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03_synchrony_anova.R
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03_synchrony_anova.R
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## ------------------------------------------ ##
# Synchrony ANOVAs
## ------------------------------------------ ##
# Written by: Nick J Lyon
# PURPOSE
## Using permutation analysis of variance (perANOVA)
## analyze effects of trait levels (separately) on synchrony (i.e., correlation)
# PRE-REQUISITES
## The following script(s) must be run--in order--for this script to work as intended
## 1. Run `synchrony_stats_prep.R`
## ------------------------------------------ ##
# Housekeeping ----
## ------------------------------------------ ##
# Load libraries
# install.packages("librarian")
librarian::shelf(tidyverse, RRPP)
# Clear environment
rm(list = ls())
# Create needed local folder(s)
dir.create(path = file.path("stats_results"), showWarnings = F)
# Read in needed file
sync_df <- read.csv(file = file.path("tidy_data", "synchrony_data.csv")) %>%
# Make a species pair column quickly
dplyr::mutate(Species_Pair = paste(Species1, Species2, sep = "__"),
.before = Species1)
# Glimpse it
dplyr::glimpse(sync_df)
# Set permutation number
iter_num <- 10000
## ------------------------------------------ ##
# Trait Level ANOVAs - Fit ----
## ------------------------------------------ ##
# Make an empty list for anova tables and one for pairwise
aov_list <- list()
pairs_list <- list()
# For each site (plus "all" sites together)
for(site in c("All", unique(sync_df$lter))){
# for(site in "All"){
# Starting message
message("Processing begun for site: ", site)
# Subset to data to relevant site
if(site == "All"){ sub_df <- sync_df } else { sub_df <- dplyr::filter(sync_df, lter == site) }
# Pollinator_code_values
if(length(unique(sub_df$Pollinator_code_values)) != 1){
## Fit anova
poll_fit <- RRPP::lm.rrpp(r.spearman ~ Pollinator_code_values,
iter = iter_num, data = sub_df)
## Strip AOV table
poll_aov <- as.data.frame(anova(poll_fit)$table) %>%
dplyr::mutate(lter = site,
model = "pollinator values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
poll_pairs <- summary(RRPP::pairwise(fit = poll_fit, groups = sub_df$Pollinator_code_values))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "pollinator values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_poll")]] <- poll_aov
pairs_list[[paste0(site, "_poll")]] <- poll_pairs }
# Seed_development_values
if(length(unique(sub_df$Seed_development_values)) != 1){
## Fit anova
seed_dev_fit <- RRPP::lm.rrpp(r.spearman ~ Seed_development_values,
iter = iter_num, data = sub_df)
## Strip AOV table
seed_dev_aov <- as.data.frame(anova(seed_dev_fit)$table) %>%
dplyr::mutate(lter = site,
model = "seed development values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
seed_dev_pairs <- summary(RRPP::pairwise(fit = seed_dev_fit,
groups = sub_df$Seed_development_values))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "seed development values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_seed_dev")]] <- seed_dev_aov
pairs_list[[paste0(site, "_seed_dev")]] <- seed_dev_pairs }
# Mycorrhiza_values
if(length(unique(sub_df$Mycorrhiza_values)) != 1){
## Fit anova
myco_fit <- RRPP::lm.rrpp(r.spearman ~ Mycorrhiza_values,
iter = iter_num, data = sub_df)
## Strip AOV table
myco_aov <- as.data.frame(anova(myco_fit)$table) %>%
dplyr::mutate(lter = site,
model = "mycorrhiza values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
myco_pairs <- summary(RRPP::pairwise(fit = myco_fit, groups = sub_df$Mycorrhiza_values))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "mycorrhiza values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_myco")]] <- myco_aov
pairs_list[[paste0(site, "_myco")]] <- myco_pairs }
# Deciduous_Evergreen_values
if(length(unique(sub_df$Deciduous_Evergreen_values)) != 1){
## Fit anova
decid_fit <- RRPP::lm.rrpp(r.spearman ~ Deciduous_Evergreen_values,
