R/statsplot2.b.R
In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research
#' @title Plots and Graphs Based on Variable Types
#' @importFrom R6 R6Class
#' @import jmvcore
#' @import magrittr
#'
statsplot2Class <- if (requireNamespace('jmvcore'))
R6::R6Class(
"statsplot2Class",
inherit = statsplot2Base,
private = list(
.run = function() {
StatStratum <- ggalluvial::StatStratum
# If no variable selected Initial Message ----
if (is.null(self$options$dep) ||
is.null(self$options$group)) {
# TODO ----
todo <- glue::glue(
"
<br>Welcome to ClinicoPath
<br><br>
This tool will help you generate plots based on variable types.
<br><br>
This function uses ggstatsplot and ggalluvial packages. Please cite jamovi and the packages as given below.
"
)
self$results$todo$setContent(todo)
return()
} else {
todo <- ""
self$results$todo$setContent(todo)
if (nrow(self$data) == 0)
stop('Data contains no (complete) rows')
# prepare main arguments ----
# mydata <- self$data
mydep <- self$data[[self$options$dep]]
mygroup <- self$data[[self$options$group]]
contin <- c("integer", "numeric", "double")
# categ <- c("factor")
distribution <- self$options$distribution
distribution <- jmvcore::composeTerm(distribution)
direction <- self$options$direction
direction <- jmvcore::composeTerm(direction)
# independent ----
if (direction == "independent") {
# independent, factor, continuous ----
if (inherits(mygroup, "factor") &&
inherits(mydep, contin)) {
# ggbetweenstats violin plots for comparisons between groups/conditions
# stat_exp <- c("independent", "factor", "continuous")
stat_exp <-
glue::glue(
"You have selected to use a violin plot to compare a continuous variable between independent groups/conditions.
"
)
# independent, continuous, continuous ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, contin)) {
# ggscatterstats scatterplots for correlations between two variables
# stat_exp <- c("independent", "continuous", "continuous")
stat_exp <-
glue::glue(
"You have selected to use a scatter plot to compare a continuous variable to another.
"
)
# independent, factor, factor ----
} else if (inherits(mygroup, "factor") &&
inherits(mydep, "factor")) {
# stat_exp <- c("independent", "factor", "factor")
stat_exp <-
glue::glue(
"You have selected to use a barplot to compare a group variable with another.
"
)
# independent, continuous, factor ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, "factor")) {
stat_exp <- glue::glue("<br>You have selected to use a barplot to compare a categorical variable with another.<br><hr>")
}
# repeated ----
} else if (direction == "repeated") {
# repeated, factor, continuous ----
if (inherits(mygroup, "factor") &&
inherits(mydep, contin)) {
# ggwithinstats violin plots for comparisons within groups/conditions
# stat_exp <-
# c("repeated", "factor", "continuous")
stat_exp <- glue::glue(
"You have selected to use a violin plot to compare a continuous variable between repeated measurements.
"
)
# repeated, continuous, continuous ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, contin)) {
# stat_exp <- c("repeated", "continuous", "continuous")
stat_exp <- glue::glue(
"Currently this tool does not support scatterplots for repeated measurements. You may refer to R-project rmcorr package."
)
# repeated, factor, factor ----
} else if (inherits(mygroup, "factor") &&
inherits(mydep, "factor")) {
# stat_exp <- c("repeated", "factor", "factor")
stat_exp <-
glue::glue(
"You have selected to compare 2 repeated factor measurements.")
# repeated, continuous, factor ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, "factor")) {
stat_exp <- glue::glue(
"Please switch the variables to generate a plot.
