# Chapter 3 Basics of ggplot2 and Correlation Plot

Load these packages by typing the codes below.

```
library(tidyverse) # it has ggplot2 package
library(cowplot) # it allows you to save figures in .png file
library(smplot2)
```

## 3.1 Uploading data

### 3.1.1 Sample data: mpg

I will be using an example from the book *R for Data Science* (https://r4ds.had.co.nz/data-visualisation.html).

**Question: Do cars with large engines use up more fuel than the those with small ones?**

First, let’s open **mpg**, which is a **data frame** stored in the **ggplot2** package.

**mpg**contains data about cars in the US. You can type`?mpg`

for more information.**displ**: the size of the car’s engine in liters**hwy**: fuel efficiency. If it’s high, then the car uses less fuel per distance.

```
## # A tibble: 234 × 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compact
## 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compact
## 3 audi a4 2 2008 4 manual(m6) f 20 31 p compact
## 4 audi a4 2 2008 4 auto(av) f 21 30 p compact
## 5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compact
## 6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compact
## 7 audi a4 3.1 2008 6 auto(av) f 18 27 p compact
## 8 audi a4 quattro 1.8 1999 4 manual(m5) 4 18 26 p compact
## 9 audi a4 quattro 1.8 1999 4 auto(l5) 4 16 25 p compact
## 10 audi a4 quattro 2 2008 4 manual(m6) 4 20 28 p compact
## # ℹ 224 more rows
```

Notice that some columns and rows are not shown. You can type `View(mpg)`

to see the entire **data frame**.

- Each row is an unique observation.
- Each column is an unique variable/condition.

## 3.2 Basics of ggplot2

### 3.2.1 Let’s make some graphs

Question: Do cars with large engines use up more fuel than the those with small ones?

To answer our question, we need to plot **mpg** data. The x-axis should be **displ**, the y-axis should be **hwy**.

We find that a smaller car has a higher efficiency and that a larger car has a lower efficiency. In other words, we see a negative relationship.

### 3.2.2 How ggplot works

When you are making a graph with **ggplot2**, always begin by typing the function `ggplot()`

.

- The
**data**you want to plot is the**first argument**here. Ex.`ggplot(data = mpg)`

.

However, `ggplot(data = mpg)`

alone does not create a graph. You will need **add** (by typing **+**) more layers, such as `geom_point()`

.

`geom_point()`

adds points to your graphs. You will need to specify (or map) x- and y-axes in the`aes()`

function, which means aesthetics. This process is called**mapping**.As you might expect, there are other

**geom**functions, such as`geom_bar()`

,`geom_boxplot()`

,`geom_errorbar()`

. They plot bar graphs, boxplots and error bars, respectively.

Here is the **template** for using **ggplot2** (copied from *R for Data Science*).

### 3.2.3 Different color of points for each unique group

You can apply different colors by the **class** of each car (each car = each row of the **mpg** data frame).

Include

`class`

variable in the`aes()`

function.This maps the third variable

`class`

into your graph.`aes()`

means aesthetic (ex. color, shape, etc).

You can also set different shapes for each group of the data.

You could also set it using aesthetic parameter such as size or transparency (not recommended) across groups. However these are not recommended as it is very hard for us to see the differences in both size and alpha as these are **continuous** parameters. But you get the idea. Using `aes()`

in a geom function (ex. `geom_point()`

), you can label different group of points.

### 3.2.4 Different color & shape for each group

You can also apply different color & shape for each group of the data.

**Exercise**: Try it on your own before you look at the code below.

### 3.2.5 Same shape across all groups

So far, you have put variables such as `shape`

and `color`

inside the function `aes()`

. This can enable you to apply different shape and color for each group.

If you put the variable for `shape`

, `color`

, `size`

outside of `aes()`

in the geom function, then all data points will have the specified `shape`

, `color`

, etc even if they are in different groups.

Notice that the `color`

is different for each group because it is inside the function `aes()`

. However, all the points are triangle because we have typed `shape = 17`

outside the function `aes()`

.

