A-app.Rmd

# (APPENDIX) Appendix {-}

# Frequently asked questions {#faq}

## Installation {#installation}

`r Biocpkg("ggtree")` is released within the Bioconductor project, you need to use `r CRANpkg("BiocManager")` to install it.

```r
## you need to install BiocManager before using it
## install.packages("BiocManager")
library(BiocManager)
install("ggtree")
```

Bioconductor release is adhere to specific R version. Please make sure you are using latest version of R if you want to install the latest release of Bioconductor packages, including `ggtree`. Beware that bugs will only be fixed in current release and develop branches. If you find a bug, please follow the guide^[<https://guangchuangyu.github.io/2016/07/how-to-bug-author/>] to report it.


## Basic R related {#faq-r}

### Use your local file {#faq-local-file}

If you are new to `R` and want to use `ggtree` for tree visualization, please do
learn some basic `R` and `ggplot2`.

A very common issue is that users always copy-paste command without looking at
the function's behavior. `system.file()` was used in the `treeio` and `ggtree` package documentation to find files in the packages.



```
system.file                package:base                R Documentation

Find Names of R System Files

Description:

     Finds the full file names of files in packages etc.

Usage:

     system.file(..., package = "base", lib.loc = NULL,
                 mustWork = FALSE)
```

For users who want to use their own files, please just use relative or absolute file path (*e.g.* `f = "your/folder/filename"`).



## Aesthetic mapping {#faq-aes-mapping}

### Inherit _aes_ {#faq-inherit-aes}

```r
ggtree(rtree(30)) + geom_point()
```

For example, we can add symbolic points to nodes with `geom_point()` directly.
The magic here is we don't need to map `x` and `y` position of the points by providing `aes(x, y)` to `geom_point()` since it was already mapped by `ggtree` function and it serves as a global mapping for all layers.

But what if we provide a `dataset` in a layer and the `dataset` doesn't contain column of `x` and/or `y`,
the layer function also try to map `x` and `y` and also others if you map them in `ggtree` function.
As these variable is not available in your `dataset`, you will get the following error:

```
Error in eval(expr, envir, enclos) : object 'x' not found
```

This can be fixed by using parameter `inherit.aes=FALSE` which will disable inheriting mapping from `ggtree` function.

### Never use `$` in aes {#faq-dollar-aes}

NEVER DO THIS^[<https://groups.google.com/d/msg/bioc-ggtree/hViM6vRZF94/MsZT8qRgBwAJ>
and <https://github.com/GuangchuangYu/ggtree/issues/106>].

See the explaination in the [ggplot2 book 2ed](https://github.com/hadley/ggplot2-book/blob/master/layers.rmd#aesthetic-mappings-secaes):

>Never refer to a variable with `$` (e.g., `diamonds$carat`) in `aes()`. This breaks containment, so that the plot no longer contains everything it needs, and causes problems if ggplot2 changes the order of the rows, as it does when facetting.


## Text & Label {#faq-text}

### Tip label truncated {#faq-label-truncated}

`r CRANpkg("ggplot2")` can't auto adjust `xlim` based on added text^[<https://twitter.com/hadleywickham/status/600280284869697538>].


```r
library(ggtree)
## example tree from https://support.bioconductor.org/p/72398/
tree <- read.tree(text= paste("(Organism1.006G249400.1:0.03977,(Organism2.022118m:0.01337,", 
             "(Organism3.J34265.1:0.00284,Organism4.G02633.1:0.00468)0.51:0.0104):0.02469);"))
p <- ggtree(tree) + geom_tiplab()  
```

In this example, the tip labels displayed on Figure \@ref(fig:truncatedTip)A are truncated. This is because the units are in two different spaces (data and pixel). Users can use `xlim` to allocate more spaces for tip labels (Figure \@ref(fig:truncatedTip)B).

```r
p + xlim(0, 0.08)
```

Another solution is to set `clip = "off"` to allow drawing outside of the plot panel. We may also need to set `plot.margin` to allocate more spaces for margin (Figure \@ref(fig:truncatedTip)C).

```r
p + coord_cartesian(clip = 'off') + 
  theme_tree2(plot.margin=margin(6, 120, 6, 6))
```

(ref:truncatedTipscap) Allocating more spaces for truncated tip lables.

