EPSCoR SCTC Aquatic Ecology
Create input files for “bioenerg_results_analysis.Rmd” script
R Session Info
sessionInfo()
## R version 3.4.3 (2017-11-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.3
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_3.4.3 backports_1.1.2 magrittr_1.5 rprojroot_1.3-2
## [5] tools_3.4.3 htmltools_0.3.6 yaml_2.1.16 Rcpp_0.12.15
## [9] stringi_1.1.6 rmarkdown_1.8 knitr_1.19 stringr_1.2.0
## [13] digest_0.6.15 evaluate_0.10.1
#require packages
library(googlesheets)
library(tidyverse)
library(forcats)
library(rmarkdown)
library(lubridate)
library(anytime)
suppressMessages(library(dplyr))
#detach("package:MASS", unload=TRUE) #pkg masks some dplyr functions
# Clear environment
rm(list=ls())
# a.) Temperature.csv; cols: day (DOY), temperature (C)
# b.) Predator.csv; cols: day (DOY), predator (j/g)
# c.) Prey.csv; cols: day (DOY), diet1, diet2, diet3... (energy densities)
# d.) Indigestible_Prey.csv, cols: day (DOY), diet1, diet2, diet3... (proportion indigestable)
# e.) Diet_Prop.csv; cols: day (DOY), diet1, diet2, diet3... (diet proportions)
# create folder w/ simulation name
# create sub folder named Main Inputs
# create files
# -- get daily mean temps for each water temperature file
# -- look up "predator" ED values for coho and chinook
# -- import "prey" ED values from googlesheet
# -- look up indigestability values for preycats
# -- get diet props for each year/spp/river/season
# export files to directory
# struggling to automate the process of reading in final csv temperature files from local folder. Accomplished with manual read.csv for now
dir15 <-"/Users/bmeyer/Google Drive/Thesis/R Analysis/Temperature Data/Final Temperature Data/2015/"
dir16 <-"/Users/bmeyer/Google Drive/Thesis/R Analysis/Temperature Data/Final Temperature Data/2016/"
BC15 <- read.csv(paste0(dir15,"2015_","LBC","_H2OTemp_Final",".csv"))
RR15 <- read.csv(paste0(dir15,"2015_","LRR","_H2OTemp_Final",".csv"))
PC15 <- read.csv(paste0(dir15,"2015_","LPC","_H2OTemp_Final",".csv"))
LKR15 <- read.csv(paste0(dir15,"2015_","LKR","_H2OTemp_Final",".csv"))
BC16 <- read.csv(paste0(dir16,"2016_","LBC","_H2OTemp_Final",".csv"))
RR16 <- read.csv(paste0(dir16,"2016_","LRR","_H2OTemp_Final",".csv"))
PC16 <- read.csv(paste0(dir16,"2016_","MPC","_H2OTemp_Final",".csv"))
LKR16 <- read.csv(paste0(dir16,"2016_","LKR","_H2OTemp_Final",".csv"))
# bind all temperature files together
# binding and transformation performed seperately for 2015 and 2016 because for some reason POSIX format of DateTime column between the years is incompatible... (2/8/2018)
fnl_temps15 <- bind_rows(BC15,RR15,PC15,LKR15) %>%
transform(DateTime = mdy_hm(DateTime)) %>%
mutate(year = year(DateTime)) %>%
transform(Stream_Name= as.factor(Stream_Name),
year = as.factor(year),
Date = mdy(Date)) %>%
select(-X,-X.1,-X.2,-X.3)
fnl_temps16 <- bind_rows(BC16,RR16,PC16,LKR16) %>%
# something weird here eliminates 2016 datetime data
transform(DateTime = as.POSIXct(DateTime)) %>%
mutate(year = year(DateTime)) %>%
transform(Stream_Name= as.factor(Stream_Name),
year = as.factor(year),
Date = as.Date(Date)) %>%
select(-X)
# bind together 2015 and 2016
fnl_temps <- bind_rows(fnl_temps15,fnl_temps16) %>%
mutate(Stream_Name_Date = paste0(Stream_Name,"_",Date))
# calculate daily mean temps
daily_mean_temps <- fnl_temps %>%
filter(!is.na(Temp_C)) %>%
group_by(Stream_Name,Date) %>%
summarise(avg_temp = mean(Temp_C)) %>%
select(Date,Stream_Name,avg_temp)
# remove temporalry objects
rm(fnl_temps15,fnl_temps16,BC15,RR15,PC15,LKR15,BC16,RR16,PC16,LKR16,dir15,dir16)
