---
title: tidyverse and ggplot integration with destiny
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{tidyverse and ggplot integration with destiny}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

Interaction with the tidyverse and ggplot2
==========================================

The [tidyverse](https://www.tidyverse.org/), [ggplot2](http://ggplot2.tidyverse.org/), and destiny are a great fit!

```{r}
suppressPackageStartupMessages({
    library(destiny)
    library(tidyverse)
    library(forcats)  # not in the default tidyverse loadout
})
```

ggplot has a peculiar method to set default scales: You just have to define certain variables.

```{r}
scale_colour_continuous <- scale_color_viridis_c
```

When working mainly with dimension reductions, I suggest to hide the (useless) ticks:

```{r}
theme_set(theme_gray() + theme(
    axis.ticks = element_blank(),
    axis.text  = element_blank()))
```

Let’s load our dataset

```{r}
data(guo_norm)
```

Of course you could use [tidyr](http://tidyr.tidyverse.org/)::[gather()](https://rdrr.io/cran/tidyr/man/gather.html) to tidy or transform the data now, but the data is already in the right form for destiny, and [R for Data Science](http://r4ds.had.co.nz/tidy-data.html) is a better resource for it than this vignette. The long form of a single cell `ExpressionSet` would look like:

```{r}
guo_norm %>%
    as('data.frame') %>%
    gather(Gene, Expression, one_of(featureNames(guo_norm)))
```

But destiny doesn’t use long form data as input, since all single cell data has always a more compact structure of genes×cells, with a certain number of per-sample covariates (The structure of `ExpressionSet`).

```{r}
dm <- DiffusionMap(guo_norm)
```

`names(dm)` shows what names can be used in `dm$<name>`, `as.data.frame(dm)$<name>`, or `ggplot(dm, aes(<name>))`:

```{r}
names(dm)  # namely: Diffusion Components, Genes, and Covariates
```

Due to the `fortify` method (which here just means `as.data.frame`) being defined on `DiffusionMap` objects, `ggplot` directly accepts `DiffusionMap` objects:

```{r}
ggplot(dm, aes(DC1, DC2, colour = Klf2)) +
    geom_point()
```

When you want to use a Diffusion Map in a dplyr pipeline, you need to call `fortify`/`as.data.frame` directly:

```{r}
fortify(dm) %>%
    mutate(
        EmbryoState = factor(num_cells) %>%
            lvls_revalue(paste(levels(.), 'cell state'))
    ) %>% ggplot(aes(DC1, DC2, colour = EmbryoState)) +
        geom_point()
```

The Diffusion Components of a converted Diffusion Map, similar to the genes in the input `ExpressionSet`, are individual variables instead of two columns in a long-form data frame, but sometimes it can be useful to “tidy” them:

```{r}
fortify(dm) %>%
    gather(DC, OtherDC, num_range('DC', 2:5)) %>%
    ggplot(aes(DC1, OtherDC, colour = factor(num_cells))) +
        geom_point() +
        facet_wrap(~ DC)
```

Another tip: To reduce overplotting, use `sample_frac(., 1.0, replace = FALSE)` (the default) in a pipeline.

Adding a constant `alpha` improves this even more, and also helps you see density:

```{r}
fortify(dm) %>%
    sample_frac() %>%
    ggplot(aes(DC1, DC2, colour = factor(num_cells))) +
        geom_point(alpha = .3)
```
