C`,
Stream,
Watershed = Basin,
VisitID = `Site ID`,
ChUnitNumber = `Unit #`,
Tier2 = Habitat,
species = Species) %>%
mutate(Date = if_else(!is.na(Time),
ymd_hms(paste(date(Date), hour(Time), minute(Time), second(Time))),
Date)) %>%
mutate(Date = if_else(year(Date) > 2018 & month(Date) > 7,
ymd_hms(paste('2017', month(Date), mday(Date), hour(Date), minute(Date), second(Date))),
Date)) %>%
select(-Time) %>%
group_by(Date, Watershed, Stream, VisitID, ChUnitNumber, Tier2, Temp, species) %>%
summarise(Pass1_M = n()) %>%
ungroup()) %>%
select(Watershed, Stream, VisitID, ChUnitNumber, Tier2, Date, Temp, everything()) %>%
arrange(VisitID, ChUnitNumber, species) %>%
filter(!is.na(Watershed))
wdfw_data = wdfw_snork %>%
group_by(Watershed, Stream, VisitID, ChUnitNumber, Date, Temp, Discharge, species) %>%
summarise_at(vars(count),
list(sum),
na.rm = T) %>%
ungroup() %>%
mutate(SurveyType = 'Snorkeling',
DCEtype = 'Count') %>%
left_join(wdfw_snork %>%
mutate(Tier2 = recode(Tier2,
'OC' = 'Off Channel',
'PP' = 'Plunge Pool',
'SP' = 'Scour Pool',
'RA' = 'Rapid',
'RI' = 'Riffle',
'SSC' = 'Small Side Channel')) %>%
select(Stream, VisitID, ChUnitNumber, Tier2) %>%
group_by(Stream, VisitID, ChUnitNumber) %>%
slice(1) %>%
ungroup()) %>%
bind_rows(wdfw_mr %>%
left_join(wdfw_flow) %>%
mutate(diff = abs(as.numeric(difftime(Date, FlowDate, units = 'days'))),
Discharge = ifelse(diff > 7,
NA,
Discharge)) %>%
select(-diff, -FlowDate) %>%
mutate(DCEtype = 'MarkRecap',
SurveyType = 'Calibration')) %>%
left_join(cu_data %>%
mutate_at(vars(ChUnitNumber),
list(as.character)) %>%
select(-Watershed) %>%
bind_rows(cu_data %>%
filter(VisitID == 4816,
ChUnitNumber %in% c(7,8)) %>%
select(VisitID, SiteName, Tier1) %>%
distinct() %>%
mutate(ChUnitNumber = '7&8'))) %>%
mutate(SiteUnit = paste(SiteName, ChUnitNumber, sep = '_'),
# Tier1 = ifelse(Tier2 == 'Run',
# 'Run',
# ifelse(Tier2 %in% c('Riffle', 'RA', 'RI'),
# 'Riffle',
# ifelse(grepl('Pool', Tier2),
# 'Pool',
# Tier2))),
Crew = 'WDFW') %>%
select(one_of(names(qci_data))) %>%
arrange(Stream, SiteName, ChUnitNumber, species)
