04-ggplot2.Rmd

---
title: "Data visualisation with ggplot2"
teaching: 45
exercises: 20
questions:
- "What are the components of a ggplot?"
- "How do I create scatterplots, boxplots, and barplots?"
- "How can I change the aesthetics (ex. colour, transparency) of my plot?"
- "How can I create multiple plots at once?"
objectives:
- "Produce scatter plots, boxplots, and time series plots using ggplot.
- "Set universal plot settings."
- "Describe what faceting is and apply faceting in ggplot."
- "Modify the aesthetics of an existing ggplot plot (including axis labels and color)."
- "Build complex and customized plots from data in a data frame."
keypoints:
- "`ggplot2` is a flexible and useful tool for creating plots in R."
- "The data set and coordinate system can be defined using the `ggplot` function."
- "Additional layers, including geoms, are added using the `+` operator."
- "Boxplots are useful for visualizing the distribuion of a continuous variable."
- "Barplot are useful for visualizing categorical data."
- "Facetting allows you to generate multiple plots based on a categorical variable."
---

```{r, include=FALSE}
source("../bin/chunk-options.R")
knitr_fig_path("04-")
```

We start by loading the required packages. **`ggplot2`** is included in the **`tidyverse`** package.

```{r load-package, message=FALSE, purl=FALSE}
library(tidyverse)
```

If not still in the workspace, load the data we saved in the previous lesson.


```{r load-data, eval=FALSE, purl=FALSE}
interviews_plotting <- read_csv("data_output/interviews_plotting.csv")

```
## Plotting with **`ggplot2`**

**`ggplot2`** is a plotting package that makes it simple to create complex plots
from data in a data frame. It provides a more programmatic interface for
specifying what variables to plot, how they are displayed, and general visual
properties. Therefore, we only need minimal changes if the underlying data change
or if we decide to change from a bar plot to a scatterplot. This helps in creating
publication quality plots with minimal amounts of adjustments and tweaking.

**`ggplot2`** functions like data in the 'long' format, i.e., a column for every dimension,
and a row for every observation. Well-structured data will save you lots of time
when making figures with **`ggplot2`**

ggplot graphics are built step by step by adding new elements. Adding layers in
this fashion allows for extensive flexibility and customization of plots.

To build a ggplot, we will use the following basic template that can be used for different types of plots:

ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  <GEOM_FUNCTION>()

- use the `ggplot()` function and bind the plot to a specific data frame using the
`data` argument

```{r, eval=FALSE, purl=FALSE}
ggplot(data = interviews_plotting)
```
- define a mapping (using the aesthetic (`aes`) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc.

```{r, eval=FALSE, purl=FALSE}
ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items))
```

- add 'geoms' – graphical representations of the data in the plot (points,
lines, bars). **`ggplot2`** offers many different geoms; we will use some
common ones today, including:

* `geom_point()` for scatter plots, dot plots, etc.
* `geom_boxplot()` for, well, boxplots!
* `geom_line()` for trend lines, time series, etc.

To add a geom to the plot use the `+` operator. Because we have two continuous variables,
let's use `geom_point()` first:

```{r first-ggplot, purl=FALSE}
ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items)) +
geom_point()
```
The `+` in the **`ggplot2`** package is particularly useful because it allows you
to modify existing `ggplot` objects. This means you can easily set up plot
templates and conveniently explore different types of plots, so the above
plot can also be generated with code like this:

```{r, first-ggplot-with-plus, eval=FALSE, purl=FALSE}
# Assign plot to a variable
interviews_plot <- ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items))

# Draw the plot
interviews_plot +
geom_point()
```

**Notes**

- Anything you put in the `ggplot()` function can be seen by any geom layers
that you add (i.e., these are universal plot settings). This includes the x- and
y-axis mapping you set up in `aes()`.
- You can also specify mappings for a given geom independently of the
mappings defined globally in the `ggplot()` function.
- The `+` sign used to add new layers must be placed at the end of the line containing
the *previous* layer. If, instead, the `+` sign is added at the beginning of the line
containing the new layer, **`ggplot2`** will not add the new layer and will return an
error message.

```{r, ggplot-with-plus-position, eval=FALSE, purl=FALSE}
# This is the correct syntax for adding layers
interviews_plot +
geom_point()

# This will not add the new layer and will return an error message
interviews_plot
+ geom_point()
```

## Building your plots iteratively

Building plots with **`ggplot2`** is typically an iterative process. We start by
defining the dataset we'll use, lay out the axes, and choose a geom:

```{r create-ggplot-object, purl=FALSE}
ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items)) +
geom_point()
```