"
)
}
}
self$results$text4$setContent(stat_exp)
}
},
.plot = function(image, ggtheme, theme, ...) {
# the plot function ----
# Error messages ----
if (is.null(self$options$dep) ||
is.null(self$options$group))
return()
if (nrow(self$data) == 0)
stop('Data contains no (complete) rows')
# prepare main arguments ----
# mydata <- self$data
mydep <- self$data[[self$options$dep]]
mygroup <- self$data[[self$options$group]]
contin <- c("integer", "numeric", "double")
# categ <- c("factor")
# distribution <- self$options$distribution
# distribution <- jmvcore::composeTerm(distribution)
# direction <- self$options$direction
# direction <- jmvcore::composeTerm(direction)
# direction ----
direction <- self$options$direction
# distribution
distribution <-
jmvcore::constructFormula(terms = self$options$distribution)
# read data ----
mydata <- self$data
# Exclude NA ----
excl <- self$options$excl
if (excl) {mydata <- jmvcore::naOmit(mydata)}
# define main arguments
# mydep <- mydata[[self$options$dep]]
# mygroup <- mydata[[self$options$group]]
dep <- self$options$dep
group <- self$options$group
dep <- jmvcore::composeTerms(listOfComponents = dep)
group <- jmvcore::composeTerm(components = group)
# if ( ! is.null(self$options$grvar) ) {
# mygrvar <- mydata[[self$options$grvar]]
# }
if ( ! is.null(self$options$grvar) ) {
grvar <- self$options$grvar
}
# define variable types ----
contin <- c("integer", "numeric", "double")
categ <- c("factor")
# independent ----
# independent, factor, continuous ----
if (direction == "independent") {
if (inherits(mygroup, "factor") && inherits(mydep, contin)) {
# ggbetweenstats violin plots for comparisons between groups/conditions
# plotData <- data.frame(gr = mygroup,
# dp = jmvcore::toNumeric(mydep))
plot <- ggstatsplot::ggbetweenstats(
data = mydata,
x = !!group,
y = !!dep,
type = distribution
)
# independent, continuous, continuous ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, contin)) {
# ggscatterstats scatterplots for correlations between two variables
# plotData <-
# data.frame(gr = jmvcore::toNumeric(mygroup),
# dp = jmvcore::toNumeric(mydep))
plot <- ggstatsplot::ggscatterstats(
data = mydata,
x = !!group,
y = !!dep,
type = distribution)
# independent, factor, factor ----
} else if (inherits(mygroup, "factor") &&
inherits(mydep, "factor")) {
# ggbarstats bar charts for categorical data
# plotData <- data.frame(gr = mygroup,
# dp = mydep)
plot <- ggstatsplot::ggbarstats(
data = mydata,
main = !!dep,
condition = !!group)
# independent, continuous, factor ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, "factor")) {
plot <- ggstatsplot::ggdotplotstats(
data = mydata,
x = !!dep,
y = !!group)
}
# repeated ----
} else if (direction == "repeated") {
# repeated, factor, continuous ----
if (inherits(mygroup, "factor") &&
inherits(mydep, contin)) {
# ggwithinstats violin plots for comparisons within groups/conditions
# plotData <- data.frame(gr = mygroup,
# dp = jmvcore::toNumeric(mydep))
plot <- ggstatsplot::ggwithinstats(
data = mydata,
x = !!group,
y = !!dep,
type = distribution,
pairwise.comparisons = TRUE
# pairwise.comparisons = pairw
)
# repeated, continuous, continuous ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, contin)) {
plot <- c("Not Available")
# my.rmc <- rmcorr::rmcorr(participant = Subject,
# measure1 = PacO2,
# measure2 = pH,
# dataset = rmcorr::bland1995)
#
# plot(my.rmc, overall = TRUE)
#
# ggplot2::ggplot(rmcorr::bland1995,
# ggplot2::aes(x = PacO2,
# y = pH,
# group = factor(Subject),
# color = factor(Subject)
# )
# ) +
# ggplot2::geom_point(ggplot2::aes(colour = factor(Subject))) +
# ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1)
# repeated, factor, factor ----
} else if (inherits(mygroup, "factor") &&
inherits(mydep, "factor")) {
# Select alluvial style ----
alluvsty <- self$options$alluvsty
if (alluvsty == "t1") {
# ggalluvial
# http://corybrunson.github.io/ggalluvial/
plotData <- data.frame(gr = mygroup,
dp = mydep)
mydata_changes <- plotData %>%
dplyr::group_by(gr, dp) %>%
dplyr::tally(x = .)
