# Chapter 7 Creating a Composite Figure by Subplotting

In this section, we will plot a composite figure. This refers to a type of plot that has many panels with common information, such as the same x- and y-axes. Subplotting is referred to when we allocate subsets of data to particular panels but not others in the composite figure. We will make each panel (i.e., subplot) one-by-one, combine them into a composite figure, add a common legend, and then annotate it with texts and shapes using the functions from *smplot2* package.

First, you need to load data (.csv file). When you are loading your own .csv file for your data analysis, make sure you place the .csv file of your interest in the folder that has been set to the **working directory**.

In this example, we will be using data from this paper:

**Seung Hyun Min, Alex S. Baldwin and Robert F. Hess. Ocular dominance plasticity: A binocular combination task finds no cumulative effect with repeated patching (2019). Vision Research, 161, 36-42.**

We will be creating similar figures to those in the paper (ex. **Figure 3A** and **Figure A2**) using **smplot**. For the PDF copy, please visit this link.

```
## # A tibble: 6 × 4
## Subject Day Time Cbratio
## <chr> <fct> <dbl> <dbl>
## 1 S1 1 0 -0.421
## 2 S2 1 0 2.82
## 3 S3 1 0 1.69
## 4 S4 1 0 2.55
## 5 S5 1 0 -0.217
## 6 S6 1 0 0.626
```

There are four columns in this data frame:

First,

`Subject`

refers to each participant. There are 10 participants total.Next,

`Day`

refers to the day of testing. The participants were tested on Day 1, 2, 3, 4 and 5. We will only use Day from 1 and 5.`Time`

refers to the number of minutes after an experimental manipulation (ex. monocular deprivation). These are 0, 3, 6, 12, 24 and 48 minutes, but in the data frame, it says 0, 1, 2, 3, 4 and 5; we will change the labels manually.The

`Cbratio`

column refers to the actual data that will be plotted here.

In the example below, the plots will have different colors based on Day (1 or 5). Therefore, the values in `Day`

column have to be discrete, not continuous. To make them discrete, one needs to convert the `Day`

column from **double** (continuous variable) to **factor** (discrete variable).

## 7.1 `filter()`

, `select()`

and `summarise()`

### 7.1.1 `filter()`

for rows

To plot data of each subject separately, we need the data frame to show data only from one subject. This can be achieved as using `filter()`

:

```
## # A tibble: 12 × 4
## Subject Day Time Cbratio
## <chr> <fct> <dbl> <dbl>
## 1 S1 1 0 -0.421
## 2 S1 1 1 0.802
## 3 S1 1 2 1.01
## 4 S1 1 3 0.634
## 5 S1 1 4 -0.245
## 6 S1 1 5 -0.834
## 7 S1 5 0 2.42
## 8 S1 5 1 1.76
## 9 S1 5 2 1.91
## 10 S1 5 3 0.609
## 11 S1 5 4 0.811
## 12 S1 5 5 0.363
```

- The first argument of
`filter()`

,`select()`

,`summarise()`

and`mutate()`

is a data frame. - The subsequent argument specifies how the data frame should be treated.
- The new printed result is a new data frame.

`filter()`

is used to filter for rows that meet the requirement of your interest.

Here is another example.

```
## # A tibble: 60 × 4
## Subject Day Time Cbratio
## <chr> <fct> <dbl> <dbl>
## 1 S1 1 0 -0.421
## 2 S2 1 0 2.82
## 3 S3 1 0 1.69
## 4 S4 1 0 2.55
## 5 S5 1 0 -0.217
## 6 S6 1 0 0.626
## 7 S7 1 0 2.62
## 8 S8 1 0 1.42
## 9 S9 1 0 1.54
## 10 S10 1 0 3.05
## # ℹ 50 more rows
```

The above code can be read as: **filter** for all rows of the data frame **df** that have `1`

in the `Day`

column.

Notice that S1 is a **character** because it has an alphabet. Therefore, it needs to be written as `'S1'`

. However, `1`

of `Day`

is **double**, which is essentially just a number digit. Therefore, it can be written as `1`

with no quotation mark.

