If you'd like to learn more about the theoretical underpinnings of ggplot2, I'd recommend reading "The Layered Grammar of Graphics", <http://vita.had.co.nz/papers/layered-grammar.pdf>.
If you run this code and get the error message "there is no package called 'tidyverse'", you'll need to first install it, then run `library()` once again.
```{r, echo = FALSE, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg that shows a negative association. Cars with engine size greater than 5 litres and highway fuel efficiency greater than 20 miles per gallon stand out from the rest of the data and are highlighted in red."}
One way to test this hypothesis is to look at the `class` value for each car.
The `class` variable of the `mpg` dataset classifies cars into groups such as compact, midsize, and SUV.
If the outlying points are hybrids, they should be classified as compact cars or, perhaps, subcompact cars (keep in mind that this data was collected before hybrid trucks and SUVs became popular).
```{r, echo = FALSE, asp = 1/4, fig.alt = "Diagram that shows four plotting characters next to each other. The first is a large circle, the second is a small circle, the third is a triangle, and the fourth is a blue circle."}
```{r, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg that shows a negative association. The points representing each car are coloured according to the class of the car. The legend on the right of the plot shows the mapping between colours and levels of the class variable: 2seater, compact, midsize, minivan, pickup, or suv."}
To map an aesthetic to a variable, associate the name of the aesthetic to the name of the variable inside `aes()`.
ggplot2 will automatically assign a unique level of the aesthetic (here a unique color) to each unique value of the variable, a process known as **scaling**.
ggplot2 will also add a legend that explains which levels correspond to which values.
The colours reveal that many of the unusual points (with engine size greater than 5 litres and highway fuel efficiency greater than 20 miles per gallon) are two-seater cars.
```{r, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg that shows a negative association. The points representing each car are sized according to the class of the car. The legend on the right of the plot shows the mapping between colours and levels of the class variable -- going from small to large: 2seater, compact, midsize, minivan, pickup, or suv."}
Or we could have mapped `class` to the *alpha* aesthetic, which controls the transparency of the points, or to the *shape* aesthetic, which controls the shape of the points.
```{r, out.width = "50%", fig.align = 'default', warning = FALSE, fig.asp = 1/2, fig.cap ="", fig.alt = "Two scatterplots next to each other, both visualizing highway fuel efficiency versus engine size of cars in ggplot2::mpg and showing a negative association. In the plot on the left class is mapped to the alpha aesthetic, resulting in different transparency levels for each level of class. In the plot on the right class is mapped the shape aesthetic, resulting in different plotting character shapes for each level of class. Each plot comes with a legend that shows the mapping between alpha level or shape and levels of the class variable."}
For each aesthetic, you use `aes()` to associate the name of the aesthetic with a variable to display.
The `aes()` function gathers together each of the aesthetic mappings used by a layer and passes them to the layer's mapping argument.
The syntax highlights a useful insight about `x` and `y`: the x and y locations of a point are themselves aesthetics, visual properties that you can map to variables to display information about the data.
```{r, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg that shows a negative association. All points are blue."}
```{r shapes, echo = FALSE, out.width = "75%", fig.asp = 1/3, fig.cap="R has 25 built in shapes that are identified by numbers. There are some seeming duplicates: for example, 0, 15, and 22 are all squares. The difference comes from the interaction of the `colour` and `fill` aesthetics. The hollow shapes (0--14) have a border determined by `colour`; the solid shapes (15--20) are filled with `colour`; the filled shapes (21--24) have a border of `colour` and are filled with `fill`.", warning = FALSE, fig.alt = "Mapping between shapes and the numbers that represent them: 0 - square, 1 - circle, 2 - triangle point up, 3 - plus, 4 - cross, 5 - diamond, 6 - triangle point down, 7 - square cross, 8 - star, 9 - diamond plus, 10 - circle plus, 11 - triangles up and down, 12 - square plus, 13 - circle cross, 14 - square and triangle down, 15 - filled square, 16 - filled circle, 17 - filled triangle point-up, 18 - filled diamond, 19 - solid circle, 20 - bullet (smaller circle), 21 - filled circle blue, 22 - filled square blue, 23 - filled diamond blue, 24 - filled triangle point-up blue, 25 - filled triangle point down blue."}
```{r, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg that shows a negative association. All points are red and the legend shows a red point that is mapped to the word 'blue'."}
Start by carefully comparing the code that you're running to the code in the book.
R is extremely picky, and a misplaced character can make all the difference.
Make sure that every `(` is matched with a `)` and every `"` is paired with another `"`.
Sometimes you'll run the code and nothing happens.
Check the left-hand of your console: if it's a `+`, it means that R doesn't think you've typed a complete expression and it's waiting for you to finish it.