#------------------------------------------------------
# Combine everything and save as a .csv file.
fish_win_data = odfw_data %>%
mutate(Crew = 'ODFW') %>%
bind_rows(qci_data) %>%
mutate_at(vars(ChUnitNumber),
list(as.character)) %>%
bind_rows(wdfw_data %>%
mutate(PercentIceCover = NA)) %>%
# make stream names match CHaMP stream names
mutate(Stream = recode(Stream,
'Yankee Fork River' = 'Yankee Fork',
'Chewuch' = 'Chewuch River',
'Chikamin' = 'Chikamin Creek',
'Chiwawa' = 'Chiwawa River',
'Entiat' = 'Entiat River',
'Little Wenatchee' = 'Little Wenatchee River',
'Mad' = 'Mad River',
'Methow' = 'Methow River',
'Napeequa' = 'Napeequa River',
'Nason' = 'Nason Creek',
'Stormy' = 'Stormy Creek',
'Twisp' = 'Twisp River',
'White' = 'White River')) %>%
mutate(Stream = if_else(SiteName == 'CBW05583-087698',
'Secesh River',
Stream)) %>%
left_join(cu_data %>%
select(SiteName, Watershed) %>%
distinct()) %>%
select(Site = SiteName,
Watershed, Stream,
SiteUnit, ChUnitNumber,
FishCrew = Crew,
SurveyType, DCEtype,
Date,
Tier1, Tier2, Discharge, Temp, PercentIceCover,
everything()) %>%
mutate(Tier2 = recode(Tier2,
'Fast Non-turbulent' = 'Run',
'Fast-NonTurbulent' = 'Run',
'SmSideChnnl' = 'Small Side Channel'),
Tier1 = recode(Tier1,
'SC' = 'SSC',
'Small Side Channel' = 'SSC')) %>%
rename(Species = species) %>%
mutate(Species = recode(Species,
'CH' = 'Chinook',
'OM' = 'Steelhead',
'BT' = 'BrookTrout')) %>%
filter(Species != 'No Data') %>%
# rename some columns to better match summer data
rename(Method = DCEtype,
Pass1.M = Pass1_M,
Pass2.C = Pass2_C,
Pass3.R = Pass3_R) %>%
mutate(Method = recode(Method,
'MarkRecap' = 'Mark Recapture'),
Method = if_else(Method == 'Count',
if_else(SurveyType == 'Snorkeling',
'Snorkel',
'Single Pass'),
Method),
Watershed = if_else(is.na(Watershed) & Stream == 'Lemhi River',
'Lemhi',
as.character(Watershed)),
Watershed = if_else(is.na(Watershed) & grepl('^DSGN4', Site),
'Upper Grande Ronde',
as.character(Watershed))) %>%
mutate_at(vars(Watershed, Stream, Method, SurveyType, FishCrew, Tier1, Tier2, Species),
list(fct_drop))
# save as csv file
write_csv(fish_win_data,
'data/prepped/fish_data_winter_prepped.csv')
# save to use as data
use_data(fish_win_data,
version = 2,
overwrite = T)
#------------------------------------------------------
# anything missing?
fish_win_data %>%
summarise_at(vars(everything()),
list(~sum(is.na(.)) / length(.))) %>%
gather(variable, percNA) %>%
arrange(desc(percNA))
xtabs(~ is.na(Pass2.C) + is.na(Pass1.M), fish_win_data)
xtabs(~ is.na(count) + is.na(Pass1.M), fish_win_data)
xtabs(~ is.na(Temp) + FishCrew, fish_win_data)
fish_win_data %>%
filter(is.na(Pass1.M)) %>%
xtabs(~ SurveyType, .)
fish_win_data %>%
filter(is.na(count)) %>%
xtabs(~ SurveyType, .)
KevinSee/QRFcapacity documentation built on Feb. 27, 2023, 3:57 p.m.
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# Author: Kevin See
# Purpose: Prep the data collected by various outfits
# Created: 5/16/2019
# Last Modified: 10/17/2019
# Notes:
#-----------------------------------------------------------------
# load needed libraries
library(lubridate)
library(readxl)
library(stringr)
library(tidyverse)
library(magrittr)
library(measurements)
#-------------------------
# set NAS prefix, depending on operating system
#-------------------------
if(.Platform$OS.type != 'unix') {
nas_prefix = "S:"
} else if(.Platform$OS.type == 'unix') {
nas_prefix = "~/../../Volumes/ABS/"
}
#-----------------------------------------------------------------
# Bring in some CHaMP habitat data at the channel unit scale, to fill in blank Tier 1 classifications.
data(champ_cu)
data(champ_site_2011_17)
cu_data = champ_cu %>%
select(VisitID, SiteName = Site, ChUnitNumber, Tier1) %>%
mutate(Tier1 = recode(Tier1,
'Fast-NonTurbulent/Glide' = 'Run',
'Fast-Turbulent' = 'Riffle',
'Slow/Pool' = 'Pool',
'Small Side Channel' = 'SC')) %>%
left_join(champ_site_2011_17 %>%
select(SiteName = Site,
VisitID,
Watershed) %>%
distinct()) %>%
select(VisitID, SiteName, Watershed, ChUnitNumber, Tier1)