**Exercise:** try changing the shape of the points to the circle with the border.

`shape = 19`

: the shape is circle without the border.`shape = 20`

: the shape is small circle without the border.`shape = 21`

, the shape is circle with the border.So let’s set

`shape`

to 21.

- Notice that the border color is different for each group, but not the color that fills the circle.
- Shapes without their borders (15-20) are filled with
`color`

. - Shapes with the border (21-24) are filled with
`fill`

and its border colored with`color`

. - So let’s change
`color = Class`

to`fill = Class`

.

### 3.2.6 How do we draw the best-fit line of the graph?

Here is our graph.

There seems to be a negative relationship. How do we draw the best-fit line of the graph’s negative relationship? Use another geom function `geom_smooth()`

.

`## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'`

### 3.2.7 geom_point() + geom_smooth()

Now let’s combine geom_point() + geom_smooth() into one graph.

```
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
geom_smooth(mapping = aes(x = displ, y = hwy))
```

`## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'`

`ggplot()`

acts as a system where you can add multiple `geom`

objects, such as `geom_point()`

and `geom_smooth()`

. You can add multiple layers of geom in a single plot, like shown here.

`ggplot()`

and **at least one geom** function are necessary to draw a graph. `ggplot()`

alone does not draw a graph. Try it on your own.

### 3.2.8 Writing shorter codes

```
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
geom_smooth(mapping = aes(x = displ, y = hwy))
```

Notice that we have typed `mapping = aes(x = displ, y = hwy)`

twice. This is **repetitive**. If you type the `mapping`

argument in `ggplot()`

, you won’t need to type them anymore in the subsequent `geom`

functions.

`## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'`

This is exactly the same as the previous graph. In both cases, the mapping has been set so that the x-axis is `displ`

and the y-axis is `hwy`

in both `geom_point()`

and `geom_smooth()`

.

Now let’s apply different color of points and the fit the line for each group.

`## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'`

Okay, this is extremely messy and probably a bad idea.

- You might have gotten
`warnings`

but you can usually ignore them.

Let’s plot the best-fit line across all groups (i.e., one best-fit line) but apply different color for each class (i.e., many colors). To do so, type `color = class`

in geom_point, not `ggplot()`

. This enables you to specify that you will apply different color for each class **only** in `geom_point()`

but not in `geom_smooth()`

.

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

`## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'`

## 3.3 Improve data visualization using smplot2

Although the default theme of ggplot2 graphs is clean, there are some things that I do not like:

The fonts are too small.

The grey background is distracting.

There are too many grids.

Let’s make this graph prettier by using functions from **smplot2**.
- In this example, let’s use `sm_hvgrid()`

. I’ve made this function as a theme suitable for correlation plots.
- Disclaimer: **smplot2** package has been built based on my preference.
- **smplot2** is not necessary to make a **ggplot** graph or change its style.

It is possible to change every aspect of the graph with **ggplot2** but this requires about 8-20 lines of codes (based on my experience). Instead, **smplot2** function does so in a few lines of code.

Now let’s remove the border within `sm_hvgrid()`

by setting `borders = FALSE`

.

```
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
geom_point() +
sm_hvgrid(borders = FALSE)
```

**Exercise:** You can also set `borders = TRUE`

and see what happens.

```
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
geom_point() +
sm_hvgrid(borders = TRUE)
```

You might notice that borders come back. This is exactly what happens when you do not include `borders`

argument in `sm_hvgrid()`

. This is because `sm_hvgrid()`

is set to `borders = TRUE`

as default.

- I think the one with the border looks better.
- You can also remove the legend by setting
`legends = FALSE`

in`sm_hvgrid()`

.

```
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
geom_point() +
sm_hvgrid(legends = FALSE)
```

**Exercise** Set `legends = TRUE`

and see what happens. Type `?sm_hvgrid`

to see why legends appear without directly writing `legends = TRUE`

.

```
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
geom_point() +
sm_hvgrid(legends = TRUE)
```

However, in this case, I think we need a legend because there are many **classes**.