Let’s try another example.

```
day1 <- filter(df, Day == 1) # save the new data frame into a new variable
filter(day1, Subject == "S1") # this new data frame contains Day 1 and Subject 1 data only.
```

```
## # A tibble: 6 × 4
## Subject Day Time Cbratio
## <chr> <fct> <dbl> <dbl>
## 1 S1 1 0 -0.421
## 2 S1 1 1 0.802
## 3 S1 1 2 1.01
## 4 S1 1 3 0.634
## 5 S1 1 4 -0.245
## 6 S1 1 5 -0.834
```

The above code can be read as: **filter** for all rows of the data frame **df** that have `1`

in the `Day`

column. Save this new data frame as `day1`

. Then, **filter** for all rows of the data frame `day1`

that have `S1`

in the `Subject`

column.

The above can also be written like the one below:

```
## # A tibble: 6 × 4
## Subject Day Time Cbratio
## <chr> <fct> <dbl> <dbl>
## 1 S1 1 0 -0.421
## 2 S1 1 1 0.802
## 3 S1 1 2 1.01
## 4 S1 1 3 0.634
## 5 S1 1 4 -0.245
## 6 S1 1 5 -0.834
```

The above can be read as: **filter** for all rows of the data frame **df** that have `1`

in the `Day`

column **AND** have `S1`

in the `Subject`

column.

```
## # A tibble: 66 × 4
## Subject Day Time Cbratio
## <chr> <fct> <dbl> <dbl>
## 1 S1 1 0 -0.421
## 2 S2 1 0 2.82
## 3 S3 1 0 1.69
## 4 S4 1 0 2.55
## 5 S5 1 0 -0.217
## 6 S6 1 0 0.626
## 7 S7 1 0 2.62
## 8 S8 1 0 1.42
## 9 S9 1 0 1.54
## 10 S10 1 0 3.05
## # ℹ 56 more rows
```

The above can be read as: **filter** for all rows of the data frame **df** that have `1`

in the `Day`

column **OR** have `S1`

in the `Subject`

column. `|`

represents **OR**, `&`

represents **AND**.

### 7.1.2 `select()`

for columns

If you wish to see the `Cbratio`

column only (i.e., data only) for rows of **df** that have `Day == 1`

and `Time == 0`

, you can write it like this:

```
day1_time0 <- filter(df, Day == 1 & Time == 0) # save the new data frame in the day1_time0 variable
select(day1_time0, Cbratio)
```

```
## # A tibble: 10 × 1
## Cbratio
## <dbl>
## 1 -0.421
## 2 2.82
## 3 1.69
## 4 2.55
## 5 -0.217
## 6 0.626
## 7 2.62
## 8 1.42
## 9 1.54
## 10 3.05
```

There are 10 rows (i.e., 10 subjects) in this filtered data frame and 1 column, which is `Cbratio`

. The above can be read as: **filter** for all rows of the data frame **df** that have `1`

in the `Day`

column **AND** have `0`

in the `Time`

column. Then, store the new data frame in `day1_time0`

. Then, select for `Cbratio`

column from `day1_time0`

.

`select()`

is used to filter for columns that meet the requirement of your interest.

### 7.1.3 `summarise()`

for grouped summaries

**df** contains individual data for all subjects on Days 1 and 5 across all time points. However, it does not contain average data either for each **Day** or **Time**.

`summarise()`

can collapse multiple rows of observations into values such as the mean.

```
## # A tibble: 1 × 1
## average
## <dbl>
## 1 1.35
```

However, in this case, we get an example of `Cbratio`

across `Subject`

, `Day`

and `Time`

. This average value itself is not so meaningful. If we wish to obtain the average for each `Day`

and `Time`

, we can use the function `group_by()`

to group data for each day and time.

As it was the case before, the first argument of

`group_by()`

is a data frame.The second argument of

`group_by()`

is the name of the column through which you would like to group the data.

```
## # A tibble: 120 × 4
## # Groups: Day, Time [12]
## Subject Day Time Cbratio
## <chr> <fct> <dbl> <dbl>
## 1 S1 1 0 -0.421
## 2 S2 1 0 2.82
## 3 S3 1 0 1.69
## 4 S4 1 0 2.55
## 5 S5 1 0 -0.217
## 6 S6 1 0 0.626
## 7 S7 1 0 2.62
## 8 S8 1 0 1.42
## 9 S9 1 0 1.54
## 10 S10 1 0 3.05
## # ℹ 110 more rows
```

The output of `group_by()`

is a new data frame (it might appear exactly the same as before, ex. **df**). However, it will respond differently to `summarise()`

because the rows of the data frame are now grouped based on day and time, as we have specified.

