data-raw/rootdist_ml/code_proc-rootdist_ml-2019and2020.R

In vanichols/maRsden: Provides easy access to data collected from the Marsden Farm at Iowa State University

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# created: july 21 2021
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# last updated:
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# purpose: Process root dist data from 2019 and 2020 ML exp
#
# NOTES: Shit I need to tak into account the soil volume...
#
#########################


##### Clear environment and load packages #####
rm(list = ls())
library(tidyverse)
library(lubridate)
library(readxl) #--used to read Excel files
library(janitor) #--used to clean data


pk <- read_csv("data-raw/plotkey/plotkey.csv") %>%
  filter(year %in% c(2019, 2020))


# 2019 --------------------------------------------------------------------

rd19raw <- read_excel("data-raw/rootdist_ml/2019 Marsden Farm Root Biomass Data single sheet.xlsx") %>%
  clean_names()

dap19 <- read_excel("data-raw/rootdist_ml/DAP-to-date.xlsx") %>%
  rename(days_after_planting = DAP) %>%
  mutate(date = ymd(date))

rd19 <-
  rd19raw %>%
  left_join(dap19) %>%
  mutate(year = year(date)) %>%
  #--he specifies the side of the plot, doesn't matter for my purposes
  mutate(plot = parse_number(plot)) %>%
  left_join(pk) %>%
  select(year, date, days_after_planting, depth, plot_id, root_weights_g, soil_volume_cm_3, mass_volume_g_cm_3) %>%
  rename("roots_gcm3" = mass_volume_g_cm_3,
         "roots_g" = root_weights_g,
         "soilvol_cm3" = soil_volume_cm_3)

rd19

# 2020 --------------------------------------------------------------------

d4 <-
  read_excel("data-raw/rootdist_ml/2020-data-from-matt/Marsden 2020 Root Biomass Data_12Feb2021.xlsx",
                 skip = 7, sheet = "D4") %>%
  clean_names() %>%
  remove_empty() %>%
  slice(1:32) %>%
  mutate(plot = parse_number(plot),
         date = ymd("2020/04/27"),
         year = year(date),
         days_after_planting = 4) %>%
  fill(plot, rotation) %>%
  mutate(plot_id = paste(year, plot, sep = "_")) %>%
  mutate(
    roots_g = as.numeric(root_weights_g),
    soilvol_cm3 = as.numeric(soil_area_cm_3),
    roots_gcm3 = as.numeric(mass_area_g_cm_3)) %>%
  select(date, plot_id, depth, days_after_planting, roots_g, soilvol_cm3, roots_gcm3)



d27 <-
  read_excel("data-raw/rootdist_ml/2020-data-from-matt/Marsden 2020 Root Biomass Data_12Feb2021.xlsx",
             skip = 3, sheet = "D27") %>%
  clean_names() %>%
  remove_empty() %>%
  slice(1:32) %>%
  mutate(plot = parse_number(plot),
         date = ymd("2020/05/22"),
         year = year(date),
         days_after_planting = 27) %>%
  fill(plot, rotation) %>%
  mutate(plot_id = paste(year, plot, sep = "_")) %>%
  mutate(
    roots_g = as.numeric(root_weights_g),
    soilvol_cm3 = as.numeric(soil_area_cm_3),
    roots_gcm3 = as.numeric(mass_area_g_cm_3)) %>%
  select(date, plot_id, depth, days_after_planting, roots_g, soilvol_cm3, roots_gcm3)

d50 <-
  read_excel("data-raw/rootdist_ml/2020-data-from-matt/Marsden 2020 Root Biomass Data_12Feb2021.xlsx",
             skip = 3, sheet = "D50") %>%
  clean_names() %>%
  remove_empty() %>%
  slice(1:32) %>%
  mutate(plot = parse_number(plot),
         date = ymd("2020/06/12"),
         year = year(date),
         days_after_planting = 50) %>%
  fill(plot, rotation) %>%
  mutate(plot_id = paste(year, plot, sep = "_")) %>%
  mutate(
    roots_g = as.numeric(root_weight_g),
    soilvol_cm3 = as.numeric(soil_area_cm_3),
    roots_gcm3 = as.numeric(mass_area_g_cm_3)) %>%
  select(date, plot_id, depth, days_after_planting, roots_g, soilvol_cm3, roots_gcm3)


