| Title: | Create Standalone and Composite Plots in 'ggplot2' for Publications |
|---|---|
| Description: | Provides functions for creating and annotating a composite plot in 'ggplot2'. Offers background themes and shortcut plotting functions that produce figures that are appropriate for the format of scientific journals. Some methods are described in Min and Zhou (2021) <doi:10.3389/fgene.2021.802894>. |
| Authors: | Seung Hyun Min [aut, cre] |
| Maintainer: | Seung Hyun Min <[email protected]> |
| License: | GPL-2 |
| Version: | 0.2.5 |
| Built: | 2025-01-20 04:26:15 UTC |
| Source: | https://github.com/smin95/smplot2 |
Adding a common legend on a combined figure
sm_add_legend( combined_plot, x, y, sampleplot, legend, direction = "vertical", border = TRUE, legend_spacing = 0.5, border_color = "black", font_size = 12 )sm_add_legend( combined_plot, x, y, sampleplot, legend, direction = "vertical", border = TRUE, legend_spacing = 0.5, border_color = "black", font_size = 12 )
combined_plot |
Combined figure, an output from sm_put_together(). |
x |
Location of the legend along the x-axis of the combined figure. The middle origin is at 0.5. |
y |
Location of the legend along the y-axis of the combined figure. The middle origin is at 0.5. |
sampleplot |
A variable containing one sample ggplot2 from which the legend can be derived. |
legend |
Pre-specified layer of legend created with sm_common_legend(). |
direction |
Direction of the legend: 'horizontal' or 'vertical'. |
border |
If set TRUE, border around the legend will be created. If set FALSE, the border will be removed. |
legend_spacing |
Spacing within the legend. |
border_color |
Color of the legend border |
font_size |
Text size of the legend |
It prints a legend on a a combined plot. It can be used to create a common legend for subplots.
library(ggplot2) library(smplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg, fill = as.factor(cyl))) + geom_point(shape = 21, color = 'white', size = 3) + sm_classic(legends=FALSE) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg, fill = as.factor(cyl))) + geom_point(shape = 21, color = 'white', size = 3) + sm_hvgrid(legends=FALSE) -> p2 combined_fig <- sm_put_together(list(p1,p2), ncol=2,nrow=1) sm_add_legend(combined_fig, x = 0.1, y = 0.1, sampleplot = p1)library(ggplot2) library(smplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg, fill = as.factor(cyl))) + geom_point(shape = 21, color = 'white', size = 3) + sm_classic(legends=FALSE) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg, fill = as.factor(cyl))) + geom_point(shape = 21, color = 'white', size = 3) + sm_hvgrid(legends=FALSE) -> p2 combined_fig <- sm_put_together(list(p1,p2), ncol=2,nrow=1) sm_add_legend(combined_fig, x = 0.1, y = 0.1, sampleplot = p1)
Add a point annotation onto the combined plot
sm_add_point(x, y, size = 10, shape = 16, color = "black", ...)sm_add_point(x, y, size = 10, shape = 16, color = "black", ...)
x |
Location of the point annotation along the x-axis of the combined figure. Default is the middle origin (0.5). Values from 0 to 1. |
y |
Location of the point annotation along the y-axis of the combined figure. Default is the middle origin (0.5). Values from 0 to 1. |
size |
Size of the point |
shape |
Shape of the point. Default is set to circle without border (16). |
color |
Color of the point. Default is set to black. |
... |
Other parameters of point that get passed to geom_point(). |
Prints a point in the combined plot.
library(ggplot2) library(smplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_hvgrid() -> p2 combined_fig <- sm_put_together(list(p1,p2), ncol=2,nrow=1) combined_fig + sm_add_point(color='red', size = 10, x = .5, y= .5)library(ggplot2) library(smplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_hvgrid() -> p2 combined_fig <- sm_put_together(list(p1,p2), ncol=2,nrow=1) combined_fig + sm_add_point(color='red', size = 10, x = .5, y= .5)
Add a text annotation onto the combined plot
sm_add_text( label, x = 0.5, y = 0.5, angle = 0, color = "black", fontface = "plain", size = 10, ... )sm_add_text( label, x = 0.5, y = 0.5, angle = 0, color = "black", fontface = "plain", size = 10, ... )
label |
Text label in strings. |
x |
Location of the text annotation along the x-axis of the combined figure. Default is the middle origin (0.5). Values from 0 to 1. |
y |
Location of the text annotation along the y-axis of the combined figure. Default is the middle origin (0.5). Values from 0 to 1. |
angle |
Angle of the text. Default is set to 0 (i.e., horizontal orientation). |
color |
Color of the text. Default is set to 'black'. |
fontface |
The default is to set the text as plain This can be changed, to either "plain", "bold", "italic", "bold.italic" . |
size |
Size of the text annotation |
... |
Other parameters of the text that will be transferred to the function annotate() from ggplot2. |
Prints a text in the combined plot.
library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_hvgrid() -> p2 combined_fig <- sm_put_together(list(p1,p2), ncol=2,nrow=1) combined_fig + sm_add_text(label='My label', x = .5, y= .5)library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_hvgrid() -> p2 combined_fig <- sm_put_together(list(p1,p2), ncol=2,nrow=1) combined_fig + sm_add_text(label='My label', x = .5, y= .5)
This is equivalent to Matlab's trapz function.
sm_auc(x, y)sm_auc(x, y)
x |
This is the scalar spacing of the coordinate. When the argument for 'x' is not provided, it will calculates the approximate integral value for ‘Y’ with unit spacing based on the length of ‘y’. |
y |
Numerical data. The length of x and y must be equal. |
A vector that is the value from the trapezoidal integration is returned.
library(smplot2) X = c(1,2,3,4,5) Y1 = c(2,3,4,2,3) Y2 = c(3,3,3,3,3) sm_auc(X,Y2) sm_auc(X,Y1)library(smplot2) X = c(1,2,3,4,5) Y1 = c(2,3,4,2,3) Y2 = c(3,3,3,3,3) sm_auc(X,Y2) sm_auc(X,Y1)
This function returns a data frame containing AUCs from a data frame that contains the original raw data. One of the two arguments 'groups' and 'conditions' must be filled. The function will throw an error if both arguments are empty.
sm_auc_all(data, subjects, groups, conditions, x, values)sm_auc_all(data, subjects, groups, conditions, x, values)
data |
Name of the variable that stores the data frame that contains the columns with the specified column names. |
subjects |
The name of the column of the data frame that contains subjects. It must be strings. |
groups |
The name of the column of the data frame that contains each group. It must be strings. |
conditions |
The name of the column of the data frame that contains each condition. It must be strings. |
x |
The name of the column of the data frame that contains the x-axis points/x coordinates from which the AUC can be calculated. It must be strings. The column must not have characters. |
values |
The name of the column of the data frame that contains the actual data, which are the y-axis points from which the AUC can be calculated. It must be strings. |
Returns a data frame containing area under curve from each subject and experimental condition and/or group.
library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,0,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Value <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c(1,2), each = length(day1)) Condition <- rep('Control', length(day1)*2) df <- cbind(Subject, Value, Condition, Day) sm_auc_all(data = df, subjects = 'Subject',values = 'Value', conditions = 'Condition',x = 'Day')library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,0,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Value <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c(1,2), each = length(day1)) Condition <- rep('Control', length(day1)*2) df <- cbind(Subject, Value, Condition, Day) sm_auc_all(data = df, subjects = 'Subject',values = 'Value', conditions = 'Condition',x = 'Day')
Generates a bar plot with optional jittered individual points and error bars. The function supports flexible customization of the bars, points, and error bars, and allows for displaying standard deviation, standard error, or confidence intervals.
sm_bar( ..., bar.params = list(width = 0.7, alpha = 1, color = "transparent", fill = "gray80"), err.params = list(linewidth = 1, color = "black"), point.params = list(size = 2.5, alpha = 0.65, shape = 16), errorbar_type = "se", point_jitter_width = 0.12, points = TRUE, borders = TRUE, legends = FALSE, seed = NULL, forget = FALSE )sm_bar( ..., bar.params = list(width = 0.7, alpha = 1, color = "transparent", fill = "gray80"), err.params = list(linewidth = 1, color = "black"), point.params = list(size = 2.5, alpha = 0.65, shape = 16), errorbar_type = "se", point_jitter_width = 0.12, points = TRUE, borders = TRUE, legends = FALSE, seed = NULL, forget = FALSE )
... |
Additional aesthetic parameters applied across points, bars, and error bars. Optional. |
bar.params |
A list of parameters for customizing the bar graph. Common parameters include:
Default: |
err.params |
A list of parameters for customizing the error bars. Common parameters include:
Default: |
point.params |
A list of parameters for customizing individual points. Common parameters include:
Default: |
errorbar_type |
A string specifying the type of error bars to display:
|
point_jitter_width |
A numeric value specifying the degree of horizontal jitter applied to individual points.
|
points |
Logical. Determines whether individual points are displayed:
|
borders |
Logical. Determines whether grid borders are displayed:
|
legends |
Logical. Determines whether legends are displayed:
|
seed |
A numeric value to set a random seed for reproducible jittered points.
