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Functions.R
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Functions.R
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# Functions
#_____________________________________________________________________________________________________#
# Clean LCA data
#_____________________________________________________________________________________________________#
clean.lca <- function(LCA_data, replicate_dat = TRUE){
# Change columns to numeric where applicable
LCA_data$Feed_soy_percent <- as.numeric(LCA_data$Feed_soy_percent)
LCA_data$Feed_othercrops_percent <- as.numeric(LCA_data$Feed_othercrops_percent)
LCA_data$Feed_FMFO_percent <- as.numeric(LCA_data$Feed_FMFO_percent)
LCA_data$Feed_animal_percent <- as.numeric(LCA_data$Feed_animal_percent)
LCA_data$Electricity_kwh <- as.numeric(LCA_data$Electricity_kwh)
LCA_data$Diesel_L <- as.numeric(LCA_data$Diesel_L)
LCA_data$Petrol_L <- as.numeric(LCA_data$Petrol_L)
LCA_data$NaturalGas_L <- as.numeric(LCA_data$NaturalGas_L)
LCA_data$Yield_t_per_Ha <- as.numeric(LCA_data$Yield_t_per_Ha)
LCA_data$Yield_kg_per_m3 <- as.numeric(LCA_data$Yield_kg_per_m3)
LCA_data$Grow_out_period_days <- as.numeric(LCA_data$Grow_out_period_days)
LCA_data$FCR_overall <- as.numeric(LCA_data$FCR_overall)
# Add country codes
LCA_data$iso3c <- countrycode(LCA_data$Country, origin = "country.name", destination = "iso3c")
# Remove experimental, polyculture, IAA, tuna, eel, and caviar studies
LCA_data <- LCA_data %>%
filter(Drop_study_flag == "") # ie, filter out Drop_study_flag %in% c(Experimental, Hypothetical, IAA, Polyculture, Tuna)
LCA_data <- LCA_data %>%
# Scale feed percents to sum to 100%
# mutate(sum_percent = Feed_soy_percent+Feed_othercrops_percent+Feed_FMFO_percent+Feed_animal_percent) %>%
# mutate(
# Feed_soy_percent = Feed_soy_percent/sum_percent,
# Feed_othercrops_percent = Feed_othercrops_percent/sum_percent,
# Feed_FMFO_percent = Feed_FMFO_percent/sum_percent,
# Feed_animal_percent = Feed_animal_percent/sum_percent
# ) %>%
# Convert all yield to the same units # FIX IT: Check this is done correctly
mutate(Yield_m2_per_t =
ifelse(m2a_t > 0, m2a_t,
ifelse(m3a_t > 0, m3a_t/1, # Add in average depth when we have this; 1 is a placeholder
ifelse(Yield_t_per_Ha > 0, 1/(Yield_t_per_Ha*1/10000),
ifelse(Yield_kg_per_m3 > 0, 1/(Yield_kg_per_m3*1*(1/1000)), # Add in average depth when we have this; 1 is a placeholder
NA))))) %>%
# Create system group
mutate(
Production_system_group = case_when(
(Production_system %in% c("Extensive raft culture", "Marine floating bag", "Integrated marine rafts", "Longline",
"Bouchot culture", "Wooden stakes", "Long-lines", "Suspended baskets",
"Bottom planted", "On or off bottom")) ~ "On- and off-bottom",
(Production_system %in% c("Intensive lake net-pen", "Marine cages", "Marine net-pen", "Reservoirs", "Lakes",
"Ponds, lakes, and reservoirs", "Lake-based net cages", "Offshore cages", "Floating cages",
"Net-pens", "Net pen", "Freshwater net pen (?)", "Saltwater net pen",
"Semi-intensive cages")) ~ "Cages & pens",
(Production_system %in% c("Intensive pond", "Extensive pond polyculture", "Semi-intensive pond", "Extensive pond",
"Earthen pond aquaculture", "Integrated pond, high input", "Integrated pond, medium inputs",
"Solid-walled aquaculture system", "Earthen pond aquaculture integrated with pigs",
"Earthern ponds", "Earthern/concrete ponds", "Ponds", "Silvo pond", "Lined pond aquaculture",
"Pond aquaculture", "Earthen pond monoculture", "Pond")) ~ "Ponds",
(Production_system %in% c("Indoor recirculating", "Flow-through", "Land-based recirculating", "Onshore tanks",
"Saltwater flow-through", "Freshwater flow-through", "Recirculating system",
"Land-based recirculating system", "Raceway", "Tanks / raceway",
"Semi-closed recirculating system", "Concrete tanks")) ~ "Recirculating and tanks",
(Production_system %in% c("Unspecified", "", "Ponds / recirculating", "Ponds / pens")) ~ "not specified"
)
) %>%
# Convert "not specified" to NA
mutate(Production_system_group = na_if(Production_system_group, "not specified")) %>%
mutate(Intensity = na_if(Intensity, ""))
# Create column clean_sci_name - use this as the "official" scientific name column
# When no valid sci name exists just use common name
# Simplify hybrid M. chrysops x M. saxatilis to its genus
# Change outdated names (P. vannamei and P hypophthalmus)
LCA_data <- LCA_data %>%
mutate(Species.scientific.name = case_when(str_detect(Species.scientific.name, " spp\\.") ~ str_replace(Species.scientific.name, pattern = " spp\\.", replacement = " spp"),
TRUE ~ Species.scientific.name)) %>%
mutate(clean_sci_name = case_when(Species.common.name == "Carps" ~ "Cyprinidae",
Species.common.name == "Common carp (33%), Grass carp (21%), Crucian carp (9%), silver carp (9%), bighead carp (7%), and other carp (21%)" ~ "Cyprinidae",
Species.common.name == "Grass carp (76%), bighead carp (5%), silver carp (8%), and crucian carp (12%)" ~ "Cyprinidae",
Species.common.name == "Freshwater prawn" ~ "Freshwater prawn",
Species.common.name == "Indo-Pacific swamp crab" ~ "Brachyura",
Species.common.name == "River eels nei" ~ "Freshwater eels",
Species.common.name == "Salmonids nei" ~ "Salmonidae",
Species.common.name == "Striped bass" ~ "Morone saxatilis",
Species.common.name == "Yellowtail_Seriola_Almaco jack" ~ "Seriola rivoliana",
TRUE ~ Species.scientific.name)) %>%
mutate(clean_sci_name = case_when(Species.scientific.name == "Chinese fed carp" ~ "Cyprinidae",
Species.scientific.name == "Ctenopharyngodon idella; Carassius carassius" ~ "Cyprinidae",