#-----------------------------------------------------------------
# Read in fish data from ODFW, QCI and WDFW.
#-----------------------------------------------------------------
# ODFW
#-----------------------------------------------------------------
# snorkel
odfw_snork = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/ODFW - Basin Data for Kevin.xlsx'),
'SnorkelCount') %>%
rename(SiteUnit = `Site_ID_+Unit`,
SiteName = `Site ID`,
ChUnitNumber = `Unit No.`,
Tier1 = `Unit Type`,
Temp = `H20 Temp`) %>%
mutate(Tier1 = recode(Tier1,
'FNT' = 'Run')) %>%
mutate(Date = if_else(!is.na(Time),
ymd_hms(paste(date(Date), hour(Time), minute(Time), second(Time))),
Date)) %>%
select(-(Tot_Count:ncol(.)), -Time) %>%
gather(species, count, `BT counted`:`CH counted`) %>%
mutate(species = str_replace(species, ' counted', '')) %>%
mutate_at(vars(SiteUnit, SiteName),
list(str_to_upper))
# mark recapture
odfw_mr = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/ODFW - Basin Data for Kevin.xlsx'),
'MarkRecap') %>%
rename(SiteName = `Site ID`,
ChUnitNumber = `Unit No.`,
Tier1 = `Unit Type`,
SampleMethod = Method) %>%
mutate(Tier1 = recode(Tier1,
'FNT' = 'Run')) %>%
select(-`N-hat(chapman)`, -Time) %>%
mutate_at(vars(SiteName),
list(str_to_upper)) %>%
gather(sample, value, `BT Marked`:`CH New`) %>%
mutate(SiteUnit = paste(SiteName, ChUnitNumber, sep = '_'),
species = str_sub(sample, 1, 2),
sample = str_sub(sample, 4),
sample = factor(sample,
levels = c('Marked', 'New', 'Recap'))) %>%
mutate(SampleMethod = str_replace(SampleMethod, 'Efish', 'efish'),
Method = 'MarkRecap') %>%
select(-SampleMethod) %>%
spread(sample, value) %>%
mutate(Capture = New + Recap) %>%
select(SiteName:Method,
Pass1_M = Marked,
Pass2_C = Capture,
Pass3_R = Recap)
# depletions
odfw_depl_org = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/ODFW - Basin Data for Kevin.xlsx'),
'Depletions') %>%
rename(SiteName = `Site ID`,
ChUnitNumber = `Unit No.`,
Tier1 = `Unit Type`) %>%
mutate(Tier1 = recode(Tier1,
'FNT' = 'Run')) %>%
mutate_at(vars(SiteName),
list(str_to_upper)) %>%
mutate(Date = if_else(!is.na(Time),
ymd_hms(paste(date(Date), hour(Time), minute(Time), second(Time))),
Date)) %>%
mutate(Method = 'Depletion',
SiteUnit = paste(SiteName, ChUnitNumber, sep = '_')) %>%
select(-matches('tot$'), -matches('Nhat'), -X, -Y, -Time) %>%
gather(variable, value, matches('Pass')) %>%
mutate(Pass = str_sub(variable, 1, 5),
Pass = str_replace(Pass, 'Pass', '')) %>%
mutate(variable = ifelse(grepl('Effort', variable),
'Effort',
variable),
species = ifelse(variable != 'Effort',
str_sub(variable, -2),
NA)) %>%
mutate(value = as.numeric(value))
# don't worry about effort, not enough data to incorporate that as variable
odfw_depl = odfw_depl_org %>%
filter(variable != 'Effort') %>%
select(-variable) %>%
spread(Pass, value) %>%
rename(Pass1_M = `1`,
Pass2_C = `2`,
Pass3_R = `3`)
# snorkel counts
odfw_snork_only = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/qry_Count_Salmonids_By_Unit_winter.xlsx'),
'Combined_Kevins_Format') %>%
select(Stream:pass3_R) %>%
rename(Pass1_M = pass1_M,
Pass2_C = pass2_C,
Pass3_R = pass3_R,
Tier1 = Tier_1) %>%
mutate(Tier1 = recode(Tier1,
'FNT' = 'Run')) %>%
# use scaled count (the raw fish count divided by % of channel unit sampled. Units were sometimes partially sampled due to ice or extremely shallow water.)