Default: |
forget |
Logical. Determines whether to apply the default aesthetic parameters:
|
A ggplot2 object representing a bar graph with optional jittered points and error bars.
library(smplot2) library(ggplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) # with aesthetic defaults of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_bar() + scale_color_manual(values = sm_color('blue','orange'))library(smplot2) library(ggplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) # with aesthetic defaults of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_bar() + scale_color_manual(values = sm_color('blue','orange'))
This function generates a Bland-Altman plot. This function requires two paired data sets as input (same length), and uses sm_statBlandAlt() to compute statistical values necessary for a Bland Altman plot. For more information on these values, please type ?sm_statBlandAlt.
The plot automatically uses sm_classic() theme. The upper dashed line indicates the upper limit (mean_diff + 1.96*sd), the middle dashed line indicates the mean difference between the two samples, and the lower dashed line indicates the lower limit (mean_diff - 1.96*sd).
To add a legend, you will need to add sm_classic(legends = TRUE). To customise the figure, you can add more geom objects.
sm_bland_altman(first, second, point_size = 3.3, diff_ci = TRUE, ...)sm_bland_altman(first, second, point_size = 3.3, diff_ci = TRUE, ...)
first |
Data from the first repetition/session |
second |
Data from the second repetition/session |
point_size |
The size of the individual points. The default is set to 3.3. |
diff_ci |
If set TRUE, then it will draw a shaded region that represents the 95 confidence interval of the difference between the two sessions from one-sample t-test. If the region (i.e. confidence interval) overlaps with zero, then there is no significant bias/difference between the two sessions/datasets. If it does not overlap with 0, then the measurement variability is significantly large. |
... |
Parameters of geom_point(), such as 'color', 'fill', 'shape', etc. |
Prints a figure, which is the Bland-Altman plot (ggplot2 object).
library(smplot2) library(tibble) first <- rnorm(20) second <- rnorm(20) df <- as_tibble(cbind(first,second)) sm_bland_altman(df$first, df$second) # when all 3 dashed lines are not shown, extend the range of the y-axis.library(smplot2) library(tibble) first <- rnorm(20) second <- rnorm(20) df <- as_tibble(cbind(first,second)) sm_bland_altman(df$first, df$second) # when all 3 dashed lines are not shown, extend the range of the y-axis.
'sm_boxplot' generates a boxplot with optional jittered individual points using ggplot2. This function allows users to customize the aesthetics of the boxplot and points separately and provides an option to control the default behavior via the 'forget' argument.
sm_boxplot( ..., boxplot.params = list(notch = FALSE, fill = "gray95", color = "black", size = 0.5, width = 0.5, outlier.shape = NA), point.params = list(alpha = 0.65, size = 2), point_jitter_width = 0.12, points = TRUE, borders = TRUE, legends = FALSE, seed = NULL, forget = FALSE )sm_boxplot( ..., boxplot.params = list(notch = FALSE, fill = "gray95", color = "black", size = 0.5, width = 0.5, outlier.shape = NA), point.params = list(alpha = 0.65, size = 2), point_jitter_width = 0.12, points = TRUE, borders = TRUE, legends = FALSE, seed = NULL, forget = FALSE )
... |
Additional aesthetic parameters applied to both the boxplot and individual points. These parameters are optional and allow global customization. |
boxplot.params |
A list of parameters for customizing the boxplot appearance, such as 'fill', 'color', 'size', 'width', and 'outlier.shape'. The default is: ‘list(notch = FALSE, fill = ’gray95', color = 'black', size = 0.5, width = 0.5, outlier.shape = NA)'. |
point.params |
A list of parameters for customizing the individual points, such as 'alpha', 'size', and 'shape'. The default is: 'list(alpha = 0.65, size = 2)'. |
point_jitter_width |
A numeric value specifying the degree of horizontal jitter applied to individual points. If set to '0', points are aligned along the y-axis without jitter. Default is '0.12'. |
points |
Logical. If 'TRUE' (default), individual points are displayed. If 'FALSE', points are hidden. |
borders |
Logical. If 'TRUE' (default), grid borders are displayed. If 'FALSE', borders are removed. |
legends |
Logical. If 'TRUE', legends are displayed. If 'FALSE' (default), legends are hidden. |
seed |
A numeric value for setting a random seed to make jittered points reproducible. Default is 'NULL' (no seed). |
forget |
Logical. If 'TRUE', ignores the default aesthetic parameters ('boxplot.params' and 'point.params') and applies only the provided customization through 'list(...)'. If 'FALSE' (default), merges the provided parameters with the defaults. |
A list of ggplot layers that can be added to a ggplot object to create a customized boxplot with optional jittered points.
library(ggplot2) library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) # with the default aesthetics of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_boxplot() + scale_color_manual(values = sm_color('blue','orange')) # Without the default aesthetics of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_boxplot(boxplot.params = list()) + scale_color_manual(values = sm_color('blue','orange'))library(ggplot2) library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) # with the default aesthetics of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_boxplot() + scale_color_manual(values = sm_color('blue','orange')) # Without the default aesthetics of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_boxplot(boxplot.params = list()) + scale_color_manual(values = sm_color('blue','orange'))
This function computes the confidence interval.
sm_ci(data, alpha = 0.05, low = TRUE)sm_ci(data, alpha = 0.05, low = TRUE)
data |
Numerical vector of data |
alpha |
Default is set to 0.05, so that 95% confidence interval is computed. |
low |
If its TRUE, it will compute the low tail of the confidence interval. If its FALSE, it will compute the high tail of the confidence interval. |
Prints a double vector that is a single end of the specified confidence interval.
library(smplot2) set.seed(1) a <- rnorm(100,1,1) sm_ci(a) sm_ci(a, low=FALSE)library(smplot2) set.seed(1) a <- rnorm(100,1,1) sm_ci(a) sm_ci(a, low=FALSE)
It has x and y axis but no grids.
sm_classic(legends = FALSE)sm_classic(legends = FALSE)
legends |
If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
Returns a background theme as a ggplot2 object.
library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_classic()library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_classic()
This is a custom color palette that SM recommends for data visualization. It returns up to 20 different colors with a high visibility.
sm_color(...)sm_color(...)