# head(as.data.frame(UCBAdmissions), n = 12)
# ggalluvial::is_alluvia_form(
# as.data.frame(UCBAdmissions),
# axes = 1:3, silent = TRUE)
# plot <- ggplot(as.data.frame(UCBAdmissions),
# aes(y = Freq, axis1 = Gender, axis2 = Dept)) +
# geom_alluvium(aes(fill = Admit), width = 1/12) +
# geom_stratum(width = 1/12, fill = "black", color = "grey") +
# geom_label(stat = "stratum", infer.label = TRUE) +
# scale_x_discrete(limits = c("Gender", "Dept"), expand = c(.05, .05)) +
# scale_fill_brewer(type = "qual", palette = "Set1") +
# ggtitle("UC Berkeley admissions and rejections, by sex and department")
stratum <- ggalluvial::StatStratum
plot <- ggplot2::ggplot(data = mydata_changes,
ggplot2::aes(
axis1 = gr,
axis2 = dp,
y = n
)) +
ggplot2::scale_x_discrete(
limits = c(self$options$group, self$options$dep),
expand = c(.1, .05)
) +
ggplot2::xlab(self$options$group) +
ggalluvial::geom_alluvium(ggplot2::aes(fill = gr,
colour = gr)) +
ggalluvial::geom_stratum() +
ggalluvial::stat_stratum(geom = "stratum") +
ggplot2::geom_label(stat = stratum, infer.label = TRUE) +
# ggalluvial::geom_stratum(stat = "stratum", label.strata = TRUE) +
# ggplot2::geom_text(stat = "stratum", infer.label = TRUE) +
# ggplot2::geom_text(label.strata = TRUE) +
# ggalluvial::geom_stratum()
ggplot2::theme_minimal()
# ggplot2::ggtitle(paste0("Changes in ", self$options$group))
#
#
# nodes = data.frame("name" =
# c(self$options$group,
# self$options$dep))
#
# links <- mydata_changes
#
# names(links) = c("source", "target", "value")
#
# plot <- networkD3::sankeyNetwork(Links = links, Nodes = nodes,
# Source = "source", Target = "target",
# Value = "value", NodeID = "name",
# fontSize= 12, nodeWidth = 30)
# library(networkD3)
# nodes = data.frame("name" =
# c("Node A", # Node 0
# "Node B", # Node 1
# "Node C", # Node 2
# "Node D"))# Node 3
# links = as.data.frame(matrix(c(
# 0, 1, 10, # Each row represents a link. The first number
# 0, 2, 20, # represents the node being conntected from.
# 1, 3, 30, # the second number represents the node connected to.
# 2, 3, 40),# The third number is the value of the node
# byrow = TRUE, ncol = 3))
# names(links) = c("source", "target", "value")
# sankeyNetwork(Links = links, Nodes = nodes,
# Source = "source", Target = "target",
# Value = "value", NodeID = "name",
# fontSize= 12, nodeWidth = 30)
# plot <- c("Under Construction")
# plot <- list(plot1,
# plot2)
} else if (alluvsty == "t2") {
# easyalluvial
# https://erblast.github.io/easyalluvial/
plot <-
easyalluvial::alluvial_wide( data = mydata,
max_variables = 5,
fill_by = 'first_variable'
)
}
# repeated, continuous, factor ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, "factor")) {
plot <- c("Not Available")
}
}
# grouped_ functions ----
if ( ! is.null(self$options$grvar) ) {
# plotData <- data.frame(gr = mygroup,
# dp = jmvcore::toNumeric(mydep),
# grvar = mygrvar )
plot <- ggstatsplot::grouped_ggbetweenstats(
data = mydata,
x = !!group,
y = !!dep,
grouping.var = !!grvar,
pairwise.comparisons = TRUE,
# pairwise.comparisons = pairw,
p.adjust.method = "bonferroni"
)
# ,
# ggplot.component = list(ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())),
# k = 3,
# title.prefix = "Movie genre",
# caption = substitute(paste(italic("Source"), ":IMDb (Internet Movie Database)")),
# palette = "default_jama",
# package = "ggsci",
# messages = FALSE,
# plotgrid.args = list(nrow = 2),
# title.text = "Differences in movie length by mpaa ratings for different genres"
}
# Print Plot ----
print(plot)
TRUE
}
)
)
sbalci/ClinicoPathJamoviModule documentation built on Feb. 25, 2025, 6:34 a.m.