# Instead of Cyprinidae, be specific that this next group are Hypophtalmichthys so that they can be pulled into their own taxa group in add_taxa_group
Species.scientific.name == "Hypophthalmichthys molitrix and Hypophthalmichthys nobilis" ~ "Mixed H. molitrix and H. nobilis",
Species.scientific.name == "Morone chrysops x M. saxatilis" ~ "Morone hybrid",
Species.scientific.name == "Labeo rohita and Catla Catla" ~ "Cyprinidae",
Species.scientific.name == "Labeo rohita and Catla catla" ~ "Cyprinidae",
Species.scientific.name == "Osteichthyes" ~ "Freshwater fishes",
Species.scientific.name == "Penaeus vannamei" ~ "Litopenaeus vannamei",
Species.scientific.name == "Pangasius hypophthalmus" ~ "Pangasianodon hypophthalmus",
TRUE ~ clean_sci_name))
# Check if any have unassigned clean_sci_name
LCA_data %>%
filter(clean_sci_name == "") %>%
select(Species.common.name, Species.scientific.name, clean_sci_name) %>%
unique()
LCA_data <- LCA_data %>%
# Divide by 5 for moist pellets
mutate(FCR = case_when(Feed_type == "Moist pellet" ~ FCR_overall/5,
TRUE ~ FCR_overall)) %>%
# Add 0s to Feed percentages where there should actually be 0s (instead of NAs)
mutate(Feed_soy_percent = if_else(is.na(Feed_soy_percent) & (!is.na(Feed_othercrops_percent) | !is.na(Feed_FMFO_percent) | !is.na(Feed_animal_percent)), true = 0, false = Feed_soy_percent),
Feed_othercrops_percent = if_else(is.na(Feed_othercrops_percent) & (!is.na(Feed_soy_percent) | !is.na(Feed_FMFO_percent) | !is.na(Feed_animal_percent)), true = 0, false = Feed_othercrops_percent),
Feed_FMFO_percent = if_else(is.na(Feed_FMFO_percent) & (!is.na(Feed_soy_percent) | !is.na(Feed_othercrops_percent) | !is.na(Feed_animal_percent)), true = 0, false = Feed_FMFO_percent),
Feed_animal_percent = if_else(is.na(Feed_animal_percent) & (!is.na(Feed_soy_percent) | !is.na(Feed_othercrops_percent) | !is.na(Feed_FMFO_percent)), true = 0, false = Feed_animal_percent)) %>%
# Normalize the FINAL feed proportion values to be greater than 0 and no less than 0.01
mutate(feed_soy_new = if_else(Feed_soy_percent == 0, true = 0.0105, false = Feed_soy_percent),
feed_crops_new = if_else(Feed_othercrops_percent == 0, true = 0.0105, false = Feed_othercrops_percent),
feed_fmfo_new = if_else(Feed_FMFO_percent == 0, true = 0.0105, false = Feed_FMFO_percent),
feed_animal_new = if_else(Feed_animal_percent == 0, true = 0.0105, false = Feed_animal_percent)) %>%
# Renomoralize values so they sum to 1
mutate(sum_for_rescaling_feed = rowSums(select(., contains("new")))) %>%
mutate(feed_soy_new = feed_soy_new / sum_for_rescaling_feed,
feed_crops_new = feed_crops_new / sum_for_rescaling_feed,
feed_fmfo_new = feed_fmfo_new / sum_for_rescaling_feed,
feed_animal_new = feed_animal_new / sum_for_rescaling_feed) %>%
# Add 0s to Energy data where there should actually be 0s (instead of NAs)
mutate(Electricity_kwh = if_else(is.na(Electricity_kwh) & (!is.na(Diesel_L) | !is.na(Petrol_L) | !is.na(NaturalGas_L)), true = 0, false = Electricity_kwh),
Diesel_L = if_else(is.na(Diesel_L) & (!is.na(Electricity_kwh) | !is.na(Petrol_L) | !is.na(NaturalGas_L)), true = 0, false = Diesel_L),
Petrol_L = if_else(is.na(Petrol_L) & (!is.na(Electricity_kwh) | !is.na(Diesel_L) | !is.na(NaturalGas_L)), true = 0, false = Petrol_L),
NaturalGas_L = if_else(is.na(NaturalGas_L) & (!is.na(Electricity_kwh) | !is.na(Diesel_L) | !is.na(Petrol_L)), true = 0, false = NaturalGas_L))
# REPLICATE DATA TO THE FARM-LEVEL
if (replicate_dat == TRUE){
# Option 1:
# Replicate data by sqrt(n_farms):
LCA_data <- LCA_data %>%
mutate(Sample_replication = round(sqrt(Sample_size_n_farms)))
LCA_data <- as.data.frame(lapply(LCA_data, rep, LCA_data$Sample_replication))
# Options 2: Replicate data by "n" farms
# LCA_data <- as.data.frame(lapply(LCA_data, rep, LCA_data$Sample_size_n_farms))
}
# FINAL STEP: Create study_id column, use this in all analyses to bind predictions from multiple models back together
LCA_data <- LCA_data %>%
mutate(study_id = row_number()) %>%
# Drop columns we no longer need
# Subset to columns to keep
select(SeaWEED.ID, Source, Sample_size_n_farms, study_id, Country, iso3c, Common.Name = Species.common.name, Scientific.Name = Species.scientific.name, clean_sci_name,
Production_system_group, Intensity, Product, Yield_m2_per_t, Grow_out_period_days, FCR = FCR_overall,
Feed_type, feed_soy_new, feed_crops_new, feed_fmfo_new, feed_animal_new,
Electricity_kwh, Diesel_L, Petrol_L, NaturalGas_L)
}
#_____________________________________________________________________________________________________#
# Rebuild FAO fish from zip file
#_____________________________________________________________________________________________________#
rebuild_fish <- function(path_to_zipfile) {
require(tools) # needed for file_path_sans_ext
require(dplyr)
require(purrr)
require(readxl) # part of tidyverse but still need to load readxl explicitly, because it is not a core tidyverse package
# The following ensures unzipped folder is created in the same directory as the zip file (can be different from the working directory)
# set outdir
if (file.exists(basename(path_to_zipfile))) { # if file is in current directory and only file name was given
outdir <- getwd()
} else if (file.exists(path_to_zipfile)) { # if file path was given
outdir <- dirname(path_to_zipfile)
} else {
stop("Check path_to_zipfile")
}
foldername <- file_path_sans_ext(basename(path_to_zipfile))
outfolder <- paste(outdir, foldername, sep = "/")
unzip(path_to_zipfile, exdir = outfolder) # Problem: if unable to unzip folder, still creates outfolder how to supress this?