iter = iter_num, data = sub_df)
## Strip AOV table
decid_aov <- as.data.frame(anova(decid_fit)$table) %>%
dplyr::mutate(lter = site,
model = "deciduous evergreen values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
decid_pairs <- summary(RRPP::pairwise(fit = decid_fit,
groups = sub_df$Deciduous_Evergreen_values))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "deciduous evergreen values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_decid")]] <- decid_aov
pairs_list[[paste0(site, "_decid")]] <- decid_pairs }
# Dispersal_syndrome_values
if(length(unique(sub_df$Dispersal_syndrome_values)) != 1){
## Fit anova
disp_fit <- RRPP::lm.rrpp(r.spearman ~ Dispersal_syndrome_values,
iter = iter_num, data = sub_df)
## Strip AOV table
disp_aov <- as.data.frame(anova(disp_fit)$table) %>%
dplyr::mutate(lter = site,
model = "dispersal syndrome values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
disp_pairs <- summary(RRPP::pairwise(fit = disp_fit,
groups = sub_df$Dispersal_syndrome_values))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "dispersal syndrome values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_disp")]] <- disp_aov
pairs_list[[paste0(site, "_disp")]] <- disp_pairs }
# Sexual_system_values
if(length(unique(sub_df$Sexual_system_values)) != 1){
## Fit anova
sex_fit <- RRPP::lm.rrpp(r.spearman ~ Sexual_system_values,
iter = iter_num, data = sub_df)
## Strip AOV table
sex_aov <- as.data.frame(anova(sex_fit)$table) %>%
dplyr::mutate(lter = site,
model = "sexual system values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
sex_pairs <- summary(RRPP::pairwise(fit = sex_fit,
groups = sub_df$Sexual_system_values))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "sexual system values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_sex")]] <- sex_aov
pairs_list[[paste0(site, "_sex")]] <- sex_pairs }
# Shade_tolerance_values
if(length(unique(sub_df$Shade_tolerance_values)) != 1){
## Fit anova
shade_fit <- RRPP::lm.rrpp(r.spearman ~ Shade_tolerance_values,
iter = iter_num, data = sub_df)
## Strip AOV table
shade_aov <- as.data.frame(anova(shade_fit)$table) %>%
dplyr::mutate(lter = site,
model = "shade tolerance values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
shade_pairs <- summary(RRPP::pairwise(fit = shade_fit, groups = sub_df$Shade_tolerance_values))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "shade tolerance values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_shade")]] <- shade_aov
pairs_list[[paste0(site, "_shade")]] <- shade_pairs }
# Growth_form_values
if(length(unique(sub_df$Growth_form_values)) != 1){
## Fit anova
growth_fit <- RRPP::lm.rrpp(r.spearman ~ Growth_form_values,
iter = iter_num, data = sub_df)
## Strip AOV table
growth_aov <- as.data.frame(anova(growth_fit)$table) %>%
dplyr::mutate(lter = site,
model = "growth form values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
growth_pairs <- summary(RRPP::pairwise(fit = growth_fit, groups = sub_df$Growth_form_values))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "growth form values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_growth")]] <- growth_aov
pairs_list[[paste0(site, "_growth")]] <- growth_pairs }
# Fleshy_fruit_values
if(length(unique(sub_df$Fleshy_fruit_values)) != 1){
## Fit anova
flesh_fit <- RRPP::lm.rrpp(r.spearman ~ Fleshy_fruit_values,
iter = iter_num, data = sub_df)
## Strip AOV table
flesh_aov <- as.data.frame(anova(flesh_fit)$table) %>%
dplyr::mutate(lter = site,
model = "fleshy fruit values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
flesh_pairs <- summary(RRPP::pairwise(fit = flesh_fit, groups = sub_df$Fleshy_fruit_values))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "fleshy fruit values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_flesh")]] <- flesh_aov
pairs_list[[paste0(site, "_flesh")]] <- flesh_pairs }
# Seed_bank_values
if(length(unique(sub_df$Seed_bank_values)) != 1){
## Fit anova
seed_bank_fit <- RRPP::lm.rrpp(r.spearman ~ Seed_bank_values,
