In blongworth/amstools: Provides tools for AMS data extraction and analysis

#Load Libraries
library(amstools)
library(amsdata)
library(ggplot2)
library(dplyr)
library(knitr)
library(odbc)

options(digits = 4)

#set parameters- from, to, sys not used if loading from file
from <- '2014-09-01'
to <- 'present' #present or date
sys <- 'both' #cfams, usams, ams1 or both

#Pull the data
ss <- getRecSR(recnum = params$rec)

# get sample name
db <- conNOSAMS()
name <- dbGetQuery(db, paste("select cl_id from logged_sample where rec_num =", params$rec))

Determination of mean fm and error for r name, rec num r sprintf('%i', params$rec).

Data pulled from snics_results by rec_num. Here's the data...

ggplot(ss, aes(fm_corr)) + geom_histogram()
ggplot(ss, aes(dc13)) + geom_histogram()

kable(select(ss, tp_num, tp_date_pressed, wheel, fm_corr, sig_fm_corr, dc13))

And the weighted mean and SD.

# means
fmmean <- mean(ss$fm_corr)
fmsd <- sd(ss$fm_corr)

That leaves us with N = r nrow(ss), mean = r mean(ss$fm_corr), weighted mean = r weighted.mean(ss$fm_corr, ss$sig_fm_corr), sd = r sd(ss$fm_corr). Mean d13C = r mean(ss$dc13, na.rm=TRUE), sd = r sd(ss$dc13, na.rm=TRUE).

Remove clear outliers. Discard points > 3 * SD.

ssnol <- ss %>% 
  filter(fm_corr > fmmean - fmsd,
         fm_corr < fmmean + fmsd)

ggplot(ssnol, aes(fm_corr)) + geom_histogram()
ggplot(ssnol, aes(dc13)) + geom_histogram()

And the weighted mean and SD without outliers.

That leaves us with N = r nrow(ssnol), mean = r mean(ssnol$fm_corr), weighted mean = r weighted.mean(ssnol$fm_corr, ssnol$sig_fm_corr), sd = r sd(ssnol$fm_corr). Mean d13C = r mean(ssnol$dc13, na.rm=TRUE), sd = r sd(ssnol$dc13, na.rm=TRUE).

blongworth/amstools documentation built on Nov. 9, 2023, 6:52 p.m.