select(-count) %>%
rename(count = `scaled_count*`) %>%
mutate(SurveyType = recode(SurveyType,
'Snorkel' = 'Snorkeling')) %>%
mutate_at(vars(matches('^Pass')),
list(as.numeric))
odfw_data = odfw_snork %>%
select(-Method) %>%
full_join(odfw_mr %>%
bind_rows(odfw_depl) %>%
select(-Date, -Temp)) %>%
mutate(Discharge = NA) %>%
mutate(SurveyType = 'Calibration') %>%
rename(DCEtype = Method) %>%
select(SiteUnit:Date,
Crew, DCEtype, SurveyType, Temp, Discharge,
everything()) %>%
bind_rows(odfw_snork_only) %>%
mutate(Tier1 = recode(Tier1,
'Fast-NonTurbulent' = 'Run',
'Fast-Turbulent' = 'Riffle',
'Slow/Pool' = 'Pool',
'SmSideChnnl' = 'Small Side Channel'))
# what's missing?
odfw_data %>%
summarise_at(vars(everything()),
list(~sum(is.na(.)) / length(.))) %>%
gather(variable, percNA)
#-----------------------------------------------------------------
# QCI
#-----------------------------------------------------------------
qci_2018 = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/QCI_winterQRF_capture.xlsx')) %>%
rename(ChUnitNumber = ChUnitNum,
Stream = StreamName,
Date = SurveyDateTime,
Temp = `Temp(°C)`,
Discharge = `Discharge(m3/s)`,
species = Species,
count = SnorkelCount,
SurveyType = Method,
Pass2_C = C,
Pass3_R = R) %>%
mutate(SiteUnit = paste(SiteName, ChUnitNumber, sep = '_'),
Discharge = as.numeric(Discharge),
Crew = 'QCI',
DCEtype = recode(DCEtype,
'MR' = 'MarkRecap'),
species = recode(species,
'Chinook' = 'CH',
'Steelhead' = 'OM'),
Tier1 = ifelse(Tier2 == 'Fast Non-turbulent',
'Run',
ifelse(Tier2 == 'Riffle',
'Riffle',
ifelse(Tier2 %in% c('Pool/Off Channel', 'Side Channel'),
'Small Side Channel',
'Pool')))) %>%
select(one_of(names(odfw_data)), PercentIceCover, Tier2) %>%
select(SiteUnit:Discharge, PercentIceCover, Tier2, everything())
# for surveys with missing discharge data, use discharge from other surveys at the same site
qci_discharge = qci_2018 %>%
select(Date, Stream, SiteName, SurveyType, Discharge) %>%
mutate(Date = floor_date(Date, unit = 'day')) %>%
distinct()
qci_2018 %<>%
left_join(qci_discharge %>%
group_by(Stream, SiteName) %>%
summarise(nTot = n_distinct(Date),
nNonNA = n_distinct(Date[!is.na(Discharge)])) %>%
left_join(qci_discharge %>%
filter(!is.na(Discharge)) %>%
group_by(SiteName) %>%
summarise_at(vars(Discharge),
list(min = min,
median = median,
mean = mean,
max = max))) %>%
ungroup() %>%
filter(nTot > nNonNA,
nNonNA > 0) %>%
select(SiteName, meanDis = mean)) %>%
mutate(Discharge = if_else(is.na(Discharge),
meanDis,
Discharge)) %>%
select(-meanDis)
