data-raw/join_all_traits.R

In akleinhesselink/sedgwicktraits: Sedgwick Annual Plant Traits

rm(list = ls())

library(tidyverse)
library(ggplot2)

outfile <- 'data-raw/cleaned_trait_data/clean_all_traits.csv'

canopy <- read_csv('data-raw/cleaned_trait_data/clean_canopy.csv')
isotopes <- read_csv('data-raw/cleaned_trait_data/clean_isotopes.csv')
heights <- read_csv('data-raw/cleaned_trait_data/clean_heights.csv')
pheno <- read_csv('data-raw/cleaned_trait_data/clean_phenology.csv')
seed_mass <- read_csv('data-raw/cleaned_trait_data/clean_seed_mass.csv')
srl <- read_csv('data-raw/cleaned_trait_data/clean_SRL.csv')
leaf_traits <- read_csv('data-raw/cleaned_trait_data/clean_leaf_traits.csv')

leaf_traits <- 
  leaf_traits %>% 
  group_by( USDA_symbol, plot, petiole ) %>% 
  summarise( SLA = mean(SLA, na.rm = T), 
             LDMC = mean(LDMC, na.rm = T), 
             LA = mean(LA, na.rm = T), 
             leaf_mass = mean(leaf_mass, na.rm = T))

leaf_traits <- 
  leaf_traits %>% 
  group_by( USDA_symbol ) %>% 
  arrange( USDA_symbol, rev( petiole )) %>% 
  filter( row_number() == 1 ) 

srl <- 
  srl %>% 
  filter( plot == 'non_plot' | USDA_symbol %in% c('BRMA3', 'HOMU', 'FEMI2', 'MICA', 'CHGL')) %>% 
  group_by( USDA_symbol ) %>% 
  summarise( `SRL (m/g)` = mean(`SRL (m/g)`, na.rm = T))

seed_mass <- 
  seed_mass %>% 
  group_by( USDA_symbol ) %>% 
  arrange( USDA_symbol, seed_mass_data_source) %>% 
  filter( row_number() == 1) %>% 
  select(USDA_symbol, seed_mass, seed_mass_data_source)
  
pheno <- 
  pheno %>% 
  group_by(USDA_symbol) %>% 
  arrange( USDA_symbol, dataset) %>% 
  filter( row_number() == 1) %>% 
  select( USDA_symbol, `phenology (DOY 50% fruit)`)

heights <- heights[ complete.cases(heights), ] 


canopy <- 
  canopy %>% 
  mutate( canopy_leaf_area = ifelse( is.na(total_area), total_area_est, total_area )) %>% 
  mutate( LAI = canopy_leaf_area/projected_area, LAR = canopy_leaf_area/total_agb_mass) %>% 
  group_by( USDA_symbol, plot) %>% 
  summarise( projected_area = mean(projected_area, na.rm = T), 
             relative_spread = mean(relative_spread, na.rm = T), 
             LAI = mean(LAI, na.rm = T), 
             LAR = mean(LAR, na.rm = T)) %>% 
  arrange( USDA_symbol, plot  ) %>% 
  filter( row_number() == 1 ) # take first one if choice between non-plot and USDA


all_traits <- 
  leaf_traits %>% 
  left_join(isotopes) %>% 
  left_join(canopy) %>% 
  left_join(heights) %>% 
  left_join(pheno) %>% 
  left_join(srl) %>% 
  left_join(seed_mass) %>% 
  mutate( notes = ifelse( petiole, 'LA with petiole', '' )) %>% 
  rename( 'leaf_size' = LA,
          'SRL' = `SRL (m/g)`, 
          'phenology' = `phenology (DOY 50% fruit)`) 

# Variable            Units 
# 'leaf_size'         (cm2)
# 'SLA'               (g/cm2) 
# 'LDMC               (mg/g) 
# 'LAI                (LA/canopy_area) 
# 'LAR                (cm2/g) 
# 'seed_mass          (g) 
# 'max_height         (cm)
# 'SRL                (m/g)
# 'relative_spread'   (lateral/height)
# 'phenology          (DOY 50% fruit)


  
all_traits %>% 
  select( USDA_symbol,
          leaf_size, 
          SLA, 
          LDMC, 
          LAI,
          LAR, 
          seed_mass,
          max_height, 
          SRL,
          relative_spread,
          projected_area,
          phenology,
          foliar_N, 
          CN_ratio, 
          d13C, 
          d15N, 
          notes, 
          seed_mass_data_source, 
          max_height_data_source) %>% 
  write_csv(path = outfile )