... |
The input has to be a character string.of a color name. There are 20 colors available from the SM palette: ''blue'‘,'’crimson'‘, '’green'‘, '’purple'‘, '’orange'', ''skyblue'‘, '’pink'‘, '’limegreen'‘, '’lightpurple'', ''brown'‘, '’red'‘, '’lightbrown'', ''asparagus'‘, '’viridian'‘, '’darkred'‘, '’lightblue'', ''light blue'‘, '’wine'‘, '’yellow'‘, '’lightgreen'' |
A character/string of hex codes
library(smplot2) sm_color('crimson') sm_color('crimson', 'green', 'blue')library(smplot2) sm_color('crimson') sm_color('crimson', 'green', 'blue')
This function is used to create a composite figure.
sm_common_axis(location, hmargin = 1, wmargin = 1)sm_common_axis(location, hmargin = 1, wmargin = 1)
location |
Location of the panel. ''topleft'': removes x-axis title, x-axis ticklabel, y-axis title. ''topright'': removes x-axis title, x-axis ticklabel, y-axis title, y-axis ticklabel. ''bottomleft'': removes x-axis title, y-axis title. ''bottomright'': removes x-axis title, y-axis title, y-axis ticklabel. ''topcenter'': removes x-axis title, x-axis ticklabel, y-axis title, y-axis ticklabel. ''bottomcenter'': removes x-axis title, y-axis title, y-axis ticklabel. ''single'': keeps all ticks but removes title ''centerleft'' : removes some ticks and titles ''centerright'' : removes some ticks and titles ''center'': removes everything |
hmargin |
The amount of height of blank space between subplots. It sets the size of the empty space (i.e., margin) between panels. T he default is set to 1, which should reduce the empty space (right and left side of each panel) between the panels. |
wmargin |
The amount of width of blank space between subplots. It sets the size of the empty space (i.e., margin) between panels. T he default is set to 1, which should reduce the empty space (right and left side of each panel) between the panels. |
Returns a ggplot2 output with ticks removed.
library(ggplot2) library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) # with aesthetic defaults of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_bar() + scale_color_manual(values = sm_color('blue','orange')) + sm_common_axis('bottomleft')library(ggplot2) library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) # with aesthetic defaults of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_bar() + scale_color_manual(values = sm_color('blue','orange')) + sm_common_axis('bottomleft')
Creating a common legend for subplots on a separate panel
sm_common_legend( x = 0.5, y = 0.5, title = FALSE, direction = "vertical", border = TRUE, legend_spacing = 0.5, border_color = "black", textRatio = 1 )sm_common_legend( x = 0.5, y = 0.5, title = FALSE, direction = "vertical", border = TRUE, legend_spacing = 0.5, border_color = "black", textRatio = 1 )
x |
Location of the legend along the x-axis. Default is the middle origin (0.5). |
y |
Location of the legend along the y-axis. Default is the middle origin (0.5). |
title |
Title of the legend. Input should be string |
direction |
Direction of the legend: 'horizontal' or 'vertical'. |
border |
If set TRUE, border around the legend will be created. If set FALSE, the border will be removed. |
legend_spacing |
Spacing within the legend. |
border_color |
Color of the legend border |
textRatio |
Size of the text relative to the plot's default. It has been set to 1.2. The larger the textRatio, the larger the texts in the legend. |
It prints a legend on a blank plot. It can be used to create a common legend for subplots.
library(ggplot2) library(smplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg, fill = as.factor(cyl))) + geom_point(shape = 21, color = 'white', size = 3) + sm_common_legend(x = .5, y = 0.5 , direction='horizontal', border=FALSE)library(ggplot2) library(smplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg, fill = as.factor(cyl))) + geom_point(shape = 21, color = 'white', size = 3) + sm_common_legend(x = .5, y = 0.5 , direction='horizontal', border=FALSE)
Common title for combined subplots
sm_common_title(title = "", size = 17, x = 0.5, y = 0.5, fontface = "bold")sm_common_title(title = "", size = 17, x = 0.5, y = 0.5, fontface = "bold")
title |
The input should be string. |
size |
Text size of the title. |
x |
Location of the title along the x-axis. Default is the middle origin (0.5). |
y |
Location of the legend along the y-axis. Default is the middle origin (0.5). |
fontface |
The default is to set the text of the title as bold. This can be changed, to either "plain", "bold", "italic", "bold.italic" . |
It prints title on a blank layer of ploting.
library(smplot2) sm_common_title('My title')library(smplot2) sm_common_title('My title')
Common x-axis label (title) for combined subplots
sm_common_xlabel(label = "", size = 17, x = 0.5, y = 0.5, fontface = "plain")sm_common_xlabel(label = "", size = 17, x = 0.5, y = 0.5, fontface = "plain")
label |
The input should be string. |
size |
Text size of the label. |
x |
Location of the label along the x-axis. Default is the middle origin (0.5). |
y |
Location of the label along the y-axis. Default is the middle origin (0.5). |
fontface |
The default is to set the text of the title as plain This can be changed, to either "plain", "bold", "italic", "bold.italic" . |
It returns a layer with the specified common x-axis label for combined plot.
library(smplot2) sm_common_xlabel('My x-axis')library(smplot2) sm_common_xlabel('My x-axis')
Common y-axis label (title) for combined subplots
sm_common_ylabel( label = "", size = 17, x = 0.5, y = 0.52, fontface = "plain", angle = 90 )sm_common_ylabel( label = "", size = 17, x = 0.5, y = 0.52, fontface = "plain", angle = 90 )
label |
The input should be string. |
size |
Text size of the label. |
x |
Location of the label along the x-axis. Default is the middle origin (0.5). |
y |
Location of the label along the y-axis. Default is the middle origin (0.5). |
fontface |
The default is to set the text of the title as plain This can be changed, to either "plain", "bold", "italic", "bold.italic" . |
angle |
Orientation of the y-axis title. Default is 90 degrees. |
It returns a layer with the specified common y-axis label for combined plot.
library(smplot2) sm_common_ylabel('My y-axis')library(smplot2) sm_common_ylabel('My y-axis')
Superimposition of the average point with horizontal and vertical error bars in the correlation plot
sm_corr_avgErr( data, x, y, point.params = list(size = 2.5), errh.params = list(height = 0), errv.params = list(width = 0), errorbar_type = "se", ... )sm_corr_avgErr( data, x, y, point.params = list(size = 2.5), errh.params = list(height = 0), errv.params = list(width = 0), errorbar_type = "se", ... )
data |
Data frame variable that is used for plotting. |
x |
Column of the data frame that represents the x-axis. |
y |
Column of the data frame that represents the y-axis. |
point.params |
List of parameters for the mean point, such as color, alpha, fill etc |
errh.params |
List of parameters for the horizontal error bar, such as color, alpha, fill etc |
errv.params |
List of parameters for the vertical points, such as color, alpha, fill etc |
errorbar_type |
This argument determines the error bar type. If it is set to 'se' , standard error bar will be shown. If it is set to 'sd', the error bar will display standard deviation. If it is set to 'ci' (default), the error bar will display 95% confidence interval. |
... |
A generic aesthetic parameter across points and error bars. This is optional. |
A point with error bars representing the average will be returned.
library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, size = 3) + sm_corr_avgErr(mtcars, drat,mpg, errorbar_type = 'se', color = sm_color('red'))library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, size = 3) + sm_corr_avgErr(mtcars, drat,mpg, errorbar_type = 'se', color = sm_color('red'))
Cohen's d is a measure of the effect size. It is often reported with p-values (ex. from a t-test or posthoc pairwise comparisons).
sm_effsize(group1, group2, absolute = TRUE)sm_effsize(group1, group2, absolute = TRUE)
group1 |
Numeric vector containing data from one sample (i.e., group 1) that is to be compared with another group. |
group2 |
Numeric vector containing data from another sample (i.e., group 2) that is to be compared with the former group. |
absolute |
If set TRUE, the function will print the absolute value of the effect size. If set FALSE, the function will print effect size of group2 - group1. For example, it will be positive if group2 has a larger mean than group 1. |
Returns a double vector that is the effect size between two samples.