### 3.3.1 Positive relationship between x- and y-axes

Let’s plot another scatterplot using **mtcars** data.

- Set the x-axis with
**drat**and y-axis with**mpg**. - Since you are making a scatterplot, you will need to use
`geom_point()`

. - Set the
**size**of all points to**3**by typing`size = 3`

. - Set the
**shape**of all points to the circle with a border by typing`shape = 21`

. - Set the
**filled color**of all points to**green**by typing`fill = '#0f993d'`

. - Set the
**border color**to**white**by typing`color = 'white'`

.- Since
`shape = 21`

refers to the circle with a border,`fill`

is the color that fills the points and`color`

is the border color.

- Since

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(
shape = 21, fill = "#0f993d", color = "white",
size = 3
)
```

**drat** and **mpg** have a positive relationship. Now let’s make it pretty by adding `sm_hvgrid()`

, which has major horizontal and vertical grids on a white background.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_hvgrid()
```

You can remove borders too by setting `borders = FALSE`

in `sm_hvgrid()`

.

### 3.3.2 Reporting statistics from a paired correlation

**smplot2** also offers a function that plots the best-fit line of a scatterplot (i.e., correlation plot) and prints statistical values, such as p- and R-values.

- p-value is used to check for statistical significance. If it’s less than 0.05, its regarded as
**statistically significant**. However, it gets smaller with a larger sample size. - R-value (correlation coefficient) measures the strength and the direction of the correlation. It ranges from -1 to 1. It does not depend on the sample size.
- Let’s add a function
`sm_statCorr()`

. The statistical results are from Pearson’s correlation test.

`sm_statCorr()`

is combined with `sm_hvgrid()`

(updated in smplot2). Therefore, when you print the statstical values using `sm_statCorr()`

, the correlation theme is automatically used.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_statCorr()
```

`## `geom_smooth()` using formula = 'y ~ x'`

Personally, in this example, I prefer to have the colors of the lines and points to be consistent.

Let’s change the color to green.

- You can also change the
`linetype`

by setting it as`linetype = 'dashed'`

or`linetype = 'solid'`

, which is the default as shown here. - Also let’s get results from Spearman’s correlation test rather than from Pearson’s.
- To do so, type
`corr_method = 'spearman'`

in the function`sm_statCorr()`

. You will get a different**R**value from 0.68, which is from Pearson’s correlation test.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_statCorr(
color = "#0f993d", corr_method = "spearman",
linetype = "dashed"
)
```

`## `geom_smooth()` using formula = 'y ~ x'`

You can also compute \(R^2\) instead of \(R\) by setting `R2 = TRUE`

. If `R2 = FALSE`

, \(R\) will be computed. The values of \(R^2\) also depend on the statistical test that is used (`corr_method`

).

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_statCorr(
color = "#0f993d", corr_method = "spearman",
linetype = "dashed", R2 = TRUE
)
```

`## `geom_smooth()` using formula = 'y ~ x'`

**Exercise**: Set`corr_method = 'pearson'`

and see what happens.

```
ggplot(data = mtcars, aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_statCorr(color = "#0f993d", corr_method = "pearson")
```

You will see that this is exactly the same as when `corr_method`

argument is not included in `sm_statCorr()`

. In short, the **default** correlation method for `sm_statCorr()`

is `'pearson'`

. So, if you don’t write anything for `corr_method`

, it will give results from Pearson’s correlation test. Type `?sm_statCorr`

to see the default of `line_type`

.

`#0f993d`

is a specific green that I like.- Now, let’s change the color. Replace
`'#0f993d'`

with`'green'`

in`geom_point()`

and`sm_statCorr`

.- This
`'green'`

is the default green color of**R**.

- This

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "green", color = "white", size = 3) +
sm_statCorr(color = "green")
```

`## `geom_smooth()` using formula = 'y ~ x'`

To precisely change the parameter for the fitted line, you can do it as shown below by filling the `fit.params`

argument in the form of `list`

, such as `fit.params = list(color = ..., linetype = ...)`

.