(ref:truncatedTipcap) **Allocating more spaces for truncated tip lables.** Long tip lables may be truncated (A). One solution is to allocate more spaces for plot panel (B) and another solution is to allow plotting labels outside the plot panel (C).


```{r truncatedTip, fig.width=12, fig.height=4, echo=FALSE, fig.cap="(ref:truncatedTipcap)", fig.scap="(ref:truncatedTipscap)"}
library(ggplot2)
library(ggtree)
## example tree from https://support.bioconductor.org/p/72398/
tree<-read.tree(text="(Organism1.006G249400.1:0.03977,(Organism2.022118m:0.01337,(Organism3.J34265.1:0.00284,Organism4.G02633.1:0.00468)0.51:0.0104):0.02469);")
p <- ggtree(tree) + geom_tiplab()
p2 <- ggtree(tree) + geom_tiplab() + xlim(0, 0.08)
p3 <- p + coord_cartesian(clip = 'off') + 
  theme_tree2(plot.margin=margin(6, 120, 6, 6))
cowplot::plot_grid(p, p2, p3, ncol=3, labels=c("A", "B", "C"))
```

### Modify (tip) labels {#faq-modify-label}


If you want to modify tip labels of the tree, you can use `treeio::rename_taxa()` to rename a `phylo` or `treedata` object.

```{r renameTaxa}
tree <- read.tree(text = "((A, B), (C, D));")
d <- data.frame(label = LETTERS[1:4], 
                label2 = c("sunflower", "tree", "snail", "mushroom"))

## rename_taxa use 1st column as key and 2nd column as value by default                
## rename_taxa(tree, d)
rename_taxa(tree, d, label, label2) %>% write.tree
```

If the input tree object is a `treedata` instance, you can use `write.beast()` to export the tree with with associated data to a BEAST compatible NEXUS file.

Renaming phylogeny tip labels seems not be a good idea, since it may introduce problems when mapping the original sequence alignment to the tree. Personally, I recommend to store the new labels as a tip annotation in `treedata` object. 

```{r warnings = F}
tree2 <- full_join(tree, d, by = "label")
tree2
```

If you just want to show different or additional information when plotting the tree, you don't need to modify tip labels. This could be easily done via the `%<+%` operator to attach the modified version of the labels and than use `geom_tiplab` to display
the modified version (Figure \@ref(fig:renameTip)).


(ref:renameTipscap) Alternative tip labels.

(ref:renameTipcap) **Alternative tip labels.** Original tip lables (A) and modified version (B).


```{r renameTip, fig.width=8, fig.height=3, fig.cap="(ref:renameTipcap)", fig.scap="(ref:renameTipscap)"}
p <- ggtree(tree) + xlim(NA, 3)
p1 <- p + geom_tiplab()

## the following command will produce identical figure of p2
## ggtree(tree2) + geom_tiplab(aes(label = label2))
p2 <- p %<+% d + geom_tiplab(aes(label=label2))
cowplot::plot_grid(p1, p2, ncol=2, labels = c("A", "B"))
```

### Formatting (tip) labels {#faq-formatting-label}

If you want to format labels, you need to set `parse=TRUE` in `geom_text`/`geom_tiplab` and the `label` should be string that can be parsed into expression and displayed as described in `?plotmath`.

For example, the tip labels contains two parts, species name and accession number and we want to display species name in _italic_, we can use command like this to format specific tip/node label (Figure \@ref(fig:formatTip)A):

```{r formatTip-A, eval=F}
set.seed(2019-06-24)
tree <- rtree(30)
p1 <- ggtree(tree) + 
  geom_tiplab(aes(subset=node==35), 
                  label='paste(italic("species name"), 
                              " accession number")', 
              parse=T) + xlim(0, 6)
```


Another example for formating all tip labels is demonstrated in Figure \@ref(fig:formatTip)B:

```{r formatTip-B, eval=F}
p2 <- ggtree(tree) + 
  geom_tiplab(aes(label=paste0('bold(', label, 
                  ')~italic(', node, ')')), 
            parse=TRUE) + xlim(0, 5)
```

The `label` can be provided by a `data.frame` that contains related information
of the taxa (Figure \@ref(fig:formatTip)C).

```{r formatTip-C, eval=F}
tree <- read.tree(text = "((a,(b,c)),d);")
genus <- c("Gorilla", "Pan", "Homo", "Pongo")
species <- c("gorilla", "spp.", "sapiens", "pygmaeus")
geo <- c("Africa", "Africa", "World", "Asia")
d <- data.frame(label = tree$tip.label, genus = genus,
                species = species, geo = geo)

p3 <- ggtree(tree) %<+% d + xlim(NA, 6) +
    geom_tiplab(aes(label=paste0('italic(', genus, 
                  ')~bolditalic(', species, ')~', geo)), 
                parse=T)
cowplot::plot_grid(p1, p2, p3, ncol=3, labels = LETTERS[1:3])                
```


(ref:formatTipscap) Formatting labels.