In this case, it's usually easy to start from scratch again by pressing ESCAPE to abort processing the current command.
The first argument of `facet_wrap()` is a formula, which you create with `~` followed by a variable name (here, "formula" is the bane if a data structure in R, not a synonym for "equation").
```{r, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg, faceted by class, with facets spanning two rows."}
```{r, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg, faceted by number of cylinders across rows and by type of drive train across columns. This results in a 4x3 grid of 12 facets. Some of these facets have no observations: 5 cylinders and 4 wheel drive, 4 or 5 cylinders and front wheel drive."}
```{r, fig.alt = "Scatterplot of number of cycles versus type of drive train of cars in ggplot2::mpg. Shows that there are no cars with 5 cylinders that are 4 wheel drive or with 4 or 5 cylinders that are front wheel drive."}
6. Which of the following two plots makes it easier to compare engine size (`displ`) across cars with different drive trains?
What does this say about when to place a faceting variable across rows or columns?
```{r, fig.alt = "Two faceted plots, both visualizing highway fuel efficiency versus engine size of cars in ggplot2::mpg, faceted by drive train. In the top plot, facet are organized across rows and in the second, across columns."}
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv ~ .)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(. ~ drv)
```
7. Recreate this plot using `facet_wrap()` instead of `facet_grid()`.
How do the positions of the facet labels change?
```{r, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg, faceted by type of drive train across rows."}
```{r, echo = FALSE, out.width = "50%", fig.align="default", message = FALSE, fig.alt = "Two plots: the plot on the left is a scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg and the plot on the right shows a smooth curve that follows the trajectory of the relationship between these variables. A confidence interval around the smooth curve is also displayed."}
```{r, message = FALSE, fig.alt = "A plot of highway fuel efficiency versus engine size of cars in ggplot2::mpg. The data are represented with smooth curves, which use a different line type (solid, dashed, or long dashed) for each type of drive train. Confidence intervals around the smooth curves are also displayed."}
One line describes all of the points that have a `4` value, one line describes all of the points that have an `f` value, and one line describes all of the points that have an `r` value.
```{r, echo = FALSE, message = FALSE, fig.alt = "A plot of highway fuel efficiency versus engine size of cars in ggplot2::mpg. The data are represented with points (coloured by drive train) as well as smooth curves (where line type is determined based on drive train as well). Confidence intervals around the smooth curves are also displayed."}
Many geoms, like `geom_smooth()`, use a single geometric object to display multiple rows of data.
For these geoms, you can set the `group` aesthetic to a categorical variable to draw multiple objects.
ggplot2 will draw a separate object for each unique value of the grouping variable.
In practice, ggplot2 will automatically group the data for these geoms whenever you map an aesthetic to a discrete variable (as in the `linetype` example).
It is convenient to rely on this feature because the group aesthetic by itself does not add a legend or distinguishing features to the geoms.
```{r, fig.width = 3, fig.align = 'default', out.width = "33%", message = FALSE, fig.alt = "Three plots, each with highway fuel efficiency on the y-axis and engine size of cars in ggplot2::mpg, where data are represented by a smooth curve. The first plot only has these two variables, the center plot has three separate smooth curves for each level of drive train, and the right plot not only has the same three separate smooth curves for each level of drive train but these curves are plotted in different colours, without a legend explaining which color maps to which level. Confidence intervals around the smooth curves are also displayed."}
```{r, message = FALSE, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg with a smooth curve overlaid. A confidence interval around the smooth curves is also displayed."}
```{r, message = FALSE, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg, where points are coloured according to the car class. A smooth curve following the trajectory of the relationship between highway fuel efficiency versus engine size of cars is overlaid along with a confidence interval around it."}
```{r, message = FALSE, fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg, where points are coloured according to the car class. A smooth curve following the trajectory of the relationship between highway fuel efficiency versus engine size of subcompact cars is overlaid along with a confidence interval around it."}
```{r, echo = FALSE, fig.width = 3, out.width = "50%", fig.align = "default", message = FALSE, fig.alt = "There are six scatterplots in this figure, arranged in a 3x2 grid. In all plots highway fuel efficiency of cars in ggplot2::mpg are on the y-axis and engine size is on the x-axis. The first plot shows all points in black with a smooth curve overlaid on them. In the second plot points are also all black, with separate smooth curves overlaid for each level of drive train. On the third plot, points and the smooth curves are represented in different colours for each level of drive train. In the fourth plot the points are represented in different colours for each level of drive train but there is only a single smooth line fitted to the whole data. In the fifth plot, points are represented in different colours for each level of drive train, and a separate smooth curve with different line types are fitted to each level of drive train. And finally in the sixth plot points are represented in different colours for each level of drive train and they have a thick white border."}
The `diamonds` dataset is in the ggplot2 package and contains information on \~54,000 diamonds, including the `price`, `carat`, `color`, `clarity`, and `cut` of each diamond.