iter = iter_num, data = sub_df)
## Strip AOV table
seed_bank_aov <- as.data.frame(anova(seed_bank_fit)$table) %>%
dplyr::mutate(lter = site,
model = "seed bank values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
seed_bank_pairs <- summary(RRPP::pairwise(fit = seed_bank_fit, groups = sub_df$Seed_bank_values))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "seed bank values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_seed_bank")]] <- seed_bank_aov
pairs_list[[paste0(site, "_seed_bank")]] <- seed_bank_pairs }
# Seed_mass_similarity
## Fit anova
seed_mass_fit <- RRPP::lm.rrpp(r.spearman ~ Seed_mass_similarity,
iter = iter_num, data = sub_df)
## Strip AOV table
seed_mass_aov <- as.data.frame(anova(seed_mass_fit)$table) %>%
dplyr::mutate(lter = site,
model = "seed mass similarity",
term = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_seed_mass")]] <- seed_mass_aov
# Phylogenetic_similarity
## Fit anova
phylo_fit <- RRPP::lm.rrpp(r.spearman ~ Phylogenetic_similarity,
iter = iter_num, data = sub_df)
## Strip AOV table
phylo_aov <- as.data.frame(anova(phylo_fit)$table) %>%
dplyr::mutate(lter = site,
model = "phylogenetic similarity",
term = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
aov_list[[paste0(site, "_phylo")]] <- phylo_aov
# Ending message
message("Finished with site: ", site) }
## ------------------------------------------ ##
# Trait Level ANOVAs - Export ----
## ------------------------------------------ ##
# Unlist and examine the ANOVA tables
aov_df <- aov_list %>%
purrr::list_rbind() %>%
# Rename problematic columns
dplyr::rename(P = `Pr(>F)`) %>%
# Arrange by sites and model term
dplyr::arrange(lter, model)
# Glimpse
dplyr::glimpse(aov_df)
# Do the same for pairwise comparisons
pairs_df <- pairs_list %>%
purrr::list_rbind() %>%
# Fix some column names
dplyr::rename(UCL_95perc = `UCL (95%)`,
P = `Pr > d`) %>%
# Arrange by sites and model term and pair
dplyr::arrange(lter, model, pairs)
# Examine
dplyr::glimpse(pairs_df)
# Assemble file names for both
(aov_name <- paste0("ANOVA_trait_aov_tables_", Sys.Date(), "_", iter_num, "perm.csv"))
(pairs_name <- paste0("ANOVA_trait_pairwise_comps_", Sys.Date(), "_", iter_num, "perm.csv"))
# Export both locally
write.csv(x = aov_df, file = file.path("stats_results", aov_name), row.names = F, na = '')
write.csv(x = pairs_df, file = file.path("stats_results", pairs_name), row.names = F, na = '')
# Clean up environment
rm(list = setdiff(ls(), c("sync_df", "iter_num")))
## ------------------------------------------ ##
# Trait Status ANOVAs - Fit ----
## ------------------------------------------ ##
# Make an empty list for anova tables for trait status
stat_list <- list()
stat_pair_list <- list()
# For each site (plus "all" sites together)
for(site in c("All", unique(sync_df$lter))){
# for(site in "All"){
# Starting message
message("Processing begun for site: ", site)
# Subset to data to relevant site
if(site == "All"){ sub_df <- sync_df } else { sub_df <- dplyr::filter(sync_df, lter == site) }
# Pollinator_code_shared
if(length(unique(sub_df$Pollinator_code_shared)) != 1){
## Fit anova
poll_fit <- RRPP::lm.rrpp(r.spearman ~ Pollinator_code_shared,
iter = iter_num, data = sub_df)
## Strip AOV table
poll_aov <- as.data.frame(anova(poll_fit)$table) %>%
dplyr::mutate(lter = site,
model = "pollinator values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
poll_pairs <- summary(RRPP::pairwise(fit = poll_fit, groups = sub_df$Pollinator_code_shared))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "pollinator values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_poll")]] <- poll_aov
stat_pair_list[[paste0(site, "_poll")]] <- poll_pairs }
# Seed_development_shared
if(length(unique(sub_df$Seed_development_shared)) != 1){
## Fit anova
seed_dev_fit <- RRPP::lm.rrpp(r.spearman ~ Seed_development_shared,
iter = iter_num, data = sub_df)
## Strip AOV table
seed_dev_aov <- as.data.frame(anova(seed_dev_fit)$table) %>%