(ref:formatTipcap) **Formatting labels.** Formatting specific tip/node label (A), all tip labels (B & C).

```{r formatTip, fig.width=12, fig.height=4, echo = F, fig.cap="(ref:formatTipcap)", fig.scap="(ref:formatTipscap)", ref.label = c('formatTip-A', 'formatTip-B', 'formatTip-C'), echo=FALSE}
```

### Avoid overlapping text labels {#faq-ggrepel}

User can use [ggrepel](https://cran.r-project.org/web/packages/ggrepel/) package to repel overlapping text labels^[<https://cran.r-project.org/web/packages/ggrepel/vignettes/ggrepel.html>].
.

For example:

(ref:repelTipscap) Repel labels.

(ref:repelTipcap) **Repel labels.** Repel labels to avoid overlapping.


```{r repelTip, fig.width=12, fig.height=8, fig.cap="(ref:repelTipcap)", fig.scap="(ref:repelTipscap)"}
library(ggrepel)
library(ggtree)
raxml_file <- system.file("extdata/RAxML", "RAxML_bipartitionsBranchLabels.H3", package="treeio")
raxml <- read.raxml(raxml_file)
ggtree(raxml) + geom_label_repel(aes(label=bootstrap, fill=bootstrap)) + 
  theme(legend.position = c(.1, .8)) + scale_fill_viridis_c()
```

### Bootstrap values from newick format {#faq-bootstrap}

It's quite command to store `bootstrap` value as node label in `newick` format. Visualizing node label is easy using `geom_text2(aes(subset = !isTip, label=label))`.

If you want to only display a subset of `bootstrap` (e.g. bootstrap > 80), you can't simply using `geom_text2(subset= (label > 80), label=label)` since `label` is a character vector, which contains node label (bootstrap value) and tip label (taxa name). If we use `geom_text2(subset=(as.numeric(label) > 80), label=label)`, it will also fail since `NAs` were introduced by coercion. We need to convert `NAs` to logical `FALSE`, this can be done by the following code:

```r
nwk <- system.file("extdata/RAxML","RAxML_bipartitions.H3", package='ggtree')
tr <- read.tree(nwk)
ggtree(tr) + geom_text2(aes(label=label, subset = !is.na(as.numeric(label)) & as.numeric(label) > 80))
```

Another solution is converting the bootstrap value outside `ggtree`.

```r
q <- ggtree(tr)
d <- q$data
d <- d[!d$isTip,]
d$label <- as.numeric(d$label)
d <- d[d$label > 80,]

q + geom_text(data=d, aes(label=label))
```

## Different x labels for different facet panels

This is not supported by `r CRANpkg("ggplot2")` in general. However, we can just draw text labels for each panels and put the labels beyond the plot panels as demonstrated in Figure \@ref(fig:xlabFacets).

(ref:xlabFacetsscap) X-axis titles for different facet panels.

(ref:xlabFacetscap) **X-axis titles for different facet panels.** 



```{r xlabFacets, fig.width=7, fig.height=8, fig.cap="(ref:xlabFacetscap)", fig.scap="(ref:xlabFacetsscap)"}
library(ggtree)
library(ggplot2)
set.seed(2019-05-02)
x <- rtree(30)
p <- ggtree(x) + geom_tiplab()
d <- data.frame(label = x$tip.label, 
                value = rnorm(30))
p2 <- facet_plot(p, panel = "Dot", data = d, 
            geom = geom_point, mapping = aes(x = value)) 
            
p2 <- p2 + theme_bw() + 
    xlim_tree(5) + xlim_expand(c(-5, 5), 'Dot') 

d = data.frame(.panel = c('Tree', 'Dot'), 
                lab = c("Distance", "Dot Units"), 
                x=c(2.5,0), y=-2)

p2 + scale_y_continuous(limits=c(0, 31), 
                        expand=c(0,0), 
                        oob=function(x, ...) x) +
    geom_text(aes(label=lab), data=d) + 
    coord_cartesian(clip='off')  + 
    theme(plot.margin=margin(6, 6, 40, 6))
```