# setwd(outfolder)
# list files
fish_files <- list.files(outfolder)
# read .xlsx file (explains data structure of time series)
# IMPORTANT: column ORDER (ABCDEF) in DS file should match columns ABCDEF in time series for looping to work below
# each row gives info for how this time series column should be merged with a code list (CL) file
ds_file <- fish_files[grep("DSD", fish_files)]
path_to_ds <- paste(outfolder, ds_file, sep = "/")
# skip removes title row
ds <- read_excel(path_to_ds, skip=1)
# manually correct ds file's codelist ID column:
ds <- ds %>%
mutate(Codelist_Code_id = case_when(
Concept_id == "SOURCE" ~ "IDENTIFIER",
Concept_id == "SYMBOL" ~ "SYMBOL",
Concept_id != "SYMBOL|SOURCE" ~ Codelist_Code_id
))
# Multiple CL files have the following column names in common: "Identifier" and "Code"
# Which means after merge, below, you get "Identifier.x" and "Identifier.y", etc.
# To disambiguate, Append Codelist with Concept_id
code_ids_to_change<-ds$Codelist_Code_id[grep("IDENTIFIER|CODE", ds$Codelist_Code_id)]
concept_ids_to_append<-ds$Concept_id[grep("IDENTIFIER|CODE", ds$Codelist_Code_id)]
new_code_ids <- paste(concept_ids_to_append, code_ids_to_change, sep = "_")
ds$Codelist_Code_id[grep("IDENTIFIER|CODE", ds$Codelist_Code_id)]<-new_code_ids
# remove non CSVs (do this to ignore "CL_History.txt" file)
fish_files <- fish_files[grep(".csv", fish_files)]
# read in time series.csv
time_files <- fish_files[grep("TS", fish_files)]
path_to_ts <- paste(outfolder, time_files, sep = "/")
time_series <- read.csv(path_to_ts)
names(time_series) <- tolower(names(time_series))
time_series_join <- time_series
for (i in 1:nrow(ds)) {
# TRUE/FALSE: is there a filename listed in Codelist_id?
if (!is.na(ds$Codelist_id[i])) {
# Use ds file to generate path_to_cl individually
code_file_i <- paste(ds$Codelist_id[i], ".csv", sep = "")
path_to_cl <- paste(outfolder, code_file_i, sep = "/")
cl_i <- read.csv(path_to_cl, check.names = FALSE) # check.names = FALSE to prevent R from adding "X" in front of column "3Alpha_Code" - creates problems because this is the matching column for merging with time series
# Many CL files have "Name" as a column, also Name_En, Name_Fr, Name_Es, etc
# Also, "Identifier", "Major Group", and "Code" are common across some CL files
# To disambiguate, append "Concept_ID" from DS file to all columns in CL that contain these terms
concept_names <- paste(ds$Concept_id[i], names(cl_i)[grep("Name|Major_Group|Identifier|Code", names(cl_i))], sep = "_")
names(cl_i)[grep("Name|Major_Group|Identifier|Code", names(cl_i))] <- concept_names
names(cl_i) <- tolower(names(cl_i)) # convert all cl headers to lowercase
merge_col <- tolower(ds$Codelist_Code_id[i]) # do the same to DS file's code ID so it matches with cl
# If factor...
#if (is.factor(cl_i[[merge_col]])) {
# ...Test if factor levels need to be merged?
#if (!nlevels(cl_i[[merge_col]]) == nlevels(time_series_join[[names(time_series_join)[i]]])) {
# combined <- sort(union(time_series_join[[names(time_series_join)[i]]], levels(cl_i[[merge_col]])))
# levels(time_series_join[[names(time_series_join)[i]]]) <- levels(cl_i[[merge_col]])
# }
#}
# This avoids warnings about unequal factor levels below
# Try converting to character first instead
if (is.factor(cl_i[[merge_col]])){
cl_i[[merge_col]]<-as.character(cl_i[[merge_col]])
time_series_join[[names(time_series_join)[i]]]<-as.character(time_series_join[[names(time_series_join)[i]]])
}
# Can't just merge by column number:
# In Time Series, column COUNTRY, AREA, SOURCE, SPECIES, and UNIT correspond to column 1 in their respective CL files
# but in Time Series, column SYMBOL corresponds to column 2
# Note: the following code does not work: #time_series_join<-left_join(time_series, cl_i, by = c(names(time_series)[i] = merge_col))
# the argument "by" needs to take on the form of join_cols as shown below
firstname <- names(time_series_join)[i]
join_cols <- merge_col
names(join_cols) <- firstname
time_series_join <- left_join(time_series_join, cl_i, by = join_cols)
# Convert back to factor
if (is.character(time_series_join[[names(time_series_join)[i]]])){
time_series_join[[names(time_series_join)[i]]]<-as.factor(time_series_join[[names(time_series_join)[i]]])
}
}
# Expected warning: Coerces from factor to character because time_series$SPECIES (nlevels=2341) and CL_FI_SPECIES_GROUPS.csv column "3alpha_code" (nlevels = 12751) have different number of factor levels
# Expected warning: Coerces from factor to chracter because time_series$UNIT and CL_FILE_UNIT.csv column "code" have different number of factor levels
# Expected warning: Coerces from factor to character because time_series$SYMBOL and CL_FI_SYMBOL.csv column "symbol" have diff number of factors
}
return(time_series_join)
}
#_____________________________________________________________________________________________________#
# Clean aquaculture priors data, add taxa grouping
# Can ignore warning: NAs introduced by coercion (inserts NAs for blank cells)
#_____________________________________________________________________________________________________#
clean_priors <- function(priors_dat){
priors_csv <- read.csv(file.path(datadir, priors_dat)) %>%
select(Group.name, Ave.FCR, Upper.FCR, Lower.FCR) %>%
# Deal with "-" entries
mutate(across(everything(), str_replace, pattern = "-", replacement = "")) %>%
# Convert data cols to numeric
mutate(across(contains(c("Yield", "FCR")), as.numeric)) %>%
# Add taxa group name:
mutate(taxa = case_when(Group.name %in% c("Mussels", "Oysters") ~ "bivalves",
Group.name %in% c("Catfish", "Miscellaneous freshwater fishes") ~ "catfish",
Group.name == "Silver and bighead carp" ~ "hypoph_carp",
Group.name == "Milkfish" ~ "milkfish",
Group.name == "Miscellaneous diadromous fishes" ~ "misc_diad",
Group.name == "Other miscellaneous marine fishes" ~ "misc_marine",