dplyr::mutate(lter = site,
model = "seed development values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
seed_dev_pairs <- summary(RRPP::pairwise(fit = seed_dev_fit,
groups = sub_df$Seed_development_shared))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "seed development values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_seed_dev")]] <- seed_dev_aov
stat_pair_list[[paste0(site, "_seed_dev")]] <- seed_dev_pairs }
# Mycorrhiza_shared
if(length(unique(sub_df$Mycorrhiza_shared)) != 1){
## Fit anova
myco_fit <- RRPP::lm.rrpp(r.spearman ~ Mycorrhiza_shared,
iter = iter_num, data = sub_df)
## Strip AOV table
myco_aov <- as.data.frame(anova(myco_fit)$table) %>%
dplyr::mutate(lter = site,
model = "mycorrhiza values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
myco_pairs <- summary(RRPP::pairwise(fit = myco_fit, groups = sub_df$Mycorrhiza_shared))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "mycorrhiza values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_myco")]] <- myco_aov
stat_pair_list[[paste0(site, "_myco")]] <- myco_pairs }
# Deciduous_Evergreen_shared
if(length(unique(sub_df$Deciduous_Evergreen_shared)) != 1){
## Fit anova
decid_fit <- RRPP::lm.rrpp(r.spearman ~ Deciduous_Evergreen_shared,
iter = iter_num, data = sub_df)
## Strip AOV table
decid_aov <- as.data.frame(anova(decid_fit)$table) %>%
dplyr::mutate(lter = site,
model = "deciduous evergreen values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
decid_pairs <- summary(RRPP::pairwise(fit = decid_fit,
groups = sub_df$Deciduous_Evergreen_shared))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "deciduous evergreen values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_decid")]] <- decid_aov
stat_pair_list[[paste0(site, "_decid")]] <- decid_pairs }
# Dispersal_syndrome_shared
if(length(unique(sub_df$Dispersal_syndrome_shared)) != 1){
## Fit anova
disp_fit <- RRPP::lm.rrpp(r.spearman ~ Dispersal_syndrome_shared,
iter = iter_num, data = sub_df)
## Strip AOV table
disp_aov <- as.data.frame(anova(disp_fit)$table) %>%
dplyr::mutate(lter = site,
model = "dispersal syndrome values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
disp_pairs <- summary(RRPP::pairwise(fit = disp_fit,
groups = sub_df$Dispersal_syndrome_shared))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "dispersal syndrome values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_disp")]] <- disp_aov
stat_pair_list[[paste0(site, "_disp")]] <- disp_pairs }
# Sexual_system_shared
if(length(unique(sub_df$Sexual_system_shared)) != 1){
## Fit anova
sex_fit <- RRPP::lm.rrpp(r.spearman ~ Sexual_system_shared,
iter = iter_num, data = sub_df)
## Strip AOV table
sex_aov <- as.data.frame(anova(sex_fit)$table) %>%
dplyr::mutate(lter = site,
model = "sexual system values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
sex_pairs <- summary(RRPP::pairwise(fit = sex_fit,
groups = sub_df$Sexual_system_shared))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "sexual system values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_sex")]] <- sex_aov
stat_pair_list[[paste0(site, "_sex")]] <- sex_pairs }
# Shade_tolerance_shared
if(length(unique(sub_df$Shade_tolerance_shared)) != 1){
## Fit anova
shade_fit <- RRPP::lm.rrpp(r.spearman ~ Shade_tolerance_shared,
iter = iter_num, data = sub_df)
## Strip AOV table
shade_aov <- as.data.frame(anova(shade_fit)$table) %>%
dplyr::mutate(lter = site,
model = "shade tolerance values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
shade_pairs <- summary(RRPP::pairwise(fit = shade_fit, groups = sub_df$Shade_tolerance_shared))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "shade tolerance values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_shade")]] <- shade_aov
stat_pair_list[[paste0(site, "_shade")]] <- shade_pairs }
# Growth_form_shared
if(length(unique(sub_df$Growth_form_shared)) != 1){
## Fit anova
growth_fit <- RRPP::lm.rrpp(r.spearman ~ Growth_form_shared,
