communicate-plots.Rmd

*Section 2* will show you how to prepare your plots for communication. You'll learn how to make your plots more legible with titles, labels, zooming, and default visual themes. 

```{r echo = FALSE, messages = FALSE, warning=FALSE}
library(ggplot2)
```


# Communication with plots

The previous sections showed you how to make plots that you can use as a tools for _exploration_. When you made these plots, you knew---even before you looked at them---which variables the plot would display and which data sets the variables would come from. You might have even known what to look for in the completed plots, assuming that you made each plot with a goal in mind. As a result, it was not very important to put a title or a useful set of labels on your plots.

The importance of titles and labels changes once you use your plots for _communication_. Your audience will not share your background knowledge. In fact, they may not know anything about your plots except what the plots themselves display. If you want your plots to communicate your findings effectively, you will need to make them as self-explanatory as possible.

Luckily, `ggplot2` provides some features that can help you.

### Labels

You can add a title to any `ggplot2` plot by adding the command `labs()` to your plot call. Set the `title` argument of `labs()` to the character string that you would like to appear as the title of your plot. `ggplot2` will place the title at the top of your plot.

```{r}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = class)) +
  geom_smooth() + 
  labs(title = "Fuel efficiency vs. Engine size")
```

You can also use `labs()` to replace the axis and legend labels in your plot, which might be a good idea if your data uses ambiguous or abbreviated variable names. To replace either of the axis labels, set the `x` or `y` arguments to a character string. `ggplot2` will replace the associated axis label with your character string.

```{r}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = class)) +
  geom_smooth() + 
  labs(title = "Fuel efficiency vs. Engine size",
       x = "Engine displacement (L)", 
       y = "Highway fuel efficiency (mpg)") 
```

To replace a legend label, set the name of the aesthetic displayed in the legend to the character string that should appear as the title of the legend. For example, the legend in our plot corresponds to the color aesthetic. We can change it's title with the command, `labs(color = "New Title")`, or, more usefully:

```{r}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = class)) +
  geom_smooth() + 
  labs(title = "Fuel efficiency vs. Engine size",
       x = "Engine displacement (L)", 
       y = "Highway fuel efficiency (mpg)",
       color = "Type of Car") 
```

### Zooming

Often, it can be helpful to zoom in on a specific region of your plot. In `ggplot2` you can do this by adding `coord_cartesian()` to your plot and setting it's `xlim` and `ylim` arguments. Pass each argument a vector of two numbers, the minimum value to display on that axis and the maximum value, e.g.

```{r}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = class)) +
  geom_smooth() + 
  coord_cartesian(xlim = c(5, 7), ylim = c(10, 30)) 
```

`coord_cartesian()` adds a cartesian coordinate system to your plot (which is the default coordinate system). However, the new coordinate system will use the zoomed in limits. 

What if your plot uses a different coordinate system? Most of the other coordinate functions also take `xlim` and `ylim` arguments. You can look up the help pages of the coordinate functions to learn more.

### Themes

Finally, you can also quickly customize the "look" of your plot by adding a theme function to your plot call. This can be a useful thing to do, for example, if you'd like to save ink when you print your plots, or if you wish to ensure that the plots photocopy well. 

`ggplot2` contains eight theme functions, listed in the table below. Each applies a different visual theme to your finished plot. You can think of the themes as "skins" for the plot. The themes change how the plot looks without changing the information that the plot displays.

To use any of the theme functions, add the function to your plot all. No arguments are necessary.

```{r}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = class)) +
  geom_smooth() + 
  theme_bw() 
```

```{r, echo = FALSE}
knitr::include_graphics("images/visualization-themes.png")
```