```{r, fig.alt = "Bar chart of number of each each cut of diamond in the ggplots::diamonds dataset. There are roughly 1500 fair diamonds, 5000 good, 12000 very good, 14000 premium, and 22000 ideal cut diamonds."}
```{r, echo = FALSE, out.width = "100%", fig.alt = 'A figure demonstrating three steps of creating a bar chart: 1. geom_bar() begins with the diamonds data set. 2. geom_bar() transforms the data with the "count" stat, which returns a data set of cut values and counts. 3. geom_bar() uses the transformed data to build the plot. cut is mapped to the x-axis, count is mapped to the y-axis.'}
```{r, fig.alt = "Bar chart of number of each each cut of diamond in the ggplots::diamonds dataset. There are roughly 1500 fair diamonds, 5000 good, 12000 very good, 14000 premium, and 22000 ideal cut diamonds."}
This works because every geom has a default stat; and every stat has a default geom.
This means that you can typically use geoms without worrying about the underlying statistical transformation.
There are three reasons you might need to use a stat explicitly:
1. You might want to override the default stat.
In the code below, I change the stat of `geom_bar()` from count (the default) to identity.
This lets me map the height of the bars to the raw values of a $y$ variable.
Unfortunately when people talk about bar charts casually, they might be referring to this type of bar chart, where the height of the bar is already present in the data, or the previous bar chart where the height of the bar is generated by counting rows.
```{r, warning = FALSE, fig.alt = "Bar chart of number of each each cut of diamond in the ggplots::diamonds dataset. There are roughly 1500 fair diamonds, 5000 good, 22000 ideal, 14000 premium, and 12000 very good, cut diamonds."}
```{r, fig.alt = "Bar chart of proportion of each each cut of diamond in the ggplots::diamonds dataset. Roughly, fair diamonds make up 0.03, good 0.09, very good 0.22, premium 26, and ideal 0.40."}
3. You might want to draw greater attention to the statistical transformation in your code.
For example, you might use `stat_summary()`, which summarises the y values for each unique x value, to draw attention to the summary that you're computing:
```{r, fig.alt = "A plot with depth on the y-axis and cut on the x-axis (with levels fair, good, very good, premium, and ideal) of diamonds in ggplot2::diamonds. For each level of cut, vertical lines extend from minimum to maximum depth for diamonds in that cut category, and the median depth is indicated on the line with a point."}
```{r, out.width = "50%", fig.align = "default", fig.alt = "Two bar charts of cut of diamonds in ggplot2::diamonds. In the first plot, the bars have coloured borders. In the second plot, they're filled with colours. Heights of the bars correspond to the number of diamonds in each cut category."}
```{r, fig.alt = "Segmented bar chart of cut of diamonds in ggplot2::diamonds, where each bar is filled with colours for the levels of clarity. Heights of the bars correspond to the number of diamonds in each cut category, and heights of the coloured segments are proportional to the number of diamonds with a given clarity level within a given cut level."}
The stacking is performed automatically by the **position adjustment** specified by the `position` argument.
If you don't want a stacked bar chart, you can use one of three other options: `"identity"`, `"dodge"` or `"fill"`.
- `position = "identity"` will place each object exactly where it falls in the context of the graph.
This is not very useful for bars, because it overlaps them.
To see that overlapping we either need to make the bars slightly transparent by setting `alpha` to a small value, or completely transparent by setting `fill = NA`.
```{r, out.width = "50%", fig.align = "default", fig.alt = "Two segmented bar charts of cut of diamonds in ggplot2::diamonds, where each bar is filled with colours for the levels of clarity. Heights of the bars correspond to the number of diamonds in each cut category, and heights of the coloured segments are proportional to the number of diamonds with a given clarity level within a given cut level. However the segments overlap. In the first plot the segments are filled with transparent colours, in the second plot the segments are only outlined with colours."}
```{r, fig.alt = "Segmented bar chart of cut of diamonds in ggplot2::diamonds, where each bar is filled with colours for the levels of clarity. Height of each bar is 1 and heights of the coloured segments are proportional to the proportion of diamonds with a given clarity level within a given cut level."}
```{r, fig.alt = "Dodged bar chart of cut of diamonds in ggplot2::diamonds. Dodged bars are grouped by levels of cut (fair, good, very good, premium, and ideal). In each group there are eight bars, one for each level of clarity, and filled with a different color for each level. Heights of these bars represent the number of diamonds with a given level of cut and clarity."}
Adding randomness seems like a strange way to improve your plot, but while it makes your graph less accurate at small scales, it makes your graph *more* revealing at large scales.
