8_ggplot.qmd

---
title: "Creating publication quality graphics with R and ggplot2"
author: "Author: Software Carpentry, with edits by Jessica Cooperstone. Date:"
date: 2024-08-19
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---

In this episode, we will learn about how to create publication-quality graphics in R using the tidyverse package [`ggplot2`](https://ggplot2.tidyverse.org/). Plotting data is a very quick and easy way to understand the relationship between your variables.

-------

## The grammar of graphics

The package [`ggplot2`](https://ggplot2.tidyverse.org/) applies a framework for plotting such that any plot can be built from the same basic building blocks. This very popular package is based on a system called the Grammar of Graphics by Leland Wilkinson which aims to create a grammatical rules for the development of graphics. It is part of a larger group of packages called “the tidyverse.”

The “gg” in ggplot stands for “grammar of graphics” and all plots share a common template. This is fundamentally different than plotting using a program like Excel, where you first pick your plot type, and then you add your data. With ggplot, you start with data, add a coordinate system, and then add “geoms,” which indicate what type of plot you want. A cool thing about ggplot is that you can add and layer different geoms together, to create a fully customized plot that is exactly what you want. If this sounds nebulous right now, that’s okay, we are going to talk more about this.

Simplified, we will provide to R:

-   our data
-   mapping aesthetics - what connects the data to the graphics
-   layers - determine which type of plot we are going to make, what cordinate system we will use, what scales we want, and other important aspects of our plot

You can think about a ggplot as being composed of layers. You start with your data, and continue to add layers until you get the plot that you want. This might sound a bit abstract so I am going to talk through this with an example.

-------

## Looking at our data and getting set up

Before we plot, let's look at the data we will use for this session. We are going to use the same `gapminder` data from one hour ago.

In case you don't have it up from before, let's load both the `tidyverse` and `gapminder` using the function `library()` so they are active.
```{r}
library(tidyverse)
library(gapminder)
```


We can use the function `View()` to look at our data. This opens our data sort of like how we might view it in Excel.
```{r, eval = FALSE}
View(gapminder)
```


We can also use the function `str()` or `glimpse()` to look at the structure of our data.
```{r}
str(gapminder)
```

`gapminder` is a tibble (very similar to a data frame), comprised of 1,704 rows and 6 columns. The columns are:

-   country (a factor)
-   continent (a factor)
-   year (an integer)
-   lifeExp (a number)
-   pop (an integer)

We are going to use the function `ggplot()` when we want to make a ggplot.

> Note, the function is called ggplot and the package is called ggplot2

If we want to learn how the function works, we can use the `?` to find out more.
```{r}
?ggplot()
```

-------

## Building our plot

We can start by providing the data as the first argument to `ggplot()`.

```{r}
ggplot(data = gapminder)
```

We have a blank plot! We haven't given enough information for R to know what we want to plot - we have simply told R what dataframe we will be using. We are getting the first “base” layer of the plot.

Instead of providing the dataframe as the first argument to `ggplot()`, we can use the pipe `%>%` (or the newer pipe `|>`) that Jelmer taught us about. This "sends" the data into the next function. I will use this syntax for the rest of the workshop.

```{r}
gapminder %>%
  ggplot()
```

> Note that both give you the same output.

If we want to make a scatterplot to understand the relationship between GDP per capita and life expectancy, we can do so by setting `x` and `y` respectively within `aes()`, or our aesthetic mappings.

```{r}
gapminder %>%
  ggplot(mapping = aes(x = gdpPercap, y = lifeExp))
```

Ok! We don't have a plot, per se, but we have more than we had before. We can now see that `gdpPercap` is on the x-axis (along with some numbers reflecting the range of our data), and `lifeExp` is on the y-axis (along with some numbers reflecting the range of our data).

> Note that the "mapping" part is actually not necessary.

```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp))
```

Now we need to tell R what geometry, or "geom" want to use. Let's make a scatterplot here.

```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point()
```

All of the "geoms" start with [`geom_*()`](https://ggplot2.tidyverse.org/reference/index.html#layers), and we can see what they all are by starting to type geom and pressing tab.

-------

### Challenge 1

Modify the plot we've made so that you can see the relationship between life expectancy and year.

<details><summary>Click for the solution</summary>

Map `x = year` and `y = lifeExp`, and use `geom_point()` since we want a scatterplot.
```{r}
gapminder %>%
  ggplot(aes(x = year, y = lifeExp)) +
  geom_point()
```

</details>

-------

### Challenge 2

Modify to color your points by continent.

<details><summary>Need a hint?</summary>

Try using the argument `color` within your aesthetic mappings.

</details>

<details><summary>Need another hint?</summary>

Try setting `color = continent` within your aesthetic mappings.