#########################
# #
# B.) Diet Input #
# #
#########################
# process 2015 & 2016 data simultaneously; separate as needed.
# arrange to identical structures then combine before proceeding
# 1.) Import and arrange data
# 2015 Diet Data: Import & Arrange
#create objects from overall diet data google sheets
SCTC2015 <- gs_title("2015 EPSCoR SCTC.xlsx")
SCTC2016 <- gs_title("2016_EPSCoR_Aquatic_Ecology_Database")
#read in worksheets containing diet data
diet15 <- SCTC2015 %>%
gs_read(ws = "2015 Diet Contents Data",col_names=TRUE) %>%
select(Sample.ID,
Fish_Species,
Sample.Event,
sample.event.num,
River,
Reach,
Site,
Sample_Date,
Prey_Type_Used,
PreyCategory,
Quantity,
Total_Prey_Dry_Mass_mg) %>%
mutate(year = "2015") %>%
transform(sample.event.num = as.integer(sample.event.num),
Quantity = as.numeric(Quantity),
Total_Prey_Dry_Mass_mg = as.numeric(Total_Prey_Dry_Mass_mg)) # columns import as character for some reason unless specified! (weird but innocuous quirk)
diet16 <- SCTC2016 %>%
gs_read(ws = "2016 Diet Contents Data",col_names=TRUE) %>%
select(Sample.ID,
Fish_Species,
Sample.Event,
sample.event.num,
River,
Reach,
Site,
Sample_Date,
Prey_Type_Used,
PreyCategory,
Quantity,
Total_Prey_Dry_Mass_mg) %>%
mutate(year = "2016")
# combine 2015 and 2016 diet data
diet <- bind_rows(diet15, diet16)
# finalize structure of diet data
diet <- diet %>%
#rename needed columns
rename(sample.id = Sample.ID,
spp = Fish_Species,
sample.event = Sample.Event,
event.num = sample.event.num,
river = River,
reach = Reach,
site = Site,
date = Sample_Date,
prey = Prey_Type_Used,
preycat = PreyCategory,
quantity = Quantity,
dm_mg = Total_Prey_Dry_Mass_mg) %>%
#select desired columns
select(sample.id,
spp,
sample.event,
event.num,
river,
reach,
site,
date,
year,
prey,
preycat,
quantity,
dm_mg) %>%
#filter out non-diet samples and blank data
filter(site!="DR1",
!is.na(spp),
!is.na(sample.event),
!is.na(prey),
!is.na(dm_mg),
!is.na(quantity),
!is.na(event.num)) %>%
#transform columns to desired classes
transform(dm_mg = as.numeric(dm_mg),
sample.event = as.factor(sample.event),
event.num = as.factor(event.num),
date = mdy(date),
spp = as.factor(spp),
quantity = as.numeric(quantity),
preycat = as.factor(preycat)) %>%
mutate(season = fct_recode(event.num, #code sampling events as seasons
"Early Summer"= "1",
"Mid-Summer" = "2",
"Late Summer" = "3",
"Fall" = "4"))
# create prey categorization scheme specific to bioenergetics modeling purposes
# -import from google sheet "EPSCoR_SCTC_Prey_Energy_Densities" and mutate new column to diet data
# -this sheet also contains energy density assignments, which must be manually input in "fishbioenergetics.r"
# at a later step.
Bioenergetics_ED <- gs_title("EPSCoR_SCTC_Prey_Energy_Densities")
ED <- Bioenergetics_ED %>%
gs_read(ws = "energy_densities", col_names = TRUE) %>%
rename(preycat = PreyCategory,
preycat_ed = PreyCategory_ED) %>%
transform(preycat = as.factor(preycat),
preycat_ed = as.factor(preycat_ed))
# amend diet data with prey categories that will be used
diet_fnl <- left_join(diet,ED,by = "preycat") %>%
select(sample.id,prey,dm_mg,diet,preycat_ed,Energy_Density_Jg_ww)
# remove temporary objects
rm(diet15,diet16,ED)
# Import resultant data from "fish_ages_all.R" script. The script uses methods described in Ch. 5 of Ogle 2016 ("Introductory fisheries analyses with R") to age all captured fish from a subset of fish scale samples. Resultant csv file was copied to the google sheet referenced here.