`## `summarise()` has grouped output by 'Day'. You can override using the `.groups` argument.`

```
## # A tibble: 12 × 3
## # Groups: Day [2]
## Day Time Average_Cbratio
## <fct> <dbl> <dbl>
## 1 1 0 1.57
## 2 1 1 2.21
## 3 1 2 2.32
## 4 1 3 0.979
## 5 1 4 1.25
## 6 1 5 1.14
## 7 5 0 1.85
## 8 5 1 1.49
## 9 5 2 1.02
## 10 5 3 1.15
## 11 5 4 0.759
## 12 5 5 0.452
```

This new data frame yields average for each Day and Time. We have now created a new column `Average_Cbratio`

which stores all the average data of `Cbratio`

.

Therefore, `group_by()`

and `summarise()`

are very useful together. They provide grouped summaries, such as the average. However, `summarise()`

alone may not be so useful. `group_by()`

alone is also rarely used.

Besides the **average**, one might also be interested in obtaining either **standard deviation** or **standard error**.

However, our **df** does not contain any data about the **standard deviation** or **standard error** per Day or Time, etc. Standard deviation can be calculated via `sd()`

and standard error can be computed with `sm_stdErr()`

.

Below, we obtain standard error with the help of the `summarise()`

function for each `Day`

and `Time`

.

```
## # A tibble: 1 × 1
## standard_error
## <dbl>
## 1 0.115
```

As we have seen before, we see that `standard_error`

has been calculated across all subjects, day and time. This is not so useful. We should use `summarise()`

with `group_by()`

so that each standard error could be for each `Day`

and `Time`

.

`## `summarise()` has grouped output by 'Day'. You can override using the `.groups` argument.`

```
## # A tibble: 12 × 3
## # Groups: Day [2]
## Day Time standard_error
## <fct> <dbl> <dbl>
## 1 1 0 0.393
## 2 1 1 0.363
## 3 1 2 0.400
## 4 1 3 0.352
## 5 1 4 0.266
## 6 1 5 0.438
## 7 5 0 0.563
## 8 5 1 0.422
## 9 5 2 0.462
## 10 5 3 0.224
## 11 5 4 0.292
## 12 5 5 0.193
```

This standard error is for each `Day`

and `Time`

across all subjects.

Now let’s obtain the **mean** and **standard error** of `Cbratio`

for each `Day`

and `Time`

across all subjects using the data frame that has been grouped by `Day`

and `Time`

via `group_by()`

.

`## `summarise()` has grouped output by 'Day'. You can override using the `.groups` argument.`

```
## # A tibble: 12 × 4
## # Groups: Day [2]
## Day Time Average StdError
## <fct> <dbl> <dbl> <dbl>
## 1 1 0 1.57 0.393
## 2 1 1 2.21 0.363
## 3 1 2 2.32 0.400
## 4 1 3 0.979 0.352
## 5 1 4 1.25 0.266
## 6 1 5 1.14 0.438
## 7 5 0 1.85 0.563
## 8 5 1 1.49 0.422
## 9 5 2 1.02 0.462
## 10 5 3 1.15 0.224
## 11 5 4 0.759 0.292
## 12 5 5 0.452 0.193
```

The original **df**, which contains data for each subject, has now been transformed to a new data frame that contains grouped summaries, such as group averages and standard errors.

If you are interested in learning more about this topic (data transformation), please check out Chapter 5 of R for Data Science by Hadley Wickham (https://r4ds.had.co.nz/transform.html).

## 7.2 Plotting the averaged data with error bars

Plotting the averaged data can be done with a data frame that contains individual observation (ex. each subject, condition, etc). This data frame can be modified to only contain summary values, such as mean and standard error, using `group_by()`

and `summarise()`

together as shown above.

We will plot a similar graph to **Figure 3A** in the Vision Research paper (Min et al., 2019) in this section.