## Plot something behind the phylogeny {#faq-under-the-tree}

The `ggtree` function plot the tree structure and normally we add layers on top of the tree.

```{r tree_behind_box}
set.seed(1982)
x <- rtree(5)
p <- ggtree(x) + geom_hilight(7, alpha=1)
```

If we want the layers behind the tree layer, we can reverse the order of all the layers.

```r
p$layers <- rev(p$layers)
```

Another solution is to use `ggplot()` instead of `ggtree()` and `+ geom_tree()` to add the layer of tree structure at the correct position of layer stack.

```r
ggplot(x) + geom_hilight(7, alpha=1) + geom_tree() + theme_tree()     
```

(ref:treeLayerOrderscap) Add layers behind tree structure.

(ref:treeLayerOrdercap) **Add layers behind tree structure.** A layer on top of the tree structure (A). Reverse layer order of A (B). Add layer behind the tree layer (C).


```{r treeLayerOrder, echo=F, fig.width=6, fig.height=3, fig.cap="(ref:treeLayerOrdercap)", fig.scap="(ref:treeLayerOrderscap)"}
g <- p
p$layers <- rev(p$layers)
cowplot::plot_grid(g, p, 
  ggplot(x) + geom_hilight(7, alpha=1) + geom_tree() + theme_tree(),
  ncol = 3, labels = LETTERS[1:3])     
```



## Enlarge center space in circular/fan layout tree {#faq-enlarge-center-space}

This question was asked several times^[<https://groups.google.com/d/msg/bioc-ggtree/gruC4FztU8I/mwavqWCXAQAJ>, <https://groups.google.com/d/msg/bioc-ggtree/UoGQekWHIvw/ZswUUZKSGwAJ> and <https://github.com/GuangchuangYu/ggtree/issues/95>], and a published example can be found in <https://www.ncbi.nlm.nih.gov/pubmed/27605062>. Increasing percentage of center white space in circular tree is useful to avoid overlapping tip labels and to increase readibility of the tree by moving all nodes and branches further out. This can be done simply by using `+xlim()` to allocate more space, just like in Figure \@ref(fig:layout2)G, or assign a long root branch that is similar to the "Root Length" parameter in FigTree. 


(ref:innerspacescap) Enlarge center space in circular tree.

(ref:innerspacecap) **Enlarge center space in circular tree.** Allocate more space by `xlim` (A) or long root branch (B).



```{r circular-space, fig.width=8, fig.height=4, fig.cap="(ref:innerspacecap)", fig.scap="(ref:innerspacescap)"}
set.seed(1982)
tree <- rtree(30)
plot_grid(
  ggtree(tree, layout='circular') + xlim(-10, NA),
  ggtree(tree, layout='circular') + geom_rootedge(5),
  labels = c("A", "B", ncol=2)
)
```

## Use the most distant tip from the root as the origin of the time scale

The `revts` will reverse the x-axis by setting the most recent tip to 0. We can use `scale_x_continuous(labels=abs)` to label x-axis using absolute values.

(ref:distantTipscap) Origin of the time scale.

(ref:distantTipcap) **Origin of the time scale.** Forward: from the root to tips (A). Backward: from the most distant tip to the root (B).


```{r distantTip, fig.cap="(ref:distantTipcap)", fig.scap="(ref:distantTipscap)", fig.width=6, fig.height=3}
tr <- rtree(10)
p <- ggtree(tr) + theme_tree2()
p2 <- revts(p) + scale_x_continuous(labels=abs)
plot_grid(p, p2, ncol=2, labels=c("A", "B"))
```

## Changing branch length of outgroup

When outgroups are on a very long branch length (Figure \@ref(fig:outgroupEdge)A), we would like to keep the out groups in the tree but ignore their branch lengths (Figure \@ref(fig:outgroupEdge)B)^[example from: <https://groups.google.com/d/msg/bioc-ggtree/T2ySvqv351g/mHsyljvBCwAJ>]. This can be easily done by modifying coordination of the out groups.