# ID googlesheet with fish age data
fish_ages <- gs_title("all_fish_ages.csv")
#read in worksheet containing diet data
fish_ages <- fish_ages %>%
gs_read(ws = "all_fish_ages.csv",col_names=TRUE) %>%
select (-X1) %>%
separate(sample.event, c("reach","year","sample.event.num"), sep = "-", remove = F) %>%
unite(river_spp, river, spp, remove = F, sep = " ") %>%
# manually order appearance of seasons and rivers for plots
transform(year = as.factor(year),
river = factor(river, levels=c("Beaver Creek",
'Russian River',
'Ptarmigan Creek',
'Kenai River')),
season = factor(season, levels = c("Early Summer",
"Mid-Summer",
"Late Summer",
"Fall")),
river_spp = as.factor(river_spp)) %>%
mutate(river_spp_yr = paste(year,river,"\n",spp)) %>%
# age 2 fish: this cohort is sparse and likely departing for the ocean.
# filter(age != 2) %>%
# mutate column for yes/no diet sampled
mutate(diet_sample= ifelse(!is.na(sample.id), "Y", "N")) %>%
mutate(Count = 1) %>%
transform(Date = mdy(Date)) %>%
mutate(doy = yday(Date)) %>%
# filter all except coho and chinook
filter(spp %in% c("Chinook", "Coho"))
#eliminate duplicate rows
fish_ages <- unique(fish_ages)
# join fish ages date with diet data
diet <- left_join(diet_fnl, fish_ages, by = "sample.id" )How many fish have diet samples, but no age designation?
# How many fish have diet samples, but no age designation?
diet_no_age <- diet %>%
filter(is.na(age)) %>%
distinct(sample.id)
diet_no_age <- nrow(diet_no_age)
#exclude these individuals
diet <- diet %>%
filter(!is.na(age))There are 17 fish with diet samples but no age assigned. These 17 fish must have both two conditions true:
Fish was not part of a river/year/spp where an age-length key could be developed.
Fish did not have a read-able scale.
We will exclude these 17 fish from analyses… for now, because we are running simulations seperately by age year/river/spp/age. Fish must have an age.
#create sum of pooled prey DM for each prey category by fish species and sample event
dm_sum_by_event <- diet %>%
group_by(year,river,spp,preycat_ed,season,age) %>%
summarise(sum(dm_mg)) %>%
rename(dm_sum =`sum(dm_mg)`) %>%
spread(preycat_ed,dm_sum) %>%
rowwise() %>%
mutate(dm_tot = sum(FishEggs, # create column for summed dry mass of all prey categories
InvertAquatic_AqOrigin,
InvertTerrestrial,
InvertTerrestrial_AqOrigin,
InvertUnknown,
SalmonEggs,
na.rm = TRUE)) %>%
mutate(FishEggs = FishEggs/dm_tot,
# change prey quantities to relative proportions by category
InvertAquatic_AqOrigin = InvertAquatic_AqOrigin/dm_tot,
InvertTerrestrial = InvertTerrestrial/dm_tot,
InvertTerrestrial_AqOrigin = InvertTerrestrial_AqOrigin/dm_tot,
InvertUnknown = InvertUnknown/dm_tot,
SalmonEggs = SalmonEggs/dm_tot) %>%
transform(year = as.factor(year)) %>%
select(-X.NA.) %>%
rename(Stream_Name = river)d.) Define temporal extents of proposed simulations
# Define temporal extents of proposed simulations
# NOTE 1/22/18: this code also exists as a script at "/Users/bmeyer/Google Drive/Thesis/R Analysis/Thesis Analyses R Project/Overall_scripts_2015_2016/sampling_periods.R". Attempted to just source("\path") this script in to the markdown document but, 'source' function prevents knitting through markdown for some reason.