- A data frame that has grouped summary information (
`group_by()`

and`summarise()`

), such as average and standard error across subject, is needed to plot a graph that shows the average data with error bars. `geom_errorbar()`

is required to plot the error bar of the sample.- Legend title has been removed with the
`theme()`

function. - Greek letter
**Delta**is printed with`\u0394`

. - X-tick labels are originally 0, 1, 2, 3, 4, 5 (as specified in the
**df**data frame). However, they can be manually changed using`labels =`

argument in the`scale_x_continuous()`

function. - Legend label can also be changed in
`labels =`

from the`scale_color_manual()`

function because each`Day`

has been defined by each`color`

; this is the case because`color = Day`

in`aes(..., ..., color = Day)`

.

```
ggplot(data = by_day_time1, aes(x = Time, y = Average, color = Day)) +
geom_point(size = 4.5) +
geom_errorbar(aes(ymin = Average - StdError, ymax = Average + StdError), size = .5, width = .05) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid(legends = TRUE) +
scale_color_manual(
values = sm_color("blue", "orange"),
labels = c("Day 1", "Day 5")
) +
ggtitle("Recovery of the patching effect") +
xlab("Time after monocular deprivation (min)") +
ylab("\u0394 Contrast balance ratio (dB)") +
theme(
legend.justification = c(1, 0),
legend.position = c(0.96, 0.67),
legend.title = element_blank()
)
```

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

## 7.3 Plotting individual data

In this section, we will plot a similar graph to **Figure A2** in the Vision Research paper (Min et al., 2019).

First, you will need several packages for this section.

- If you do not have the
**gridExtra**and**grid**packages in your RStudio, please install them using the codes below. It might take less than a minute.

- Then load all these packages below.

Now let’s plot data for each subject (S1-S9) except S10. Each panel shows the data of each subject for both Days 1 and 5.

```
df_s1 <- filter(df, Subject == "S1")
# rows of df that only contain S1 in the Subject column
# use df_s1 to plot the data of S1
plot_s1 <- ggplot(data = df_s1, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
# axis text size is 1.5x the original font size.
print(plot_s1)
```

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

Then make each one for the other subjects (S2-S9).

```
df_s2 <- filter(df, Subject == "S2")
plot_s2 <- ggplot(data = df_s2, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
# axis text size is 1.5x the original font size.
print(plot_s2)
```

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

```
df_s3 <- filter(df, Subject == "S3")
plot_s3 <- ggplot(data = df_s3, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
# axis text size is 1.5x the original font size.
print(plot_s3)
```

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

```
df_s4 <- filter(df, Subject == "S4")
plot_s4 <- ggplot(data = df_s4, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
# axis text size is 1.5x the original font size.
print(plot_s4)
```

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

```
# Subject 5
df_s5 <- filter(df, Subject == "S5")
# rows of df that only contain S5 in the Subject column
plot_s5 <- ggplot(data = df_s5, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid(legends = FALSE) +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
print(plot_s5)
```

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

```
# Subject 6
df_s6 <- filter(df, Subject == "S6")
# rows of df that only contain S6 in the Subject column
plot_s6 <- ggplot(data = df_s5, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
# axis text size is 1.5x the original font size.
print(plot_s6)
```

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

```
df_s7 <- filter(df, Subject == "S7")
plot_s7 <- ggplot(data = df_s7, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
# axis text size is 1.5x the original font size.
print(plot_s7)
```

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

```
df_s8 <- filter(df, Subject == "S8")
plot_s8 <- ggplot(data = df_s8, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
# axis text size is 1.5x the original font size.
print(plot_s8)
```

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

```
# Subject 9
df_s9 <- filter(df, Subject == "S9")
plot_s9 <- ggplot(data = df_s9, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
# axis text size is 1.5x the original font size.
print(plot_s9)
```