`fit.params`

feeds arguments for aesthetics into`geom_smooth()`

such as`color`

so that the fitted regression line can have specified aesthetics.

In fact, this is the major update of **smplot2** across most visualizing functions from **smplot**. As you will notice later, this offers more aesthetic flexibility for users.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_statCorr(
corr_method = "spearman",
fit.params = list(
color = "#0f993d",
linetype = "dashed"
)
)
```

`## `geom_smooth()` using formula = 'y ~ x'`

You can also remove the `borders`

of the plot using `sm_statCorr`

. This computes R.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_statCorr(
corr_method = "spearman",
fit.params = list(
color = "#0f993d",
linetype = "dashed"
),
borders = FALSE
)
```

`## `geom_smooth()` using formula = 'y ~ x'`

This instead computes \(R^2\).

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_statCorr(
corr_method = "spearman",
fit.params = list(
color = "#0f993d",
linetype = "dashed"
),
borders = FALSE, R2 = TRUE
)
```

`## `geom_smooth()` using formula = 'y ~ x'`

Which one do you prefer? With or without the border?

### 3.3.3 Changing the text

If you do not like the fact that the correlation stats are displayed as `R = 0.65, p < 0.001`

and that you rather show it as `R = 0.65`

and `p < 0.001`

in separate lines, you can do it too.

In `R`

, `\n`

means addition of a new line. So you can set the argument `separate_by = '\n'`

, which essentially means `R = 0.65\n p < 0.001`

, which will create two lines.

You would test this directly by typing in R console.

```
## R = 0.65
## p < 0.001
```

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_statCorr(
corr_method = "spearman",
fit.params = list(
color = "#0f993d",
linetype = "dashed"
),
separate_by = "\n"
)
```

`## `geom_smooth()` using formula = 'y ~ x'`

Another way is just do directly compute it yourself using `cor()`

or `cor.test()`

, and annotate the statistical results on the plot. This might be a better alternative if you wish to precisely modify how the text is presented.

```
##
## Spearman's rank correlation rho
##
## data: mtcars$drat and mtcars$mpg
## S = 1901.7, p-value = 5.381e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.6514555
```

Store the results from `cor.test()`

into a variable called `res`

, which is short for **results**.

`## [1] 5.381347e-05`

You can extract certain component of the results from `cor.test()`

from the variable `res`

by using `$`

. `res$p.value`

extracts the p-value, and `res$estimate`

extracts the correlation coefficient `R`

.

```
## rho
## 0.6514555
```

We will round the values to 2 decimal points using `round(..., 2)`

, where 2 denotes the number of decimal places. `paste0()`

is used to construct strings from various vectors, such as the numerical values extracted from `res`

.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
annotate("text", x = 3, y = 32, label = paste0(
"R =",
round(res$estimate, 2),
"\n p < 0.001"
)) +
sm_statCorr(
show_text = FALSE,
fit.params = list(
color = "#0f993d",
linetype = "dashed"
)
)
```

`## `geom_smooth()` using formula = 'y ~ x'`

### 3.3.4 Plotting the average with standard errors

**smplot2** also offers `sm_corr_avgErr()`

, which is a function that enables the users to plot the mean with errors, such as standard error (`errorbar_type = 'se'`

as default), standard deviation (`errorbar_type = 'sd'`

) or 95% confidence interval (`errorbar_type = 'ci'`

).

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_corr_avgErr(data = mtcars, x = drat, y = mpg)
```

The black point in the middle represents the mean with vertical and horizontal standard errors.

The `data`

argument of `sm_corr_avgErr`

requires the variable that stores the data frame that is used to plot the data, which is `mtcars`

. `x`

argument is the variable that is plotted along the x-axis. `y`

argument is the variable that is plotted along the y-axis. Actually these arguments are nearly identical to what you have provided `ggplot(data = ..., aes(x = ..., y = ...)`

.

