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<h1 class="title toc-ignore">Multivariate</h1>
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<div id="ordinations" class="section level3">
<h3>Ordinations</h3>
<p>Many ecological analyses involve some sort of multivariate measurement of “species” within “sites”. For example, this might include counts of plant species along a sampling transect, relative sequencing abundance of bacterial species within a host gut, or chemical composition of soils.</p>
<p>The vegan package in R provides excellent tutorials and resources for conducting the appropriate analyses for a given multivariate problem. Here, we will simply explore different ways of visualizing these outputs.</p>
<p>Let’s start by simulating some data.</p>
<p>I’m a (very) amateur baker (read: I like to eat sweet things, but I don’t have a clue about how/why baking recipes work), so I collected some data about baking ratios for several common desserts:</p>
<pre class="r"><code># create the data frame
desserts = data.frame(dessert = c("pound_cake","pancakes","meringue","choux_pastry","pie","fritter", "cookie","custard","biscuit", "crepe","muffin"),
flour = c(2,1,2,0,1,3,2,3,3,0.5,2), sugar = c(1,0,2,0,0,0,1,0,0,0,0),
eggs = c(1,1,1,2,0,1,0,2,0,1,1), fat = c(1,0.5,0,1,2,0,2,0,1,0,1),
liquid = c(0,2,0,2,1,2,0,1,2,1,2))</code></pre>
<p>Now we will visualize these data using NMDS (Non-metric multidimensional scaling). We could also use a number of other multivariate methods (e.g. PCA, rda, dbrda, cca). A lot of ecological datasets violate assumptions of normality and often NMDS is a pretty robust method; therefore, we will work through this example using the metaMDS function in the vegan package.</p>
<pre class="r"><code># load vegan library
library(vegan)
# run NMDS
desord = metaMDS(desserts[,-1])</code></pre>
<pre><code>## Run 0 stress 0.1036346
## Run 1 stress 0.1036346
## ... New best solution
## ... Procrustes: rmse 5.30892e-05 max resid 0.0001064708
## ... Similar to previous best
## Run 2 stress 0.1193745
## Run 3 stress 0.1036346
## ... Procrustes: rmse 0.0001642725 max resid 0.0003294312
## ... Similar to previous best
## Run 4 stress 0.1318933
## Run 5 stress 0.1318934
## Run 6 stress 0.2899938
## Run 7 stress 0.1036347
## ... Procrustes: rmse 0.0002570188 max resid 0.0005205672
## ... Similar to previous best
## Run 8 stress 0.171437
## Run 9 stress 0.1036347
## ... Procrustes: rmse 0.0002334495 max resid 0.0004741269
## ... Similar to previous best
## Run 10 stress 0.1036346
## ... Procrustes: rmse 7.797331e-05 max resid 0.0001588556
## ... Similar to previous best
## Run 11 stress 0.1383341
## Run 12 stress 0.1036346
## ... Procrustes: rmse 6.190355e-05 max resid 0.000125205
## ... Similar to previous best
## Run 13 stress 0.1786696
## Run 14 stress 0.1193745
## Run 15 stress 0.1703785
## Run 16 stress 0.1318934
## Run 17 stress 0.1036346
## ... Procrustes: rmse 6.185104e-05 max resid 0.0001253576
## ... Similar to previous best
## Run 18 stress 0.1036346
## ... Procrustes: rmse 0.0002117138 max resid 0.0004247419
## ... Similar to previous best
## Run 19 stress 0.1036346
## ... Procrustes: rmse 0.0001906893 max resid 0.0003862021
## ... Similar to previous best
## Run 20 stress 0.1318933
## *** Solution reached</code></pre>
<p>Unfortunately, the basic vegan plotting function doesn’t make a particularly compelling plot:</p>
<pre class="r"><code>plot(desord)</code></pre>
<p><img src="Code_for_multivariate_plots_demo_files/figure-html/unnamed-chunk-3-1.png" width="672" /></p>
<p>Not to fear! We can extract these data and make our own ggplot2-based plot.</p>
<pre class="r"><code># create dataframe using information stored as points in the NMDS object
dessert_data = data.frame(NMDS1 = desord$points[,1], NMDS2= desord$points[,2],
dessert = desserts$dessert)
head(dessert_data)</code></pre>
<pre><code>## NMDS1 NMDS2 dessert
## 1 -0.48446362 0.1261595 pound_cake
## 2 0.36062179 -0.1195179 pancakes
## 3 -0.60830809 0.5137318 meringue
## 4 0.75160092 -0.2512453 choux_pastry
## 5 -0.06593439 -0.7494342 pie
## 6 0.10414680 0.2649756 fritter</code></pre>
<pre class="r"><code># create dataframe of ingredient information using the 'species' scores
