library(ggplot2) library(dplyr) library(tidyr) library(purrr) library(tidypaleo) knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.height = 3, fig.width = 5, dpi = 150 )
Load the packages:
library(tidyverse) library(tidypaleo)
Preparing the data:
alta_lake_geochem
alta_nested <- nested_data( alta_lake_geochem, qualifiers = c(age, depth, zone), key = param, value = value, trans = scale ) alta_nested
alta_nested %>% unnested_data(data) alta_nested %>% unnested_data(qualifiers, data)
pca <- alta_nested %>% nested_prcomp() pca
plot(pca) pca %>% unnested_data(qualifiers, scores) pca %>% unnested_data(variance) pca %>% unnested_data(loadings)
keji_nested <- keji_lakes_plottable %>% group_by(location) %>% nested_data(qualifiers = depth, key = taxon, value = rel_abund) keji_nested %>% unnested_data(qualifiers, data)
coniss <- keji_nested %>% nested_chclust_coniss() plot(coniss, main = location)
plot(coniss, main = location, xvar = qualifiers$depth, labels = "")
coniss %>% select(location, zone_info) %>% unnest(zone_info)
keji_nested %>% nested_chclust_coniss(n_groups = c(3, 2)) %>% select(location, zone_info) %>% unnested_data(zone_info)
halifax_nested <- halifax_lakes_plottable %>% nested_data(c(location, sample_type), taxon, rel_abund, fill = 0) halifax_nested %>% unnested_data(qualifiers, data)
hclust <- halifax_nested %>% nested_hclust(method = "average") plot( hclust, labels = sprintf( "%s (%s)", qualifiers$location, qualifiers$sample_type ) )
alta_nested %>% nested_analysis(vegan::rda, data) %>% plot()
biplot(pca)
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