iter = iter_num, data = sub_df)
## Strip AOV table
growth_aov <- as.data.frame(anova(growth_fit)$table) %>%
dplyr::mutate(lter = site,
model = "growth form values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
growth_pairs <- summary(RRPP::pairwise(fit = growth_fit, groups = sub_df$Growth_form_shared))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "growth form values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_growth")]] <- growth_aov
stat_pair_list[[paste0(site, "_growth")]] <- growth_pairs }
# Fleshy_fruit_shared
if(length(unique(sub_df$Fleshy_fruit_shared)) != 1){
## Fit anova
flesh_fit <- RRPP::lm.rrpp(r.spearman ~ Fleshy_fruit_shared,
iter = iter_num, data = sub_df)
## Strip AOV table
flesh_aov <- as.data.frame(anova(flesh_fit)$table) %>%
dplyr::mutate(lter = site,
model = "fleshy fruit values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
flesh_pairs <- summary(RRPP::pairwise(fit = flesh_fit, groups = sub_df$Fleshy_fruit_shared))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "fleshy fruit values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_flesh")]] <- flesh_aov
stat_pair_list[[paste0(site, "_flesh")]] <- flesh_pairs }
# Seed_bank_shared
if(length(unique(sub_df$Seed_bank_shared)) != 1){
## Fit anova
seed_bank_fit <- RRPP::lm.rrpp(r.spearman ~ Seed_bank_shared,
iter = iter_num, data = sub_df)
## Strip AOV table
seed_bank_aov <- as.data.frame(anova(seed_bank_fit)$table) %>%
dplyr::mutate(lter = site,
model = "seed bank values",
term = row.names(.),
.before = dplyr::everything())
## Strip pairwise comparisons out as well
seed_bank_pairs <- summary(RRPP::pairwise(fit = seed_bank_fit, groups = sub_df$Seed_bank_shared))$summary.table %>%
as.data.frame() %>%
dplyr::mutate(lter = site,
model = "seed bank values",
pairs = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_seed_bank")]] <- seed_bank_aov
stat_pair_list[[paste0(site, "_seed_bank")]] <- seed_bank_pairs }
# Seed_mass_similarity
## Fit anova
seed_mass_fit <- RRPP::lm.rrpp(r.spearman ~ Seed_mass_similarity,
iter = iter_num, data = sub_df)
## Strip AOV table
seed_mass_aov <- as.data.frame(anova(seed_mass_fit)$table) %>%
dplyr::mutate(lter = site,
model = "seed mass similarity",
term = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_seed_mass")]] <- seed_mass_aov
# Phylogenetic_similarity
## Fit anova
phylo_fit <- RRPP::lm.rrpp(r.spearman ~ Phylogenetic_similarity,
iter = iter_num, data = sub_df)
## Strip AOV table
phylo_aov <- as.data.frame(anova(phylo_fit)$table) %>%
dplyr::mutate(lter = site,
model = "phylogenetic similarity",
term = row.names(.),
.before = dplyr::everything())
## Add to their respective lists
stat_list[[paste0(site, "_phylo")]] <- phylo_aov
# Ending message
message("Finished with site: ", site) }
## ------------------------------------------ ##
# Trait Status ANOVAs - Export ----
## ------------------------------------------ ##
# Unlist and examine the ANOVA tables
stat_df <- stat_list %>%
purrr::list_rbind() %>%
# Rename problematic columns
dplyr::rename(P = `Pr(>F)`) %>%
# Arrange by sites and model term
dplyr::arrange(lter, model)
# Glimpse
dplyr::glimpse(stat_df)
# Do the same for pairwise comparisons
stat_pair_df <- stat_pair_list %>%
purrr::list_rbind() %>%
# Fix some column names
dplyr::rename(UCL_95perc = `UCL (95%)`,
P = `Pr > d`) %>%
# Arrange by sites and model term and pair
dplyr::arrange(lter, model, pairs)
# Examine
dplyr::glimpse(stat_pair_df)
# Assemble file names for both
(stat_name <- paste0("ANOVA_trait_status_aov_tables_", Sys.Date(), "_", iter_num, "perm.csv"))
(stat_pair_name <- paste0("ANOVA_trait_status_pairwise_comps_", Sys.Date(), "_", iter_num, "perm.csv"))
# Export both locally
write.csv(x = stat_df, file = file.path("stats_results", stat_name), row.names = F, na = '')
write.csv(x = stat_pair_df, file = file.path("stats_results", stat_pair_name), row.names = F, na = '')
# Clean up environment
rm(list = setdiff(ls(), c("sync_df", "iter_num")))
# End ----