library(smplot2) group1 <- rnorm(10,0,1) group2 <- rnorm(10,1,1) sm_effsize(group1, group2)library(smplot2) group1 <- rnorm(10,0,1) group2 <- rnorm(10,1,1) sm_effsize(group1, group2)
Forest plot
sm_forest( ..., point.params = list(size = 2.5, alpha = 0.3), avgPoint.params = list(size = 5.5, shape = 18), err.params = list(color = "black"), ref.params = list(size = 0.4, color = "gray80", linetype = "dashed"), xintercept = 0, sep_level = 2, point_jitter_width = 0, errorbar_type = "ci", points = TRUE, refLine = TRUE, borders = TRUE, legends = FALSE, seed = NULL, forget = FALSE )sm_forest( ..., point.params = list(size = 2.5, alpha = 0.3), avgPoint.params = list(size = 5.5, shape = 18), err.params = list(color = "black"), ref.params = list(size = 0.4, color = "gray80", linetype = "dashed"), xintercept = 0, sep_level = 2, point_jitter_width = 0, errorbar_type = "ci", points = TRUE, refLine = TRUE, borders = TRUE, legends = FALSE, seed = NULL, forget = FALSE )
... |
A generic aesthetic parameter across points, lines and error bars. This is optional. |
point.params |
List of parameters for individual points, such as color, alpha, fill etc |
avgPoint.params |
List of parameters for the average point, such as color, alpha, fill etc |
err.params |
List of parameters for the error bar from the average point, such as color, alpha etc |
ref.params |
List of parameters for the vertical reference line, such as color, alpha etc |
xintercept |
Location of the vertical reference line along the x coordinate. |
sep_level |
A numerical value that controls the level of the separation between the individual points and the average point. If it's 0, all of these are clustered together. If it's higher (and more positive), the text annotations will increasingly go below the mean point. Default is set to 2. The values can be negative so that the points can be above the mean point. There is no limit of the range for this argument. |
point_jitter_width |
A numerical value that determines the degree of the jitter for each point. If its 0, all the points will have no jitter (aligned along the y-axis). |
errorbar_type |
This argument determines the error bar type. If it is set to 'se' , standard error bar will be shown. If it is set to 'sd', the error bar will display standard deviation. If it is set to 'ci' (default), the error bar will display 95% confidence interval. |
points |
If points is set TRUE, individual points are shown. If FALSE, they are not shown. |
refLine |
If it is set TRUE, the reference line at a specified location along the x-axis is shown. If it is set FALSE, it is not shown. |
borders |
If the border needs to be displayed, the input should be TRUE. If the border is not needed, the input should be FALSE. |
legends |
If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
seed |
Random seed |
forget |
Forget the defaults when list() is called for a specific parameter (ex. point.params). Set to TRUE when when users want to map aesthetics to different groups more flexibly.. Set to FALSE by default. |
A forest plot generated using ggplot2
library(smplot2) library(ggplot2) day1 = rnorm(20,0,1) day2 = rnorm(20,5,1) day3 = rnorm(20,6,1.5) day4 = rnorm(20,7,2) Subject <- rep(paste0('S',seq(1:20)), 4) Data <- data.frame(Value = matrix(c(day1,day2,day3,day4),ncol=1)) Day <- rep(c('Day 1', 'Day 2', 'Day 3', 'Day 4'), each = length(day1)) df2 <- cbind(Subject, Data, Day) ggplot(data = df2, aes(x = Value, y = Day, color = Day, fill = Day)) + sm_forest(sep_level = 2, point_jitter_width = .12, errorbar_type = 'ci', point.params = list(alpha=0.2)) + scale_color_manual(values = sm_palette(4))library(smplot2) library(ggplot2) day1 = rnorm(20,0,1) day2 = rnorm(20,5,1) day3 = rnorm(20,6,1.5) day4 = rnorm(20,7,2) Subject <- rep(paste0('S',seq(1:20)), 4) Data <- data.frame(Value = matrix(c(day1,day2,day3,day4),ncol=1)) Day <- rep(c('Day 1', 'Day 2', 'Day 3', 'Day 4'), each = length(day1)) df2 <- cbind(Subject, Data, Day) ggplot(data = df2, aes(x = Value, y = Day, color = Day, fill = Day)) + sm_forest(sep_level = 2, point_jitter_width = .12, errorbar_type = 'ci', point.params = list(alpha=0.2)) + scale_color_manual(values = sm_palette(4))
Annotation of the error range on the forest plot
sm_forest_annot( data, x, y, errorbar_type = "ci", text.params = list(size = 4, color = "black"), sep_level = 2, ... )sm_forest_annot( data, x, y, errorbar_type = "ci", text.params = list(size = 4, color = "black"), sep_level = 2, ... )
data |
Data frame variable that is used for plotting. |
x |
Column of the data frame that represents the x-axis. |
y |
Column of the data frame that represents the y-axis. |
errorbar_type |
This argument determines the errorbar type. If it is set to 'se', standard error bar will be shown. If it is set to 'sd' (default), the error bar will display standard deviation. If it is set to 'ci', the error bar will display 95% confidence interval. |
text.params |
List of parameters for the text annotation, such as color, size etc |
sep_level |
A numerical value that controls the level of the separation between the text annotation and the average point. If it's 0, all of these are clustered together. If it's higher (and more positive), the text annotations will increasingly go above the mean point. Default is set to 2. The values can be negative so that the texts can be below the mean point. There is no limit of the range for this argument. Ideally, this should equal to the sep_level in sm_forest(). |
... |
Parameters for the text annotation, such as size and color etc. |
Annotations showing the range of uncertainty will printed on the forest plot.
library(ggplot2) library(smplot2) day1 = rnorm(20,0,1) day2 = rnorm(20,5,1) day3 = rnorm(20,6,1.5) day4 = rnorm(20,7,2) Subject <- rep(paste0('S',seq(1:20)), 4) Data <- data.frame(Value = matrix(c(day1,day2,day3,day4),ncol=1)) Day <- rep(c('Day 1', 'Day 2', 'Day 3', 'Day 4'), each = length(day1)) df2 <- cbind(Subject, Data, Day) ggplot(data = df2, aes(x = Value, y = Day, color = Day)) + sm_forest(point_jitter_width = 0.12, sep_level = 3) + scale_color_manual(values = sm_palette(4)) + sm_forest_annot(data = df2, x = Value, y = Day, sep_level = 3)library(ggplot2) library(smplot2) day1 = rnorm(20,0,1) day2 = rnorm(20,5,1) day3 = rnorm(20,6,1.5) day4 = rnorm(20,7,2) Subject <- rep(paste0('S',seq(1:20)), 4) Data <- data.frame(Value = matrix(c(day1,day2,day3,day4),ncol=1)) Day <- rep(c('Day 1', 'Day 2', 'Day 3', 'Day 4'), each = length(day1)) df2 <- cbind(Subject, Data, Day) ggplot(data = df2, aes(x = Value, y = Day, color = Day)) + sm_forest(point_jitter_width = 0.12, sep_level = 3) + scale_color_manual(values = sm_palette(4)) + sm_forest_annot(data = df2, x = Value, y = Day, sep_level = 3)
A graph with a horizontal grid is plotted. Border can be added or removed. This is useful for plotting a bar graph.
sm_hgrid(legends = FALSE, borders = TRUE)sm_hgrid(legends = FALSE, borders = TRUE)
legends |
If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
borders |
If the border needs to be displayed, the input should be TRUE. If the border is not needed, the input should be FALSE. |
Returns a background theme with major horizontal grids (ggplot2 output).
library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_hgrid()library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_hgrid()
Histogram with kernel density estimation (Gaussian) and rugs
sm_hist( ..., hist.params = list(binwidth = 1/2, fill = sm_color("blue"), color = "white", alpha = 0.4), density.params = list(color = sm_color("blue"), size = 0.8, fill = "transparent"), rug.params = list(color = sm_color("blue"), alpha = 0.8, size = 0.4), histogram = TRUE, density = TRUE, rug = TRUE, borders = FALSE, legends = FALSE, forget = FALSE )sm_hist( ..., hist.params = list(binwidth = 1/2, fill = sm_color("blue"), color = "white", alpha = 0.4), density.params = list(color = sm_color("blue"), size = 0.8, fill = "transparent"), rug.params = list(color = sm_color("blue"), alpha = 0.8, size = 0.4), histogram = TRUE, density = TRUE, rug = TRUE, borders = FALSE, legends = FALSE, forget = FALSE )
... |
A generic aesthetic parameter across points and the boxplot. This is optional. |
hist.params |
List of parameters for the histogram, such as binwidth, color, alpha, fill etc. |
density.params |
List of parameters for the density estimation, such as color, size, alpha etc |
rug.params |
List of parameters for the rugs, such as color, size, alpha etc |
histogram |
TRUE if the histogram needs to be shown. FALSE if the histogram needs to be hidden. |
density |
TRUE if the density plot needs to be shown. FALSE if the density plot needs to be hidden. |
rug |
TRUE if the rugs need to be shown. FALSE if the rugs need to be hidden. |
borders |
If the border needs to be displayed, the input should be TRUE. If the border is not needed, the input should be FALSE. |
legends |
If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
forget |
Forget the defaults when list() is called for a specific parameter. Set to TRUE when when users want to map aesthetics to different groups more flexibly.. Set to FALSE by default. |
Returns a histogram generated using ggplot2.
