waves: Vis-NIR Spectral Analysis Wrapper

# Jenna Hershberger
# jmh579@cornell.edu
# March 27, 2020
# Updated May 27, 2020 (pared down)

# Raw data from:
# Rapid analyses of dry matter content and carotenoids in fresh cassava roots using a portable
# visible and near infrared spectrometer (Vis/NIRS). PLoS One 12(12): 1–17.
# doi: 10.1371/journal.pone.0188918.

# Raw files downloaded directly from PLoS One website on 03/25/2020
# https://doi.org/10.1371/journal.pone.0188918

library(readxl)
library(tidyverse)

#### Load raw files and format into ikeogu.2017 ####
# C16I66 <- read_excel("./data-raw/Ikeogu_et_al_2017/C16I66.xlsx") %>%
#   rename_at(vars(`350`:`2500`), ~ paste0("X", 350:2500)) %>%
#   mutate(study.name = "C16I66") %>%
# #  mutate(location = "CIAT") %>%
# #  mutate(year = 2016) %>%
#   mutate(prep.method = "intact") %>%
#   rename(sample.id = Sampleid) %>%
#   rename(DMC.oven = DMLAB) %>%
#   rename(TCC = TCCLAB) %>%
#   dplyr::select(study.name,
#                 #location, year,
#                 prep.method, sample.id, DMC.oven, TCC, X350:X2500)
#
# C16M66 <- read_excel("./data-raw/Ikeogu_et_al_2017/C16M66.xlsx") %>%
#   rename_at(vars(`350`:`2500`), ~ paste0("X", 350:2500)) %>%
#   mutate(study.name = "C16M66") %>%
# #  mutate(location = "CIAT") %>%
# #  mutate(year = 2016) %>%
#   mutate(prep.method = "mashed") %>%
#   rename(sample.id = Sampleid) %>%
#   rename(DMC.oven = DMLAB) %>%
#   rename(TCC = TCCLAB) %>%
#   dplyr::select(study.name,
#                 #location, year,
#                 prep.method, sample.id, DMC.oven, TCC, X350:X2500)


C16Mcal <- read_excel("./data-raw/Ikeogu_et_al_2017/C16Mcal.xlsx") %>%
  rename_at(vars(`350`:`2500`), ~ paste0("X", 350:2500)) %>%
  mutate(study.name = "C16Mcal") %>%
  #  mutate(location = "CIAT") %>%
  #  mutate(year = 2016) %>%
  #  mutate(prep.method = "mashed") %>%
  rename(sample.id = Sampleid) %>%
  rename(DMC.oven = DMLAB) %>%
  rename(TCC = TCCLAB) %>%
  dplyr::select(
    study.name,
    # location, year, prep.method,
    sample.id, DMC.oven, TCC, X350:X2500
  )

C16Mval <- read_excel("./data-raw/Ikeogu_et_al_2017/C16Mval.xlsx") %>%
  rename_at(vars(`350`:`2500`), ~ paste0("X", 350:2500)) %>%
  mutate(study.name = "C16Mval") %>%
  #  mutate(location = "CIAT") %>%
  #  mutate(year = 2016) %>%
  #  mutate(prep.method = "mashed") %>%
  rename(sample.id = Sampleid) %>%
  rename(DMC.oven = DMLAB) %>%
  rename(TCC = TCCLAB) %>%
  dplyr::select(
    study.name,
    # location, year, prep.method,
    sample.id, DMC.oven, TCC, X350:X2500
  )

# U15I <- read_excel("./data-raw/Ikeogu_et_al_2017/U15I.xlsx") %>%
#   rename_at(vars(`350`:`2500`), ~ paste0("X", 350:2500)) %>%
#   mutate(study.name = "U15I") %>%
#   mutate(location = "Umudike") %>%
#   mutate(year = 2015) %>%
#   mutate(prep.method = "intact") %>%
#   rename(sample.id = `Sample Number`) %>%
#   rename(DMC.oven = DM) %>%
#   # The existing TCC column was measured with different methods in this trial as opposed to the
#   # CIAT trials, so it has been removed here to reduce confusion.
#   mutate(TCC = NA) %>%
#   dplyr::select(study.name, location, year, prep.method, sample.id, DMC.oven, TCC, X350:X2500)
#
# U16I <- read_excel("./data-raw/Ikeogu_et_al_2017/U16I.xlsx") %>%
#   rename_at(vars(`350`:`2500`), ~ paste0("X", 350:2500)) %>%
#   mutate(study.name = "U16I") %>%
#   mutate(location = "Umudike") %>%
#   mutate(year = 2016) %>%
#   mutate(prep.method = "intact") %>%
#   rename(sample.id = Sampleid) %>%
#   rename(DMC.oven = DMLAB) %>%
#   mutate(TCC = NA) %>%
#   dplyr::select(study.name, location, year, prep.method, sample.id, DMC.oven, TCC, X350:X2500)
#
# U16M <- read_excel("./data-raw/Ikeogu_et_al_2017/U16M.xlsx") %>%
#   rename_at(vars(`350`:`2500`), ~ paste0("X", 350:2500)) %>%
#   mutate(study.name = "U16M") %>%
#   mutate(location = "Umudike") %>%
#   mutate(year = 2016) %>%
#   mutate(prep.method = "mashed") %>%
#   rename(sample.id = Sampeid) %>%
#   rename(DMC.oven = DMLAB) %>%
#   mutate(TCC = NA) %>%
#   dplyr::select(study.name, location, year, prep.method, sample.id, DMC.oven, TCC, X350:X2500)

# ikeogu.2017 <- rbind(C16I66, C16M66, C16Mcal, C16Mval, U15I, U16I, U16M)
# ikeogu.2017 <- rbind(C16I66, C16M66, C16Mcal, C16Mval)
ikeogu.2017 <- rbind(C16Mcal, C16Mval)
ikeogu.2017$DMC.oven <- as.numeric(ikeogu.2017$DMC.oven)
ikeogu.2017$TCC <- as.numeric(ikeogu.2017$TCC)

# At this point, ikeogu.2017 is ready for export
usethis::use_data(ikeogu.2017, overwrite = TRUE)