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

## 7.4 Putting multiple plots together (a composite figure) - `sm_put_together()`

Each panel output has to be stored in a `list`

using the function `list()`

. X-axis label can be specified using `sm_common_xlabel()`

, which has `x`

and `y`

arguments for setting the location of the label. Y-axis label is specified using `sm_common_ylabel()`

with the same `x`

and `y`

arguments. The title can be set using `sm_common_title()`

.

```
all_plots <- list(
plot_s1, plot_s2, plot_s3,
plot_s4, plot_s5, plot_s6,
plot_s7, plot_s8, plot_s9
)
xlabel <- sm_common_xlabel("Minutes after monocular deprivation", x = 0.52, y = 0.6)
ylabel <- sm_common_ylabel("\u0394 Contrast balance ratio (dB)")
title <- sm_common_title("Individual data", y = 0.2)
plots_tgd <- sm_put_together(all_plots,
title = title, xlabel = xlabel,
ylabel = ylabel, ncol = 3, nrow = 3,
hmargin = -5, wmargin = -5
)
```

Now let’s put them together in a 3x3 figure (3 rows, 3 columns) using the function `sm_put_together()`

. So, `ncol`

and `nrow`

are set to 3 in `sm_put_together()`

. The function automatically removes tick labels in both x- and y-axes in inner panels, and keeps them on the outer panels, so that combined plot looks clean. Also, the `hmargin`

argument can be set to adjust the blank space of height between panels; the `wmargin`

argument can be set to adjust the blank space of width between panels. Their values can be both negative (less blank space) and positive (more blank space). I suggest you use values from *-5 to 5* for `hmargin`

and `wmargin`

.

When you are saving the graph as an image file, `nrow`

and `ncol`

in `save_plot()`

have to match the values in `sm_put_together()`

as shown above. `save_plot()`

, which is a function from the *cowplot* package, is a function that saves a selected graph into an image or PDF file (or eps, etc).

Open `together1.png`

in your directory folder. The figure is clean but it lacks a few things: 1) label for each panel, 2) legend. With examples below, we will add them.

```
all_plots2 <- sm_panel_label(all_plots,
x = 0.1, y = 0.9, panel_tag = "1", panel_pretag = "S", text_size = 5,
text_color = "black", fontface = "bold"
)
```

Here, we include a label for each panel, which represents the data of each subject, using `sm_panel_label()`

.

The function `sm_panel_label()`

has a few arguments. `x`

and `y`

determine the location of the panel label; 0.5 represents its origin in the middle of the panel. `panel_tag`

determines the character string that will be used for enumeration. In this example, `panel_tag = "1"`

is chosen so there will be a sequence of numbers. Other options include: 1) `panel_tag = "A"`

for uppercase letters, 2) `panel_tag = "a"`

for small case letters, 3) `panel_tag = "I"`

for upper roman numerals, and 4) `panel_tag = "i"`

for lower roman numerals. There are also tag labels that can set to be *consistent* across panels: these are `panel_pretag`

and `panel_posttag`

. `panel_pretag`

comes before `panel_tag`

(as shown in this example, in the form of `S`

, ex. `S1`

), and `panel_posttag`

comes after `panel_tag`

.

```
plots_tgd2 <- sm_put_together(all_plots2,
title = title, xlabel = xlabel,
ylabel = ylabel, ncol = 3, nrow = 3,
hmargin = -5, wmargin = -5
)
plots_tgd3 <- sm_add_legend(plots_tgd2,
x = .88, y = 0.05, sampleplot = all_plots2[[1]],
direction = "horizontal", border = FALSE
)
```

As before, we then combine the list of plots with panel label (`all_plots2`

) and the labels for the common x- and y-axes, as well as the title of the combined figure using `sm_put_together()`

. The arguments of `sm_put_together()`

have to be written in this order unless they are specified (ex. `sm_put_together(all_plots=all_plots, xlabel=xlabel, title=title)`

). We reduce the margin of width and height blank space between panels by setting `hmargin=-5`

and `vmargin=-5`

.

Next, we add legend using `sm_add_legend()`

. It has a number of arguments. First, the combined figure (output from `sm_put_together()`

) must be provided. Next, `x`

and `y`

are the location of the legend in the coordinates of the combined figure (0 to 1), where `x=0.5`

and `y=0.5`

represents the center of the combined figure. `sampleplot`

has to be provided, which in this case one of the plots in the `plots_tgd2`

list, so that the function `sm_add_legend()`

can derive a legend for the whole combined plot. The `direction`

(or orientation) of the legend can also be set as `horizontal`

or `vertical`

; in this case, we set it to `direction = horizontal`

.