# # what's missing?
# qci_2018 %>%
# summarise_at(vars(everything()),
# list(~sum(is.na(.)) / length(.))) %>%
# gather(variable, percNA)
#
# xtabs(~ DCEtype + SurveyType + is.na(count), qci_2018)
# get QCI/ABS data from 2018-2019
qci_2019 = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/winterQRF_18to19_capture.xlsx')) %>%
rename(ChUnitNumber = HabitatUnit,
Stream = StreamName,
Date = SampledDate,
Tier2 = HabitatType,
species = Species,
SurveyType = CaptureMethod,
Pass1_M = M,
Pass2_C = C,
Pass3_R = R) %>%
mutate_at(vars(ChUnitNumber, starts_with('Pass')),
list(as.numeric)) %>%
mutate(SiteName = str_remove(SiteName, '_QRF2018$'),
SiteName = str_remove(SiteName, '_QRF2019$'),
SiteName = str_replace(SiteName,
'^LEM-',
'CBW05583-'),
SiteName = str_replace(SiteName,
'^SFS-',
'CBW05583-'),
SiteName = if_else(SiteName == 'LittleSprings1',
paste0('LEM00002-', SiteName),
SiteName),
SiteName = if_else(grepl('Big0Springs', SiteName),
paste0('LEM00001-', SiteName),
SiteName),
SiteName = if_else(grepl('CBW05583-181535', SiteName),
'CBW05583-181535',
SiteName)) %>%
mutate(SiteUnit = paste(SiteName, ChUnitNumber, sep = '_'),
Crew = 'QCI',
DCEtype = if_else(is.na(Pass2_C),
'Count',
'MarkRecap'),
species = recode(species,
'Chinook' = 'CH',
'Steelhead' = 'OM'),
Tier1 = ifelse(Tier2 %in% c('Fast Non-turbulent', 'FNT', 'Run'),
'Run',
ifelse(Tier2 %in% c('Riffle', 'LSC Riffle'),
'Riffle',
ifelse(Tier2 == 'Rapid',
'Rapid',
ifelse(Tier2 %in% c('OCA', 'SC', 'SSC'),
'Small Side Channel',
'Pool'))))) %>%
select(one_of(names(odfw_data)), Tier2) %>%
select(SiteUnit:Discharge, Tier2, everything())
qci_2019 %>%
select(Site = SiteName, Date) %>%
distinct() %>%
anti_join(champ_site_2011_17 %>%
select(Site) %>%
distinct())
qci_data = qci_2018 %>%
bind_rows(qci_2019)
#-----------------------------------------------------------------
# WDFW
#-----------------------------------------------------------------
# Flow data
wdfw_flow = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/WDFW Night Snorkel Data.xlsx'),
'Flow',
range = 'O4:Q39') %>%
rename(Stream = 1,
VisitID = `Site ID`,
FlowCFS = `Flow CFS`) %>%
filter(!grepl('_2$', VisitID)) %>%
mutate(Discharge = conv_unit(FlowCFS,
'ft3_per_sec',
'm3_per_sec'),
VisitID = as.integer(str_replace(VisitID, '_1$', ''))) %>%
select(-FlowCFS) %>%
full_join(read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/WDFW Night Snorkel Data.xlsx'),
'Flow',
range = 'A1:D632') %>%
rename(VisitID = `Stream ID`) %>%
distinct() %>%
mutate(Date = if_else(!is.na(Time),
ymd_hms(paste(date(Date), hour(Time), minute(Time), second(Time))),
Date)) %>%
select(-Time) %>%
filter(!grepl('_2$', VisitID)) %>%
mutate(VisitID = as.integer(str_replace(VisitID, '_1$', '')))) %>%
select(FlowDate = Date, everything())
# snorkel
wdfw_snork = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/WDFW Night Snorkel Data.xlsx'),
'Raw Snorkel Data') %>%
select(Date,
Time = `Start Time`,
