In blongworth/HybridGIS: Data, Functions, and Analysis for the NOSAMS Hybrid Gas Ion Source

knitr::opts_chunk$set(echo = TRUE)

Reanalysis of carbonate data with most current data reduction method.

library(tidyverse)
library(here)
library(amstools)
library(HybridGIS)

theme_set(theme_bw())
options(digits = 3)
options(scipen = 4)

# TODO: Handle case of only one blank

wheels <- c("020521", "030421", "040121", "041521", "051421", "052621", "061121", "061821")
files <- here("data", paste0("USAMS", wheels, "R.txt"))
dates <- as.Date(c("2021-02-05", "2021-03-05", "2021-04-09", "2021-04-16", "2021-05-14", "2021-05-28", "2021-06-11", "2021-06-18"))
stds <- list(c(5,8), NULL, c(26,37), NULL, c(10,15,65,70), NULL, NULL, NULL)
blanks <- list(7, c(7,8), c(28,29,30), c(27,28,29), c(20,35,50), NULL, NULL, 23)
outliers <- list(NULL, tibble(pos = c(6, 8, 8, 8), meas = c(18, 1, 2, 3)),
                 tibble(pos = c(28,29), meas=c(1, 1)),
                 NULL,
                 tibble(pos=10, meas=1),
                 tibble(pos=c(8,9,13), meas=c(6,1,1)),
                 tibble(pos=c(4,5,5,6,7,8,15), meas=c(1,1,4,10,3,3,2)),
                 NULL)


results <- pmap(list(files, dates, stds, blanks, outliers), reduce_hgis)
raw_df <- map_dfr(results, 1)
results_df <- map_dfr(results, 2)

write_csv(raw_df, here("data_analysed/carb_all_raw.csv"))
write_csv(results_df, here("data_analysed/carb_all_results.csv"))

Plot raw data

map(results, 1) %>% 
  map(plot_hgis_time, y_var = norm_ratio, outlier = ok_calc)

map(results, 1) %>% 
  map(plot_hgis_time, y_var = he12C, outlier = ok_calc)

results_df %>% 
  filter(!is.na(rec_num),
         sample_type == "B") %>% 
  select(sample_name, rec_num, wheel, sample_type, fm_consensus, fm_corr, sig_fm_corr) %>% 
  arrange(pos)

results_df %>% 
  filter(!is.na(rec_num),
         !is.na(sig_fm_corr)) %>% 
  compare_replicates() %>% 
  arrange(rec_num) %>% 
  select(Name, N)

cons_df <- compare_consensus(results_df)

cons_df %>% 
  select(wheel, pos, sample_name, fm_consensus, fm_corr, sig_fm_corr, fm_diff, sigma) %>% 
  filter(wheel == "USAMS061821") %>% 
  arrange(desc(fm_diff))

cons_df %>% 
  filter(wheel != "USAMS020521") %>% 
plot_hgis_consensus()

cons_df %>% 
  filter(wheel != "USAMS020521") %>% 
  group_by(Name, fm_consensus) %>% 
  summarize(across(c(fm_corr, sig_fm_corr, fm_diff, sigma), list(mean = mean, sd = sd)))

cons_df %>% 
  ungroup() %>% 
  filter(wheel != "USAMS020521") %>% 
  summarize(across(c(sig_fm_corr, fm_diff, sigma), list(mean = mean, sd = sd)))

blongworth/HybridGIS documentation built on Dec. 19, 2021, 10:41 a.m.