Group.name %in% c("Common carp", "Crucian carp", "Grass carp", "Other Carps, barbels and other cyprinids") ~ "oth_carp",
Group.name == "Aquatic plants" ~ "plants",
Group.name == "Salmon" ~ "salmon",
Group.name == "Shrimps, prawns" ~ "shrimp",
Group.name == "Tilapias and other cichlids" ~ "tilapia",
Group.name == "Trouts" ~ "trout",
TRUE ~ "not_grouped")) %>%
group_by(taxa) %>%
summarise(Ave.FCR = mean(Ave.FCR, na.rm = TRUE),
Upper.FCR = mean(Upper.FCR, na.rm = TRUE),
Lower.FCR = mean(Lower.FCR, na.rm = TRUE)) %>%
ungroup() %>%
# Manually add other freshwater fishes to catfish
filter(taxa != "not_grouped") %>%
arrange(taxa)
}
#_____________________________________________________________________________________________________#
# Clean WILD CAPTURE priors data, add taxa grouping
# Can ignore warning: NAs introduced by coercion (inserts NAs for blank cells)
#_____________________________________________________________________________________________________#
clean_wild_priors <- function(wild_priors_dat = "Priors - Capture.csv"){
priors_csv <- read.csv(file.path(datadir, wild_priors_dat)) %>%
select(Group.name, Mean.FUI, Median.FUI) %>%
# Convert to GHG values (as in Parker et al. 2018, see Rob's note in CSV file)
mutate(Mean.GHG = Mean.FUI*3.3*1.33,
Median.GHG = Median.FUI*3.3*1.33) %>%
mutate(Group.name = case_when(str_detect(Group.name, "Lobsters") ~ "Lobsters",
str_detect(Group.name, "Shrimps") ~ "Shrimps",
TRUE ~ Group.name)) %>%
drop_na()
}
#_____________________________________________________________________________________________________#
# Add taxa grouping
#_____________________________________________________________________________________________________#
add_taxa_group <- function(lca_dat_clean, fishstat_dat){
isscaap_lookup <- fishstat_dat %>%
select(species_scientific_name, isscaap_group) %>%
unique()
lca_dat_out <- lca_dat_clean %>%
left_join(isscaap_lookup, by = c("clean_sci_name" = "species_scientific_name"))
# Which sci_names have NA for isscaap_group after joining
lca_dat_out %>%
filter(is.na(isscaap_group)) %>%
select(clean_sci_name, isscaap_group) %>%
unique()
lca_dat_out <- lca_dat_out %>%
# First pass is to assign NAs to ISSCAAP group
mutate(isscaap_group = case_when(clean_sci_name %in% c("Mixed Hypophthalmichthys molitrix and H. nobilis",
"Ctenopharyngodon idella") ~ "Carps, barbels and other cyprinids",
clean_sci_name %in% c("Freshwater prawn", "Macrobrachium amazonicum") ~ "Freshwater crustaceans",
clean_sci_name %in% c("Morone hybrid") ~ "Miscellaneous diadromous fishes",
clean_sci_name %in% c("Gracilaria chilensis") ~ "Red seaweeds",
clean_sci_name %in% c("Litopenaeus vannamei") ~ "Shrimps, prawns",
TRUE ~ isscaap_group))
# Inspect assignment to isscaap_group
lca_dat_out %>%
select(clean_sci_name, isscaap_group) %>%
unique() %>%
arrange(isscaap_group)
# Once all groups have an isscaap group, Create taxa groups either manually or from isscaap_group
lca_dat_out <- lca_dat_out %>%
# Split up carps
mutate(taxa_group_name = case_when(clean_sci_name %in% c("Mixed H. molitrix and H. nobilis",
"Hypophthalmichthys molitrix") ~ "Silver and bighead carp",
clean_sci_name %in% c("Carassius carassius",
"Ctenopharyngodon idella",
"Cyprinidae",
"Cyprinus carpio",
"Mixed Ctenopharyngodon idella and Carassius carassius",
"Mixed Labeo rohita and Catla catla") ~ "Other carps, barbels and cyprinids",
# Remove catfish from Miscellaneous freshwater fishes as its own grouping
clean_sci_name %in% c("Clarias batrachus", "Clarias gariepinus", "Pangasianodon hypophthalmus", "Pangasius spp") ~ "Catfish",
# Split salmons and trouts
str_detect(Common.Name, "salmon|Salmonids") ~ "Salmon",
str_detect(Common.Name, "trout") ~ "Trout",
str_detect(Common.Name, "char") ~ "Miscellaneous diadromous fishes",
# Remove milkfish from misc diadromous fishes
clean_sci_name == "Chanos chanos" ~ "Milkfish",
# Combine groups into misc marine fishes
isscaap_group %in% c("Miscellaneous coastal fishes", "Miscellaneous demersal fishes", "Miscellaneous pelagic fishes", "Flounders, halibuts, soles") ~ "Miscellaneous marine fishes",
# Combine seaweeds into aquatic plants
isscaap_group %in% c("Brown seaweeds", "Red seaweeds") ~ "Aquatic plants",
# Combine bivalves
isscaap_group %in% c("Mussels", "Oysters") ~ "Bivalves",
TRUE ~ isscaap_group))
# Inspect assignment of taxa_group_name
lca_dat_out %>%
select(clean_sci_name, isscaap_group, taxa_group_name) %>%
unique() %>%
arrange(taxa_group_name)
# Clean up taxa_group_names
lca_dat_out <- lca_dat_out %>%
mutate(taxa = case_when(taxa_group_name == "Aquatic plants" ~ "plants",
taxa_group_name == "Bivalves" ~ "bivalves",
taxa_group_name == "Catfish" ~ "catfish",
taxa_group_name == "Crabs, sea-spiders" ~ "crabs",
taxa_group_name == "Freshwater crustaceans" ~ "fresh_crust",
taxa_group_name == "Milkfish" ~ "milkfish",
taxa_group_name == "Miscellaneous diadromous fishes" ~ "misc_diad",
taxa_group_name == "Miscellaneous freshwater fishes" ~ "misc_fresh",
taxa_group_name == "Miscellaneous marine fishes" ~ "misc_marine",
taxa_group_name == "Other carps, barbels and cyprinids" ~ "oth_carp",
taxa_group_name == "Salmon" ~ "salmon",
taxa_group_name == "Shrimps, prawns" ~ "shrimp",
taxa_group_name == "Silver and bighead carp" ~ "hypoph_carp",
taxa_group_name == "Tilapias and other cichlids" ~ "tilapia",
taxa_group_name == "Trout" ~ "trout",
TRUE ~ "unassigned"))
}
#_____________________________________________________________________________________________________#
# Replicate data based on Sample_size_n_farms column
#_____________________________________________________________________________________________________#
rep_data <- function(lca_dat_clean){
