Find here the species names, ANIC museum codes (when available) and abbreviations for each specimen used in our experiment.
library(here)
Beetles <- read.csv(here::here("Data/SupplementaryBeetleDataBase.csv"))
library(DT)
datatable(Beetles)
The following is the list of packages we used to perform our statistical analysis
# For PCA
install.packages("Hmisc")
install.packages("psych")
install.packages("plot.matrix")
# For plots
install.packages("ggplot2")
install.packages("RColorBrewer")
install.packages("gridExtra")
install.packages("devtools")
devtools::install_github("eliocamp/ggnewscale")
# spectral Data
install.packages("pavo")
# For MCMCglmm
install.packages("MCMCglmm")
devtools::install_github("YuLab-SMU/ggtree")
install.packages("geiger")
install.packages("phangorn")
# For PGLS
install.packages("caper")
install.packages("phytools")
install.packages("coda")
# Data wrangling
install.packages("dplyr")
install.packages("tidyverse")
install.packages("tidyr")
install.packages("reshape2")
# MarkDown HTML
install.packages("kableExtra")
install.packages("ggExtra")
install.packages("jtools")
install.packages("DT")We used a .R file to contain and load all the libraries:
This is the way we curated the raw data frames to make sure we include only the relevant species and organisms
# Import data
refl1 <- read.csv(here::here("Data/1_Reflectance_HRBatch.csv")) # HR batch
refl2 <- read.csv(here::here("Data/2_Reflectance_FirstBatch.csv")) # original
TopTrans <- read.csv(here::here("Data/3_Transmittance_HRBatch.csv")) # transmittance
theorySun <- read.csv(here::here("Data/4_SunIrradiance.csv")) # Sun irradiance
# Fix the column names and convert to rspec object
refl1 <-
refl1 %>%
dplyr::select(sort(tidyselect::peek_vars())) %>%
dplyr::select(wl, everything()) %>%
filter(wl >= 400 & wl <= 1700) %>%
dplyr::select(
-clor2, # same as clor 12 in refl2
-rpmt1, -vrid1, -vrid2, -ocul1, # locality missing
-rose1
) %>% # no ecol variables available
dplyr::rename(
anom03 = anom1, anom02 = anom2, atki03 = atki1, atki04 = atki2,
aurs03 = aurs1, aurs04 = aurs2, clor17 = clor1, conc05 = conc1,
conc06 = conc2, ecry04 = ecry1, ecry05 = ecry2, fchi03 = fchi1,
fchi04 = fchi2, gray04 = gray1, gray05 = gray2, hirs15 = hirs1,
hirs16 = hirs2, lats04 = lats1, lats05 = lats2, mcla03 = mcla1,
macl06 = mclm1, macl07 = mclm2, narm04 = narm1, narm05 = narm2,
pczo05 = ocul2, pczv04 = oliv1, pczv05 = oliv2, opal03 = opal1,
pali08 = pali1, pali09 = pali2, poro16 = poro1, poro17 = poro2,
pczp05 = ppsi1, pczp06 = ppsi2, prsi04 = prsi1, prsi05 = prsi2,
prsi06 = prsi3, pvul05 = pvul1, pvul06 = pvul2, rayn04 = rayn1,
rayn05 = rayn2, repa03 = rpan1, reps03 = rpmm1, smgg03 = smgd1,
smgg04 = smgd2, tars05 = tars1, tars06 = tars2, xyle07 = xyln1,
xyle08 = xyln2, xyle09 = xyln3
) %>% # rename to fuse with the other data set
as.rspec(.) %>%
procspec(., fixneg = "zero")
refl2 <-
refl2 %>%
dplyr::select(sort(tidyselect::peek_vars())) %>%
dplyr::select(wl, everything()) %>%
filter(wl >= 400 & wl <= 1700) %>%
dplyr::select(
-ambl03, -anom02, -black, -mima03,
-oliv03, -para01, -para02, -saul03,
-velu00, -vrdi01, -xyle03
) %>% # removed. Location not available
dplyr::rename(
velu05 = flin01, velu04 = flin02,
narm03 = namr03, rept01 = rep201,
rept02 = rep202, rept03 = rep203,
rubi03 = sutr01, rubi04 = sutr02,
rubi05 = sutr03, rubi06 = sutr04
) %>% # renamed
as.rspec(.) %>%
procspec(., fixneg = "zero")
TopTrans <-
TopTrans %>%
dplyr::select(sort(tidyselect::peek_vars())) %>%
dplyr::select(wl, everything()) %>%
filter(wl >= 400 & wl <= 1700) %>%
dplyr::select(
-clor2t, # same as clor 12 in refl2
-rpmt1t, -vrid1t, -vrid2t, -ocul1t, # locality missing
-rose1t
) %>% # no ecol variables available
dplyr::rename(
anom03 = anom1t, anom02 = anom2t, atki03 = atki1t, atki04 = atki2t,
aurs03 = aurs1t, aurs04 = aurs2t, clor17 = clor1t, conc05 = conc1t,
conc06 = conc2t, ecry04 = ecry1t, ecry05 = ecry2t, fchi03 = fchi1t,
fchi04 = fchi2t, gray04 = gray1t, gray05 = gray2t, hirs15 = hirs1t,
hirs16 = hirs2t, lats04 = lats1t, lats05 = lats2t, mcla03 = mcla1t,
macl06 = mclm1t, macl07 = mclm2t, narm04 = narm1t, narm05 = narm2t,
pczo05 = ocul2t, pczv04 = oliv1t, pczv05 = oliv2t, opal03 = opal1t,
pali08 = pali1t, pali09 = pali2t, poro16 = poro1t, poro17 = poro2t,
pczp05 = ppsi1t, pczp06 = ppsi2t, prsi04 = prsi1t, prsi05 = prsi2t,
prsi06 = prsi3t, pvul05 = pvul1t, pvul06 = pvul2t, rayn04 = rayn1t,
rayn05 = rayn2t, repa03 = rpan1t, reps03 = rpmm1t, smgg03 = smgd1t,
smgg04 = smgd2t, tars05 = tars1t, tars06 = tars2t, xyle07 = xyln1t,
xyle08 = xyln2t, xyle09 = xyln3t
) %>% # rename to fuse with the other data set
as.rspec(.) %>%
procspec(., fixneg = "zero")
# Convert the sun irradiance into an rspec object
theorySun <-
theorySun %>%
filter(wl >= 400 & wl <= 1700) %>%
as.rspec(.) %>%
procspec(., fixneg = "zero")
# Produce the final data frames for calculating irradiance
Transmitance <-
TopTrans %>%
mutate(Sun = theorySun$irradiance) # Add Sun irradiance as the last column
Reflectance <-
data.frame(refl1, refl2[, 2:length(refl2)]) %>%
dplyr::select(sort(tidyselect::peek_vars())) %>%
select(wl, everything()) %>%
as.rspec(.) %>%
procspec(., fixneg = "zero") %>%
mutate(Sun = theorySun$irradiance) # Add Sun irradiance as the last columnCreate files