Then, we start modifying this plot to extract more information from it. For
instance, we can add transparency (`alpha`) to avoid overplotting:

```{r adding-transparency, purl=FALSE}
ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items)) +
geom_point(alpha = 0.5)
```

That only helped a little bit with the overplotting problem. We can also
introduce a little bit of randomness into the position of our points
using the `geom_jitter()` function.

```{r adding-jitter, purl=FALSE}
ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items)) +
geom_jitter(alpha = 0.5)
```


We can also add colors for all the points:

```{r adding-colors, purl=FALSE}
ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items)) +
geom_jitter(alpha = 0.5, color = "blue")
```

Or to color each species in the plot differently, you could use a vector as an input to the argument **color**. **`ggplot2`** will provide a different color corresponding to different values in the vector. Here is an example where we color by **`village`**:


```{r color-by-species, purl=FALSE}
ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items)) +
geom_jitter(aes(alpha = 0.5, color = village))
```

There appears to be a positive trend between number of household
members and number of items owned (from the list provided). This trend
does not appear to be different by village.

> ## Exercise
>
> Use what you just learned to create a scatter plot of `rooms` by
> `village` with the `respondent_wall_type` showing in different colors. Is this a good
> way to show this type of data?
>
> > ## Solution
> > ```{r scatter-challenge, answer=TRUE, purl=FALSE}
> > ggplot(data = interviews_plotting, aes(x = village, y = rooms)) +
> > geom_jitter(aes(color = respondent_wall_type))
> > ```
> {: .solution}
{: .challenge}


## Boxplot

We can use boxplots to visualize the distribution of rooms for each
wall type:

```{r boxplot, purl=FALSE}
ggplot(data = interviews_plotting, aes(x = respondent_wall_type, y = rooms)) +
geom_boxplot()
```

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

```{r boxplot-with-points, purl=FALSE}
ggplot(data = interviews_plotting, aes(x = respondent_wall_type, y = rooms)) +
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, color = "tomato")
```

We can see that muddaub houses and sunbrick houses tend to be smaller than
burntbrick houses.

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?

> ## Exercise
>
> Boxplots are useful summaries, but hide the *shape* of the distribution. For
> example, if the distribution is bimodal, we would not see it in a
> boxplot. An alternative to the boxplot is the violin plot, where the shape
(of the density of points) is drawn.
>
> - Replace the box plot with a violin plot; see `geom_violin()`.
> > ## Solution
> > ```{r}
> > ggplot(data = interviews_plotting, aes(x = respondent_wall_type, y = rooms)) +
> > geom_violin(alpha = 0) +
> > geom_jitter(alpha = 0.5, color = "tomato")
> > ```
> {: .solution}
>
> So far, we've looked at the distribution of room number within wall type. Try making a
> new plot to explore the distribution of another variable within wall type.
>
> - Create a boxplot for `liv_count` for each wall type. Overlay the boxplot layer on a jitter
>   layer to show actual measurements.
>
> > ## Solution
> > ggplot(data = interviews_plotting, aes(x = respondent_wall_type, y = liv_count)) +
> > geom_boxplot(alpha = 0) +
> > geom_jitter(alpha = 0.5)
> {: .solution}
>
> - Add color to the data points on your boxplot according to whether
> the respondent is a member of an irrigation association (`memb_assoc`).
>
> > ## Solution
> > ggplot(data = interviews_plotting, aes(x = respondent_wall_type, y = liv_count)) +
> > geom_boxplot(alpha = 0) +
> > geom_jitter(aes(alpha = 0.5, color = memb_assoc))
> {: .solution}

## Barplots

Barplots are also useful for visualizing categorical data. By default,
`geom_bar` accepts a variable for x, and plots the number of instances 
each value of x (in this case, wall type) appears in the dataset.

```{r}
ggplot(data = interviews_plotting, aes(x = respondent_wall_type)) +
geom_bar()
```

We can use the `fill` asthetic for the `geom_bar()` geom to color bars by
the portion of each count that is from each village.

```{r}
ggplot(data = interviews_plotting, aes(x = respondent_wall_type)) +
geom_bar(aes(fill = village))
```

This creates a stacked bar chart. These are generally more difficult to
read than side-by-side bars. We can separate the portions of the stacked
bar that correspond to each village and put them side-by-side by
using the `position` argument for `geom_bar()` and setting it to "dodge".


```{r}
ggplot(data = interviews_plotting, aes(x = respondent_wall_type)) +
geom_bar(aes(fill = village), position = "dodge")
```