</details>

<details><summary>Click for the solution</summary>

```{r}
gapminder %>%
  ggplot(aes(x = year, y = lifeExp, color = continent)) + 
  geom_point()
```

</details>

-------

# Adding more layers

Instead of making a scatter plot, we might want to make a line plot. Using `ggplot` this is as easy as changing out your geom. 

```{r}
gapminder %>%
  ggplot(aes(x = year, y = lifeExp, color = continent)) +
  geom_line()
```

The plot is jumping around a lot since for each year, we have life expectancy data for each country. Our plot here is not a summary of that data, but instead all of that data together, on top of itself.

We might want to have one line for each country, we can do this by specifying `group = country` within our aesthetic mappings.

```{r}
gapminder %>% 
  ggplot(aes(x = year, y = lifeExp, color = continent, group = country)) +
  geom_line()
```

A nice thing about `ggplot` is that you don't actually need to decide if you want to have lines or points, you can have both!
```{r}
gapminder %>% 
  ggplot(aes(x = year, y = lifeExp, color = continent, group = country)) +
  geom_line() +
  geom_point()
```

Now we see a point for each observation and each point and line is colored based on continent.

You can also set your mappings globally (within `ggplot()`) or locally (within a specific geom). Let's see what the difference is. Let's see what happens when we move `color = continent` into `geom_point(aes(color = continent))`.
```{r}
gapminder %>% 
  ggplot(aes(x = year, y = lifeExp, group = country)) +
  geom_point(aes(color = continent)) +
  geom_line()
```

We can see that now only the points are colored by continent, and the lines are all black (the default color for one group).

> Note, the layers are added in the order you indicate, so if you change the order, your plot will change.

-------

### Challenge 3
Change the order of the point and line layers - what happens?

<details><summary>Click for the solution</summary>

Points then lines (points are on the bottom)
```{r}
gapminder %>%
  ggplot(aes(x = year, y = lifeExp, group = country)) +
  geom_point() +
  geom_line(aes(color = continent))
```

Lines then points (lines are on the bottom)
```{r}
gapminder %>%
  ggplot(aes(x = year, y = lifeExp, group = country)) +
  geom_line(aes(color = continent)) +
  geom_point() 
```

</details>

-------

## Transformations and statistics

Sometimes we might want to apply some kind of transformation to our data while plotting so we can better see relationships between our variables.

Let's start with a base plot to see the relationship between GDP per capita (`gdpPercap`) and life expectancy (`lifeExp`).
```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point()
```

The presence of some outliers for `gdpPercap` make it hard to see this relationship. We can try log base 10 transforming the x-axis to see if this helps using a [`scale_*()`](https://ggplot2.tidyverse.org/reference/index.html#scales) function.
```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
  scale_x_log10() # log 10 scales the x axis
```

I can also make our points a little bit transparent to ease our overplotting problem (where too many point are on top of each other, making each point hard to see) by setting `alpha = `. Alpha ranges from 0 (totally transparent) to 1 (totally opaque). Note that I did not put  `alpha = 0.5` within an `aes()` - we are not mapping alpha to some variable, we are simply setting what alpha should be. 
```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + # not inside the aes
  scale_x_log10()
```

Now we can better see the parts of the plot that are very dark are where there are a lot of data points.

We can also add a smoothed line of fit to our data by setting `method = "lm"` within `geom_smooth()` to fit a linear model.
```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + # not inside the aes
  scale_x_log10() +
  geom_smooth(method = "lm") # smooth with a linear model ie "lm"
```

We can adjust the thickness of the line by setting `linewidth` within `geom_smooth()`.
```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + # not inside the aes
  scale_x_log10() +
  geom_smooth(method = "lm", linewidth = 3) # smooth with a linear model ie "lm"
```

That's a ridiculously thick line.

-------

### Challenge 4A

Modify the color and the size of the points in the previous example. You'll also probably want to make the linewidth less ridiculous.

<details><summary>Need a hint?</summary>

Don't put `color` and `size` inside `aes()`.

</details>

<details><summary>Want another hint?</summary>

The equivalent of `linewidth` for points is `size`.

</details>

<details><summary>Click for the solution</summary>

```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5, color = "purple", size = 0.5) + # not inside the aes
  scale_x_log10() +
  geom_smooth(method = "lm", linewidth = 1) 
```

</details>

-------

### Challenge 4B

Modify the plot from 4A so points are a different shape and colored by continent with new trendlines

<details><summary>Need a hint?</summary>

Don't put `color` and `size` inside `aes()`.

</details>

<details><summary>Want another hint?</summary>

The equivalent of `linewidth` for points is `size`.