ingredient_data = as.data.frame(scores(desord, "species"))
ingredient_data$ingredient = row.names(ingredient_data)
head(ingredient_data)</code></pre>
<pre><code>## NMDS1 NMDS2 ingredient
## flour -0.2288977 0.1283596 flour
## sugar -1.1138513 0.4866187 sugar
## eggs 0.3352142 0.3436351 eggs
## fat -0.2236500 -0.7130399 fat
## liquid 0.4657341 -0.1422781 liquid</code></pre>
<p>Now we can make a plot:</p>
<pre class="r"><code># First, let's visualize the desserts in 2-D space
ggplot(dessert_data, aes(x=NMDS1, y=NMDS2))+
geom_point(aes(col=dessert))+
geom_text(aes(label=dessert, col=dessert), nudge_y = -0.025)+
theme_bw(base_size=14)</code></pre>
<p><img src="Code_for_multivariate_plots_demo_files/figure-html/unnamed-chunk-5-1.png" width="672" /></p>
<p>It’s a bit annoying that some of our labels are not entirely visible. One easy solution is to use the ggrepel package:</p>
<pre class="r"><code>library(ggrepel)
ggplot(dessert_data, aes(x=NMDS1, y=NMDS2))+
geom_point(aes(col=dessert))+
geom_text_repel(aes(label=dessert, col=dessert))+
theme_bw(base_size=14)</code></pre>
<p><img src="Code_for_multivariate_plots_demo_files/figure-html/unnamed-chunk-6-1.png" width="672" /></p>
<p>Let’s say that we want to get really fancy and substitute icons for each of our points. R has several different shapes that we could use, but none of them are particularly representative of our desserts. However, we can take advantage of a) emojis or b) MAKING OUR OWN ICONS!!!</p>
<p>First, let’s install the emo package</p>
<pre class="r"><code>#remotes::install_github("hadley/emo")
library(emo)</code></pre>
<p>This is a pretty cool package that will retrieve color emojis given a keyword. For example:</p>
<pre class="r"><code>ji("cake")</code></pre>
<pre><code>## <U+0001F370></code></pre>
<p>If we want to see all the emojis, we can access the ji_keyword object</p>
<pre class="r"><code>head(summary(ji_keyword))</code></pre>
<pre><code>## Length Class Mode
## 12 -none- character
## -1 1 -none- character
## “acceptable” 1 -none- character
## “application” 1 -none- character
## “bargain” 1 -none- character
## “congratulations” 1 -none- character</code></pre>
<pre class="r"><code>dim(summary(ji_keyword)) # there are over 5000 keywords</code></pre>
<pre><code>## [1] 5586 3</code></pre>
<p>Let’s see if we can find some good emojis for our desserts</p>
<pre class="r"><code>ji("cake")
ji("cloud") # for meringue
ji("custard")
ji("pancake")
ji("pie")
ji("cupcake") # for muffin
ji("cookie")
ji("bread") # for choux
ji("hotdog") # for fritter
ji("circle") # for crepe
ji("moon_cake") # for biscuit
ji("doughnut") # for pound cake</code></pre>
<p>Now we can map these to values</p>
<pre class="r"><code>library(purrr)
library(dplyr)
# create a new column with our substitue names
dessert_data = dessert_data %>%
mutate(dessert_id2 = dessert) %>%
mutate(dessert_id2 = recode(dessert_id2, meringue="cloud", muffin="cupcake",
fritter="hotdog", crepe="circle", biscuit="moon_cake", pound_cake="doughnut", choux_pastry="bread")) %>%
mutate(emoji = map_chr(dessert_id2, ji))
head(dessert_data)</code></pre>
<pre><code>## NMDS1 NMDS2 dessert dessert_id2 emoji
## 1 -0.48446362 0.1261595 pound_cake doughnut <U+0001F369>
## 2 0.36062179 -0.1195179 pancakes pancakes <U+0001F95E>
## 3 -0.60830809 0.5137318 meringue cloud <U+2601><U+FE0F>
## 4 0.75160092 -0.2512453 choux_pastry bread <U+0001F35E>
## 5 -0.06593439 -0.7494342 pie pie <U+0001F967>
## 6 0.10414680 0.2649756 fritter hotdog <U+0001F32D></code></pre>
<pre class="r"><code>ggplot(dessert_data, aes(x = NMDS1, y = NMDS2))+
geom_text(aes(label=emoji, col=emoji))+
geom_text_repel(aes(label=dessert))+
guides(col="none")+
theme_bw()</code></pre>
<p><img src="Code_for_multivariate_plots_demo_files/figure-html/unnamed-chunk-11-1.png" width="672" /></p>
<p>Hooray!</p>
<p>So obviously, not all of these are perfect substitutes. What if we could substitute in our OWN icons? WHAT?</p>
<p>To do this, we can take advantage of the ggimage package:</p>
<pre class="r"><code>library(ggimage)</code></pre>
<p>We now need to find some images to use! I created a folder (named “baking_pics”) to store my images.</p>