d68 <-
  read_excel("data-raw/rootdist_ml/2020-data-from-matt/Marsden 2020 Root Biomass Data_12Feb2021.xlsx",
             skip = 3, sheet = "D68") %>%
  clean_names() %>%
  remove_empty() %>%
  slice(1:32) %>%
  mutate(plot = parse_number(plot),
         date = ymd("2020/06/30"),
         year = year(date),
         days_after_planting = 68) %>%
  fill(plot, rotation) %>%
  mutate(plot_id = paste(year, plot, sep = "_")) %>%
  mutate(
    roots_g = as.numeric(root_weight_g),
    soilvol_cm3 = as.numeric(soil_area_cm_3),
    roots_gcm3 = as.numeric(mass_area_g_cm_3)) %>%
  select(date, plot_id, depth, days_after_planting, roots_g, soilvol_cm3, roots_gcm3)


d96 <-
  read_excel("data-raw/rootdist_ml/2020-data-from-matt/Marsden 2020 Root Biomass Data_12Feb2021.xlsx",
             skip = 3, sheet = "D96") %>%
  clean_names() %>%
  remove_empty() %>%
  slice(1:32) %>%
  mutate(plot = parse_number(plot),
         date = ymd("2020/07/28"),
         year = year(date),
         days_after_planting =96) %>%
  fill(plot, rotation) %>%
  mutate(plot_id = paste(year, plot, sep = "_")) %>%
  mutate(
    roots_g = as.numeric(root_weights_g),
    soilvol_cm3 = as.numeric(soil_area_cm_3),
    roots_gcm3 = as.numeric(mass_area_g_cm_3)) %>%
  select(date, plot_id, depth, days_after_planting, roots_g, soilvol_cm3, roots_gcm3)

d117 <-
  read_excel("data-raw/rootdist_ml/2020-data-from-matt/Marsden 2020 Root Biomass Data_12Feb2021.xlsx",
             skip = 3, sheet = "D117") %>%
  clean_names() %>%
  remove_empty() %>%
  slice(1:32) %>%
  mutate(plot = parse_number(plot),
         date = ymd("2020/08/18"),
         year = year(date),
         days_after_planting =117) %>%
  fill(plot, rotation) %>%
  mutate(plot_id = paste(year, plot, sep = "_")) %>%
  mutate(
    roots_g = as.numeric(root_weights_g),
    soilvol_cm3 = as.numeric(soil_area_cm_3),
    roots_gcm3 = as.numeric(mass_area_g_cm_3)) %>%
  select(date, plot_id, depth, days_after_planting, roots_g, soilvol_cm3, roots_gcm3)

rd20 <-
  d4 %>%
  bind_rows(d27) %>%
  bind_rows(d50) %>%
  bind_rows(d68) %>%
  bind_rows(d96) %>%
  bind_rows(d117) %>%
  mutate(year = year(date))


# write data ----------------------------------------------

mrs_rootdist_ml <-
  rd19 %>%
  bind_rows(rd20) %>%
  mutate(roots_gcm3 = roots_g/soilvol_cm3,
         roots_kgha =
           roots_gcm3 * (1/1000) * (100^3) * 10000 * 0.15) %>% #--each depth is 15 cm
  rename("dap" = days_after_planting) %>%
  arrange(year, date, plot_id, depth)


mrs_rootdist_ml %>% write_csv("data-raw/rootdist_ml/mrs_rootdist_ml.csv")
usethis::use_data(mrs_rootdist_ml, overwrite = T)


# sum over entire profile -------------------------------------------------

#--plot 22, day 117 in 2020 has NAs. Eliminate it from the sum data
mrs_rootdist_ml %>%
  filter(plot_id == "2020_22") %>%
  arrange(-dap)

mrs_rootdist_mlsum <-
  mrs_rootdist_ml %>%
  filter(!(plot_id == "2020_22" & dap == 117)) %>%
  group_by(year, date, dap, plot_id) %>%
  summarise_if(is.numeric, sum, na.rm = T) %>%
  mutate(roots_gcm3 = roots_g/soilvol_cm3,
         roots_kgha = roots_gcm3 * (1/1000) * (100^3) * 10000 * 0.6)

mrs_rootdist_mlsum %>%
  left_join(pk) %>%
  ggplot(aes(dap, roots_kgha, color = rot_trt)) +
  geom_point() +
  facet_grid(year~.)

mrs_rootdist_mlsum %>% write_csv("data-raw/rootdist_ml/mrs_rootdist_mlsum.csv")
usethis::use_data(mrs_rootdist_mlsum, overwrite = T)