In gholtgrieve/HEEL: Miscellaneous Functions From HEEL Members

LABORATORY

UW Facility for Compound-Specific Isotope Analysis of Environmental Samples (known informally a NACHO)
College of the Environment
University of Washington
Director is Gordon Holtgrieve, gholt@uw.edu, 206-227-9930

METHOD

Your solid samples were analyzed for bulk $\delta$^13^C and $\delta$^15^N on a ThermoFinnigan Delta V with a Carlo Erba elemental analyzer in continuous flow mode following the general method of Fry et al. 1992. Automated analysis system for coupled d13C and d15N measurements. Analytical Chemistry 64, 288-291.

ANALYSIS

Date of Analysis (YYYY-MM-DD): r params$analysis.date
Processed data folder file path: r params$processed.data.dir
Run type: bulk C and N
Run comments: r params$run.comments

REFERENCE MATERIALS

All internationally recognized reference material accepted values can be found at the CIAAW. Typically we use IsoLab working standards GA1, GA2, and Bristol Bay Sockeye (salmon) on NACHO. You can find information about these standards on the IsoLab web page. Below are data specific to this run:

library(tibble)
library(knitr)
library(dplyr)
library(pander)

known.stds <- dplyr::filter(params$known.standard.values, group %in% params$standards.in.run)

knitr::kable(tibble(known.stds), format = "simple", digits = c(NA, 2,2, 1, 1),
             caption="Lab reference materials used in this run and their accepted values.")

knitr::kable(params$measured.standard.means, format = "simple", digits = 1,
             caption = "Mean measured d13C and d15N of working standards both raw and adjusted to international standards.")

knitr::kable(tibble(params$calibration.coefficients), format = "simple", digits = 4, 
             caption="Linear calibration curve coefficents used for this run (y=mx + b).")

knitr::kable(tibble(params$error.analysis.results), format = "simple", digits = 2, 
             caption="Accuracy and precision of standards for this run.")

ZEROS & BLANKS

Blanks are empty tins while zeros are no tin or sample. The table below, if given, contains the data for blanks and zeros from this run. No table indicates blanks and zeros were not measurable. A blank correction has not been implemented in the script.

if (params$blank.flag) knitr::kable(params$blank.CN, format = "simple", caption="Blank data.")
if (params$zero.flag) knitr::kable(params$zero.CN, format = "simple", caption="Zero data.")

MASS EFFECTS

Your data were analysed for effects of sample mass on peak area and isotopic ratios. Ideally, there should be a strong linear response of mass C (or N) in the standard on peak area 44 (or 28) and no effect of peak area on $\delta$^13^C or $\delta$^15^N.

# library(ggplot2)
# library(ggpubr)
# library(gridExtra)
# 
# standard.CN <- params$standard.CN
# 
# ##These plots do not work!!!####
# p1 <- ggplot(standard.CN[standard.CN$group=="GA1"|standard.CN$group=="GA2",], aes(x = mass.N.mg, y = Area.28)) +
#   geom_point() +
#   labs(x = "mass (mg)", title = "Area Response vs. Input N") +
#   geom_smooth(method=lm, formula = y ~ x, se = FALSE) +
#   theme_minimal() +
#   theme(legend.position = "none") +
#   ggpubr::stat_cor(aes(label = paste(after_stat(rr.label), after_stat(p.label), sep = "~`,`~")),
#                label.y.npc = "center") +
#   ggpubr::stat_regline_equation()
# 
# p2 <- ggplot(standard.CN[standard.CN$group=="GA1"|standard.CN$group=="GA2",], aes(x = mass.C.mg, y = Area.44)) +
#   geom_point() +
#   labs(x = "mass (mg)", title = "Area Response vs. Input C") +
#   geom_smooth(method=lm, formula = y ~ x, se = FALSE) +
#   ggpubr::stat_cor(aes(label = paste(after_stat(rr.label), after_stat(p.label), sep = "~`,`~")),
#                label.y.npc = "center") +
#   theme_minimal() +
#   ggpubr::stat_regline_equation()
# 
#   p21 <- gridExtra::grid.arrange(p2, p1, ncol = 2)

#The table below seems to work.  
knitr::kable(params$standard.coefficients, format = "simple", digits = 4,
              caption="Linear model coefficients of mass effects.")

SAMPLE MASS CHECK

Any samples listed below are 30% below the target mass nitrogen and/or carbon. Samples at or below 30% of the target amount of nitrogen and/or carbon are highly suspect and should be reanalyzed.

output <- tibble(Analysis = c("C", "N"), Unique.ID = NA)

output$Unique.ID[1] <- params$peak.area.flags %>% filter(area.44.30pct.threshold.flag == T) %>% select(unique.ID)
output$Unique.ID[2] <- params$peak.area.flags %>% filter(area.28.30pct.threshold.flag == T) %>% select(unique.ID)

pander::pander(output)

Any samples listed below are 50% below the target mass for nitrogen. $\delta$^15^N is suspect. Consider reanalyzing the sample.

output <- tibble(params$peak.area.flags %>% filter(area.28.50pct.threshold.flag == T) %>% select(unique.ID))

pander::pander(output)

Any samples listed below are 300% above the target mass for carbon. $\delta$^13^C is suspect. Consider reanalyzing the sample.

output <- tibble(params$peak.area.flags %>% filter(area.44.300pct.threshold.flag == T) %>% select(unique.ID))

pander::pander(output)

SAMPLE DATA

Below is a short summary of your sample data. A more complete data set has been saved as a .csv file in your chosen processed data folder.

if(params$return.mass.percent.CN == F){
  sample.CN <- params$sample.CN[c("Identifier.1", "d.13C.12C.VPDB", "d.15N.14N.air", "mg.C", "mg.N")]
} else {
  sample.CN <- params$sample.CN[c("Identifier.1", "d.13C.12C.VPDB", "d.15N.14N.air", "mass.percent.C", "mass.percent.N")]
}


knitr::kable(tibble(sample.CN), format = "simple", caption="Sample Data", digits = c(NA, 2, 2, 2, 2))

DATA REDUCTION DETAILS

library(knitr)
x <- matrix(c("File rendered on:", format(Sys.time(), "%d %b %Y"),
              "R version:", as.character(getRversion()),
              "HEEL package version:", as.character(packageVersion("HEEL"))), ncol=2, byrow=T)
print(knitr::kable(x, row.names = F))