This is a nicer graphic, but we're more likely to be interested in the
proportion of each housing type in each village than in the actual
count of number of houses of each type (because we might have
sampled different numbers of households in each village). To compare
proportions, we will first create a new data frame (`percent_wall_type`)
with a new column named "percent" representing the percent of
each house type in each village. We will remove houses with
cement walls, as there was only one in the dataset.


```{r}
percent_wall_type <- interviews_plotting %>%
filter(respondent_wall_type != "cement") %>%
group_by(village, respondent_wall_type) %>%
summarize(count = n()) %>%
mutate(percent = count / sum(count))
```

Now we can use this new data frame to create our plot showing the
percentage of each house type in each village.

```{r}
ggplot(percent_wall_type, aes(x = village, y = percent, fill = respondent_wall_type)) +
geom_bar(stat = "identity", position = "dodge")
```

> ## Exercise
>
> Create a bar plot showing the proportion of respondents in each
> village who are or are not part of an irrigation association
> (`memb_assoc`). Include only respondents who answered that question
> in the calculations and plot. Which village had the lowest proportion of
> respondents in an irrigation association?
>
> > ## Solution
> > ```{r}
> > percent_memb_assoc <- interviews_plotting %>%
> > filter(memb_assoc != "NULL") %>%
> > group_by(village, memb_assoc) %>%
> > summarize(count = n()) %>%
> > mutate(percent = count / sum(count))
> >
> > ggplot(percent_memb_assoc, aes(x = village, y = percent, fill = memb_assoc)) +
> > geom_bar(stat = "identity", position = "dodge")
> > ```
> >
> > Ruaca had the lowest proportion of members in an irrigation association.
> {: .solution}
{: .challenge}


## Adding Labels and Titles

By default, the axes labels on a plot are determined by the name of the variable
being plotted. However, **`ggplot2`** offers lots of cusomization options,
like specifing the axes labels, and adding a title to the plot with 
relatively few lines of code. We will add more informative x and y axis
labels to our plot of proportion of house type by village and also add
a title.

```{r}
ggplot(percent_wall_type, aes(x = village, y = percent, fill = respondent_wall_type)) +
    geom_bar(stat = "identity", position = "dodge") +
    ylab("Percent") +
    xlab("Wall Type") +
    ggtitle("Proportion of wall type by village")
```

## Faceting

Rather than creating a single plot with side-by-side bars for each
village, we may want to create multiple plot, where each plot shows the
data for a single village. This would be especially useful if we had
a large number of villages that we had sampled, as a large number of
side-by-side bars will become more difficult to read.

**`ggplot2`** has a special technique called *faceting* that allows the user to split one
plot into multiple plots based on a factor included in the dataset. We
will use it to split our barplot of housing type proportion by village
so that each village has it's own panel in a multi-panel plot:

```{r}
ggplot(percent_wall_type, aes(x = respondent_wall_type, y = percent)) +
geom_bar(stat = "identity", position = "dodge") +
ylab("Percent") +
xlab("Wall Type") +
ggtitle("Proportion of wall type by village") +
facet_wrap(~ village)
```

Click the "Zoom" button in your RStudio plots pane to view a larger
version of this plot.

Usually plots with white background look more readable when printed.  We can set
the background to white using the function `theme_bw()`. Additionally, you can remove
the grid:

```{r, purl=FALSE}
ggplot(percent_wall_type, aes(x = respondent_wall_type, y = percent)) +
geom_bar(stat = "identity", position = "dodge") +
ylab("Percent") +
xlab("Wall Type") +
ggtitle("Proportion of wall type by village") +
facet_wrap(~ village) +
theme_bw() +
theme(panel.grid = element_blank())
```

What if we wanted to see the proportion of respondents in each village
who owned a particular item? We can calculate the percent of people
in each village who own each item and then create a faceted series of
bar plots where each plot is a particular item. First we need to
calculate the percentage of people in each village who own each item:

```{r}
percent_items <- interviews_gather %>%
group_by(item_owned, village) %>%
summarize(count = n()) %>%
# add a column with the number of people in each village
mutate(people_in_village = case_when(village == "Chirodzo" ~ 39,
village == "God" ~ 43,
village == "Ruaca" ~ 49)) %>%
mutate(percent = count / people_in_village)
```{r}

To calculate this percentage data frame, we needed to use the `case_when()` parameter within `mutate()`. In our earlier examples,
we knew that each house was one and only one of the types specified.
However, people can (and do) own more than one item, so we can't use
the sum of the count column to give us the denominator in our percentage
calculation. Instead, we need to specify the number of respondents in each
village. Using this data frame, we can now create a multi-paneled bar plot.

```{r}
ggplot(percent_items, aes(x = village, y = percent)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ item_owned) +
theme_bw() +
theme(panel.grid = element_blank())
```

## **`ggplot2`** themes

In addition to `theme_bw()`, which changes the plot background to white, **`ggplot2`**
comes with several other themes which can be useful to quickly change the look
of your visualization. The complete list of themes is available
at <http://docs.ggplot2.org/current/ggtheme.html>. `theme_minimal()` and
`theme_light()` are popular, and `theme_void()` can be useful as a starting
point to create a new hand-crafted theme.

