The goal of mxsrquick is to is to make MixSIAR project run quicker
You can install the development version of stepurr from GitHub with:
# install.packages("devtools")
# devtools::install_github("mncube/mxsrquick")
This is a basic example which shows you how to solve a common problem:
library(mxsrquick)
#> Loading required package: tidyverse
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v ggplot2 3.3.5 v purrr 0.3.4
#> v tibble 3.1.4 v dplyr 1.0.7
#> v tidyr 1.1.3 v stringr 1.4.0
#> v readr 2.0.1 v forcats 0.5.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#Create a dataframe which mimics isospace source data
iso_data <- data.frame(iso_a = c(2.2, 4.4, 3.3, 5.1, 3.4),
iso_b = c(1.6, 3.9, 5.2, 4.2, 3.7),
prot = c("bug", "bug", "bug", "plant", "plant"))
#Create an isospace plot using source groups' means and standard deviations
#Use tdf1 and tdf2 to correct for trophic discrimination factors
source_biplot(data = iso_data, group = prot,
var1 = iso_a, var2 = iso_b,
tdf1 = c(2, 1), tdf2 = c(1, 1),
x_lab = "A", y_lab = "B")
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