The border of the legend can also be included by setting `border = TRUE`

. We can also increase the amount of spacing within the legend using `legend_spacing`

argument from `sm_add_legend`

. This change is subtle but you will definitely notice it.

```
plots_tgd3b <- sm_add_legend(plots_tgd2,
x = .88, y = 0.05, sampleplot = all_plots2[[1]],
direction = "horizontal", border = TRUE, legend_spacing = 1
)
```

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

You can even make a separate legend on your own with full customization using `sm_common_legend()`

, then add the legend to the combined plot using `sm_add_legend()`

. We will do this by first making a combined figure with two rows and five columns (`nrow=2`

and `ncol=5`

). The legend can be on the 10th panel because its empty.

```
legend <- ggplot(data = df_s9, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 2.5) +
scale_color_manual(
values = sm_color("blue", "orange"),
labels = c("Data from Day 1 ", "Data from Day 5 ")
) +
scale_shape_manual(
values = c(21, 22),
labels = c("Data from Day 1 ", "Data from Day 5 ")
) +
sm_common_legend(title = FALSE, legend_spacing = 1)
```

Basically, `legend`

that is created with `sm_common_legend()`

is a new plot with no points; however, you still need to draw the same points and include other features from other sub-panels to make sure that they are included in the legend. Notice that legend labels have been customized as well by adjusting the `labels`

within `scale_color_manual()`

and `scale_shape_manual()`

. The title has been removed by setting `title = FALSE`

.

The `legend`

can then be added to the combined plot using `sm_add_legend()`

. If you provide a separate legend, then other arguments of the function will be ignored, such as `direction`

and `border`

from `sm_add_legend()`

because these arguments are used to derive a legend based on `sampleplot`

. `x`

and `y`

describe the coordinates of the legend in the composite plot (output from `sm_put_together()`

: `plots_tgd2`

), and these are supplied in the `sm_add_legend()`

.

Also, besides `sm_add_legend()`

, `sm_put_together()`

can also legend onto the combined plot if the user supplies a separate legend input: ex. `sm_put_together(legend = legend)`

, where `legend`

is a separate legend created as shown above. If the `legend`

is provided in `sm_put_together()`

, the function will automatically add legend onto the last panel of the combined plot, so make sure that the last empty panel is empty. The `x`

and `y`

coordinates howeevr are determined using `sm_common_legend()`

, which we have opted for their defaults `x=0.5`

and `y=0.5`

, ex. `sm_common_legend(title=FALSE, legend_spacing=1, x=0.5, y=0.5)`

in the above.

To demonstrate this function, we will make a combined figure with 2 rows and 5 columns.

```
xlabel <- sm_common_xlabel("Minutes after monocular deprivation", x = 0.51, y = 0.6)
ylabel <- sm_common_ylabel("\u0394 Contrast balance ratio (dB)", x = 0.7)
title <- sm_common_title("Individual data", y = 0.3, x = 0.51, size = 18)
plots_tgd4 <- sm_put_together(all_plots,
title = title, xlabel = xlabel,
ylabel = ylabel, legend = legend,
ncol = 5, nrow = 2, hmargin = -5, wmargin = -5
)
```

The arguments of `sm_put_together()`

have to be written in this order unless they are specified (ex. `sm_put_together(all_plots=all_plots, xlabel=xlabel, title=title)`

).

The `title`

, `xlabel`

, `xlabel2`

, `ylabel`

and `ylabel2`

can be provided as string characters, as shown below. If characters are provided rather than the outputs of `sm_common_title()`

, `sm_common_xlabel()`

and `sm_common_ylabel()`

, then their locations cannot be modified. Their sizes can be adjusted using `labelRatio`

argument. A value of 1 refers to the optimized text size, and 1.1 will be 0.1x larger than the optimized size. The text size optimization and the text locations are set by a simple algorithm that relies on the plot’s layout and other information to give optimal values. `labelRatio`

only adjusts the text sizes when the label inputs are supplied as character strings, and they wont change text sizes when these have been created with `sm_common_title()`

, `sm_common_xlabel()`

and `sm_common_ylabel()`

.

