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 <- as.Date('2014-09-01')
sys <- 'both' #cfams, usams, ams1 or both

#Pull the data
ss <- getStandards(from, sys = sys, rec = 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), from r from to present.

Data pulled from no_os by rec_num.

ggplot(ss, aes(f_modern)) +
  geom_histogram() +
  ggtitle("Fm distribution")

Remove clear outliers. Discard points > 1.5 * interquartile range.

ss <- ss %>%
  mutate(f_modern = removeOutliers(f_modern))

# weighted means
wm <- weighted.mean(ss$f_modern, ss$f_ext_error, na.rm = TRUE)

That leaves us with N = r length(ss$f_modern[!is.na(ss$f_modern)]), mean = r mean(ss$f_modern, na.rm = TRUE), weighted mean = r wm, sd = r sd(ss$f_modern, na.rm = TRUE).

ggplot(ss, aes(f_modern)) + 
  geom_histogram() + 
  ggtitle("Fm distribution", subtitle = "Outliers removed")

kable(select(ss, tp_num, tp_date_pressed, wheel, gf_co2_qty, f_modern, f_ext_error),
      caption = "Data without outliers")

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