Chapter 11 Themes, Colors, Correlations, Boxplots, Violins and Bars (Part 1)

This and the next chapters discuss in brief of all visualization functions of smplot2. Most of these functions have already been described in depth in the preceding chapters. Those that have not, such as sm_hgrid(), sm_hist() and sm_bland_altman() will be discussed in detail.

If you seek certain bits of information about the package on this page, the best is to use it via CTRL + F and type certain key words as this page will be long. This page is meant to be a manual, not a linear tutorial. So I recommend that the reader not read this page linearly.

This chapter does not describe analysis functions of smplot2, such as sm_effsize(), sm_power() and sm_auc(); these functions are described in the Data Analysis section of the book.

If you are still not sure about any of the functions, please type ? before the function names, ex. ?sm_bar.

11.1 Installation of the Package

install.packages("smplot2")

To use the package, load it to memory in RStudio.

library(smplot2)

11.2 How ... and xxx.params arguments work

smplot2's parameters

Figure 11.1: smplot2’s parameters

These are both additional arguments in the visualizing functions of smplot2. xxx.params comes in various forms, such as:

  • err.params: controls the error bar’s appearance.
  • point.params: controls the points’ appearance.
  • line.params: controls the lines’ appearance.
  • avgLine.params: controls the mean line’s appearance.
  • avgPoint.params: controls the average point’s appearance.
  • bar.params: controls the bar’s appearance.
  • boxplot.params: controls the boxplot’s appearance.
  • violin.params: controls the violin’s appearance.

... is another additional argument that I refer to as the generic parameter. It can affect different components of a graph all at once if there is no default.

  • However, these xxx.params can have defaults on their own depending on the function. To remove the defaults, arguments have to be written in this format xxx.params = list() within the functions.

This is probably the most substantial difference between smplot and smplot2.

11.3 Modifying the default aesthetics

Users can modify the default appearance of the visualization functions from the smplot2 package in two different ways:

  • First, they can modify one component of a graph while keeping the rest of the default the same (second panel of the figure). This involves users to re-specify one aspect of the appearance, such as the bar’s alpha level here.

  • Second, they can reset the defaults by setting forget = TRUE and calling list() for a certain plot component, such as bar.params of the bar plot (third panel). This will ensure that the defaults of the plot component (i.e., bar) are forgotten, so users should re-specify each part of the appearance of the bar. This allows users to be more flexible with the aesthetics.

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_bar() +
  scale_color_manual(values = sm_color("blue", "orange")) +
  ggtitle("Bar's default in smplot2") -> p1

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_bar(bar.params = list(alpha = 0.3)) +
  # Previously, needed to write:
  # bar.params = list(alpha = 0.5, width = 0.7, color = 'transparent', fill = 'gray80')
  scale_color_manual(values = sm_color("blue", "orange")) +
  ggtitle("Bar's default with new alpha") -> p2

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_bar(bar.params = list(), forget = TRUE) +
  # As previously, forgets defaults of a plot component (ex. bar) when list() is called.
  scale_color_manual(values = sm_color("blue", "orange")) +
  ggtitle("Bar's default forgotten") -> p3

sm_put_together(list(p1, p2, p3), ncol = 3, nrow = 1, tickRatio = 1)

In the visualization functions of smplot2, argument for aesthetics that are not re-specified in list() follow the defaults of smplot2. If only one of them gets re-specified, then that one will get modified as specified but others will still retain the default appearance.

11.4 Color palette

Unlike some other visualization packages, smplot2 only offers single color palette (only up to 20 colors). This choice of design was partly motivated by the fact that too many choices would often confuse the users, as I have found myself doing so.

These 20 colors can be accessed using the function sm_palette().

  • sm_palette() accepts the number of colors (up to 20) and returns the hex codes accordingly.

A specific color can be called upon by the function sm_color().

  • sm_color() accepts the character string of the color name.
smplot2's color palette

Figure 11.2: smplot2’s color palette

These colors make up the entire palette of smplot2.

sm_palette(20)
##  [1] "#7f404a" "#5b4080" "#408073" "#8c994d" "#cc9666" "#cc1489" "#1262b3" "#cc3d3d" "#da73e6"
## [10] "#66b1cc" "#0f993d" "#7f5d0d" "#7b3dcc" "#45e0e6" "#63e617" "#e57717" "#c9b9c6" "#ffe764"
## [19] "#ffb359" "#9ee1a8"

If you only need 5 colors, then type this instead.

sm_palette(5)
## [1] "#cc1489" "#1262b3" "#5b4080" "#e57717" "#0f993d"

This is the hex code for darkred.

sm_color("darkred")
## [1] "#7f404a"

You can also call two (same / different) colors at the same time.

sm_color("darkred", "darkred")
## [1] "#7f404a" "#7f404a"
sm_color("darkred", "viridian")
## [1] "#7f404a" "#408073"

If you want to use 1 color (ex. darkred) from smplot2 and another color (#b37982) from somewhere else, you will need to resort to using their hex codes.