# No longer need to clean sample size column - now has column Sample_size_n_farms
# lca_dat_clean_rep <- lca_dat_clean %>%
# # Clean up sample size column
# # First ignore numbers that are percentages, then find and extract any numbers, then fill the rest in with 1s
# mutate(clean_sample_size = case_when(str_detect(Sample_size, "%") ~ 1,
# str_detect(Sample_size, "[0-9]+") ~ as.numeric(str_extract(Sample_size, pattern = "[0-9]+")),
# TRUE ~ 1))
lca_dat_clean_rep <- as.data.frame(lapply(lca_dat_clean, rep, lca_dat_clean$Sample_size_n_farms))
}
#_____________________________________________________________________________________________________#
# Clean feed footprint data
#_____________________________________________________________________________________________________#
clean.feedFP <- function(feedFP_data){
feedFP_data <- feedFP_data %>%
#filter(Units != "kg PO4-eq") %>% # Not currently using
group_by(Input.type, Impact.category, Units, Allocation) %>%
# Revise this to change from arithmetic mean to a weighted mean
summarise(impact_val = mean(Value, na.rm = TRUE), SD = sd(Value, na.rm = TRUE), .groups = 'drop') %>%
mutate(impact_factor = case_when(
(Units == "kg CO2-eq / kg") ~ "Carbon",
(Units == "m3 / kg") ~ "Water",
(Units == "m2a / kg") ~ "Land",
(Units == "kg N-eq / kg") ~ "Nitrogen",
(Units == "kg P-eq / kg") ~ "Phosphorus"
)) %>%
select(impact_factor, Impact.category, Allocation, impact_val, SD, Units)
}
#_____________________________________________________________________________________________________#
# Estimate off farm, feed-associated footprint
#_____________________________________________________________________________________________________#
estimate.feedFP <- function(LCA_data, Feed_data, FP_option, allocation){
# FP options are "Carbon", "Water", "Nitrogen", "Phosphorus", and "Land"
# Filter to FP option
Feed_data <- Feed_data %>%
filter(FP == FP_option) %>%
filter(Allocation.method == allocation)
soy <- Feed_data %>% filter(Category == "Soy")
soy <- soy$FP_val
othercrops <- Feed_data %>% filter(Category == "Crop") %>% select(FP_val)
othercrops <- othercrops$FP_val
FMFO <- Feed_data %>% filter(Category == "Fishery") %>% select(FP_val)
FMFO <- FMFO$FP_val
animal <- Feed_data %>% filter(Category == "Animal by-products") %>% select(FP_val)
animal <- animal$FP_val
# Calculate estimated FP
estimate_FP <- LCA_data$FCR * (soy*(LCA_data$Feed_soy_percent/100) +
othercrops*(LCA_data$Feed_othercrops_percent/100) +
FMFO * (LCA_data$Feed_FMFO_percent/100) +
animal * (LCA_data$Feed_animal_percent/100))
return(estimate_FP)
}
#_____________________________________________________________________________________________________#
# Estimate on farm GHG footprint
#_____________________________________________________________________________________________________#
estimate.onfarm.GHG <- function(LCA_data, energy_data){
diesel_perL_CO2eq <- 1 # Placeholder
petrol_perL_CO2eq <- 1 # Placeholder
naturalgas_perL_CO2eq <- 1 # Placeholder
LCA_data <- LCA_data %>%
left_join(energy_data, by = "iso3c")
estimate_FP <- rowSums(cbind(c(LCA_data$Electricity_kwh * LCA_data$GWP_perkWh_CO2eq),
c(LCA_data$Diesel_L * diesel_perL_CO2eq),
c(LCA_data$Petrol_L * petrol_perL_CO2eq),
c(LCA_data$NaturalGas_L * naturalgas_perL_CO2eq)),
na.rm = TRUE)
return(estimate_FP)
}
#_____________________________________________________________________________________________________#
# Estimate on farm land footprint
#_____________________________________________________________________________________________________#
estimate.onfarm.land <- function(LCA_data){
estimate_FP <- LCA_data$Yield_t_per_Ha/LCA_data$harvest
return(estimate_FP)
}
#_____________________________________________________________________________________________________#
# Estimate on farm water footprint
#_____________________________________________________________________________________________________#
estimate.onfarm.water <- function(LCA_data, evap_data, aerated = FALSE){
# Requires calculating land footprint first, saved in a column "onfarm.land"
if(aerated == TRUE){
aeration.factor <- 1.25 # Placeholder of 25% increase in evaporation rate
} else{
aeration.factor <- 1
}
# Join lca and evap data by country
LCA_data <- LCA_data %>%
left_join(evap_data, by = "iso3c")
# Need land FP in HA, evap rate in volume/HA/day and grow out period in days
estimate_FP <- LCA_data$onfarm.land * aeration.factor*LCA_data$evap_rate * LCA_data$Grow_out_period_days
return(estimate_FP)
}
#_____________________________________________________________________________________________________#
# Estimate on farm N and P footprint
#_____________________________________________________________________________________________________#
estimate.feed.NP <- function(LCA_data, Feed_data, FP){
# FP set to N or P
if(FP == "N"){
Feed_data <- Feed_data %>%
filter(element == "N")
}else if(FP == "P"){
Feed_data <- Feed_data %>%
filter(element == "P")
}else print("error: FP must equal N or P")
soy <- Feed_data %>% filter(ingredient == "Soy")
soy <- soy$value
othercrops <- Feed_data %>% filter(ingredient == "Crop")
othercrops <- othercrops$value
FMFO <- Feed_data %>% filter(ingredient == "Fishery")
FMFO <- FMFO$value
animal <- Feed_data %>% filter(ingredient == "Animal by-products")
animal <- animal$value
# Calculate estimated FP
estimate_FP <- soy*(LCA_data$Feed_soy_percent/100) +
othercrops*(LCA_data$Feed_othercrops_percent/100) +
FMFO * (LCA_data$Feed_FMFO_percent/100) +
animal * (LCA_data$Feed_animal_percent/100)
return(estimate_FP)
}
#_____________________________________________________________________________________________________#
# Estimate on-farm N and P emissions
#_____________________________________________________________________________________________________#
clean_feedNutrition <- function(feedNutrition_data){
# Clean columns
feedNutrition_data$Phosphorus <- as.numeric(feedNutrition_data$Phosphorus....)
