Update RDA example using plotLoadings (#603)

Co-authored-by: seraidarian <ely.seraidarian@ecole.ensicaen.fr>
Co-authored-by: Tuomas Borman <60338854+TuomasBorman@users.noreply.github.com>
Co-authored-by: Leo Lahti <leo.lahti@iki.fi>
Co-authored-by: Muluh <127390183+Daenarys8@users.noreply.github.com>
Co-authored-by: Daenarys8 <Daenarys8@users.noreply.github.com>
Co-authored-by: TuomasBorman <TuomasBorman@users.noreply.github.com>
Co-authored-by: TuomasBorman <tvborm@utu.fi>

inst/pages/beta_diversity.qmd

@@ -607,27 +607,7 @@ how the groups tend to differ from each other in terms of community
 composition.
 
 ```{r plot-top-coef-anova, fig.cap = ""}
-# Extract loadings for first eigenvector
-rda <- reducedDim(tse2, "RDA")
-rda <- attr(rda, "rda")
-coef <- rda$CCA$v
-coef <- coef[, 1, drop = FALSE]
-
-# Get the taxa with biggest weights
-top_coef <- head(coef[rev(order(abs(coef))), , drop = FALSE], 20)
-# Sort weights in increasing order
-top_coef <- top_coef[order(top_coef), ]
-
-# Create data.frame
-df <- data.frame(
-    x = top_coef,
-    y = factor(names(top_coef), unique(names(top_coef))))
-
-# Create a plot
-ggplot(df, aes(x = x, y = y)) +
-    geom_bar(stat = "identity") +
-    labs(x = "", y= "", title = "Top Taxa") +
-    theme_bw()
+plotLoadings(tse2, "RDA", ncomponents = 2, n = 20)
 ```
 
 In the example above, the largest differences between the two groups

oma_packages/oma_packages.csv

@@ -32,7 +32,7 @@ grid
 gtools
 gsEasy
 igraph
-kableExtra
+IntegratedLearner
 knitr
 Maaslin2
 mia
@@ -68,7 +68,6 @@ SpiecEasi
 SPRING
 stats
 stringr
-SuperLearner
 tidyverse
 topGO
 vegan