#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)
.
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