feedNutrition_data <- feedNutrition_data %>%
mutate(Nitrogen = Crude.protein..../ 6.25)
feedNutrition_data$Nitrogen <- as.numeric(feedNutrition_data$Nitrogen) #N in % of DM
#soy: Entry numbers: 601-620
s=c(602,604,608,610,612,614,616,618,620)
#animal by products, Entry number: meat byproducts 385-392, poultry 479-481 (take only 100% DM)
a=c(386,388,390,392,480,482,484)
#other crops: cassava 141-142, peanut extr. 461-464, linseed extr. 345-348 ,
#corn gluten meal 223-228, pea (not protein concentrate) 455-456, rape 493-496, sunflower oil and meal 633-636,
#wheat byproduct 683-688, Sorghum grain 573-580, triticale 667-668, Navy beans (instead of faba) 57-58,
cr=c(142,462,464,346,348,224,226,228,456,494,496,634,636,684,686,688,578,580,668,58)
#fishmeal and oil
# fish 301-304, fish alewife meal 305-306,anchovy meal 307-308, catfish meal 313-314, herring meal 317-318,
# mackerel meal 322, manhaden meal 323-324, redfish meal 325-326, salmon meal 329-330, sardine meal 333-334
#tuna meal 337-338, white fish 341-342
f=c(302,304,306,308,314,318,322,324,326,330,334,338,342)
# Create dataframe with crop N and P values
out <- data.frame(ingredient = c("Animal by-products", "Animal by-products", "Crop", "Crop", "Fishery", "Fishery", "Soy", "Soy"),
element = rep(c("N", "P"), 4), value = NA, sd = NA)
# Calculate the mean and standard deviation by group
# Change this to calculated weighted mean
out$value[out$ingredient=="Animal by-products" & out$element == "N"] <-
mean(feedNutrition_data$Nitrogen[which(feedNutrition_data$Entry.Number %in% a)],na.rm=TRUE)
out$sd[out$ingredient=="Animal by-products" & out$element == "N"] <-
sd(feedNutrition_data$Nitrogen[which(feedNutrition_data$Entry.Number %in% a)],na.rm=TRUE)
out$value[out$ingredient=="Animal by-products" & out$element == "P"] <-
mean(feedNutrition_data$Phosphorus[which(feedNutrition_data$Entry.Number %in% a)],na.rm=TRUE)
out$sd[out$ingredient=="Animal by-products" & out$element == "P"] <-
sd(feedNutrition_data$Phosphorus[which(feedNutrition_data$Entry.Number %in% a)],na.rm=TRUE)
out$value[out$ingredient=="Crop" & out$element == "N"] <-
mean(feedNutrition_data$Nitrogen[which(feedNutrition_data$Entry.Number %in% cr)],na.rm=TRUE)
out$sd[out$ingredient=="Crop" & out$element == "N"] <-
sd(feedNutrition_data$Nitrogen[which(feedNutrition_data$Entry.Number %in% cr)],na.rm=TRUE)
out$value[out$ingredient=="Crop" & out$element == "P"] <-
mean(feedNutrition_data$Phosphorus[which(feedNutrition_data$Entry.Number %in% cr)],na.rm=TRUE)
out$sd[out$ingredient=="Crop" & out$element == "P"] <-
sd(feedNutrition_data$Phosphorus[which(feedNutrition_data$Entry.Number %in% cr)],na.rm=TRUE)
out$value[out$ingredient=="Fishery" & out$element == "N"] <-
mean(feedNutrition_data$Nitrogen[which(feedNutrition_data$Entry.Number %in% f)],na.rm=TRUE)
out$sd[out$ingredient=="Fishery" & out$element == "N"] <-
sd(feedNutrition_data$Nitrogen[which(feedNutrition_data$Entry.Number %in% f)],na.rm=TRUE)
out$value[out$ingredient=="Fishery" & out$element == "P"] <-
mean(feedNutrition_data$Phosphorus[which(feedNutrition_data$Entry.Number %in% f)],na.rm=TRUE)
out$sd[out$ingredient=="Fishery" & out$element == "P"] <-
sd(feedNutrition_data$Phosphorus[which(feedNutrition_data$Entry.Number %in% f)],na.rm=TRUE)
out$value[out$ingredient=="Soy" & out$element == "N"] <-
mean(feedNutrition_data$Nitrogen[which(feedNutrition_data$Entry.Number %in% s)],na.rm=TRUE)
out$sd[out$ingredient=="Soy" & out$element == "N"] <-
sd(feedNutrition_data$Nitrogen[which(feedNutrition_data$Entry.Number %in% s)],na.rm=TRUE)
out$value[out$ingredient=="Soy" & out$element == "P"] <-
mean(feedNutrition_data$Phosphorus[which(feedNutrition_data$Entry.Number %in% s)],na.rm=TRUE)
out$sd[out$ingredient=="Soy" & out$element == "P"] <-
sd(feedNutrition_data$Phosphorus[which(feedNutrition_data$Entry.Number %in% s)],na.rm=TRUE)
out <- out %>%
mutate(feed_type = case_when(
(ingredient == "Soy") ~ "soy",
(ingredient == "Crop") ~ "crops",
(ingredient == "Fishery") ~ "fmfo",
(ingredient == "Animal by-products") ~ "animal"
)) %>%
select(-c("ingredient", "sd")) %>%
pivot_wider(names_from = "element", values_from = "value") %>%
mutate(N = N/100, P = P/100) # Divide by 100 because N and P data is in percent
return(out)
}
# Refer to clean_fish_NP.R for functions to estimate fish N and P content
#_____________________________________________________________________________________________________#
# Calculate the weighted averages for the feed components
#_____________________________________________________________________________________________________#
# Calculate soy weightings
# Since the specific soy products don't map onto the FAO items, use all soy exports for all soy products
calc_soy_weights <- function(faostat = faostat, feed_fp = feed_fp, deforestation_free = FALSE){
weightings <- faostat %>%
filter(Unit == "tonnes") %>%
filter(Item %in% c("Soybeans", "Cake, soybeans", "Oil, soybean")) %>%
{if (deforestation_free == TRUE) filter(., iso3c != "BRA")
else .} %>%
group_by(iso3c) %>%
summarise(Exports = sum(Value, na.rm = TRUE)) %>%
filter(Exports > 0) %>%
left_join(feed_fp %>% filter(Input.type == "Soy"), by = c("iso3c")) %>%
filter(is.na(Input.type) == FALSE) %>%
group_by(iso3c) %>%
summarise(Exports = sum(Exports, na.rm = TRUE)) %>%
mutate(weighting = Exports/sum(Exports)) %>%
select(iso3c, weighting)
weighted_soy <- feed_fp %>%
filter(Input.type == "Soy") %>%
left_join(weightings, by = "iso3c") %>%
filter(is.na(weighting) == FALSE) %>%
group_by(Input.type, Input, Impact.category, Allocation, Units) %>%
# Normalize weightings to sum to 1
mutate(reweighting = weighting/sum(weighting, na.rm = TRUE)) %>%
summarise(Value = sum(Value * reweighting)) %>%
# If weighting ingredient types, do here along with country weightings
group_by(Input.type, Impact.category, Allocation, Units) %>%
summarise(ave_stressor = mean(Value, na.rm = TRUE))
return(weighted_soy)
}
# Calculate crop weightings
calc_crop_weights <- function(faostat = faostat, feed_fp = feed_fp, deforestation_free = FALSE){