</details>

<details><summary>Click for the solution</summary>

All points are now triangles.
```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point(shape = 17, alpha = 0.5) + 
  scale_x_log10() +
  geom_smooth(method = "lm", linewidth = 1) # smooth with a linear model ie "lm"
```

All points are now open triangles, we are setting continent to `fill` and making the outline black.
```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, fill = continent)) +
  geom_point(shape = 24, alpha = 0.5, color = "black") + 
  scale_x_log10() +
  geom_smooth(method = "lm", linewidth = 1) # smooth with a linear model ie "lm"
```

Mapping `shape` to `continent`
```{r}
gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point(aes(shape = continent), alpha = 0.5) + 
  scale_x_log10() +
  geom_smooth(method = "lm", linewidth = 1) # smooth with a linear model ie "lm"
```

</details>

------

## Multi-panel figures

[Small multiples](https://en.wikipedia.org/wiki/Small_multiple#:~:text=A%20small%20multiple%20(sometimes%20called,was%20popularized%20by%20Edward%20Tufte.) are a useful way to look at data across the same scale to understand patterns.

Let's say we want to understand how life expectancy changes over time throughout the Americas? Instead of making an individual plot for each country, we can use `facet_wrap()` to have `ggplot` make our plots all at once.

First let's use what [Jelmer taught us](https://github.com/osu-codeclub/carpentries-aug-2024/wiki/2b_dplyr#filter-to-pick-rows-observations) to `filter` for only the observations from the Americas.

```{r}
gapminder_americas <- gapminder %>%
  filter(continent == "Americas")
```

Then, this new data frame `gapminder_americas` can be the data for our next plot. Let's look first without faceting.
```{r}
gapminder_americas %>%
  ggplot(aes(x = year, y = lifeExp, group = country)) +
  geom_line()
```

Faceting allows us to better see each country on its own.
```{r}
gapminder_americas %>%
  ggplot(aes(x = year, y = lifeExp)) +
  geom_line() +
  facet_wrap(vars(country)) + # make facets by country
  theme(axis.text.x = element_text(angle = 45)) # years on the x on a 45 deg angle
```

-------

## Modifying text

The plots we've made so far could really benefit from some better labels. We can set what we want or plot labels to be as arguments in `labs().`
```{r}
gapminder_americas %>%
  ggplot(aes(x = year, y = lifeExp)) +
  geom_line() +
  facet_wrap(vars(country)) + # make facets by country
  theme(axis.text.x = element_text(angle = 45)) + # years on the x on a 45 deg angle
  labs(x = "Year",              # x axis title
       y = "Life expectancy",   # y axis title
       title = "Figure 1. Life expectancy in the Americas from 1952-2007", # main title of figure
      )
```

-------

## Exporting a plot
Often we want to take our plot we have made using R and save it for use someplace else. You can export using the Export button in the Plots pane (bottom right) but you are limited on the parameters for the resulting figure.

We can do this with more control using the function [`ggsave()`](https://ggplot2.tidyverse.org/reference/ggsave.html). 

First we will save our plot as an object using the assignment operator `<-`.

```{r}
life_exp_americas_plot <- gapminder_americas %>%
  ggplot(aes(x = year, y = lifeExp)) +
  geom_line() +
  facet_wrap(vars(country)) + # make facets by country
  theme(axis.text.x = element_text(angle = 45)) + # years on the x on a 45 deg angle
  labs(x = "Year", # x axis title
       y = "Life expectancy", # y axis title
       title = "Figure 1. Life expectancy in the Americas from 1952-2007", # main title of figure
      )
```

Then we can save it. I am indicating here to save the plot in a folder called `results` in my working directory, as a file called `lifeExp.png`. If you want your file to go within a folder, you have to first create that folder.

```{r}
ggsave(filename = "results/lifeExp.png", # file path and name
       plot = life_exp_americas_plot,  # what to save
       width = 18, 
       height = 12, 
       dpi = 300, # dots per inch,  ie resolution
       units = "cm") # units for width and height
```

> Note to learn more about the arguments in `ggsave()` you can always run `?ggsave()`.

### Challenge 5

Create some box plots that compare life expectancy between the continents over the time period provided. Try and make your plot look nice, add labels and stuff!

<details><summary>Need a hint?</summary>

The geom for making a boxplot is `geom_boxplot()`.

</details>

<details><summary>Want another hint?</summary>

Set labels within `labs().` Adjust theming with `theme()`. Check out the [complete themes](https://ggplot2.tidyverse.org/reference/ggtheme.html).

</details>

<details><summary>Click for the solution</summary>

```{r}
gapminder %>%
  ggplot(aes(x = continent, y = lifeExp, fill = continent)) +
  geom_boxplot() +
  facet_wrap(vars(year)) +
  theme_classic() + # my favorite complete theme 
  theme(axis.title.x = element_blank(), # remove x-axis title
        axis.text.x = element_blank(), # remove x-axis labels
        axis.ticks.x = element_blank()) + # remove x-axis ticks
  labs(y = "Life Expectancy (years)",
       fill = "Continent") # change the label on top of the legend
```

</details>

------