In bsaul/elktoeChemistry: Data and code for Elktoe Chemistry project

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  echo    = FALSE,
  message = FALSE,
  warning = FALSE
)

# devtools::load_all()
library(elktoeChemistry)
library(dplyr)
library(ggplot2)
library(ggbeeswarm)
analysis_data  <- readRDS(params$inputs$analysis)

dt <- analysis_data(
     contrast = "all",
     test_data_FUN = function(data) data,
     elements  = "As_ppm_m75",
     signals   = c("mad_10"),
     group_by_valve = TRUE,
    transect_opts  = list(
       .layers    = c("ipx", "ncr", "psm", "pio", "opx"),
       .min_n_obs = 0L
     )
   )

Layer Distance within each valve/transect

d_by_layer <-
 dt$data[[1]] %>%
 group_by(id, transect, layer) %>%
 summarize(d = max(distance) - min(distance)) %>%
 mutate(
  layer = factor(layer, levels = c("ipx", "ncr", "psm", "pio", "opx"), order = TRUE)
 )

d_by_layer %>%
  group_by(layer) %>%
  summarize(
    mean = mean(d),
    sd   = sd(d),
    median = median(d)
  )

ggplot(
  data = d_by_layer,
  aes(x = layer, y = d)
) + 
 geom_quasirandom() + 
 facet_grid(
  layer ~ .,
  scales = "free"
 )

Layer Distances within each valve/transect (in most recent annuli)

d_by_annuli <-
 dt$data[[1]] %>%
 filter(annuli == "A") %>%
 group_by(id, transect, layer, annuli) %>%
 summarize(d = max(distance) - min(distance)) %>%
 mutate(
  layer = factor(layer, levels = c("ipx", "ncr", "psm", "pio", "opx"), order = TRUE)
 )

d_by_annuli %>%
  group_by(layer, annuli) %>%
  summarize(
    mean = mean(d),
    sd   = sd(d),
    median = median(d)
  )

ggplot(
  data = d_by_annuli,
  aes(x = layer, y = d)
) + 
 geom_quasirandom() + 
 facet_grid(
  layer ~ .,
  scales = "free"
 )

bsaul/elktoeChemistry documentation built on Nov. 17, 2022, 8:10 a.m.