Create a variable that stores these two colors using <-.

my_colors <- c(sm_color("darkred"), "#b37982")
my_colors
## [1] "#7f404a" "#b37982"

my_colors have two colors: one from smplot2 and another from somewhere else.

11.5 Graph themes

smplot2 offers many graph themes. They are minimalistic and have similar font size across titles and texts. They are most ideal when the panel is square (ratio = 0.95- 1.05). They also have arguments borders and legends, which could be used to remove / add borders (borders = FALSE / borders = TRUE) or remove / add legends (legends = FALSE / legends = TRUE). These are most appropriate for scientific publications.

  • sm_hgrid() is the default theme for most visualizing functions of smplot2, such as sm_boxplot(), sm_bar(), sm_forest() etc. It has major horizontal grids.

  • sm_vgrid() has major vertical grids and is not used as default for the visualizing functions.

  • sm_hvgrid() has both major horizontal and vertical grids. This is identical to sm_hvgrid().

  • This theme is most appropriate when the x- and y-axes have the same range.

  • sm_hvgrid_minor() has vertical and horizontal major and minor grids.

  • sm_classic() has no grid and half-border. This is a classic shape. This is useful for all types of plots. The default theme of sm_bland_altman() is sm_classic().

  • sm_minimal has no grid. This is useful when a graph has a lot of annotation, such as texts and arrows.

  • sm_slope_theme() is a theme for a slope chart. It removes everything except the y-axis. This is the default theme of sm_slope().

Let’s explore these themes one-by-one. First, let’s create p1, which has the default theme of ggplot2.

library(tidyverse)
p1 <- ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 2)
p1

We can incorporate sm_hvgrid().

p1 + sm_hvgrid()

We can also remove borders and legends by setting them as FALSE.

p2 <- p1 + sm_hvgrid(borders = FALSE, legends = FALSE)
p2

You can also apply smplot’s colors by using scale_color_manual().

p2 + scale_color_manual(values = sm_palette(7))

We can also apply sm_hgrid().

p1 + sm_hgrid()

Let’s try sm_vgrid().

p1 + sm_vgrid()

You can remove the legend as shown below.

p1 + sm_vgrid(legends = FALSE)

sm_classic() looks very clean as well.

p1 + sm_classic()

You can add a legend.

p1 + sm_classic(legends = TRUE)

Another choice is sm_minimal(), which has no grids.

p1 + sm_minimal()

You can choose to include the legend in sm_minimal().

p1 + sm_minimal(legends = TRUE)

11.6 Correlation plot - sm_statCorr() and sm_corr_avgErr()

When using ggplot2 to plot correlation, the data frame has to have at least two columns, each of which for x and y axes.

p1 <- ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) +
  geom_point(shape = 21, fill = sm_color("green"), color = "white", size = 3)
p1

In the data frame mtcars, one column is for the x-axis (drat), another column is for y-axis (mpg). In short, the data frame structure is different to the one used to plot bar sm_bar(), sm_boxplot(), sm_raincloud() etc.

There are two associated functions in smplot2 with correlation.

  • sm_statCorr() draws the fitted linear regression line, prints the R (or \(R^2\)) and p values from the specified method of correlation test. It also calls sm_hvgrid().
    • Important: sm_statCorr() recognizes the data for the y- and x-axes from the mapping = aes() in ggplot() function. Therefore there is no need to provide data, x and y arguments in this function.
  • sm_corr_avgErr() draws the average point with vertical and horizontal error bars. The arguments sm_corr_avgErr(data = ..., x = ..., y = ...) have to be included; these are identical to what are provided in ggplot().
    • Important: sm_corr_avgErr() does not recognize the data from mapping = aes() in ggplot(). Therefore, you should specify the three arguments.
p1 + sm_statCorr()

11.6.1 sm_statCorr()

sm_statCorr() has various arguments.