library(ggplot2) library(smplot2) set.seed(2) data=data.frame(value=rnorm(1000)) data2 = data.frame(value=rnorm(1000,5,1)) data$day <- 'day1' data2$day <- 'day2' rbind(data,data2) -> df ggplot(data = data, aes(x=value)) + sm_hist() ggplot(data = df, aes(x=value, fill=day, color = day)) + sm_hist(hist.params = list(binwidth = 1/2, alpha = 0.3), density.params = list(fill='transparent', size = 0.8), rug.params = list(alpha = 0.8)) + scale_color_manual(values = sm_palette(2)) + scale_fill_manual(values = sm_palette(2))library(ggplot2) library(smplot2) set.seed(2) data=data.frame(value=rnorm(1000)) data2 = data.frame(value=rnorm(1000,5,1)) data$day <- 'day1' data2$day <- 'day2' rbind(data,data2) -> df ggplot(data = data, aes(x=value)) + sm_hist() ggplot(data = df, aes(x=value, fill=day, color = day)) + sm_hist(hist.params = list(binwidth = 1/2, alpha = 0.3), density.params = list(fill='transparent', size = 0.8), rug.params = list(alpha = 0.8)) + scale_color_manual(values = sm_palette(2)) + scale_fill_manual(values = sm_palette(2))
This theme has major vertical and horizontal grids. This is useful for plotting correlations. sm_corr_theme() is exactly the same as sm_hvgrid().
sm_hvgrid(legends = TRUE, borders = TRUE)sm_hvgrid(legends = TRUE, borders = TRUE)
legends |
If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
borders |
If the border needs to be displayed, the input should be 'TRUE'. If the border is not needed, the input should be 'FALSE'. |
Returns a background theme that has both horizontal and vertical major grids (ggplot2 output).
library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_hvgrid()library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_hvgrid()
This theme has vertical and horizontal grids.
sm_hvgrid_minor(legends = TRUE, borders = TRUE)sm_hvgrid_minor(legends = TRUE, borders = TRUE)
legends |
If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
borders |
If the border needs to be displayed, the input should be 'TRUE'. If the border is not needed, the input should be 'FALSE'. |
Returns a background theme that has both horizontal and vertical major and minor grids (ggplot2 output).
library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_hvgrid_minor()library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_hvgrid_minor()
This theme has no major grid.
sm_minimal(legends = FALSE)sm_minimal(legends = FALSE)
legends |
If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
Returns a background theme that has no grids (ggplot2 output).
library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_minimal()library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_minimal()
This is a custom color palette of smplot2. It returns up to 20 different colors with a high contrast.
sm_palette(colorNum = 10)sm_palette(colorNum = 10)
colorNum |
Number of colors (1-20). |
Returns a hex code in string vector. The input determines the length of the output.
library(smplot2) #' sm_palette(3) # returns 3 colorslibrary(smplot2) #' sm_palette(3) # returns 3 colors
Writing a label for each panel of a combined figure
sm_panel_label( all_plots, x, y, panel_tag = "1", panel_pretag, panel_posttag, text_size = 5.5, text_color = "black", fontface = "plain", ... )sm_panel_label( all_plots, x, y, panel_tag = "1", panel_pretag, panel_posttag, text_size = 5.5, text_color = "black", fontface = "plain", ... )
all_plots |
all_plots should be a list vector, which should contain all panels that are to be combined into one figure. |
x |
Location of the label along the x-axis (0 to 1). 0.5 is the middle origin. |
y |
Location of the label along the y-axis (0 to 1). 0.5 is the middle origin. |
panel_tag |
A character vector that defines how each panel is enumerated. Options include: 'a', 'A', '1', 'I' or 'i'.'a' is for lowercase letters. 'A' is for uppercase letters. '1' is for integers. 'I' is for upper case roman numerals. 'i' is for lower case roman numerals. Each panel will display a unique string based on the set enumeration. |
panel_pretag |
A character vector that is identical across panels BEFORE the panel_tag. |
panel_posttag |
A character vector that is identical across panels AFTER the panel_tag. |
text_size |
Text size of the panel label |
text_color |
Text color of the panel label |
fontface |
Fontface of the panel label. Options include "plain", "bold", "italic" and others that are provided by ggplot2. |
... |
Additional parameters for adjusting the appearance of the sticker. Same parameters for annotate() from ggplot2. |
It returns a list of plots with panel labels.
library(ggplot2) library(smplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_hvgrid() -> p2 sm_panel_label(list(p1,p2), x = 0.1, y = 0.9, panel_tag ='1', panel_pretag = 'S', text_size = 4, text_color = 'black')library(ggplot2) library(smplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_hvgrid() -> p2 sm_panel_label(list(p1,p2), x = 0.1, y = 0.9, panel_tag ='1', panel_pretag = 'S', text_size = 4, text_color = 'black')
Remove xticklabels and yticklabels in selected panels for proper subplotting
sm_plot_clean(all_plots, ncol, nrow, wmargin = wmargin, hmargin = hmargin)sm_plot_clean(all_plots, ncol, nrow, wmargin = wmargin, hmargin = hmargin)
all_plots |
all_plots should be list, which should contain all panels that are to be combined into one figure. |
ncol |
Number of columns in the combined plot |
nrow |
Number of rows in the combined plot |
wmargin |
The amount of width of blank space between subplots. It sets the size of the empty space (i.e., margin) between panels. T he default is set to 1, which should reduce the empty space (right and left side of each panel) between the panels. |
hmargin |
The amount of height of blank space between subplots. It sets the size of the empty space (i.e., margin) between panels. T he default is set to 1, which should reduce the empty space (right and left side of each panel) between the panels. |
Returns a list of plots with new layouts.
library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_hvgrid() -> p2 sm_plot_clean(list(p1,p2), ncol=2,nrow=1,wmargin=-2, hmargin=-2)library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_hvgrid() -> p2 sm_plot_clean(list(p1,p2), ncol=2,nrow=1,wmargin=-2, hmargin=-2)
This is a common plot with mean point, standard error (se, sd or 95 uniquely shadow, which is a faint display of individual points behind the mean.
sm_pointplot( ..., avgPoint.params = list(size = 2.5), avgLine.params = list(linewidth = 1), point.params = list(alpha = 0.35, color = "gray", fill = "gray"), line.params = list(alpha = 0.35, color = "gray"), err.params = list(linewidth = 1), errorbar_type = "se", show_shadow = FALSE, group = NULL, borders = TRUE, legends = FALSE, forget = FALSE )sm_pointplot( ..., avgPoint.params = list(size = 2.5), avgLine.params = list(linewidth = 1), point.params = list(alpha = 0.35, color = "gray", fill = "gray"), line.params = list(alpha = 0.35, color = "gray"), err.params = list(linewidth = 1), errorbar_type = "se", show_shadow = FALSE, group = NULL, borders = TRUE, legends = FALSE, forget = FALSE )
... |
A generic aesthetic parameter across points, lines and errorbars. This is optional. This will be extremely useful for dodging each line using position_dodge(). |
avgPoint.params |
List of parameters for the average point, such as color, alpha, fill etc |
avgLine.params |
List of parameters for the average line, such as color, alpha etc |
point.params |
List of parameters for the points in the shadow, such as color, alpha, fill etc |
line.params |
List of parameters for the lines in the shadow, such as color, alpha etc |
err.params |
List of parameters for the error bar from the average plot, such as color, alpha etc |
errorbar_type |
This argument determines the errorbar type. If it is set to 'se', standard error bar will be shown. If it is set to 'sd' (default), the error bar will display standard deviation. If it is set to 'ci', the error bar will display 95% confidence interval. |
show_shadow |
If it is TRUE, it will show the shadow. If it is FALSE, it will not show the shadow (default). |
group |
If show_shadow = TRUE, this argument is required. This is the variable that each plot from the shadow should be grouped along aesthetically. It should be grouped for each individual observation, ex. sm_pointplot(group = Subject), whereby Subject is the column that holds identifers for each observation. |
borders |
If the border needs to be displayed, the input should be TRUE. If the border is not needed, the input should be FALSE. |
legends |
If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
forget |
Forget the defaults when list() is called for a specific parameter (ex. point.params). Set to TRUE when when users want to map aesthetics to different groups more flexibly.. Set to FALSE by default. |
Returns a pointplot generated using ggplot2.