## 7.5 Other important inputs of `sm_put_together()`

These inputs are automatically optimized (or adjusted) as default if the user does not supply input.

`wRatio`

: controls the relative width of the panels in the left-most column of the composite plot with ticks for the**primary y-axis**.`wRatio2`

: controls the relative width of the panels in the right-most column of the composite plot with ticks for the**secondary y-axis**. This gets activated if one of the plots in the input list has secondary y-axis.`hRatio`

: controls the relative height of the panels in the bottom row of the composite plot with ticks for the**primary x-axis**.`hRatio2`

: controls the relative height of the panels in the top row of the composite plot with ticks for the**secondary x-axis**. This gets activated if one of the plots in the input list has secondary x-axis.`tickRatio`

: controls the relative text size of the tick labels on both axes of the outer panels in the composite plot.

However, it is possible that the optimization might not yield the best composite output, especially if users change the font sizes and because I cannot simply anticipate all possible plotting situations. **So, users are encouraged to modify these arguments using values from 1.1 to 1.4**.

`labelRatio`

: controls the size of the titles (labels) of both axes. This gets activated only when users supply character inputs for the labels (`title`

,`xlabel`

,`ylabel`

,`xlabel2`

or`ylabel2`

). Instead, it gets ignored when users supply outputs from`sm_common_title()`

,`sm_common_xaxis()`

and`sm_common_yaxis()`

.

## 7.6 Adding annotations to the composite figure

*smplot2* enables users to add annotations on the final, combined figure with a full flexibility. Here are some examples using both functions of *smplot2* and `annotate()`

from `ggplot2`

. Theoretically, users can also use functions from other packages. Except for `sm_add_legend()`

, `sm_add_point()`

and `sm_add_text()`

functions are added modularly to the composite plot (output from `sm_put_together()`

) as shown below.

### 7.6.1 Text annotations

Text annotations can be added to the composite figure using `sm_add_text()`

. The `label`

argument can be used to specify the text label. `x`

and `y`

are the coordinate values of the text in the composite figure, and they have to be between 0 and 1; 0.5 represents the origin (center) of the composite figure. `size`

determines the text size and the `fontface`

can be either `bold`

, `plain`

, `italic`

or `italic.bold`

.

### 7.6.2 Point annotations

Point annotations can be added to the composite figure using `sm_add_point()`

. `x`

and `y`

are the coordinate values of the point in the composite figure, and they have to be between 0 and 1; 0.5 represents the origin (center) of the composite figure. `size`

determines the point size and the `shape`

number input should be the same as the ones in *ggplot2* (ex. 21 is circle with border). `fill`

should be provided for shapes with border. If the shape has no border, then `color`

is sufficient to set the color that fills the point.

### 7.6.3 Line annotations

Line annotations can be added to the composite figure using `annotate()`

. `x`

and `y`

are the starting coordinate values of the line in the composite figure. `xend`

and `yend`

are the ending coordinate values of the line. These coordinate values have to be between 0 and 1; 0.5 represents the origin (center) of the composite figure. `linewidth`

determines the width of the line and the `color`

should adjust the color of the line. There is no `fill`

argument.

```
plots_tgd4e <- plots_tgd4c +
annotate("segment",
x = 0.5, y = 0.475, xend = 0.5,
yend = 0.525, color = "black"
) +
annotate("segment",
x = 0.49, y = 0.5, xend = 0.51,
yend = 0.5, color = "black"
)
```

You can also use third-party annotation functions to annotate the composite figure generated from `sm_put_together()`

.

## 7.7 Why use `sm_put_together()`

? (Advanced)

The primary reason for its existence is that the function has been designed to change the workflow of *ggplot2* for subplotting and creating a composite figure by integrating the programmatic approach, which is not commonly used among users of *ggplot2* because they otherwise risk losing flexibility for aesthetics and control.

A programming approach refers to a means that involves creating programming constructs, such as a for loop, if conditionals or vertorization, to minimize the length of code and improve the speed for computing to perform a complex task. Creating a composite plot with many panels, such as the example here, is a type of complex data visualization whose procedure can appear to be repetitive and cumbersome.