############
#
# 2015
#
###########
# Verify that sheet is present, create object of sheet with all tabs
SCTC2015 <- gs_title("2015 EPSCoR SCTC.xlsx")
#read in specific worksheet containing fishing data
effort2015 <- SCTC2015 %>%
gs_read(ws = "B Fishing Data",col_names=TRUE, skip=2) %>%
#rename needed columns
rename(site.id=Site_ID,
sample.event=Sample_Event) %>%
#select desired columns
select(site.id,River,sample.event,sample.event.num,deploy_dt,collect_dt) %>%
#filter out blanks
filter(!is.na(sample.event)) %>%
# transform columns to desired classes
transform(site.id=as.factor(site.id),
sample.event=as.factor(sample.event),
sample.event.num=as.factor(sample.event.num))
###################
#
# 2016
#
###################
# Verify that sheet is present, create object of sheet with all tabs
SCTC2016 <- gs_title("2016_EPSCoR_Aquatic_Ecology_Database")
#read in specific worksheet containing fishing data
effort2016 <- SCTC2016 %>%
gs_read(ws = "B Fishing Data",col_names=TRUE, skip=2) %>%
#rename needed columns
rename(site.id=Site_ID,
sample.event=Sample_Event) %>%
#select desired columns
select(site.id,River,sample.event,sample.event.num,deploy_dt,collect_dt) %>%
#filter our blanks
filter(!is.na(sample.event)) %>%
# transform columns to desired classes
transform(site.id=as.factor(site.id),
sample.event=as.factor(sample.event),
sample.event.num=as.factor(sample.event.num))
# bind 2015 & 2016 data; format datetimes
effort <- bind_rows(effort2015,effort2016) %>%
transform(collect_dt = anytime(collect_dt),
deploy_dt = anytime(deploy_dt)) %>%
mutate(year = year(collect_dt))
# Define Temporal Extents of proposed simulations
# "Simulation" temporal extent
# Time period from first date of each season to first date of following season
# prepare data to tibble form
sim_extents <- effort %>%
select(-site.id) %>%
group_by(River, year, sample.event.num) %>%
nest() %>%
mutate(season.start = map_dbl(data, ~min(.$deploy_dt)),
season.end = map_dbl(data, ~min(.$deploy_dt))
) %>%
transform(season.start = anytime(season.start),
season.end = anytime(season.end)) %>%
arrange(River,year,sample.event.num)
# prepare data such that end of one season is beginning of next
t <- sim_extents %>%
mutate(season.end_x = lead(season.end)) %>%
select(-season.end) %>%
arrange(River,year,sample.event.num)
# prepare data such that final season of each year has duration of 30 days.
# 2015 had 3 seasons, 2016 had 4 seasons.
t_end15 <- t %>%
filter(sample.event.num == 3,
year == 2015) %>%
mutate(season.end = season.start + days(30)) %>%
select(-season.end_x)
t_end16 <- t %>%
filter(sample.event.num == 4,
year == 2016) %>%
mutate(season.end = season.start + days(30)) %>%
select(-season.end_x)
t_end15_1 <- t %>%
filter(sample.event.num != 3,
year != 2016) %>%
rename(season.end = season.end_x)
t_end16_1 <- t %>%
filter(sample.event.num != 4,
year != 2015) %>%
rename(season.end = season.end_x)
# final version of simulation extents
sim_extents <- bind_rows(t_end15_1,t_end15,t_end16_1,t_end16) %>%
rename(Stream_Name = River,
sim.start = season.start,
sim.end = season.end) %>%
transform(year = as.factor(year)) %>%
mutate(season = fct_recode(as.factor(sample.event.num), #code sampling events as seasons
"Early Summer"="1",
"Mid-Summer" = "2",
"Late Summer" ="3",
"Fall" = "4")) %>%
select(-data)
# remove temporary objects
rm(t,t_end15,t_end15_1,t_end16,t_end16_1,effort2015,effort2016)
# make plot that visualizes extent of each simulation period faceted by year and river
# simulation extents all run 30 days forward from 1st sampling date in season
sim_extents_mod <- sim_extents %>%
mutate(sim.start.doy = yday(sim.start),
sim.end.doy = yday(sim.end),
year = year(sim.start))
theme_bm <- theme(axis.text.x = element_text(size=18),
axis.text.y = element_text(size=18), # x axis text
axis.title.x = element_text(size=18, face = "bold"), # x axis title
axis.title.y = element_text(size=18, face = "bold"),
strip.text.x = element_text(size = 16, face = "bold"),
legend.text = element_text(size = 16))