  • fit.params feeds arguments to geom_smooth()control the aesthetics such as color, linetype, fullrange etc. These parameters can be used as part of ... the generic parameter.
p1 + sm_statCorr(fit.params = list(
  color = "black",
  linetype = "dashed"
))

  • fullrange controls whether the fitted line spans the entire length of the x-axis. If fullrange = TRUE, it will do so. If not, it will only span between the minimum and maximum x points of the dataset.
p1 + sm_statCorr(fullrange = T) +
  scale_x_continuous(limits = c(2, 5))

p1 + sm_statCorr(fullrange = F) +
  scale_x_continuous(limits = c(2, 5))

  • corr_method is the method of correlation test that is to be used. The default is set to the pearson correlation test (corr_method = 'pearson').
p1 + sm_statCorr(corr_method = "spearman")

  • separate_by is the string that separates between the R (or \(R^2\)) print and the p print.
p1 + sm_statCorr(separate_by = "\n", R2 = TRUE)

  • label_x and label_y controls the location of the statistical annotations. text_size controls their size.
p1 + sm_statCorr(
  fit.params = list(
    color = "black",
    linetype = "dashed"
  ),
  label_x = 2, label_y = 0, text_size = 5, R2 = TRUE
)

11.6.2 sm_corr_avgErr()

  • data is the variable that contains the data frame.

  • x is the column name of the variable that is plotted in the x-axis.

  • y is the column name of the variable that is plotted in the y-axis.

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) +
  sm_hvgrid()

  • point.params() feeds arguments to geom_point() to set color, alpha, size etc for 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.

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()

  • errorbar_type sets the type of the error bar, 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,
    errorbar_type = "se"
  ) +
  sm_hvgrid()

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,
    errorbar_type = "ci"
  ) +
  sm_hvgrid()

11.6.3 Structure of data frame

As previously mentioned, the structure of a data frame is unique for correlation plot.

  • Column 1 has to be the data for x-axis.
  • Column 2 has to be the data for y-axis. .

Let’s explore this difference by generating fake dataset and reorganizing it for different plot types.

# Example
set.seed(11) # generate random data
method1 <- c(rnorm(19, 0, 1), 2.5)
method2 <- c(rnorm(19, 0, 1), 2.5)
Subject <- rep(paste0("S", seq(1:20)), 2)
Data <- data.frame(Value = matrix(c(method1, method2), ncol = 1))
Method <- rep(c("Method 1", "Method 2"), each = length(method1))
df_general <- cbind(Subject, Data, Method) # used for sm_bar(), sm_boxplot(), sm_violin(), etc

df_corr <- data.frame(first = method1, second = method2) # used for correlation

We have created two data frames: df_general and df_corr. Let’s take a look at their structures.

head(df_general)
##   Subject       Value   Method
## 1      S1 -0.59103110 Method 1
## 2      S2  0.02659437 Method 1
## 3      S3 -1.51655310 Method 1
## 4      S4 -1.36265335 Method 1
## 5      S5  1.17848916 Method 1
## 6      S6 -0.93415132 Method 1

Notice that df_general has three columns. The first column is subject, second column is data (i.e., Value) and third column is measurement group.

head(df_corr)
##         first      second
## 1 -0.59103110 -0.65571812
## 2  0.02659437 -0.68251762
## 3 -1.51655310 -0.01585819
## 4 -1.36265335 -0.44260479
## 5  1.17848916  0.35255750
## 6 -0.93415132  0.07317058

Notice that df_corr has two columns, each of which represents a measurement group.

# correlation plot using data frame 'df_corr'
ggplot(data = df_corr, mapping = aes(x = first, y = second)) +
  geom_point(
    shape = 21, fill = sm_color("crimson"), color = "white",
    size = 3
  ) +
  sm_hvgrid(borders = FALSE) +
  scale_y_continuous(limits = c(-2.5, 2.5)) +
  scale_x_continuous(limits = c(-2.5, 2.5)) +
  sm_statCorr(
    color = sm_color("crimson"), fullrange = TRUE,
    label_x = -2.2, label_y = 2.3,
    linetype = "dashed"
  ) +
  ggtitle("Correlation plot") +
  xlab("Method 1") +
  ylab("Method 2")

# bar graph using data frame 'df_general'
ggplot(data = df_general, mapping = aes(
  x = Method, y = Value,
  color = Method
)) +
  sm_bar() +
  scale_color_manual(values = sm_color("crimson", "green")) +
  scale_y_continuous(limits = c(-2, 3))

11.6.4 Correlation plot with both regression and reference lines

You can also add a reference line (slope = 1) in a correlation plot.

This can be done with geom_abline(). In this example, the reference line’s slope is set to 1 and it has a dashed line style.