You can control the aesthetics with a high flexibility using `point.params`

, `errh.params`

and `errv.params`

.

`point.params`

feeds the arguments to`geom_point()`

, such as`color`

,`alpha`

, etc, to plot the average point.`errv.params`

feeds the arguments to`geom_errorbar()`

, such as`color`

,`size`

and`width`

etc, to plot the vertical (y-axis) error bar.`errh.params`

feeds the arguments to`geom_errorbarh()`

, such as`color`

,`size`

and`height`

etc, to plot the horizontal (x-axis) error bar.

If you write `xxx.params = list()`

, even with empty ones, you will remove the defaults of `sm_corr_avgErr()`

, which has default of `width = 0`

(for `errv.params`

) / `height = 0`

(for `errh.params`

). `width`

and `height`

are equivalent.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_corr_avgErr(
data = mtcars, x = drat, y = mpg,
errh.params = list(),
errv.params = list()
) +
sm_hvgrid()
```

After removing the defaults, we see that `width`

of `errv.params`

and `height`

of `errh.params`

are no longer 0. We can control both `width`

and `height`

separately.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_corr_avgErr(
data = mtcars, x = drat, y = mpg,
errh.params = list(height = 1),
errv.params = list(width = 0.12)
) +
sm_hvgrid()
```

In this case, `height`

is much longer because the scale of y-axis is much longer than that of x-axis. We can also set unique colors to horizontal and vertical error bars.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_corr_avgErr(
data = mtcars, x = drat, y = mpg,
errh.params = list(height = 1, color = "#1262b3"),
errv.params = list(width = 0.12, color = "#cc3d3d")
) +
sm_hvgrid()
```

The error bar might appear too thin and the average point too small. We can control the `size`

to make it more noticeable in the plot.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = "#0f993d", color = "white", size = 3) +
sm_corr_avgErr(
data = mtcars, x = drat, y = mpg,
point.params = list(size = 4),
errh.params = list(
height = 1, color = "#1262b3",
size = 0.8
),
errv.params = list(
width = 0.12, color = "#cc3d3d",
size = 0.8
)
) +
sm_hvgrid()
```

We can control the transparency of the individual points and fine tune the aesthetics of the average point to make it more noticeable in the sea of individual points.

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(
shape = 21, fill = "#0f993d", color = "white", size = 3,
alpha = 0.6
) +
sm_corr_avgErr(
data = mtcars, x = drat, y = mpg,
point.params = list(
size = 4, shape = 21, color = "white",
fill = "black"
),
errh.params = list(
height = 1, color = "#1262b3",
size = 0.8
),
errv.params = list(
width = 0.12, color = "#cc3d3d",
size = 0.8
)
) +
sm_hvgrid()
```

Notice that the transparency of the individual point allows us see that there are some points with some serious overlap.

### 3.3.5 `fill = '#0f993d'`

vs `fill = 'green'`

I personally like `#0f993d`

more. However, **R** does not recognize this color as `green`

. So how are you supposed to remember the color code?

You do not have to. You can type `sm_color('green')`

instead. This is a function from the **smplot** package.

`sm_color()`

accepts the name of the color. If you want to get the hex codes (color codes) for red and green, type `sm_color('red','green')`

.

Again, `sm_color()`

has been built based on my preference. So it returns the hex codes of colors that I use most often.

There are many more color themes that are available in R. For more information, please check out Chapter 28 of *R for Data Science* (https://r4ds.had.co.nz/graphics-for-communication.html).