(ref:outgroupEdgescap) Changing branch length of outgroup.

(ref:outgroupEdgecap) **Changing branch length of outgroup.** Original tree (A) and reduced outgroup branch length version (B).


```{r outgroupEdge, fig.cap="(ref:outgroupEdgecap)", fig.scap="(ref:outgroupEdgescap)", fig.width=6, fig.height=5}
x <- read.tree("data/long-branch-example.newick")
m <- MRCA(x, 75, 76)
y <- groupClade(x, m)
p <- p1 <- ggtree(y, aes(linetype = group)) + 
  geom_tiplab(size = 2) +
  theme(legend.position = 'none')
p$data[p$data$node %in% c(75, 76), "x"] <- mean(p$data$x)
plot_grid(p1, p, ncol=2)
```

## Edit tree graphic details {#export-edit}


It can be hard to modify plot details for ordinary users using `r CRANpkg("ggplot2")`/`r Biocpkg("ggtree")`. We recommend using the `r CRANpkg("export")` package to export `r Biocpkg("ggtree")` output to 'Microsoft Office' Document and edit the tree graphic in 'PowerPoint'.




# Tips for using `ggtree` with `ggimage` {#ggimage-tips}

`r Biocpkg("ggtree")` supports annotating tree with silhouette images via the `r CRANpkg("ggimage")` package. `r CRANpkg("ggimage")` provides grammar of graphic syntax to work with image files. It allows processing images on the fly via the `image_fun` parameter, which accepts a function to process `magick-image` object. The `r CRANpkg("magick")` package provides several functions and these functions can be combined to perform a particular task.

## Example 1: Remove background of images {#ggimage-rm-image-bg}

(ref:ggimagebgscap) Remove image background.

(ref:ggimagebgcap) **Remove image background.** Plotting silhouette images on phylogenetic tree without (A) and with (B) background remove.


```{r ggimagebg, fig.width=8, fig.height=4, fig.cap="(ref:ggimagebgcap)", fig.scap="(ref:ggimagebgscap)"} 
set.seed(1982)
x <- rtree(5)
p <- ggtree(x) + theme_grey()
p1 <- p + geom_nodelab(image="img/frogs/frog.jpg", geom="image", size=.12) + 
          ggtitle("original image")
p2 <- p + geom_nodelab(image="img/frogs/frog.jpg", geom="image", size=.12,
            image_fun= function(.) magick::image_transparent(., "white")) + 
            ggtitle("image with background removed")
plot_grid(p1, p2, ncol=2)
```

## Example 2: Plot tree on a background image {#ggimage-bgimage}

The `geom_bgimage` add a layer of the image and put the layer to the bottom of the layer stack. It is a normal layer and doesn't change the structure of the output `ggtree` object. Users can add annotation layers as without the background image layer.

(ref:bgimagescap) Use image file as tree background.

(ref:bgimagecap) **Use image file as tree background.**


```{r bgimage, fig.width=6, fig.height=4, fig.cap="(ref:bgimagecap)", fig.scap="(ref:bgimagescap)"} 
ggtree(rtree(20), size=1.5, color="white") + 
  geom_bgimage('img/blackboard.jpg') + 
  geom_tiplab(color="white", size=5, family='xkcd')
```

# Comic (xkcd-like) phylogenetic tree {#commicR}

```{r ggsvg, fig.show='hide'}
library(htmltools)    
library(XML)    
library(gridSVG)    
library(ggplot2)    
library(ggtree)
library(comicR)    

p <- ggtree(rtree(30), layout="circular")  +
    geom_tiplab(aes(label=label), color="purple")
print(p)
svg <- grid.export(name="", res=100)$svg
```

(ref:comicRscap) Remove image background.

(ref:comicRcap) **Remove image background.** Plotting silhouette images on phylogenetic tree without (A) and with (B) background remove.


```{r comicR} 
tagList(
    tags$div(
             id = "ggtree_comic",
             tags$style("#ggtree_comic text {font-family:Chalkduster;}"),
             HTML(saveXML(svg)),
             comicR("#ggtree_comic", ff=5)
         )
) # %>% html_print
```



# Print ASCII-art rooted tree {#ascii-tree}

```{r asciiTree, comment=NA}
library(data.tree)
tree <- rtree(10)
d <- as.data.frame(as.Node(tree))
names(d) <- NULL
print(d, row.names=FALSE)
```

It is neat to print ASCII-art of phylogeny. Sometimes we don't want to plot the tree, but just take a glance at the tree structure without leaving the focus from R console. However, it is not a good idea to print the whole tree as ASCII text if the tree is large. Sometimes we just want to look at a specific portion of the tree and their immediate relatives. At this scenario, we can use `treeio::tree_subset()` function (see [session 2.4](subsetting-tree-with-data)) to extract selected portion of a tree. Then we can print ASCII-art of the tree subset to explore evolutionary relationship of interested species in R console.