Because this is such a useful operation, ggplot2 comes with a shorthand for `geom_point(position = "jitter")`: `geom_jitter()`.
To learn more about a position adjustment, look up the help page associated with each adjustment: `?position_dodge`, `?position_fill`, `?position_identity`, `?position_jitter`, and `?position_stack`.
```{r, fig.alt = "Scatterplot of highway fuel efficiency versus city fuel efficiency of cars in ggplot2::mpg that shows a positive association. The number of points visible in this plot is less than the number of points in the dataset."}
Coordinate systems are probably the most complicated part of ggplot2.
The default coordinate system is the Cartesian coordinate system where the x and y positions act independently to determine the location of each point.
There are a number of other coordinate systems that are occasionally helpful.
- `coord_flip()` switches the x and y axes.
This is useful (for example), if you want horizontal boxplots.
It's also useful for long labels: it's hard to get them to fit without overlapping on the x-axis.
```{r, fig.width = 3, out.width = "50%", fig.align = "default", fig.alt = "Two side-by-side box plots of highway fuel efficiency of cars in ggplot2::mpg. A separate box plot is created for cars in each level of class (2seater, compact, midsize, minivan, pickup, subcompact, and suv). In the first plot class is on the x-axis, in the second plot class is on the y-axis. The second plot makes it easier to read the names of the levels of class since they're listed down the y-axis, avoiding overlap."}
However, note that you can achieve the same result by flipping the aesthetic mappings of the two variables.
```{r, fig.width = 3, fig.align = "default", fig.alt = "Side-by-side box plots of highway fuel efficiency of cars in ggplot2::mpg. A separate box plot is drawn along the y-axis for cars in each level of class (2seater, compact, midsize, minivan, pickup, subcompact, and suv)."}
```{r, fig.width = 3, out.width = "50%", fig.align = "default", message = FALSE, fig.alt = "Two maps of the boundaries of New Zealand. In the first plot the aspect ratio is incorrect, in the second plot it's correct."}
```{r, fig.width = 3, out.width = "50%", fig.align = "default", fig.asp = 1, fig.alt = "Two plots: on the left is a bar chart of cut of diamonds in ggplot2::diamonds, on the right is a Coxcomb chart of the same data."}
```{r, fig.asp = 1, out.width = "50%", fig.alt = "Scatterplot of highway fuel efficiency versus engine size of cars in ggplot2::mpg that shows a negative association. The plot also has a straight line that follows the trend of the relationship between the variables but doesn't go through the cloud of points, it's beneath it."}
Our new template takes seven parameters, the bracketed words that appear in the template.
In practice, you rarely need to supply all seven parameters to make a graph because ggplot2 will provide useful defaults for everything except the data, the mappings, and the geom function.
The seven parameters in the template compose the grammar of graphics, a formal system for building plots.
The grammar of graphics is based on the insight that you can uniquely describe *any* plot as a combination of a dataset, a geom, a set of mappings, a stat, a position adjustment, a coordinate system, and a faceting scheme.
To see how this works, consider how you could build a basic plot from scratch: you could start with a dataset and then transform it into the information that you want to display (with a stat).
```{r, echo = FALSE, out.width = "100%", fig.alt = "A figure demonstrating the steps for going from raw data (ggplot2::diamonds) to table of counts where each row represents one level of cut and a count column shows how many diamonds are in that cut level. Steps 1 and 2 are annotated: 1. Begin with the diamonds dataset. 2. Compute counts for each cut value with stat_count()."}
```{r, echo = FALSE, out.width = "100%", fig.alt = "A figure demonstrating the steps for going from raw data (ggplot2::diamonds) to table of counts where each row represents one level of cut and a count column shows how many diamonds are in that cut level. Each level is also mapped to a color. Steps 3 and 4 are annotated: 3. Represent each observation with a bar. 4. Map the fill of each bar to the ..count.. variable."}
You'd then select a coordinate system to place the geoms into.
You'd use the location of the objects (which is itself an aesthetic property) to display the values of the x and y variables.
At that point, you would have a complete graph, but you could further adjust the positions of the geoms within the coordinate system (a position adjustment) or split the graph into subplots (faceting).
You could also extend the plot by adding one or more additional layers, where each additional layer uses a dataset, a geom, a set of mappings, a stat, and a position adjustment.
```{r, echo = FALSE, out.width = "100%", fig.alt = "A figure demonstrating the steps for going from raw data (ggplot2::diamonds) to bar chart where each bar represents one level of cut and filled in with a different color. Steps 5 and 6 are annotated: 5. Place geoms in a Cartesian coordinate system. 6. Map the y values to ..count.. and the x values to cut."}