weightings <- faostat %>%
filter(Unit == "tonnes") %>%
{if (deforestation_free == TRUE) filter(., iso3c != "ARG")
else .} %>%
group_by(iso3c, Item) %>%
summarise(Exports = sum(Value, na.rm = TRUE)) %>%
filter(Exports > 0) %>%
mutate(Input = case_when(
(Item %in% c("Cassava Equivalent")) ~ "Cassava",
(Item %in% c("Maize")) ~ "Maize",
(Item %in% c("Cake, maize")) ~ "Corn gluten meal",
(Item %in% c("Cake, groundnuts")) ~ "Peanut meal",
(Item %in% c("Rape and Mustard Oils")) ~ "Rapeseed oil",
(Item %in% c("Cake, rapeseed")) ~ "Rapeseed meal",
(Item %in% c("Wheat")) ~ "Wheat",
(Item %in% c("Bran, wheat")) ~ "Wheat bran",
(Item %in% c("Rice")) ~ "Rice bran",
(Item %in% c("Cake, sunflower")) ~ "Sunflower meal"
)) %>%
filter(is.na(Input) == FALSE) %>%
left_join(feed_fp %>% filter(Input.type == "Crop"), by = c("iso3c", "Input")) %>%
filter(is.na(Input.type) == FALSE) %>%
ungroup() %>%
group_by(Input, iso3c) %>%
summarise(Exports = sum(Exports, na.rm = TRUE)) %>%
mutate(weighting = Exports/sum(Exports)) %>%
select(iso3c, Input, weighting)
weighted_crop <- feed_fp %>%
filter(Input.type == "Crop") %>%
left_join(weightings, by = c("iso3c", "Input")) %>%
filter(is.na(weighting) == FALSE) %>%
# If weighting soy ingredient types, do here along with country weightings
group_by(Input.type, Input, Impact.category, Allocation, Units) %>%
# Normalize weightings to sum to 1
mutate(reweighting = weighting/sum(weighting, na.rm = TRUE)) %>%
summarise(Value = sum(Value * reweighting)) %>%
# If weighting ingredient types, do here along with country weightings
group_by(Input.type, Impact.category, Allocation, Units) %>%
summarise(ave_stressor = mean(Value, na.rm = TRUE))
return(weighted_crop)
}
# Calculate chicken weightings
calc_chicken_weights <- function(faostat = faostat, feed_fp = feed_fp){
weightings <- faostat %>%
filter(Unit == "tonnes") %>%
filter(Item %in% c("Poultry Meat")) %>%
mutate(iso3c = ifelse(iso3c %in% c("FRA", "ITA"), "EUR", iso3c)) %>%
group_by(iso3c) %>%
summarise(Exports = sum(Value, na.rm = TRUE)) %>%
filter(Exports > 0) %>%
left_join(feed_fp %>%
filter(Input %in% c("Chicken by-product meal", "Chicken by-product oil")), by = c("iso3c")) %>%
filter(is.na(Input.type) == FALSE) %>%
group_by(iso3c) %>%
summarise(Exports = sum(Exports, na.rm = TRUE)) %>%
mutate(weighting = Exports/sum(Exports)) %>%
select(iso3c, weighting)
weighted_chicken <- feed_fp %>%
filter(Input %in% c("Chicken by-product meal", "Chicken by-product oil")) %>%
left_join(weightings, by = "iso3c") %>%
filter(is.na(weighting) == FALSE) %>%
# If weighting soy ingredient types, do here along with country weightings
group_by(Input.type, Input, Impact.category, Allocation, Units) %>%
# Normalize weightings to sum to 1
mutate(reweighting = weighting/sum(weighting, na.rm = TRUE)) %>%
summarise(Value = sum(Value * reweighting)) %>%
# If weighting ingredient types, do here along with country weightings
group_by(Input.type, Impact.category, Allocation, Units) %>%
summarise(ave_stressor = mean(Value, na.rm = TRUE))
return(weighted_chicken)
}
# Calculate fishery weights
calc_fishery_weights <- function(fmfo_prod = fmfo_prod, feed_fp = feed_fp){
weighted_fishery <- feed_fp %>%
filter(Input.type == c("Fishery")) %>%
left_join(fmfo_prod, by = c("Input" = "Name")) %>%
group_by(Input.type, Impact.category, Allocation, Units) %>%
# Normalize weightings to sum to 1
mutate(reweighting = Weighting/sum(Weighting, na.rm = TRUE)) %>%
summarise(Value = sum(Value * reweighting)) %>%
group_by(Impact.category, Allocation, Units) %>%
summarise(ave_stressor = mean(Value, na.rm = TRUE))
return(weighted_fishery)
}
# Calculate fish byproduct weights
calc_byproduct_weights <- function(fmfo_prod = fmfo_prod, feed_fp = feed_fp){
weighted_fishbyproduct <- feed_fp %>%
filter(Input.type == c("Fishery by-product")) %>%
left_join(fmfo_prod, by = c("Input" = "Name")) %>%
group_by(Input.type, Impact.category, Allocation, Units) %>%
# Normalize weightings to sum to 1
mutate(reweighting = Weighting/sum(Weighting, na.rm = TRUE)) %>%
summarise(Value = sum(Value * reweighting)) %>%
group_by(Impact.category, Allocation, Units) %>%
summarise(ave_stressor = mean(Value, na.rm = TRUE))
return(weighted_fishbyproduct)
}
# Combine fishery and byproduct weights
combine_fish_weights <- function(weighted_fishery = weighted_fishery, weighted_fishbyproduct = weighted_fishbyproduct){
weighted_fish <- weighted_fishery %>%
left_join(weighted_fishbyproduct, by = c("Impact.category", "Allocation", "Units")) %>%
mutate(ave_stressor = 0.675*ave_stressor.x + 0.325*ave_stressor.y) %>%
select(-ave_stressor.x, -ave_stressor.y)
return(weighted_fish)
}
#_____________________________________________________________________________________________________#
# Create visualizations of brms gamma (or dirichlet) regression outputs
#_____________________________________________________________________________________________________#
plot_for_si <- function(name_of_fit, name_of_data, name_of_var, regression_type = "gamma"){
# Mac
datadir <- "/Volumes/jgephart/BFA Environment 2/Data"
outdir <- "/Volumes/jgephart/BFA Environment 2/Outputs"
library(tidyverse)
library(modelr)
library(ggdist)
library(tidybayes)
library(ggplot2)
library(cowplot)
library(rstan)
library(brms)
library(ggrepel)
library(RColorBrewer)
# SET THEME
plot_theme <- theme(title = element_text(size = 20),
axis.title.x = element_text(size = 20),
axis.text=element_text(size=20, color = "black"))
compiled_dat_clean <- read.csv(file.path(outdir, "lca_clean_with_groups.csv"))
brms_output <- get(name_of_fit)
full_dat <- get(name_of_data)
# FCR
# name_of_fit <- "fit_fcr_no_na"
# name_of_data <- "full_fcr_dat"
# name_of_var <- "fcr"
# Yield
# name_of_fit <- "fit_yield_no_na"
# name_of_data <- "full_yield_dat"
# name_of_var <- "yield"
# Electricity
# name_of_fit <- "fit_electric_no_na"
# name_of_data <- "full_electric_dat"
# name_of_var <- "electric"
# Diesel
# name_of_fit <- "fit_diesel_no_na"
# name_of_data <- "full_diesel_dat"
# name_of_var <- "diesel"
# Petrol
# name_of_fit <- "fit_petrol_no_na"
# name_of_data <- "full_petrol_dat"
# name_of_var <- "petrol"
# Natural gas
# name_of_fit <- "fit_natgas_no_na"
# name_of_data <- "full_natgas_dat"
# name_of_var <- "natgas"
if (regression_type == "gamma") {