# correlation plot using data frame 'df_corr'
ggplot(data = df_corr, mapping = aes(x = first, y = second)) +
  geom_point(
    shape = 21, fill = sm_color("crimson"), color = "white",
    size = 3
  ) +
  sm_hvgrid(borders = FALSE) +
  geom_abline(slope = 1, linetype = "dashed") +
  scale_y_continuous(limits = c(-2.8, 2.8), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-2.8, 2.8), expand = c(0, 0)) +
  sm_statCorr(
    color = sm_color("crimson"), fullrange = TRUE,
    label_x = -2.2, label_y = 2.3,
    linetype = "dashed"
  ) +
  ggtitle("Correlation plot") +
  xlab("Method 1") +
  ylab("Method 2")

11.7 Boxplot - sm_boxplot()

sm_boxplot() generates a boxplot and individual points at the same time.

  • It automatically uses sm_hgrid() as its default theme.
    • It also has arguments borders and legends as part of sm_hgrid().

It has several arguments. To explore them, let’s generate some random data first.

set.seed(1) # generate random data
day1 <- rnorm(16, 0, 1)
day2 <- rnorm(16, 5, 1)
day3 <- rnorm(16, 10, 1)
Subject <- rep(paste0("S", seq(1:16)), 3)
Data <- data.frame(Value = matrix(c(day1, day2, day3), ncol = 1))
Day <- rep(c("Day 1", "Day 2", "Day 3"), each = length(day1))
df <- cbind(Subject, Data, Day)

Here is the default boxplot.

ggplot(data = df, mapping = aes(x = Day, y = Value)) +
  sm_boxplot()

  • point.param sends arguments to geom_point() such as color, size and alpha to plot the jittered individual points.

  • boxplot.param sends arguments to geom_boxplot() such as fill, color, width and notch to plot the boxplot.

In this example, ... is fed into point.params to control the color of the points, but not of the boxplot because boxplot.params already has a color default (type ?sm_boxplot to see the defaults).

ggplot(data = df, mapping = aes(x = Day, y = Value)) +
  sm_boxplot(color = sm_color("darkred"))

You can be quite flexible with the aesthetics.

ggplot(data = df, mapping = aes(x = Day, y = Value)) +
  sm_boxplot(point.params = list(
    fill = sm_color("darkred"),
    size = 2.2,
    shape = 21,
    color = "white",
    alpha = 1
  ))

You can also change the border color of the boxplot and its width using boxplot.params.

ggplot(data = df, mapping = aes(x = Day, y = Value)) +
  sm_boxplot(
    boxplot.params = list(
      color = sm_color("skyblue"),
      width = 0.65
    ),
    point.params = list(
      alpha = 0.65,
      size = 2.5
    )
  )

Notice that the border color of the boxplot and the fact that outliers are not separately labeled are identical to the default settings because these are kept unless the user re-specifies them. Only the width of the boxplots and their filling colors have been changed because we have specified them by overwriting over the defaults.

Now let’s apply different color for each Day.

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_boxplot() +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

You can change the shape of the boxplot by setting notch = TRUE within boxplot.params.

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_boxplot(boxplot.params = list(notch = TRUE, color = "black")) +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

A notched boxplot shows the confidence interval around the median (+/- 1.58 * interquartile range / sqrt(n)).

  • The notches are used for group comparison. -If the notch of each box does not overlap, there is a strong likelihood that the medians are significantly different between groups.

  • point_jitter_width lets you control the width of the jitter of the individual points.

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_boxplot(point_jitter_width = 0.2) +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

11.7.1 Plotting individual points with unique colors

sm_boxplot() can draw individual points with unique colors if you set color = Subject within ggplot().

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Subject)) +
  sm_boxplot() +
  scale_color_manual(values = sm_palette(16))

We can increase the size of the points.

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Subject)) +
  sm_boxplot(size = 2.2) +
  scale_color_manual(values = sm_palette(16))

11.8 Violin Plot - sm_violin()

sm_violin() plots a violin plot, individual points and lines that indicate means and +/- 1 standard deviation at the same time.

  • It is very similar to sm_boxplot() except there is no option for notch = TRUE in sm_violin().
  • sm_violin() automatically uses sm_hgrid() as its default theme.
    • It also has arguments borders and legends.
ggplot(data = df, mapping = aes(x = Day, y = Value)) +
  sm_violin()

# a violin plot with different colored points and lines
ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_violin() +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

You can specify the aesthetics of sm_violin() using point.params, err.params and violin.params.

  • point.params feeds the arguments to geom_point(), such as color, alpha, etc, to plot the individual points.

  • err.params feeds the arguments to geom_linerange(), such as color, size etc, to plot the error bar in either standard error (errorbar_type = se), standard deviation (errorbar_type = sd) or 95% confidence interval (errorbar_type = ci).