```
ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
geom_point(shape = 21, fill = sm_color("green"), color = "white", size = 3) +
sm_hvgrid() +
sm_statCorr(color = sm_color("green"))
```

**Exercise** Change the color of the points and the best-fit line to `blue`

using `sm_color()`

. If you want to see all the color options for `sm_color()`

, type `?sm_color`

. There are 16 colors total.

### 3.3.6 Different color for each group but with other colors

Let’s go back to the **mpg** data. Set the x-axis with **displ** and y-axis with **hwy**. Then make a scatterplot using `geom_point()`

.
- Set the size of the points to 2 across all groups. So type `size = 2`

outside of `aes()`

in `geom_point()`

.

Let’s apply different color for each `class`

of the cars by writing `color = class`

in `aes()`

from `ggplot()`

.

`fill = class`

is needed when the shape of the point is set to 21-25.

To use other colors, we could use a function from **ggplot2** called `scale_color_manual()`

.

`scale_fill_manual()`

is used when the shape of the point has borders (shape = 21-25).

To find how many colors we need total, we need to find how many groups exist.

In R, you can extract data from one column by using `$`

. You can try it with different variables too. `unique()`

returns unique values in the selected data.
Then compute the number of unique values using `length()`

function.

`## [1] 7`

`sm_palette`

accepts the number of colors as input. It returns colors that I use most often. Now that we know we need 7 colors total, we can type `sm_palette(7)`

or `sm_palette(number_of_classes)`

for `values`

in `scale_color_manual()`

.

```
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
geom_point(size = 2) +
scale_color_manual(values = sm_palette(number_of_classes)) +
sm_hvgrid()
```

Let’s store this graph using a **variable** called `figure1`

.

```
figure1 <- ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
geom_point() +
scale_color_manual(values = sm_palette(number_of_classes)) +
sm_hvgrid()
```

**Notice that when you store a figure into a variable, the figure is not displayed** when you run the code that makes the figure, ex. `figure1 <- ggplot(data = mpg, mapping = ...`

. To display the figure, please type the variable name in the console.

### 3.3.7 Let’s save the plot as an image in your folder **LearnR** by using the variable **figure1**.

To save the figure as an image, we will use the function `save_plot()`

from the **cowplot** package.

There is one important argument:

`base_asp`

.- This is the ratio of your image (width/height). I usually set it to 1.4. So let’s type
`base_asp = 1.4`

in`save_plot()`

. - If
`base_asp`

is larger than 1, it gets wider than its height. This is recommended when you have a legend. - If there is no legend, then
`base_asp = 1`

is recommended.

- This is the ratio of your image (width/height). I usually set it to 1.4. So let’s type

**Exercise**: try to save it again with a name **figure1b.png** by typing:

How’s the picture? Why does it look different? Type `?save_plot`

to see what the default `base_asp`

is.

Done! The graph (in png file) should be in your **LearnR** folder.

**Exercise:** Try to open Microsoft Word or PowerPoint and upload **figure1**. The figure should look the same as it appears in the slides.

**Exercise:** Remove the legend and save the scatterplot with `base_asp = 1`

.

Congratulations! You can now make **correlation plots** with R.

## 3.4 Summary

- You have learned the basics of
**ggplot**.- You begin by writing a
`ggplot()`

function. - If aesthetics (color, shape, etc) are specified outside of
`aes()`

function, then there is no group difference. - If aesthetics are specified in
`aes()`

, different groups of data will have different looks. - You have learned to add geom layers such as
`geom_point()`

, which shows points, and`geom_smooth()`

, which plots the best-fit function. - You have learned to plot
`geom_point()`

and`geom_smooth()`

in the same graph.

- You begin by writing a
**smplot2**functions can be used to improve**ggplot2**visually.- For correlation plots, add
`sm_hvgrid()`

. - You can report statistical results and plot linear regression from correlation by
`sm_statCorr()`

, which also provides`sm_hvgrid()`

. So there is no need to write`sm_hvgrid()`

when using`sm_statCorr()`

. - You can also select colors using
`sm_color()`

.

- For correlation plots, add
- Save the graph as an image file in your working directory.
- Working directory has to be set in RStudio (
**Session -> Set Working Directory -> Choose Directory**) - Then use
`save_plot()`

from**cowplot**to save the image in your directory (folder**LearnR**).

- Working directory has to be set in RStudio (