# test plot
s <- ggplot(sim_extents_mod, aes(x=sim.start.doy, xend = sim.end.doy,
y=as.factor(year), yend=as.factor(year), color = season)) +
theme_bw() +
ggtitle("Bioenergetics Simulation Periods") +
geom_segment(aes(color = season), size = 10) +
facet_wrap(~Stream_Name) +
xlab("Day of Year") +
ylab("Year") +
theme(plot.title = element_text(size= 24, face = "bold", hjust = 0.5)) +
theme(legend.title=element_text(size=24)) +
guides(color = guide_legend(override.aes = list(size=8))) +
scale_color_manual(name="Season",
breaks = c("Early Summer",
"Mid-Summer",
"Late Summer",
"Fall"),
values=c("red", "purple", "turquoise", "green")) +
#
theme_bm
s
# match temporal extent of simulated diet proportion data to simulation date if it exists.
diet_prop_seasons <- left_join(dm_sum_by_event,sim_extents,by=c("Stream_Name","season","year")) %>%
mutate(days = round(sim.end-sim.start))
# extend diet props down length of season
sim_diet_days <- diet_prop_seasons[rep(seq_len(nrow(diet_prop_seasons)),diet_prop_seasons[,"days"]), ]
# add a row # ("SimDay") per row within each desired sim group,
sim_diet_days <- sim_diet_days %>%
mutate(sim_name = paste0(year,"_",
season,"_",
Stream_Name,"_",
spp,"_",
"Age","_",
age)) %>%
group_by(sim_name) %>%
mutate(SimDay = row_number()) %>%
mutate(Date = sim.start + days(SimDay)) %>%
transform(Date = date(Date))
# join daily mean temperatures with diet proportions
sim_diet_days <- left_join(sim_diet_days,daily_mean_temps,by=c("Date","Stream_Name"))
# arrange and rename columns to format identical as in "InputFileTemplate" for bioenergetics modeling
sim_diet_days <- sim_diet_days %>%
rename(Diet1 = FishEggs,
Diet2 = InvertAquatic_AqOrigin,
Diet3 = InvertTerrestrial,
Diet4 = InvertTerrestrial_AqOrigin,
Diet5 = InvertUnknown,
Diet6 = SalmonEggs,
Temp = avg_temp) %>%
# add in blank columns 7-10 to match InputFileTemplate format
mutate(Diet7 = 0,
Diet8 = 0,
Diet9 = 0,
Diet10 = 0) %>%
# select desired columns
select(Date,SimDay,Temp,
contains("Diet"),
sim_name,spp) %>%
# ensure that list of dataframes and list of dataframe names match up order in next step
arrange(sim_name) %>%
# round decimals to two digits
mutate_if(is.numeric, funs(round(., 1))) %>%
# filter out simulation days with no temperature data; trim lengths of sims to days w/ sims
filter(!is.na(Temp))
#replace all NA's with 0's
sim_diet_days[is.na(sim_diet_days)] <- 0
# eliminate simulations where diet inputs are all zeros
# a.) create dataframe that conatins sim names with zero diet content
zero_sims <- sim_diet_days %>%
select(-spp) %>%
group_by(sim_name) %>%
summarise_each(funs(sum)) %>%
# sum across columns with diet proportions
mutate(diet_prop_sum = rowSums(.[3:12])) %>%
select(sim_name, diet_prop_sum) %>%
# filter to create dataframe of simulations with zero diet content
filter(diet_prop_sum == 0)
# export names of simulations with zero diet mass to google sheet for later reference
# code below hastagged out b/c there are already no samples included w/ zero diet mass
## remove any existing googlesheet resulting from previous runs of this script
#zero_sims_gs <- gs_title("zero_diet_mass_sims")
#gs_delete(zero_sims_gs)
## write new sheet with data
#gs_new("zero_diet_mass_sims", input = zero_sims)
# b.) use above table to remove simulatins with zero diet content
#sim_diet_days <- anti_join(sim_diet_days,zero_sims)
# arrange columns in desired order
sim_diet_days <- sim_diet_days %>%
select(Date,SimDay,Temp,Diet1,Diet2,Diet3,Diet4,Diet5,Diet6,Diet7,Diet8,Diet9,Diet10,sim_name,spp)
# create table connecting SimDay, Sim_Name, and Date information
sim_dates_x <- sim_diet_days %>%
select(sim_name,SimDay,Date)
# export table connecting Sim.Day, Sim_Name, and Date information for later use in analysis
sim_dates <- gs_title("sim_dates")
gs_delete(sim_dates)
gs_new("sim_dates", ws_title ="Sheet1", input = sim_dates_x)
# remove Date column from sim_diet_days
sim_diet_days <- sim_diet_days %>%
select(-Date)
write.csv(sim_dates_x, file = "tmp.csv")
# choose data for subset of simulations selected in script "bioenerg_dat_input_selection.Rmd"
# a.) import sheet with list of simulations subsetted (output table from "bioenerg_dat_input_selection.Rmd")
sim_size_inputs_list <- gs_title("sim_size_inputs")
sim_size_inputs_list <- sim_size_inputs_list %>%
gs_read(ws = "lw_data") %>%
select(sim_name)
# b.) Filter table of diet proportion data to include only chosen subset of simulations
sim_diet_days <- semi_join(sim_diet_days,sim_size_inputs_list)