`r Biocpkg("ggtree")` supports parsing tip labels as emoji to create [phylomoji](#phylomoji). With the `r CRANpkg("data.tree")` and `r CRANpkg("emojifont")` packages, we can also print phylomoji as ASCII text.

```{r asciiTreeEmoji, comment=NA}
library(emojifont)

tt <- '((snail,mushroom),(((sunflower,evergreen_tree),leaves),green_salad));'
tree <- read.tree(text = tt)
tree$tip.label <- emoji(tree$tip.label)
d <- as.data.frame(as.Node(tree))
names(d) <- NULL
print(d, row.names=FALSE)
```

# Zoom in selected portion {#facet-zoom}


(ref:facetZoomscap) Zoom in selected clade.

(ref:facetZoomcap) **Zoom in selected clade.**


```{r facetZoom, fig.width=6, fig.height=4, fig.cap="(ref:facetZoomcap)", fig.scap="(ref:facetZoomscap)"}
set.seed(2019-08-05)
x <- rtree(30)
nn <- tidytree::offspring(x, 43, self_include=TRUE)
ggtree(x) + ggforce::facet_zoom(xy = node %in% nn)
```


# Run ggtree in Jupyter notebook

If you have [Jupyter notebook](https://jupyter.org/) installed on your system, you can install [IRkernel](https://irkernel.github.io/) with the following command in R:

```r
install.packages("IRkernel")
IRkernel::installspec()
```

Then you can use ggtree and other R packages in Jupyter notebook. Here is a screenshot of recreating Figure \@ref(fig:phylomoji1) in Jupyter notebook.

(ref:jupyterscap) ggtree in Jupyter notebook.

(ref:jupytercap) **ggtree in Jupyter notebook.** Running ggtree in Jupyter notebook via R kernel. 


```{r jupyter, echo=F, fig.cap="(ref:jupytercap)", fig.scap="(ref:jupyterscap)"}
## htmltools::includeHTML("img/ggtree_jupyter.html")
knitr::include_graphics("img/Screenshot_2019-06-24_ggtree-jupyter.png")
```