library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = cyl, y = mpg)) + sm_pointplot()library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = cyl, y = mpg)) + sm_pointplot()
Post-hoc power analysis using two-sample or paired t-test
sm_power(group1, group2, paired, sig.level = 0.05, power = 0.8)sm_power(group1, group2, paired, sig.level = 0.05, power = 0.8)
group1 |
Numeric vector containing data from one sample (i.e., group 1) that is to be compared with another group. |
group2 |
Numeric vector containing data from another sample (i.e., group 2) that is to be compared with the former group. |
paired |
A logical indicating whether your two samples (group1 and group2) are paired. |
sig.level |
Significance level (Type I error probability). Default is set to 0.05. |
power |
Power of test (1 minus Type II error probability). Default is set to 0.8. |
Returns a result with a class of "power.htest" from the pwr package.
library(smplot2) group1 <- rnorm(10,0,1) group2 <- rnorm(10,1,1) sm_power(group1, group2, paired = TRUE)library(smplot2) group1 <- rnorm(10,0,1) group2 <- rnorm(10,1,1) sm_power(group1, group2, paired = TRUE)
'sm_put_together' combines multiple ggplot objects into a single composite figure. The function optimizes the layout by considering the presence of tick labels and axis labels in the input plots, ensuring a clean and well-aligned output.
sm_put_together( all_plots, title = NULL, xlabel = NULL, ylabel = NULL, legend = NULL, ncol = NULL, nrow = NULL, xlabel2 = NULL, ylabel2 = NULL, tickRatio = NULL, panel_scale = 0.9, wRatio = NULL, hRatio = NULL, hmargin = 0, wmargin = 0, remove_ticks = "some", wRatio2 = NULL, hRatio2 = NULL, labelRatio = 1 )sm_put_together( all_plots, title = NULL, xlabel = NULL, ylabel = NULL, legend = NULL, ncol = NULL, nrow = NULL, xlabel2 = NULL, ylabel2 = NULL, tickRatio = NULL, panel_scale = 0.9, wRatio = NULL, hRatio = NULL, hmargin = 0, wmargin = 0, remove_ticks = "some", wRatio2 = NULL, hRatio2 = NULL, labelRatio = 1 )
all_plots |
A list of ggplot objects to be combined. Each plot should ideally include tick labels on both x and y axes to allow the function to optimize the layout effectively. |
title |
A ggplot layer or character string specifying the title of the combined figure. If supplied as a string, the function dynamically adjusts its size and placement. Optional. |
xlabel |
A ggplot layer or character string specifying the label for the x-axis of the combined figure. Optional. |
ylabel |
A ggplot layer or character string specifying the label for the y-axis of the combined figure. Optional. |
legend |
A ggplot legend layer to be added to the combined figure. Optional. |
ncol |
Number of columns in the combined figure grid. If not supplied, the function determines this based on the number of plots. |
nrow |
Number of rows in the combined figure grid. If not supplied, the function determines this based on the number of plots. |
xlabel2 |
A secondary x-axis label layer or character string. Optional. |
ylabel2 |
A secondary y-axis label layer or character string. Optional. |
tickRatio |
A scaling factor for tick label size. By default, this is adjusted dynamically based on the number of rows and columns in the grid. Larger values increase tick label size. Optional. |
panel_scale |
A numeric value between 0 and 1 that scales the individual panels to reduce empty space within and around each panel. Default is '0.9'. |
wRatio |
A scaling factor for the width of the first column relative to other columns. By default, this is computed based on the input plots. Users can override it with a numeric value. Optional. |
hRatio |
A scaling factor for the height of the last row relative to other rows. By default, this is computed based on the input plots. Users can override it with a numeric value. Optional. |
hmargin |
Vertical margin between subplots. Positive values increase the spacing, while negative values reduce it. Default is '0'. |
wmargin |
Horizontal margin between subplots. Positive values increase the spacing, while negative values reduce it. Default is '0'. |
remove_ticks |
Specifies whether to remove ticks from the inner panels: '"some"' (default) removes ticks only from inner plots, '"all"' removes ticks from all plots, and '"none"' keeps all ticks. |
wRatio2 |
A scaling factor for the width of the last column relative to other columns. By default, this is computed based on the input plots. Optional. |
hRatio2 |
A scaling factor for the height of the first row relative to other rows. By default, this is computed based on the input plots. Optional. |
labelRatio |
A scaling factor for the text size of titles, axis labels, and secondary labels when provided as character strings. Default is '1'. Larger values increase text size proportionally. |
Users can supply custom titles and axis labels either as ggplot layers created with 'sm_common_xlabel()', 'sm_common_ylabel()', and 'sm_common_title()' or as character strings directly. The function attempts to adjust the size and placement of titles and labels dynamically based on the input plots.
While the 'all_plots' argument is required, all other arguments are optional, and defaults are provided for most parameters.
A composite ggplot object combining the input plots into a grid layout.
library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_hvgrid() -> p2 title <- sm_common_title('My title') xlabel <- sm_common_xlabel('My x-axis') ylabel <- sm_common_ylabel('My y-axis') sm_put_together(list(p1,p2), title=title, xlabel=xlabel, ylabel=ylabel, ncol=2,nrow=1) sm_put_together(list(p1,p2), title='My title', xlabel='My x-axis', ylabel='My y-axis', labelRatio = 1.1, ncol=2,nrow=1)library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) -> p1 ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_hvgrid() -> p2 title <- sm_common_title('My title') xlabel <- sm_common_xlabel('My x-axis') ylabel <- sm_common_ylabel('My y-axis') sm_put_together(list(p1,p2), title=title, xlabel=xlabel, ylabel=ylabel, ncol=2,nrow=1) sm_put_together(list(p1,p2), title='My title', xlabel='My x-axis', ylabel='My y-axis', labelRatio = 1.1, ncol=2,nrow=1)
Creates a raincloud plot, a combination of jittered points, boxplots, and violin plots. Inspired by the 'raincloudplots' R package by Jordy van Langen, this function offers enhanced customization and automatic sorting of data based on the x-axis factor levels.
This function allows detailed control over the aesthetics of individual components (boxplot, violin, and points) and provides options to adjust layout and orientation.
sm_raincloud( ..., boxplot.params = list(), violin.params = list(alpha = 0.3, color = "transparent"), point.params = list(alpha = 1, size = 3, shape = 21, color = "transparent"), which_side = "r", sep_level = 2, point_jitter_width = 0.12, vertical = TRUE, points = TRUE, borders = TRUE, legends = FALSE, seed = NULL, forget = FALSE )sm_raincloud( ..., boxplot.params = list(), violin.params = list(alpha = 0.3, color = "transparent"), point.params = list(alpha = 1, size = 3, shape = 21, color = "transparent"), which_side = "r", sep_level = 2, point_jitter_width = 0.12, vertical = TRUE, points = TRUE, borders = TRUE, legends = FALSE, seed = NULL, forget = FALSE )
... |
Additional aesthetic parameters applied across all elements (points, boxplot, and violin plot). Optional. |
boxplot.params |
A list of parameters for customizing the boxplot. Common options include:
Default: |
violin.params |
A list of parameters for customizing the violin plot. Common options include:
Default: |
point.params |
A list of parameters for customizing individual points. Common options include:
Default: |
which_side |
Specifies the side of the violin and boxplots. Options:
The |
sep_level |
A numeric value (0-4) controlling the separation level among the boxplot, violin plot, and points:
Default: |
point_jitter_width |
A numeric value determining the horizontal jitter for individual points:
|
vertical |
Logical. Specifies the orientation of the plot:
|
points |
Logical. Determines whether individual points are displayed:
|
borders |
Logical. Determines whether grid borders are displayed:
|
legends |
Logical. Determines whether legends are displayed:
|
seed |
A numeric value to set a random seed for reproducible jittered points.