# Combine modeled data (both data and predictions) with the full clean LCA dataset and output this
dat_for_si <- compiled_dat_clean %>%
left_join(full_dat, by = c("study_id", "clean_sci_name", "taxa", "Intensity" = "intensity", "Production_system_group" = "system"))
write.csv(dat_for_si, file.path(outdir, paste("lca_clean_with_model_predictions-", name_of_var, ".csv", sep = "")), row.names = FALSE)
summary(brms_output)
# Default posterior predictive check is a density plot:
# Specify response variable in resp
pp_check(brms_output, resp = "y", nsamples = 50) +
ggtitle(paste("Posterior predictive check: ", name_of_var, sep = ""))
ggsave(filename = file.path(outdir, paste("plot_gamma-regression_", name_of_var, "_post-pred-checks_density.png", sep = "")), width = 8, height = 11.5)
pp_check(brms_output, type = "scatter_avg", nsamples = 1000, resp = "y") +
ggtitle(paste("Posterior predictive check: ", name_of_var, sep = ""))
ggsave(filename = file.path(outdir, paste("plot_gamma-regression_", name_of_var, "_post-pred-checks_scatter.png", sep = "")), width = 8, height = 11.5)
# Other posterior predictive checks
# pp_check(brms_output, type = "error_hist", nsamples = 5, resp = "y")
# pp_check(brms_output, type = "stat_2d", resp = "y")
# pp_check(brms_output, type = "stat", resp = "y")
get_variables(brms_output)
# Plot coefficients
p <- mcmc_plot(brms_output, pars = c("^b_"))
coeff_data <- p$data %>%
mutate(effect = case_when(m > 0 ~ "positive",
m < 0 ~ "negative",
TRUE ~ "none"))
ggplot(data = coeff_data) +
geom_segment(data = coeff_data, mapping = aes(xend = hh, yend = parameter, x = ll, y = parameter)) +
geom_segment(data = coeff_data, mapping = aes(xend = h, yend = parameter, x = l, y = parameter), size = 2) +
geom_point(aes(x = m, y = parameter, color = effect), size = 3) +
labs(x = "", y = "", title = paste("Coefficients for ", name_of_var, sep = "")) +
theme_classic() +
plot_theme
ggsave(filename = file.path(outdir, paste("plot_gamma-regression_", name_of_var, "_coeffs.png", sep = "")), height = 8.5, width = 11)
# Boxplot of data + predictions per taxa group
ggplot(full_dat, aes(x = !!sym(name_of_var), y = taxa)) +
geom_boxplot(outlier.shape = NA) + geom_jitter(aes(color = data_type)) +
theme_classic() +
plot_theme
ggsave(filename = file.path(outdir, paste("plot-data_", name_of_var, "-ghg_tx-level.png", sep = "")), width = 11, height = 8.5)
# Make separate plot for each taxa group
for (i in 1:length(unique(full_dat$taxa))){
taxa_i <- unique(full_dat$taxa)[i]
dat_taxa_i <- full_dat %>%
filter(taxa == taxa_i) %>%
mutate(rowname = row_number()) %>%
replace_na(replace = list(taxa = "unknown", intensity = "unknown", system = "unknown"))
p <- ggplot() +
geom_pointinterval(aes(y = rowname, x = !!sym(name_of_var), xmin = .lower, xmax = .upper, shape = system, point_color = intensity), size = 2, data = dat_taxa_i) +
geom_point(aes(y = rowname, x = !!sym(name_of_var), shape = system, color = intensity), data = dat_taxa_i, size = 3) +
#scale_interval_shape(drop = FALSE) +
scale_shape_discrete(drop = FALSE) +
#coord_cartesian(xlim = c(0, 10)) +
theme_classic() +
labs(x = name_of_var, y = "", title = taxa_i) +
theme(axis.text = element_text(size = 16),
axis.title = element_text(size = 20),
axis.text.y = element_blank())
plot(p)
file_i <- paste("plot_gamma-regression_", name_of_var, "_missing-dat-predictions_taxa-", taxa_i, ".png", sep = "")
ggsave(filename = file.path(outdir, file_i), width = 8, height = 11.5)
}
}
if (regression_type == "dirichlet"){
full_dat_for_merge <- full_dat %>%
select(-c(.row, rowname, .lower, .upper, .width, .point, .interval)) %>%
pivot_wider(names_from = .category, values_from = feed_proportion) %>%
group_by(study_id, clean_sci_name, taxa, intensity, system, data_type) %>%
# SUM to collapse into a single row
mutate(feed_soy = sum(feed_soy, na.rm = TRUE),
feed_crops = sum(feed_crops, na.rm = TRUE),
feed_fmfo = sum(feed_fmfo, na.rm = TRUE),
feed_animal = sum(feed_animal, na.rm = TRUE)) %>%
distinct()
# Combine modeled data (both data and predictions) with the full clean LCA dataset and output this
dat_for_si <- compiled_dat_clean %>%
left_join(full_dat_for_merge, by = c("study_id", "clean_sci_name", "taxa", "Intensity" = "intensity", "Production_system_group" = "system"))
write.csv(dat_for_si, file.path(outdir, paste("lca_clean_with_model_predictions-", name_of_var, ".csv", sep = "")), row.names = FALSE)
summary(brms_output)
## PP_CHECK not implemented for dirichlet regression models
# pp_check(brms_output, nsamples = 50) +
# ggtitle("Posterior predictive check")
# ggsave(filename = file.path(outdir, "plot_gamma-regression_post-pred-checks_density.png"), width = 11.5, height = 8)
# pp_check(brms_output, type = "error_hist", nsamples = 5)
# pp_check(brms_output, type = "scatter_avg", nsamples = 1000)
# pp_check(brms_output, type = "stat_2d")
# pp_check(brms_output, type = "stat")
get_variables(brms_output)
# SET THEME
plot_theme <- theme(title = element_text(size = 20),
axis.title.x = element_text(size = 20),
axis.text=element_text(size=20, color = "black"))
# Plot coefficients (separate for each feed component)
feed_coeffs <- c("b_mufeedcrops", "b_mufeedfmfo", "b_mufeedanimal")
for (i in 1:length(feed_coeffs)){
p <- mcmc_plot(brms_output, pars = feed_coeffs[i])
coeff_data <- p$data %>%
mutate(effect = case_when(m > 0 ~ "positive",
m < 0 ~ "negative",
TRUE ~ "none"))
p_custom <- ggplot(data = coeff_data) +
geom_segment(data = coeff_data, mapping = aes(xend = hh, yend = parameter, x = ll, y = parameter)) +
geom_segment(data = coeff_data, mapping = aes(xend = h, yend = parameter, x = l, y = parameter), size = 2) +
geom_point(aes(x = m, y = parameter, color = effect), size = 3) +
labs(x = "", y = "") +
theme_classic() +
plot_theme
plot(p_custom)