# Figures and Tables



```{r facet-geom, echo=FALSE,results='asis'}

x <- "ggalt\tgeom_dumbbell\tcreates dumbbell charts\n
ggbio\tgeom_alignment\tshows interval data as alignment\n
ggfittext\tgeom_fit_text\tshrinks, grows or wraps text to fit inside a defined rectangular area\n
gggenes\tgeom_gene_arrow\tdraws genes as arrows\n
ggimage\tgeom_image\tvisualizes image files\n
ggimage\tgeom_phylopic\tqueries image files from phylopic database and visualizes them\n
ggplot2\tgeom_hline\tadds horizontal lines\n
ggplot2\tgeom_jitter\tadds a small amount of random variation to the location of each point\n
ggplot2\tgeom_label\tdraws a rectangle behind the text\n
ggplot2\tgeom_point\tcreats scatterplots\n
ggplot2\tgeom_raster\ta high performance special case for all the tiles are the same size\n
ggplot2\tgeom_rect\tdraws rectangle by using the locations of the four coners\n
ggplot2\tgeom_segment\tdraws a straight line between points\n
ggplot2\tgeom_spoke\ta polar parameterisation of 'geom_segment()'\n
ggplot2\tgeom_text\tadds text to the plot\n
ggplot2\tgeom_tile\tdraws rectangle by using the center of the tile and its size\n
ggplot2\tgeom_vline\tadds vertical lines\n
ggrepel\tgeom_text_repel\tadds text to the plot. The text labels repel away from each other and away from the data points\n
ggrepel\tgeom_label_repel\tdraws a rectangle underneath the text. The text labels repel away from each other and away from the data ponts\n
ggridges\tgeom_density_ridges\tarranges multiple density plots in a staggered fashion\n
ggridges\tgeom_density_ridges_gradient\tworks just like 'geom_density_ridges' except that the 'fill' aesthetic can vary along the x axis\n
ggridges\tgeom_ridgeline\tplots the sum of the 'y' and 'height' aesthetics versus 'x', filling the area between 'y' and 'y + height' with a color\n
ggridges\tgeom_ridgeline_gradient\tworks just like 'geom_ridgeline' except that the 'fill' aesthetic can vary along the x axis\n
ggstance\tgeom_barh\thorizontal version of 'geom_bar()'\n
ggstance\tgeom_boxploth\thorizontal version of 'geom_boxplot()'\n
ggstance\tgeom_crossbarh\thorizontal version of 'geom_crossbar()'\n
ggstance\tgeom_errorbarh\thorizontal version of 'geom_errorbarh()'\n
ggstance\tgeom_histogramh\thorizontal version of 'geom_histogram()'\n
ggstance\tgeom_linerangeh\thorizontal version of 'geom_linerange()'\n
ggstance\tgeom_pointrangeh\thorizontal version of 'geom_pointrange()'\n
ggstance\tgeom_violinh\thorizontal version of 'geom_violin()'\n
ggtree\tgeom_motif\tdraws aligned motifs\n
"

xx <- strsplit(x, "\n\n")[[1]]
y <- strsplit(xx, "\t") %>% do.call("rbind", .)
y <- as.data.frame(y)
colnames(y) <- c("Package", "Geom Layer", "Description")

require(kableExtra)
caption = "Geometric layers that supported by 'geom_facet()' and 'facet_plot()'"
knitr::kable(y, caption=caption, booktabs = T) %>% 
  collapse_rows(columns = 1, latex_hline = "major", valign ="top") %>%
  kable_styling(latex_options = c("striped", "scale_down"),
                bootstrap_options = c("striped", "hover")) #%>% landscape
```


<!--
#``` r treeio, echo=F, message=FALSE}
library(kableExtra)
ff <- matrix(c(
  'read.beast',  "parsing output of BEAST",
  "read.codeml", "parsing output of CodeML (rst and mlc files)",
  "read.codeml_mlc", "parsing mlc file (output of CodeML)",
  "read.hyphy", "parsing output of HYPHY",
  "read.jplace", "parsing jplace file including output of EPA and pplacer",
  "read.nhx", "parsing NHX file including output of PHYLDOG and RevBayes",
  "read.paml_rst", "parsing rst file (output of BaseML or CodeML)",
  "read.phylip", "parsing phylip file",
  "read.phyloT", "parsing output of phyloT (http://phylot.biobyte.de/)",
  "read.r8s", "parsing output of r8s",
  "read.raxml", "parsing output of RAxML",
  "Nnode", "get number of internal nodes",
  "Nnode2", "get number of all nodes (including internal and external nodes)",
  "Ntip", "get number of all tips (external nodes)",
  "as.phylo", "convert tree object to phylo object",
  "as.treedata", "convert tree object to treedata object",
  "drop.tip", "remove tips in a tree",
  "get.fields", "get available tree attributes stored in tree object",
  "get.placements", "get placement result of jplace object",
  "get.subs", "get substitution by comparing parent node and child node",
  "get.tipseq", "get tip sequences",
  "get.treetext", "get newick text of a tree object",
  "groupClade", "add clade grouping information to tree object",
  "groupOTU", "grouping OTUs by tracing back to most recent commond ancestor",
  "merge_tree", "merging tree objects into one",
  "write.jpace", "write tree with associated annotation data to a jplace file"
  ), ncol=2, byrow=TRUE)
ff <- as.data.frame(ff)
colnames(ff) <- c("Function", "Description")
knitr::kable(ff, caption = "Functions defined in treeio.", booktabs = T) #%>%
  #kable_styling(latex_options = c("striped", "hold_position"), full_width = T)
```


-->



# Session Info {#session-info}

The book was written using R Markdown and was compiled by `r CRANpkg("bookdown")` package. Here is the session information on the system on which this thesis was compiled:

```{r sessionInfo, echo=FALSE, cache=FALSE}
options(width=90)
devtools::session_info()
## sessionInfo() %>% capture.output %>%
##   `[`(., -c(5:8)) %>% paste('\n') %>% cat
## options(width=80)
```