Temp = `Water Temp C`,
Snorkelers:Stream,
Watershed = Basin,
VisitID = `Visit ID`,
SiteID = `Site ID`,
SiteLength = `Site L (m)`,
NumSnork = `# Snrk`,
ChUnit = `Unit #`,
Tier2 = Type,
PercSnork = `% Snrk`,
Visibility = `Vis (0-3)`,
ChUnitNotes = `Channel Unit Notes`,
SiteNotes = `Site Notes`,
`BT <80`:`Sculpin`) %>%
mutate(Date = if_else(!is.na(Time),
ymd_hms(paste(date(Date), hour(Time), minute(Time), second(Time))),
Date)) %>%
select(-matches('Total'), -matches('^WH'), -Time) %>%
mutate(ChUnitNumber = str_replace(ChUnit, '[[:alpha:]]', '')) %>%
mutate_at(vars(Temp),
list(as.numeric)) %>%
filter(PercSnork > 0,
NumSnork > 0) %>%
# correct a few dates
mutate(Date = if_else(VisitID == 4806,
min(Date[VisitID == 4806]),
Date)) %>%
left_join(wdfw_flow) %>%
mutate(diff = abs(as.numeric(difftime(Date, FlowDate, units = 'days'))),
Discharge = ifelse(diff > 7,
NA,
Discharge)) %>%
select(-diff, -`Lamprey J`, -Sculpin) %>%
gather(spp_size, count, `BT <80`:`CH >100`) %>%
mutate(species = str_extract(spp_size, '[:alpha:]+'),
species = factor(species,
levels = c('CH', 'OM', 'BT')),
sizeCls = str_replace(spp_size, '[:alpha:]+', ''),
sizeCls = str_trim(sizeCls),
sizeCls = factor(sizeCls,
levels = c('<100', '100+', '<80', '80-129', '130-199', '200+'))) %>%
select(-spp_size) %>%
mutate(count = count / (PercSnork / 100)) %>%
select(Date:SiteNotes, ChUnitNumber, Discharge, species, sizeCls, count) %>%
mutate_at(vars(count),
list(~ as.integer(round(.)))) %>%
mutate(count = ifelse(is.na(count),
0, count))
# WDFW mark-recapture / snorkel calibration surveys
# by size class
wdfw_mr_size_cls = read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/WDFW Night Snorkel Data.xlsx'),
'Correction Factor',
range = 'S40:BP60') %>%
rename(Stream = 1,
VisitID = 2,
Tier2 = 3) %>%
select(-starts_with('Total'), -15, -27, -39) %>%
mutate(id = 1:n()) %>%
gather(variable, value, -(Stream:Tier2), -id) %>%
mutate(sizeCls = str_remove(variable, '\\.\\.[:digit:]+'),
index = str_split(variable, '\\.\\.', simplify = T)[,2],
index = as.integer(index),
spp = ifelse(sizeCls %in% c('<100', '100+'),
'CH',
ifelse(index %in% c(6:9, 18:21, 30:33, 42:45),
'OM',
'BT')),
pass = ifelse(spp == 'CH',
ifelse(index %in% c(4,5),
'count',
ifelse(index %in% c(16, 17),
'Pass1_M',
ifelse(index %in% c(28,29),
'Pass3_R',
ifelse(index %in% c(40,41),
'Pass2_unmarked',
NA)))),
NA),
pass = ifelse(spp == 'OM',
ifelse(index %in% c(6:9),
'count',
ifelse(index %in% c(18:21),
'Pass1_M',
ifelse(index %in% c(30:33),
'Pass3_R',
ifelse(index %in% c(42:45),
'Pass2_unmarked',
pass)))),
pass),
pass = ifelse(spp == 'BT',
ifelse(index %in% c(10:13),
'count',
ifelse(index %in% c(22:25),
'Pass1_M',
ifelse(index %in% c(34:37),
'Pass3_R',
ifelse(index %in% c(46:49),
'Pass2_unmarked',
pass)))),
pass)) %>%
mutate(pass = factor(pass),
pass = fct_relevel(pass, 'count'),
spp = factor(spp,
levels = c('CH', 'OM', 'BT')),
sizeCls = factor(sizeCls,