The
[ggthemes](https://cran.r-project.org/web/packages/ggthemes/vignettes/ggthemes.html) package
provides a wide variety of options (including an Excel 2003 theme).
The [**`ggplot2`** extensions website](https://www.ggplot2-exts.org) provides a list
of packages that extend the capabilities of **`ggplot2`**, including additional
themes.

> ## Exercise
> Experiment with at least two different themes. Build the previous plot
> using each of those themes. Which do you like best?
{: .challenge}

## Customization

Take a look at the [**`ggplot2`** cheat sheet](https://www.rstudio.com/wp-content/uploads/2016/11/ggplot2-cheatsheet-2.1.pdf), and
think of ways you could improve the plot.

Now, let's change names of axes to something more informative than 'village'
and 'percent' and add a title to the figure:

```{r, purl=FALSE}
ggplot(percent_items, aes(x = village, y = percent)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ item_owned) +
labs(title = "Percent of respondents in each village who owned each item",
x = "Village",
y = "Percent of Respondents") +
theme_bw()
```

The axes have more informative names, but their readability can be improved by
increasing the font size:

```{r number-species-year-with-right-labels-xfont-size, purl=FALSE}
ggplot(percent_items, aes(x = village, y = percent)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ item_owned) +
labs(title = "Percent of respondents in each village who owned each item",
x = "Village",
y = "Percent of Respondents") +
theme_bw() +
theme(text=element_text(size = 16))
```

Note that it is also possible to change the fonts of your plots. If you are on
Windows, you may have to install
the [**`extrafont`** package](https://github.com/wch/extrafont), and follow the
instructions included in the README for this package.

After our manipulations, you may notice that the values on the x-axis are still not
properly readable. Let's change the orientation of the labels and adjust them
vertically and horizontally so they don't overlap. You can use a 90-degree
angle, or experiment to find the appropriate angle for diagonally oriented
labels:

```{r number-species-year-with-theme, purl=FALSE}
ggplot(percent_items, aes(x = village, y = percent)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ item_owned) +
labs(title = "Percent of respondents in each village who owned each item",
x = "Village",
y = "Percent of Respondents") +
theme_bw() +
theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 45, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
text = element_text(size = 16))
```

If you like the changes you created better than the default theme, you can save them as
an object to be able to easily apply them to other plots you may create:


```{r number-species-year-with-right-labels-xfont-orientation, purl=FALSE}
grey_theme <- theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 45, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
text = element_text(size = 16))


ggplot(percent_items, aes(x = village, y = percent)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ item_owned) +
labs(title = "Percent of respondents in each village who owned each item",
x = "Village",
y = "Percent of Respondents") +
grey_theme
```

> ### Challenge
>
> With all of this information in hand, please take another five minutes to
> either improve one of the plots generated in this exercise or create a
> beautiful graph of your own. Use the RStudio [**`ggplot2`** cheat sheet](https://www.rstudio.com/wp-content/uploads/2016/11/ggplot2-cheatsheet-2.1.pdf)
> for inspiration. Here are some ideas:
>
> * See if you can make the bars white with black outline.
> * Try using a different color palette (see
>   http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/).

After creating your plot, you can save it to a file in your favorite format. The Export tab in the **Plot** pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters.

Instead, use the `ggsave()` function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments (`width`, `height` and `dpi`).

Make sure you have the `fig_output/` folder in your working directory.

```{r ggsave-example, eval=FALSE, purl=FALSE}
my_plot <- ggplot(percent_items, aes(x = village, y = percent)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ item_owned) +
labs(title = "Percent of respondents in each village who owned each item",
x = "Village",
y = "Percent of Respondents") +
theme_bw() +
theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 45, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
text = element_text(size = 16))

ggsave("fig_output/name_of_file.png", my_plot, width = 15, height = 10)
```

Note: The parameters `width` and `height` also determine the font size in the saved plot.


```{r, child="_page_built_on.Rmd"}
```