03-ggplot2.Rmd

---
layout: topic
title: Data visualization with ggplot2
minutes: 60
---

```{r, echo=FALSE, purl=FALSE}
knitr::opts_chunk$set(fig.keep='last')
```

```{r setup, echo=FALSE, purl=FALSE}
source("setup.R")
```

> ### Learning Objectives
>
> -	Visualize some of the [mammals data](https://dx.doi.org/10.6084/m9.figshare.1314459.v5)
> from [`portal_data_joined.csv`](http://kbroman.org/datacarp/portal_data_joined.csv)
> -	Understand how to plot these data using R ggplot2 package. For more details
> on using ggplot2 see [official documentation](http://docs.ggplot2.org/current/).
> -	Building step by step complex plots with the ggplot2 package

```{r load_data_and_clean, echo=FALSE, message=FALSE}
library(dplyr)
surveys <- read.csv("http://kbroman.org/datacarp/portal_data_joined.csv")
surveys_complete <- surveys %>%
    filter(species_id != "", !is.na(weight)) %>%
    filter(!is.na(hindfoot_length), sex != "")
species_counts <- surveys_complete %>%
    group_by(species_id) %>%
    tally
frequent_species <-  species_counts %>%
    filter(n >= 10) %>%
    select(species_id)
reduced <- surveys_complete %>%
    filter(species_id %in% frequent_species$species_id)
```

I've placed these reduced data on the web, so you can download them
directly.

```{r load_clean_data, eval=FALSE}
reduced <- read.csv("http://kbroman.org/datacarp/portal_data_reduced.csv")
```

Or download the file and then load it

```{r download_then_load, eval=FALSE}
download.file("http://kbroman.org/datacarp/portal_data_reduced.csv",
              "CleanData/portal_data_reduced.csv")
reduced <- read.csv("CleanData/portal_data_reduced.csv")
```


## Plotting with ggplot2

There are two main systems for making plots in R: "base graphics"
(which are the traditional plotting functions distributed with R) and
ggplot2, written by Hadley Wickham following Leland Wilkinson's book
_Grammar of Graphics_. We're going to show you how to use
ggplot2. It's may seem a bit complicated at first, but once you get a
hang of it, you'll be able to make really useful visualizations quite rapidly.

We first need to load the dplyr and ggplot2 packages.

```{r, message=FALSE}
library(ggplot2)
library(dplyr)
```

I'll assume the data are available, and we'll focus on the "cleaned"
version, `reduced`.

Let's first make a scatterplot of hindfoot length vs weight. Here's
the code to do it.

```{r first-ggplot}
ggplot(reduced, aes(x = weight, y = hindfoot_length)) + geom_point()
```

Two key concepts in the grammar of graphics: _aesthetics_ map features
of the data (for example, the `weight` variable) to features of the
visualization (for example the y-axis coordinate), and
_geoms_ concern what actually gets plotted (here, each row in the data
becomes a point in the plot).

Another key aspect of ggplot2: the `ggplot()` function creates a
graphics object; additional controls are added with the `+`
operator. The actual plot is made when the object is printed.

```{r explicit_printing, eval=FALSE}
p1 <- ggplot(reduced, aes(x=weight, y=hindfoot_length))
p2 <- p1 + geom_point()
print(p2)
```

```{r explicit_printing_run, echo=FALSE}
p1 <- ggplot(reduced, aes(x=weight, y=hindfoot_length))
p2 <- p1 + geom_point()
```

If we saved the pieces like this, we could apply other options
afterwards. For example, if we wanted `weight` on a log scale:

```{r weight_on_log_scale}
p2 + scale_x_log10()
```

This makes it kind of easy to try out different things. For example,
we could plot the x-axis on a square root scale.

```{r weight_on_sqrt_scale}
p2 + scale_x_sqrt()
```


### Challenge

Make a scatterplot of `hindfoot_length` vs `weight`, but only for the
`species_id`, `"DM"`.

<!-- end challenge -->



## Other aesthetics

For scatterplot, additional aesthetics include `shape`, `size`,
`color`, and "`alpha`" (for transparency of points).

Let's make a template for our plot, to make modifications easier.

```{r create-ggplot-object}
surveys_plot <- ggplot(reduced, aes(x = weight, y = hindfoot_length))
```

- adding transparency (alpha)

```{r adding-transparency}
surveys_plot + geom_point(alpha = 0.1)
```

- adding colors

```{r adding-colors}
surveys_plot + geom_point(alpha = 0.1, color = "slateblue")
```

```{r changing the size of the points}
surveys_plot + geom_point(alpha = 0.1, color = "slateblue", size=0.5)
```

Things get more interesting when we assign these aesthetics to data.


```{r scatter_colored_by_species}
surveys_plot + geom_point(aes(color = species_id))
```


### Challenge

Use dplyr to calculate the mean `weight` and `hindfoot_length` as well
as the sample size for each species.