levels = c('<100', '100+', '<80', '80-129', '130-199', '200+'))) %>%
select(-variable, -index) %>%
arrange(id, Stream, VisitID, spp, pass, sizeCls) %>%
spread(pass, value) %>%
mutate(Pass2_C = Pass2_unmarked + Pass3_R) %>%
select(Stream:Tier2, id, species = spp, sizeCls, count, Pass1_M, Pass2_C, Pass3_R) %>%
arrange(id, VisitID, species, sizeCls)
# lump across size classes
wdfw_mr = wdfw_mr_size_cls %>%
group_by(Stream, VisitID, Tier2, id, species) %>%
summarise_at(vars(count:Pass3_R),
list(sum)) %>%
ungroup() %>%
mutate(VisitID = as.integer(str_sub(VisitID, 1, 4))) %>%
mutate(Tier2 = recode(Tier2,
'Fast Non Turb' = 'Run')) %>%
select(-id) %>%
left_join(read_excel(paste0(nas_prefix, 'data/qrf/fish/winter/WDFW Night Snorkel Data.xlsx'),
'Correction Factor',
range = 'A1:Q392') %>%
select(Date,
Time = `Start Time`,
Temp = `Water Temp C`,
Stream,
Watershed = Basin,
VisitID = `Site ID`,
ChUnitNumber = `Unit #`,
Tier2 = Habitat,
species = Species) %>%
mutate(Date = if_else(!is.na(Time),
ymd_hms(paste(date(Date), hour(Time), minute(Time), second(Time))),
Date)) %>%
mutate(Date = if_else(year(Date) > 2018 & month(Date) > 7,
ymd_hms(paste('2017', month(Date), mday(Date), hour(Date), minute(Date), second(Date))),
Date)) %>%
select(-Time) %>%
group_by(Date, Watershed, Stream, VisitID, ChUnitNumber, Tier2, Temp, species) %>%
summarise(Pass1_M = n()) %>%
ungroup()) %>%
select(Watershed, Stream, VisitID, ChUnitNumber, Tier2, Date, Temp, everything()) %>%
arrange(VisitID, ChUnitNumber, species) %>%
filter(!is.na(Watershed))
wdfw_data = wdfw_snork %>%
group_by(Watershed, Stream, VisitID, ChUnitNumber, Date, Temp, Discharge, species) %>%
summarise_at(vars(count),
list(sum),
na.rm = T) %>%
ungroup() %>%
mutate(SurveyType = 'Snorkeling',
DCEtype = 'Count') %>%
left_join(wdfw_snork %>%
mutate(Tier2 = recode(Tier2,
'OC' = 'Off Channel',
'PP' = 'Plunge Pool',
'SP' = 'Scour Pool',
'RA' = 'Rapid',
'RI' = 'Riffle',
'SSC' = 'Small Side Channel')) %>%
select(Stream, VisitID, ChUnitNumber, Tier2) %>%
group_by(Stream, VisitID, ChUnitNumber) %>%
slice(1) %>%
ungroup()) %>%
bind_rows(wdfw_mr %>%
left_join(wdfw_flow) %>%
mutate(diff = abs(as.numeric(difftime(Date, FlowDate, units = 'days'))),
Discharge = ifelse(diff > 7,
NA,
Discharge)) %>%
select(-diff, -FlowDate) %>%
mutate(DCEtype = 'MarkRecap',
SurveyType = 'Calibration')) %>%
left_join(cu_data %>%
mutate_at(vars(ChUnitNumber),
list(as.character)) %>%
select(-Watershed) %>%
bind_rows(cu_data %>%
filter(VisitID == 4816,
ChUnitNumber %in% c(7,8)) %>%
select(VisitID, SiteName, Tier1) %>%
distinct() %>%
mutate(ChUnitNumber = '7&8'))) %>%
mutate(SiteUnit = paste(SiteName, ChUnitNumber, sep = '_'),
# Tier1 = ifelse(Tier2 == 'Run',
# 'Run',
# ifelse(Tier2 %in% c('Riffle', 'RA', 'RI'),
# 'Riffle',
# ifelse(grepl('Pool', Tier2),
# 'Pool',
# Tier2))),
Crew = 'WDFW') %>%
select(one_of(names(qci_data))) %>%
arrange(Stream, SiteName, ChUnitNumber, species)