Default: |
forget |
Logical. Determines whether to apply the default aesthetic parameters:
|
A list of ggplot2 layers for creating a raincloud plot.
library(ggplot2) library(smplot2) set.seed(2) # generate random data day1 = rnorm(20,0,1) day2 = rnorm(20,5,1) day3 = rnorm(20,6,1.5) day4 = rnorm(20,7,2) Subject <- rep(paste0('S',seq(1:20)), 4) Data <- data.frame(Value = matrix(c(day1,day2,day3,day4),ncol=1)) Day <- rep(c('Day 1', 'Day 2', 'Day 3', 'Day 4'), each = length(day1)) df2 <- cbind(Subject, Data, Day) ggplot(data=df2, aes(x = Day, y = Value, color = Day, fill = Day)) + sm_raincloud() + xlab('Day') + scale_fill_manual(values = sm_palette(4))library(ggplot2) library(smplot2) set.seed(2) # generate random data day1 = rnorm(20,0,1) day2 = rnorm(20,5,1) day3 = rnorm(20,6,1.5) day4 = rnorm(20,7,2) Subject <- rep(paste0('S',seq(1:20)), 4) Data <- data.frame(Value = matrix(c(day1,day2,day3,day4),ncol=1)) Day <- rep(c('Day 1', 'Day 2', 'Day 3', 'Day 4'), each = length(day1)) df2 <- cbind(Subject, Data, Day) ggplot(data=df2, aes(x = Day, y = Value, color = Day, fill = Day)) + sm_raincloud() + xlab('Day') + scale_fill_manual(values = sm_palette(4))
Generates a slope chart, which is particularly useful for comparing effects between two time points. The function supports grouped data and provides various customization options for lines, points, error bars, and x-axis ticks. Users can specify the type of error bars and control whether to display mean and error bars.
The mapping in 'ggplot()' requires grouping each observation to ensure correct pairing of points.
sm_slope( ..., labels, group, line.params = list(color = "gray53", linewidth = 0.4, alpha = 0.4), point.params = list(size = 2.5, shape = 21, color = "white"), avgLine.params = list(linewidth = 1), avgPoint.params = list(size = 4), err.params = list(linewidth = 1), xTick.params = list(position = "top", expand = c(0.17, 0.1), drop = FALSE), errorbar_type = "sd", many_groups = FALSE, show_err = FALSE, show_mean = FALSE, legends = FALSE, forget = FALSE )sm_slope( ..., labels, group, line.params = list(color = "gray53", linewidth = 0.4, alpha = 0.4), point.params = list(size = 2.5, shape = 21, color = "white"), avgLine.params = list(linewidth = 1), avgPoint.params = list(size = 4), err.params = list(linewidth = 1), xTick.params = list(position = "top", expand = c(0.17, 0.1), drop = FALSE), errorbar_type = "sd", many_groups = FALSE, show_err = FALSE, show_mean = FALSE, legends = FALSE, forget = FALSE )
... |
Additional aesthetic parameters applied across points and lines, such as |
labels |
A vector specifying the labels for the x-axis ticks. This is a required argument.
For example: |
group |
The name of the variable used to group individual data points. This is a required argument. |
line.params |
A list of parameters for individual lines. Common parameters include:
Default: |
point.params |
A list of parameters for individual points. Common parameters include:
Default: |
avgLine.params |
A list of parameters for the average line. Common parameters include:
Default: |
avgPoint.params |
A list of parameters for the average points. Common parameters include:
Default: |
err.params |
A list of parameters for error bars. Common parameters include:
Default: |
xTick.params |
A list of parameters for customizing the x-axis ticks. Common options include:
|
errorbar_type |
A string specifying the type of error bars to display:
|
many_groups |
Logical. Determines whether the average line is plotted for each group:
Default: |
show_err |
Logical. Determines whether to display error bars:
|
show_mean |
Logical. Determines whether to display the average line and points:
|
legends |
Logical. Determines whether to display legends:
|
forget |
Logical. Determines whether to apply the default aesthetic parameters:
|
A list of ggplot2 layers for creating a slope chart.
library(ggplot2) library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) ggplot(data=df, aes(x = Day, y = Value, fill = Day)) + sm_slope(labels = c('Day 1', 'Day 2'), group = Subject) + scale_fill_manual(values= sm_color('blue','orange')) ggplot(data = df, aes(x = Day, y = Value, fill = Day)) + sm_slope(labels = c('Day 1','Day 2'),group = Subject, point.params = list(alpha = 0.3, size = 2.5, color = 'white', shape = 21, fill = sm_color('skyblue')), line.params = list(color = sm_color('skyblue'), alpha = 0.3), avgPoint.params = list(color='transparent', shape = 21, size = 4, fill = sm_color('blue')), avgLine.params = list(color = sm_color('blue'), linewidth = 1), show_mean = TRUE)library(ggplot2) library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) ggplot(data=df, aes(x = Day, y = Value, fill = Day)) + sm_slope(labels = c('Day 1', 'Day 2'), group = Subject) + scale_fill_manual(values= sm_color('blue','orange')) ggplot(data = df, aes(x = Day, y = Value, fill = Day)) + sm_slope(labels = c('Day 1','Day 2'),group = Subject, point.params = list(alpha = 0.3, size = 2.5, color = 'white', shape = 21, fill = sm_color('skyblue')), line.params = list(color = sm_color('skyblue'), alpha = 0.3), avgPoint.params = list(color='transparent', shape = 21, size = 4, fill = sm_color('blue')), avgLine.params = list(color = sm_color('blue'), linewidth = 1), show_mean = TRUE)
This function returns a data frame containing slope (from linear regression) from a data frame that contains the original raw data.
The user can use lm() from base R to compute the slope as well.
sm_slope_all(data, subjects, groups, conditions, x, values)sm_slope_all(data, subjects, groups, conditions, x, values)
data |
Name of the variable that stores the data frame that contains the columns with the specified column names. |
subjects |
The name of the column of the data frame that contains subjects. It must be strings. |
groups |
The name of the column of the data frame that contains each group. It must be strings. |
conditions |
The name of the column of the data frame that contains each condition. It must be strings. |
x |
The name of the column of the data frame that contains the x-axis points/x coordinates from which the slopes can be calculated. It must be strings. The column must not have characters. |
values |
The name of the column of the data frame that contains the actual data, which are the y-axis points from which the slope can be calculated. It must be strings. |
Returns a data frame containing slopes for each subject and each experimental condition and/or group.
library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,0,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Value <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c(1,2), each = length(day1)) Condition <- rep('Control', length(day1)*2) df <- cbind(Subject, Value, Condition, Day) sm_slope_all(data = df, subjects = 'Subject',values = 'Value', conditions = 'Condition',x = 'Day')library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,0,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Value <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c(1,2), each = length(day1)) Condition <- rep('Control', length(day1)*2) df <- cbind(Subject, Value, Condition, Day) sm_slope_all(data = df, subjects = 'Subject',values = 'Value', conditions = 'Condition',x = 'Day')
Generates a slope chart with mean for a single group. This is useful for comparing the effect between two time points. The function includes options for shadow lines and points, mean lines, points, and error bars, all of which can be customized.
Note: This functionality can also be reproduced using 'sm_slope()' with appropriate customization. In 'ggplot()', the mapping requires grouping each observation to correctly pair points.
sm_slope_mean( ..., labels, group, main_color = sm_color("blue"), main_shape = 21, back_alpha = 0.25, line_width = 0.25, avgline_width = 1, point_size = 2.5, avgpoint_size = 4, err_width = 1, xTick.params = list(position = "top", expand = c(0.17, 0.1), drop = FALSE), errorbar_type = "sd", show_err = FALSE, legends = FALSE )sm_slope_mean( ..., labels, group, main_color = sm_color("blue"), main_shape = 21, back_alpha = 0.25, line_width = 0.25, avgline_width = 1, point_size = 2.5, avgpoint_size = 4, err_width = 1, xTick.params = list(position = "top", expand = c(0.17, 0.1), drop = FALSE), errorbar_type = "sd", show_err = FALSE, legends = FALSE )
... |
Additional aesthetic parameters applied across points, lines, and error bars. Optional. |
labels |
A vector specifying the labels for the x-axis ticks. This is a required argument.