<pre class="r"><code># store file paths to images
images = list.files("baking_pics/")
images = paste("baking_pics/", images, sep="")
# substitute the image file in the dessert data frame
dessert_data = dessert_data %>%
mutate(picture = NA)
dessert_data$picture[grep("crepe",dessert_data$dessert)] = "baking_pics/crepe.png"
dessert_data$picture[grep("fritter",dessert_data$dessert)] = "baking_pics/fritter.png"
dessert_data$picture[grep("choux_pastry",dessert_data$dessert)] = "baking_pics/eclair.png"
dessert_data$picture[grep("pound_cake",dessert_data$dessert)] = "baking_pics/poundcake.png"
dessert_data$picture[grep("meringue",dessert_data$dessert)] = "baking_pics/meringue.png"
dessert_data$picture[grep("muffin",dessert_data$dessert)] = "baking_pics/muffin.png"</code></pre>
<p>Before plotting the emojis and pictures together, let’s now mutate the bad/fake emojis to NA:</p>
<pre class="r"><code>dessert_data = dessert_data %>%
mutate_at(vars(emoji), funs(ifelse(is.na(picture), ., NA)))</code></pre>
<p>Now let’s plot them together!</p>
<pre class="r"><code>ggplot(dessert_data, aes(x=NMDS1, y=NMDS2))+
geom_image(aes(image = picture),size=0.1)+
geom_text_repel(aes(label = emoji, col=emoji), size=12)+
guides(col="none")+
theme_bw()</code></pre>
<p><img src="Code_for_multivariate_plots_demo_files/figure-html/unnamed-chunk-15-1.png" width="672" /></p>
<p>Alright!</p>
<p>Granted, this does look heinous, but if you are a talented artist, you can see how this method is great for automatically adding your own artwork to figures so that you don’t have to futz around in powerpoint or illustrator :)</p>
<p>The last step that I’ll cover is adding the ingredient information to plot.</p>
<pre class="r"><code>head(ingredient_data)</code></pre>
<pre><code>## NMDS1 NMDS2 ingredient
## flour -0.2288977 0.1283596 flour
## sugar -1.1138513 0.4866187 sugar
## eggs 0.3352142 0.3436351 eggs
## fat -0.2236500 -0.7130399 fat
## liquid 0.4657341 -0.1422781 liquid</code></pre>
<p>We can do this by simply adding another layer to the plot:</p>
<pre class="r"><code>ggplot(dessert_data, aes(x=NMDS1, y=NMDS2))+
geom_image(aes(image = picture),size=0.1)+
geom_text_repel(aes(label = emoji, col=emoji), size=12)+
geom_text_repel(data=ingredient_data, aes(x=NMDS1, y=NMDS2, label=ingredient))+
guides(col="none")+
theme_bw()</code></pre>
<p><img src="Code_for_multivariate_plots_demo_files/figure-html/unnamed-chunk-17-1.png" width="672" /></p>
<p>And just because we can, let’s play with the fonts.</p>
<p>You can check out the fonts installed on your (Windows) system using:</p>
<pre class="r"><code>#windowsFonts()</code></pre>
<p>This usually doesn’t return a huge array of options. But we can use the extrafont package to have access to some more:</p>
<pre class="r"><code># install this dependency before loading the package
#remotes::install_version("Rttf2pt1", version = "1.3.8")
library(extrafont)
# need to import and load the fonts - this will take a few minutes but only has to be done once
#font_import()
#loadfonts(device="win")
head(windowsFonts())</code></pre>
<pre><code>## $serif
## [1] "TT Times New Roman"
##
## $sans
## [1] "TT Arial"
##
## $mono
## [1] "TT Courier New"
##
## $`Agency FB`
## [1] "Agency FB"
##
## $Algerian
## [1] "Algerian"
##
## $`Arial Black`
## [1] "Arial Black"</code></pre>
<pre class="r"><code># hooray! fonts!</code></pre>
<p>Let’s pick some zany fonts for our ingredients</p>
<pre class="r"><code># make a vector
font_vec = c("Papyrus", "Jokerman", "Bauhaus 93", "Goudy Stout", "Blackadder ITC")
ggplot(dessert_data, aes(x=NMDS1, y=NMDS2))+
geom_image(aes(image = picture),size=0.1)+
geom_text_repel(aes(label = emoji, col=emoji), size=12)+
geom_text_repel(data=ingredient_data, aes(x=NMDS1, y=NMDS2, label=ingredient,
family=font_vec))+
guides(col="none")+
theme_bw()</code></pre>
<p><img src="Code_for_multivariate_plots_demo_files/figure-html/unnamed-chunk-20-1.png" width="672" /></p>
<p>Yes, that’s right, I used Papyrus.</p>
<p><img src="https://media.giphy.com/media/l1J9NQvtuCQzUbH2g/giphy-downsized-large.gif" /></p>
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