Make a scatterplot of mean `hindfoot_length` vs mean `weight`, with
the sizes of the points corresponding to the sample size.

<!-- end challenge -->

## Layers

You can use `geom_line` to make a line plot. For example, we could
plot the counts of species by year.

```{r plot_counts_by_year}
count_by_year <- reduced %>%
    group_by(year) %>%
    tally

p <- ggplot(count_by_year, aes(x=year, y=n))
p + geom_line()
```

You can use _both_ `geom_line` and `geom_point` to make a line plot
with points at the data values.

```{r line_plus_point}
p + geom_line() + geom_point()
```

This brings up another important concept with ggplot2: _layers_. A
given plot can have multiple layers of geometric objects, plotted one
on top of the other.

If you make the lines and points different colors, we can see that the
points are placed _on top_ of the lines.

```{r line_plus_point_wcolors}
p + geom_line(color="lightblue") + geom_point(color="violetred")
```

If we switch the order of `geom_point` and `geom_line`, we'll reverse
the layers.

```{r line_plus_point_wcolors_alt}
p + geom_point(color="violetred") + geom_line(color="lightblue")
```

Note that aesthetics included in the call to `ggplot()` (or completely
separately) are made to be the defaults for all layers, but we can
separately control the aesthetics for each layer. For example, we
could color the points by year:

```{r points_colored_by_year}
p + geom_line() + geom_point(aes(color=year))
```

Compare that to the following:

```{r points_colored_by_year_2}
p + geom_line() + geom_point() + aes(color=year)
```

### Challenge

Make a plot of counts of `species_id` `"DM"` and `"DS"` by year.

<!-- end challenge -->

## Groups

Suppose, in that last challenge, we'd wanted to have black lines but
the points colored by species. We might have done this:

```{r misgrouped}
counts_dm_ds <- reduced %>% filter(species_id %in% c("DM", "DS")) %>%
    group_by(species_id, year) %>% tally
p <- ggplot(counts_dm_ds, aes(x=year, y=n))
p + geom_line() + geom_point(aes(color=species_id))
```

The points get connected left-to-right, which is not what we want.

If we make the `color=species_id` aesthetic _global_, we don't have
this problem.

```{r color_global}
p + geom_line() + geom_point() + aes(color=species_id)
```

Alternatively, we can use the `group` aesthetic, which indicates that
certain data points go together. This way the lines can be a constant
color.

```{r group_aes}
p + geom_line(aes(group=species_id)) + geom_point(aes(color=species_id))
```

We could also make the group aesthetic global

```{r group_global}
p + aes(group=species_id) + geom_line() + geom_point(aes(color=species_id))
```


## Univariate geoms

We've focused so far on scatterplots, but one can also create
one-dimensional summaries, such as histograms or boxplots.


### Challenge

Try using `geom_histogram()` to make a histogram visualization of the
distribution of `weight`.

Hint: You want `weight` as the x-axis aesthetic. Try specifying `bins`
in `geom_histogram()`.

<!-- end challenge -->



### Boxplot

Visualising the distribution of weight within each species.

```{r boxplot}
ggplot(reduced, aes(x = species_id, y = hindfoot_length)) +
    geom_boxplot()
```

By adding points to boxplot, we can have a better idea of the number of
measurements and of their distribution:

```{r boxplot-with-points}
ggplot(reduced, aes(x = species_id, y = hindfoot_length)) +
    geom_boxplot(alpha = 0) +
    geom_jitter(alpha = 0.3, color = "tomato")
```

Notice how the boxplot layer is behind the jitter layer? What do you need to
change in the code to put the boxplot in front of the points such that it's not
hidden.


### Challenge

A variant on the box plot is the violin plot. Use `geom_violin()` to
make violin plots of `hindfoot_length` by `species_id`.

<!-- end challenge -->


## Faceting

ggplot has a special technique called *faceting* that allows to split one plot
into multiple plots based on a factor included in the dataset. We will use it to
make one plot for a time series for each species.

```{r first-facet}
yearly_counts <- reduced %>% group_by(year, species_id) %>% tally
ggplot(yearly_counts, aes(x = year, y = n, group = species_id, colour = species_id)) +
    geom_line() +
    facet_wrap(~ species_id)
```

Now we would like to split line in each plot by sex of each individual
measured. To do that we need to make counts in data frame grouped by sex.

### Challenge

- Calculate counts grouped by year, species_id, and sex

- make the faceted plot splitting further by sex (within each panel)

- color by sex rather than species

<!--  end challenge -->



Suppose I make a similar plot of average weight by species:

```{r average-weight-timeseries}
yearly_weight <- reduced %>%
                 group_by(year, species_id, sex) %>%
                 summarise(avg_weight = mean(weight, na.rm = TRUE))
ggplot(yearly_weight, aes(x=year, y=avg_weight, color = species_id, group = species_id)) +
    geom_line() +
    facet_wrap(~ species_id)
```

Why do we see those steps in the plot?