#------------------------------------------------------
# Combine everything and save as a .csv file.
fish_win_data = odfw_data %>%
mutate(Crew = 'ODFW') %>%
bind_rows(qci_data) %>%
mutate_at(vars(ChUnitNumber),
list(as.character)) %>%
bind_rows(wdfw_data %>%
mutate(PercentIceCover = NA)) %>%
# make stream names match CHaMP stream names
mutate(Stream = recode(Stream,
'Yankee Fork River' = 'Yankee Fork',
'Chewuch' = 'Chewuch River',
'Chikamin' = 'Chikamin Creek',
'Chiwawa' = 'Chiwawa River',
'Entiat' = 'Entiat River',
'Little Wenatchee' = 'Little Wenatchee River',
'Mad' = 'Mad River',
'Methow' = 'Methow River',
'Napeequa' = 'Napeequa River',
'Nason' = 'Nason Creek',
'Stormy' = 'Stormy Creek',
'Twisp' = 'Twisp River',
'White' = 'White River')) %>%
mutate(Stream = if_else(SiteName == 'CBW05583-087698',
'Secesh River',
Stream)) %>%
left_join(cu_data %>%
select(SiteName, Watershed) %>%
distinct()) %>%
select(Site = SiteName,
Watershed, Stream,
SiteUnit, ChUnitNumber,
FishCrew = Crew,
SurveyType, DCEtype,
Date,
Tier1, Tier2, Discharge, Temp, PercentIceCover,
everything()) %>%
mutate(Tier2 = recode(Tier2,
'Fast Non-turbulent' = 'Run',
'Fast-NonTurbulent' = 'Run',
'SmSideChnnl' = 'Small Side Channel'),
Tier1 = recode(Tier1,
'SC' = 'SSC',
'Small Side Channel' = 'SSC')) %>%
rename(Species = species) %>%
mutate(Species = recode(Species,
'CH' = 'Chinook',
'OM' = 'Steelhead',
'BT' = 'BrookTrout')) %>%
filter(Species != 'No Data') %>%
# rename some columns to better match summer data
rename(Method = DCEtype,
Pass1.M = Pass1_M,
Pass2.C = Pass2_C,
Pass3.R = Pass3_R) %>%
mutate(Method = recode(Method,
'MarkRecap' = 'Mark Recapture'),
Method = if_else(Method == 'Count',
if_else(SurveyType == 'Snorkeling',
'Snorkel',
'Single Pass'),
Method),
Watershed = if_else(is.na(Watershed) & Stream == 'Lemhi River',
'Lemhi',
as.character(Watershed)),
Watershed = if_else(is.na(Watershed) & grepl('^DSGN4', Site),
'Upper Grande Ronde',
as.character(Watershed))) %>%
mutate_at(vars(Watershed, Stream, Method, SurveyType, FishCrew, Tier1, Tier2, Species),
list(fct_drop))
# save as csv file
write_csv(fish_win_data,
'data/prepped/fish_data_winter_prepped.csv')
# save to use as data
use_data(fish_win_data,
version = 2,
overwrite = T)
#------------------------------------------------------
# anything missing?
fish_win_data %>%
summarise_at(vars(everything()),
list(~sum(is.na(.)) / length(.))) %>%
gather(variable, percNA) %>%
arrange(desc(percNA))
xtabs(~ is.na(Pass2.C) + is.na(Pass1.M), fish_win_data)
xtabs(~ is.na(count) + is.na(Pass1.M), fish_win_data)
xtabs(~ is.na(Temp) + FishCrew, fish_win_data)
fish_win_data %>%
filter(is.na(Pass1.M)) %>%
xtabs(~ SurveyType, .)
fish_win_data %>%
filter(is.na(count)) %>%
xtabs(~ SurveyType, .)
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