In this short example, I recreate the composite plot generated above using the function `lapply()`

, which creates an iterative loop, producing one ggplot2 object per loop. There will be nine loops total, and these will be stored in the output object `all_figs`

.

```
subj_list <- paste0("S", 1:9)
all_figs <- lapply(1:length(subj_list), function(i) {
df_subj <- df %>% filter(Subject == subj_list[[i]])
ggplot(data = df_subj, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
})
```

We see that `all_figs`

has nine plots and that it is a list structure, which can be handled by `sm_put_together()`

to generate a composite plot.

```
plots_tgd5 <- sm_put_together(all_figs,
title = "Individual data",
xlabel = "Minutes after monocular deprivation",
ylabel = "\u0394 Contrast balance ratio (dB)", legend = legend,
labelRatio = 0.95, ncol = 5, nrow = 2, hmargin = -5, wmargin = -5
)
```

`sm_put_together()`

can also handle nested list objects (i.e., list of list), so users can fully integrate complex programming constructs into their visualization routines to perform sophisticated data visualizations without relying on external packages.

To create a mosaic, where there is an empty panel in the every other panel, simply create an empty plot `ggplot(NULL) + sm_common_legend()`

inside the `lapply()`

construct.

```
ncol <- 5
nrow <- 4
numPanels <- ncol * nrow
subj_list <- paste0("S", 1:10)
i <- 0
figs_mosaic <- lapply(1:numPanels, function(iPlot) {
if (iPlot %% 2 == 0) {
ggplot(NULL) +
sm_common_legend() # empty panel
} else if (iPlot %% 2 == 1) {
i <<- i + 1
df_subj <- df %>% filter(Subject == subj_list[[i]])
ggplot(data = df_subj, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
}
})
```

`sm_put_together()`

provides a limitless possibility of creating a composite plot with expressive layouts of subplots, as shown below.

```
mosaic_tgd <- sm_put_together(figs_mosaic,
title = "Mosaic of individual data",
xlabel = "Minutes after monocular deprivation",
ylabel = "\u0394 Contrast balance ratio (dB)",
labelRatio = 0.95, ncol = ncol, nrow = nrow, hmargin = -5, wmargin = -5
)
mosaic_tgd1 <- sm_add_legend(mosaic_tgd, x = 0.555, y = 0.2, legend = legend)
```

In this last example, we will organize the subplots using the configuration of a lower triangular matrix.

```
ncol <- 4
nrow <- 4
numPanels <- ncol * nrow
subj_list <- paste0("S", 1:10)
i <- 0
low_triangle <- lapply(1:numPanels, function(iPlot) {
if (iPlot %in% c(2, 3, 4, 7, 8, 12)) {
ggplot(NULL) +
sm_common_legend() # empty panel
} else {
i <<- i + 1
df_subj <- df %>% filter(Subject == subj_list[[i]])
ggplot(data = df_subj, aes(x = Time, y = Cbratio, color = Day)) +
geom_point(size = 4.5) +
geom_smooth(method = "lm", se = F, size = 0.9) +
# lm = linear regression method
scale_x_continuous(
breaks = unique(df$Time),
labels = c("0", "3", "6", "12", "24", "48")
) +
sm_hgrid() +
scale_color_manual(values = sm_color("blue", "orange")) +
scale_y_continuous(limits = c(-3, 5.5))
}
})
# Combine the subplots
low_tr_tgd <- sm_put_together(low_triangle,
title = "Low triangle of individual data",
xlabel = "Minutes after monocular deprivation",
ylabel = "\u0394 Contrast balance ratio (dB)",
labelRatio = 0.95, ncol = ncol, nrow = nrow, hmargin = -5, wmargin = -5
)
# Add legend
low_tr_tgd1 <- sm_add_legend(low_tr_tgd, x = 0.89, y = 0.35, legend = legend)
# Add annotation
low_tr_tgd2 <- low_tr_tgd1 + sm_add_text("Programmatic approach",
alpha = 0.5, x = 0.7, y = 0.65,
color = "black", size = 48, angle = 315,
fontface = "bold"
)
# Save figure
save_plot("low_tr.png", low_tr_tgd2,
ncol = ncol, nrow = nrow,
base_height = 3.4, base_width = 3
)
```