    • err.params can be used to control the average point as well because it is connected to the error bar.
ggplot(data = df, mapping = aes(
  x = Day, y = Value, color = Day,
  group = Day
)) +
  sm_violin(err.params = list(
    color = "black",
    size = 1.2
  )) +
  scale_color_manual(values = sm_palette(3))

  • violin.params feeds the arguments to geom_violin(), such as color and fill, to plot the violin.

  • point_jitter_width lets you control the width of the jitter of the individual points.

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_violin(point_jitter_width = 0.2) +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

  • points = FALSE removes all the individual points.
ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_violin(points = FALSE) +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

11.8.1 Plotting individual points with unique colors

Set color = Subject to plot individual points with unique colors.

ggplot(data = df, mapping = aes(
  x = Day, y = Value, color = Subject,
  group = Day
)) +
  sm_violin(point.params = list(alpha = 0.5)) +
  scale_color_manual(values = sm_palette(16))

11.9 Bar plot - sm_bar()

sm_bar() automatically uses sm_hgrid().

  • It also has arguments borders and legends, the latter of which has been set to FALSE as default.

Let’s use data (df) we generated before to explore this function.

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_bar() +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

In this case, the error bar represents standard error. If you prefer to show standard deviation, then you should set errorbar_type = 'sd' in sm_bar().

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_bar(errorbar_type = "sd") +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

95% confidence interval can also be displayed with errorbar_type = 'ci'.

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_bar(errorbar_type = "ci") +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

Jitter’s width can be controlled using point_jitter_width.

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_bar(
    errorbar_type = "ci",
    point_jitter_width = 0.2
  ) +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

You can also adjust different parameters of the bar graph using bar.params, err.params and point.params.

  • bar.params = list() sends arguments, such as color, fill, width and alpha to geom_bar() so that a bar is plotted.
ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_bar(bar.params = list(width = 0.7, alpha = 0.5)) +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

  • err.params = list() sends arguments, such as color, to geom_linerange() so that an error bar is plotted.
ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_bar(
    errorbar_type = "sd",
    bar.params = list(width = 0.7, alpha = 0.5),
    err.params = list(
      color = "gray40",
      size = 1
    )
  ) +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

  • point.params = list() feeds arguments, such as color, fill, and alpha to geom_point() to so that individual points are plotted.
ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) +
  sm_bar(
    errorbar_type = "sd",
    bar.params = list(width = 0.7, alpha = 0.5),
    err.params = list(
      color = "gray40",
      size = 1
    ),
    point.params = list(
      size = 2.5, shape = 15,
      alpha = 0.6
    )
  ) +
  scale_color_manual(values = sm_color("blue", "darkred", "viridian"))

11.9.1 Plotting individual points with unique colors

As before with sm_boxplot() and sm_violin(), to plot individual points with unique colors, set color = Subject.

ggplot(data = df, mapping = aes(x = Day, y = Value, color = Subject)) +
  sm_bar() +
  scale_color_manual(values = sm_palette(16))

sm_bar() can also print distinct box colors across distinct x levels. This requires the defaults of bar.params to be reset. Therefore, it needs to be written as bar.params = list() by setting forget = TRUE, which is an argument that makes the function forget its default when list() is called.

If you want the visualization functions to forget their defaults when list() is called, set forget = TRUE. This is necessary when you want to map aesthetics to different variables more flexibly.

ggplot(data = df, mapping = aes(
  x = Day, y = Value, color = Subject,
  fill = Day
)) +
  sm_bar(
    bar.params = list(alpha = 0.7, color = "transparent"),
    forget = TRUE
  ) +
  scale_color_manual(values = sm_palette(16)) +
  scale_fill_manual(values = sm_color("blue", "darkred", "viridian"))

To set one color across all points, you need to remove color = Subject. point.params can be specified to control the aesthetics of the points

ggplot(data = df, mapping = aes(
  x = Day, y = Value,
  fill = Day
)) +
  sm_bar(
    bar.params = list(alpha = 0.7, color = "transparent"),
    point.params = list(
      shape = 21, fill = "gray90",
      color = "black", size = 2.2
    ),
    forget = TRUE
  ) +
  scale_fill_manual(values = sm_color("blue", "darkred", "viridian"))

This is an example where the bar and points have unique colors per group.

ggplot(data = df, mapping = aes(
  x = Day, y = Value,
  fill = Day
)) +
  sm_bar(
    bar.params = list(alpha = 0.5, color = "transparent"),
    point.params = list(
      shape = 21, alpha = 1,
      size = 2.2,
      color = "white"
    ),
    forget = TRUE
  ) +
  scale_fill_manual(values = sm_color("blue", "darkred", "viridian"))