**Oops** need to group by sex

```{r average-weight-timeseries-fixed}
yearly_weight <- reduced %>%
                 group_by(year, species_id, sex) %>%
                 summarise(avg_weight = mean(weight, na.rm = TRUE))
ggplot(yearly_weight, aes(x=year, y=avg_weight, color = sex, group = sex)) +
    geom_line() +
    facet_wrap(~ species_id)
```

### facet_grid

The `facet_wrap` geometry extracts plots into an arbitrary number of dimensions
to allow them to cleanly fit on one page. On the other hand, the `facet_grid`
geometry allows you to explicitly specify how you want your plots to be
arranged via formula notation (`rows ~ columns`; a `.` can be used as
a placeholder that indicates only one row or column).

```{r average-weight-time-facet_sex_rows}
## One column, facet by rows
yearly_weight %>% filter(species_id %in% c("DM", "DO", "DS")) %>%
    ggplot(aes(x=year, y=avg_weight, color = species_id, group = species_id)) +
    geom_line() +
    facet_grid(sex ~ .)
```

```{r average-weight-time-facet_sex_columns}
# One row, facet by column
yearly_weight %>% filter(species_id %in% c("DM", "DO", "DS")) %>%
    ggplot(aes(x=year, y=avg_weight, color = species_id, group = species_id)) +
    geom_line() +
    facet_grid( ~ sex)
```


```{r average-weight-time-facet_sex_columns_species_rows}
# separate panel for each sex and species
yearly_weight %>% filter(species_id %in% c("DM", "DO", "DS")) %>%
    ggplot(aes(x=year, y=avg_weight, color = species_id, group = species_id)) +
    geom_line() +
    facet_grid(species_id ~ sex)
```

## Saving plots to a file

If you want to save a plot, to share with others, use the `ggsave`
function.

The default is to save the last plot that you created, but I think
it's safer to first save the plot as an object and pass that to
`ggsave`. Also give the height and width in inches.

```{r ggsave_example, eval=FALSE}
p <- ggplot(reduced, aes(x=weight, y=hindfoot_length)) + geom_point()
ggsave("scatter.png", p, height=6, width=8)
```

The image file type is taken from the file name extension.
To make a PDF instead:

```{r ggsave_pdf, eval=FALSE}
ggsave("scatter.pdf", p, height=6, width=8)
```

Use `scale` to adjust the sizes of things, for example for a talk/poster
versus a paper/report. Use `scale < 1` to make the various elements
bigger relative to the plotting area.

```{r ggsave_scale, eval=FALSE}
ggsave("scatter_2.png", p, height=6, width=8, scale=0.8)
```

## Customizing plots

### Axis limits

When faceting, the different panels are given common x- and y-axis
limits. If we were to create separate plots (say one for each
country), we would need to do a bit extra to ensure that common axis
limits are used.

Recall the `scale_x_log10()` function that we had used to create the log
scale for the x axis. This can take an argument `limits` (a
vector of length 2) defining the minimum and maximum values plotted.

There is also a `scale_y_log10()` function, but if you want to change
the y-axis limits without going to a log scale, you would use
`scale_y_continuous()`. (Similarly, there's a `scale_x_continuous`.)

For example, to plot the data for China, using axis limits defined by
the full data, we'd do the following:

```{r limits}
xrange <- range(reduced$weight)
yrange <- range(reduced$hindfoot_length)

p <- reduced %>% filter(species_id=="DM") %>%
    ggplot(aes(x=weight, y=hindfoot_length)) +
    geom_point()
p + scale_x_log10(limits=xrange) +
    scale_y_continuous(limits=yrange)
```

### Color choices

If you don't like the choices for point colors, you can customize
them in a number of ways. First, you can use `scale_color_manual()`
with a vector of your preferred choices. (If it's `fill` rather than
`color` that you want to change, you'll need to use `scale_fill_manual()`.)

```{r custom_colors}
p <- reduced %>% filter(species_id %in% c("DM", "DS", "DO")) %>%
    ggplot(aes(x=weight, y=hindfoot_length)) +
    geom_point(aes(color=species_id))

colors <- c("blue", "green", "orange")
p + scale_color_manual(values=colors)
```

You can also use RGB hex values.

```{r custom_colors_rgb}
hexcolors <- c("#001F3F", "#0074D9", "#01FF70")
p + scale_color_manual(values=hexcolors)
```


## Themes

Not everyone gray background and such in the default ggplot plots.

But you can apply one of a variety of "themes" to control the overall
appearance of plots.

One that a lot of people like is `theme_bw()`. Add it to a plot, and
the overall appearance changes.

```{r theme}
p + theme_bw()
```