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In the previous section, we used facet_wrap() to create a grid of plots based on one or more factors. This function is usually more efficient in using the available screen space than facet_grid(), which creates a grid based on two factors. However, in this specific case, where we want to compare the data for 2013 and 2014 in two separate rows, facet_grid() is a better choice because it allows us to align the plots by year and avoid unnecessary empty spaces.

Let's see how facet_grid() works with an example. We will use the mpg dataset, which contains information about the fuel economy of different car models. We want to create a grid of scatter plots that show the relationship between engine displacement and highway miles per gallon for each combination of manufacturer and year. To do this, we can use the following code:

```r

library(ggplot2)

ggplot(mpg, aes(x = displ, y = hwy)) +

geom_point() +

facet_grid(manufacturer year)

```

This code produces a grid of 15 rows and 2 columns, where each row corresponds to a different manufacturer and each column corresponds to a different year (either 1999 or 2008). We can see that there is a negative correlation between displacement and highway mpg for most car models, and that some manufacturers have more variation in their fuel efficiency than others.

One advantage of facet_grid() is that it allows us to compare the plots across both rows and columns. For example, we can see how the fuel efficiency of Honda cars changed from 1999 to 2008, or how Honda cars compare to other manufacturers in 2008. However, one drawback of facet_grid() is that it can create very small plots when there are many levels of the factors. In this case, some of the plots are hard to read because the points are too crowded.

To make the plots bigger, we can use the theme() function to adjust the aspect ratio and the size of the text. For example, we can use the following code to make the plots wider and the text larger:

```r

ggplot(mpg, aes(x = displ, y = hwy)) +

geom_point() +

facet_grid(manufacturer year) +

theme(aspect.ratio = 0.5,

text = element_text(size = 12))

```

This code produces a grid of plots that are easier to see and read. However, we still have a lot of empty space in some of the plots, especially for manufacturers that have only a few car models. To avoid this, we can use facet_wrap() instead of facet_grid(), and specify the number of columns we want in the grid. For example, we can use the following code to create a grid of 6 columns:

```r

ggplot(mpg, aes(x = displ, y = hwy)) +

geom_point() +

facet_wrap(manufacturer year, ncol = 6) +

theme(text = element_text(size = 12))

```

This code produces a grid of plots that are more compact and balanced. We can see that facet_wrap() rearranges the order of the factors to fit them into the grid. We can also see that facet_wrap() creates a single strip for each factor level, instead of a separate strip for each row and column as facet_grid() does. This makes the grid more consistent and less redundant. 061ffe29dd