rm(list = ls())
library(tidyverse)
library(sedgwickspecies)
library(stringr)
library(lubridate)
alias <- read_csv('data-raw/alias.csv')
agb <- read_csv('data-raw/raw_trait_data/aboveground_biomass_weights.csv') %>%
filter( plot == 'non_plot') %>%
rename( 'alias' = species) %>%
left_join(alias) %>%
select(-alias)
leaf_traits <-
read_csv('data-raw/cleaned_trait_data/clean_leaf_traits.csv') %>%
mutate( year = year( date ))
leaf_traits %>% filter( USDA_symbol == 'ERGR5') %>% View()
canopy_area1 <- readRDS('temp/direct_canopy_leaf_area.RDS')
#------------------------------------------------------ #
avg_SLA <-
leaf_traits %>%
filter( plot == 'non_plot') %>%
filter( !censor) %>%
group_by( USDA_symbol, petiole, year ) %>%
summarise( m_SLA = mean(SLA, na.rm = T) )
avg_SLA %>% View()
agb <-
agb %>%
filter( plot == 'non_plot') %>%
select( species, plant_number, type, tissue_type, aboveground_biomass_g, year, date_collected, notes) %>%
rename( 'alias' = species) %>%
left_join(alias) %>%
select(-alias) %>%
group_by(USDA_symbol, plant_number, tissue_type) %>%
rename('mass_g' = aboveground_biomass_g) %>%
group_by(year, USDA_symbol, type, plant_number, tissue_type) %>%
summarise( mass_g = sum(mass_g) ) %>%
mutate( tissue_type = ifelse( is.na(tissue_type), 'unclassified', tissue_type)) %>%
spread(tissue_type, mass_g, fill = 0) %>%
mutate( total = leaves + stem + unclassified) %>%
ungroup() %>%
left_join(avg_SLA) %>%
mutate( leaf_area_by_weight = leaves*m_SLA)
canopy_area1 %>%
left_join(agb, by = c('year', 'USDA_symbol', 'plant_number')) %>%
select( year, USDA_symbol, plant_number, canopy_leaf_area, leaf_area_by_weight) %>% View()
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