# How many simulations included?
n_sims <- nrow(sim_size_inputs_list)
# restructure table to prep for txt format export
# acquire string of column names for desired final table
colnames_df <- sim_diet_days %>%
ungroup() %>%
select(-spp,-sim_name)
sim_col_names <- toString(names(colnames_df))
# remove commas from character string
sim_col_names <- gsub(",", "", sim_col_names, fixed = TRUE)
# unite content of data columns in to text string
sim_diet_days <- sim_diet_days %>%
# name of new column is "foo", later replaced with string of all col names
unite("foo",SimDay,Temp,
contains("Diet"),
sep = " ")
# assign column names
colnames(sim_diet_days) <- c(sim_col_names,"sim_names","spp")
#recreate grouping variable
sim_diet_days <- sim_diet_days %>%
group_by(sim_names)
# create individual dataframes for each species
sim_diet_days_coho <- sim_diet_days %>%
filter(spp == "Coho") %>%
select(-spp)
sim_diet_days_chnk <- sim_diet_days %>%
filter(spp == "Chinook") %>%
select(-spp)
# split up all data in to lists of individual simulations by year, river, species, age, and season
sim_list_coho <- split.data.frame(sim_diet_days_coho, sim_diet_days_coho$sim_names)
sim_list_chnk <- split.data.frame(sim_diet_days_chnk, sim_diet_days_chnk$sim_names)
# remove column containing name of simulation from all objects in list
sim_list_coho <- lapply(sim_list_coho, function(x) x[!(names(x) %in% c("sim_names"))])
sim_list_chnk <- lapply(sim_list_chnk, function(x) x[!(names(x) %in% c("sim_names"))])
Note: for some reason txt files are not exporting to the proper sub-folder; moved manually after export from here… 2/18/18
# get names of all simulations in a list
sim_names_coho <- sim_diet_days_coho %>%
select(sim_names) %>%
distinct()
sim_names_coho <- as.list(sim_names_coho)
# set working directory to destination for data
setwd("/Users/bmeyer/Google Drive/Thesis/R Analysis/Bioenergetics/coho_biol_input")
# clear out working directory folder if it already has existing files
do.call(file.remove, list(list.files("/Users/bmeyer/Google Drive/Thesis/R Analysis/Bioenergetics/coho_biol_input", full.names = TRUE)))
# write dataframes as individual txt files to working directory
lapply(names(sim_list_coho),
function(x, sim_list_coho)
write.table(sim_list_coho[[x]],
paste(x, ".txt", sep = ""),
col.names=TRUE,
row.names=FALSE,
sep="\t",
quote=FALSE),sim_list_coho)
# individual simulations now saved as txt files in working directory.
# these contain diet proportions for each SimDay and the mean temperature
# at the lower watershed site.
# get names of all simulations in a list
sim_names_chnk <- sim_diet_days_chnk %>%
select(sim_names) %>%
distinct()
sim_names_chnk <- as.list(sim_names_chnk)
# set working directory to destination for data
setwd("/Users/bmeyer/Google Drive/Thesis/R Analysis/Bioenergetics/chnk_biol_input")
# clear out working directory folder if it already has existing files
do.call(file.remove, list(list.files("/Users/bmeyer/Google Drive/Thesis/R Analysis/Bioenergetics/chnk_biol_input", full.names = TRUE)))
# write dataframes as individual txt files to working directory
lapply(names(sim_list_chnk),
function(x, sim_list_chnk)
write.table(sim_list_chnk[[x]],
paste(x, ".txt", sep = ""),
col.names=TRUE,
row.names=FALSE,
sep="\t",
quote=FALSE),sim_list_chnk)
# individual simulations now saved as txt files in working directory.
# these contain diet proportions for each SimDay and the mean temperature
# at the lower watershed site.
Final outcome: diet proportions at scale of year/river/spp/age by day w/ water temp, stored in local directory