For example: |
group |
The name of the variable used to group individual data points. This is a required argument. |
main_color |
The main color of the slope chart, shared across:
Default: |
main_shape |
The shape of the points in the slope chart:
Default: |
back_alpha |
Transparency (alpha) for the shadow lines and points:
Default: |
line_width |
Line width for the shadow lines connecting points. Default: |
avgline_width |
Line width for the average line. Default: |
point_size |
Size of the points in the shadow. Default: |
avgpoint_size |
Size of the points representing the mean of the data. Default: |
err_width |
Line width for the error bars. Default: |
xTick.params |
A list of parameters for customizing the x-axis ticks. Options include:
|
errorbar_type |
A string specifying the type of error bars to display:
|
show_err |
Logical. Determines whether to display error bars:
|
legends |
Logical. Determines whether to display legends:
|
A slope chart with mean, represented as a ggplot2 object.
library(ggplot2) library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) ggplot(data=df, aes(x = Day, y = Value)) + sm_slope_mean(labels = c('Day 1', 'Day 2'), group = Subject, back_alpha = .3, main_color = sm_color('green'))library(ggplot2) library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) ggplot(data=df, aes(x = Day, y = Value)) + sm_slope_mean(labels = c('Day 1', 'Day 2'), group = Subject, back_alpha = .3, main_color = sm_color('green'))
In this plot, all aspects except for the left-handed spine are missing. This format is appropriate for the slope chart.
sm_slope_theme(legends = TRUE)sm_slope_theme(legends = TRUE)
legends |
#' If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
Returns a background theme that is suitable for a slope chart (ggplot2 output).
library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_slope_theme()library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_slope_theme()
Bland-Altman plot is drawn to show measurement variability/reliabiilty of a task. This function requires two paired datasets (same length). It returns a list of difference (by element), mean, standard deviation of the difference, mean difference, upper and lower limits. These values are necessary to draw a Bland Altman plot.
The list returned from this function can be directly used as an argument for sm_bland_altman(), which draws a Bland-Altman plot using ggplot2.
Another output 'data' is a tibble with two columns: 1) Mean across each pair for each element (ex. a mean of the 1st element from the first set and 1st element from the second set), 2) Difference between each pair for every element. The output 'data' should be used as a argument for data in ggplot() when plotting.
sm_statBlandAlt(first, second)sm_statBlandAlt(first, second)
first |
Data from the first repetition/session |
second |
Data from the second repetition/session |
A list is returned, which has all numerical results that are relevant to drawing a Bland-Altman plot.
library(smplot2) library(tibble) first <- rnorm(20) second <- rnorm(20) df <- as_tibble(cbind(first,second)) # requires library(tidyverse) sm_statBlandAlt(df$first, df$second)library(smplot2) library(tibble) first <- rnorm(20) second <- rnorm(20) df <- as_tibble(cbind(first,second)) # requires library(tidyverse) sm_statBlandAlt(df$first, df$second)
This combines two different functions: 1) 'geom_smooth()' from ggplot, and 2) 'stat_cor()' from ggpubr. 'geom_smooth()' is used to fit the best-fit model, whereas 'stat_cor()' is used to print correlation results at an optimized location.
Updates from smplot2 include more flexibility, less input arguments and its pairing with 'sm_hvgrid()' / 'sm_corr_theme()'.
sm_statCorr( ..., fit.params = list(), corr_method = "pearson", alternative = "two.sided", separate_by = ",", label_x = NULL, label_y = NULL, text_size = 4, show_text = TRUE, borders = TRUE, legends = FALSE, r2 = FALSE, R2 )sm_statCorr( ..., fit.params = list(), corr_method = "pearson", alternative = "two.sided", separate_by = ",", label_x = NULL, label_y = NULL, text_size = 4, show_text = TRUE, borders = TRUE, legends = FALSE, r2 = FALSE, R2 )
... |
Arguments for the properties of regression line, such as 'linetype', 'color', etc. For more information, type ?geom_smooth |
fit.params |
Paramters for the fitted line, such as color, linetype and alpha. |
corr_method |
Method of the correlation test. Options include: 'pearson', 'kendall', or 'spearman'. |
alternative |
Specifies the alternative hypothesis (H1). 'two.sided' is the standard way. 'greater' is a positive association, whereas 'less' is a negative association. |
separate_by |
This marks how the p- and r- values should be separated. The default option is: ',' For more information, check out stat_cor() from the ggpubr package. |
label_x |
Location of the statistical value prints along the figure's x-axis. It asks for a number within the x-axis limit. |
label_y |
Location of the statistical value prints along the figure's y-axis. It requires a number within the y-axis limit. |
text_size |
Size (numerical value) of the texts from correlation. |
show_text |
If the statistical result needs to be displayed, the input should be TRUE (default). If the statistical result is not needed, the input should be FALSE. |
borders |
If the border needs to be displayed, the input should be TRUE. If the border is not needed, the input should be FALSE. |
legends |
If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
r2 |
FALSE or TRUE. TRUE if user wants to compute R2. FALSE if R needs to be computed. |
R2 |
Same as r2. |
Plots a best-fitted linear regression on a correlation plot with results from correlation statistical tests.
library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_statCorr() # computes R ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_statCorr(R2 = TRUE) # computes R2library(smplot2) library(ggplot2) ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_statCorr() # computes R ggplot(data = mtcars, mapping = aes(x = drat, y = mpg)) + geom_point(shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_statCorr(R2 = TRUE) # computes R2
Standard error
sm_stdErr(data)sm_stdErr(data)
data |
Numerical vector of data. |
A double vector is returned with a standard error of the input (given sample).
library(smplot2) sm_stdErr(rnorm(10,0,1))library(smplot2) sm_stdErr(rnorm(10,0,1))
This theme has major vertical grids.
sm_vgrid(legends = TRUE, borders = TRUE)sm_vgrid(legends = TRUE, borders = TRUE)
legends |
If the legend needs to be displayed, the input should be TRUE. If the legend is not needed, the input should be FALSE. |
borders |
If the border needs to be displayed, the input should be TRUE. If the border is not needed, the input should be FALSE. |
Returns a background theme with major vertical grids (ggplot2 output).
library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_vgrid()library(ggplot2) library(smplot2) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) + sm_vgrid()
Generates a violin plot with optional jittered individual points and error bars. This function allows for flexible customization of the violin, points, and error bars, and supports displaying standard deviation, standard error, or confidence intervals as error bars.
sm_violin( ..., violin.params = list(fill = "gray90", color = "transparent"), err.params = list(size = 1.2, linewidth = 1.2), point.params = list(alpha = 0.25, size = 2), errorbar_type = "sd", point_jitter_width = 0.17, points = TRUE, borders = TRUE, legends = FALSE, seed = NULL, forget = FALSE )sm_violin( ..., violin.params = list(fill = "gray90", color = "transparent"), err.params = list(size = 1.2, linewidth = 1.2), point.params = list(alpha = 0.25, size = 2), errorbar_type = "sd", point_jitter_width = 0.17, points = TRUE, borders = TRUE, legends = FALSE, seed = NULL, forget = FALSE )
... |
Additional aesthetic parameters applied across points, the violin plot, and error bars. Optional. |
violin.params |
A list of parameters for customizing the violin plot. Common parameters include:
Default: |
err.params |
A list of parameters for customizing the error bars. Common parameters include:
Default: |
point.params |
A list of parameters for customizing individual points. Common parameters include:
Default: |
errorbar_type |
A string specifying the type of error bars to display:
|
point_jitter_width |
A numeric value specifying the degree of horizontal jitter applied to individual points.
|
points |
Logical. Determines whether individual points are displayed:
|
borders |
Logical. Determines whether grid borders are displayed:
|
legends |
Logical. Determines whether legends are displayed:
|
seed |
A numeric value to set a random seed for reproducible jittered points.
Default: |
forget |
Logical. Determines whether to apply the default aesthetic parameters:
|
A ggplot2 object containing a violin plot with optional jittered points and error bars.
library(ggplot2) library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) # with aesthetic defaults of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_violin() + scale_color_manual(values = sm_color('blue','orange')) # without aesthetic defaults of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_violin(violin.params = list()) + scale_color_manual(values = sm_color('blue','orange'))library(ggplot2) library(smplot2) set.seed(1) # generate random data day1 = rnorm(16,2,1) day2 = rnorm(16,5,1) Subject <- rep(paste0('S',seq(1:16)), 2) Data <- data.frame(Value = matrix(c(day1,day2),ncol=1)) Day <- rep(c('Day 1', 'Day 2'), each = length(day1)) df <- cbind(Subject, Data, Day) # with aesthetic defaults of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_violin() + scale_color_manual(values = sm_color('blue','orange')) # without aesthetic defaults of smplot ggplot(data = df, mapping = aes(x = Day, y = Value, color = Day)) + sm_violin(violin.params = list()) + scale_color_manual(values = sm_color('blue','orange'))