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#' @title Plots and Graphs Based on Variable Types
#' @importFrom R6 R6Class
#' @import jmvcore
#' @import magrittr
#'
statsplot2Class <- if (requireNamespace('jmvcore'))
R6::R6Class(
"statsplot2Class",
inherit = statsplot2Base,
private = list(
.run = function() {
StatStratum <- ggalluvial::StatStratum
# If no variable selected Initial Message ----
if (is.null(self$options$dep) ||
is.null(self$options$group)) {
# TODO ----
todo <- glue::glue(
"
<br>Welcome to ClinicoPath
<br><br>
This tool will help you generate plots based on variable types.
<br><br>
This function uses ggstatsplot and ggalluvial packages. Please cite jamovi and the packages as given below.
"
)
self$results$todo$setContent(todo)
return()
} else {
todo <- ""
self$results$todo$setContent(todo)
if (nrow(self$data) == 0)
stop('Data contains no (complete) rows')
# prepare main arguments ----
# mydata <- self$data
mydep <- self$data[[self$options$dep]]
mygroup <- self$data[[self$options$group]]
contin <- c("integer", "numeric", "double")
# categ <- c("factor")
distribution <- self$options$distribution
distribution <- jmvcore::composeTerm(distribution)
direction <- self$options$direction
direction <- jmvcore::composeTerm(direction)
# independent ----
if (direction == "independent") {
# independent, factor, continuous ----
if (inherits(mygroup, "factor") &&
inherits(mydep, contin)) {
# ggbetweenstats violin plots for comparisons between groups/conditions
# stat_exp <- c("independent", "factor", "continuous")
stat_exp <-
glue::glue(
"You have selected to use a violin plot to compare a continuous variable between independent groups/conditions.
"
)
# independent, continuous, continuous ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, contin)) {
# ggscatterstats scatterplots for correlations between two variables
# stat_exp <- c("independent", "continuous", "continuous")
stat_exp <-
glue::glue(
"You have selected to use a scatter plot to compare a continuous variable to another.
"
)
# independent, factor, factor ----
} else if (inherits(mygroup, "factor") &&
inherits(mydep, "factor")) {
# stat_exp <- c("independent", "factor", "factor")
stat_exp <-
glue::glue(
"You have selected to use a barplot to compare a group variable with another.
"
)
# independent, continuous, factor ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, "factor")) {
stat_exp <- glue::glue("<br>You have selected to use a barplot to compare a categorical variable with another.<br><hr>")
}
# repeated ----
} else if (direction == "repeated") {
# repeated, factor, continuous ----
if (inherits(mygroup, "factor") &&
inherits(mydep, contin)) {
# ggwithinstats violin plots for comparisons within groups/conditions
# stat_exp <-
# c("repeated", "factor", "continuous")
stat_exp <- glue::glue(
"You have selected to use a violin plot to compare a continuous variable between repeated measurements.
"
)
# repeated, continuous, continuous ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, contin)) {
# stat_exp <- c("repeated", "continuous", "continuous")
stat_exp <- glue::glue(
"Currently this tool does not support scatterplots for repeated measurements. You may refer to R-project rmcorr package."
)
# repeated, factor, factor ----
} else if (inherits(mygroup, "factor") &&
inherits(mydep, "factor")) {
# stat_exp <- c("repeated", "factor", "factor")
stat_exp <-
glue::glue(
"You have selected to compare 2 repeated factor measurements.")
# repeated, continuous, factor ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, "factor")) {
stat_exp <- glue::glue(
"Please switch the variables to generate a plot.
"
)
}
}
self$results$text4$setContent(stat_exp)
}
},
.plot = function(image, ggtheme, theme, ...) {
# the plot function ----
# Error messages ----
if (is.null(self$options$dep) ||
is.null(self$options$group))
return()
if (nrow(self$data) == 0)
stop('Data contains no (complete) rows')
# prepare main arguments ----
# mydata <- self$data
mydep <- self$data[[self$options$dep]]
mygroup <- self$data[[self$options$group]]
contin <- c("integer", "numeric", "double")
# categ <- c("factor")
# distribution <- self$options$distribution
# distribution <- jmvcore::composeTerm(distribution)
# direction <- self$options$direction
# direction <- jmvcore::composeTerm(direction)
# direction ----
direction <- self$options$direction
# distribution
distribution <-
jmvcore::constructFormula(terms = self$options$distribution)
# read data ----
mydata <- self$data
# Exclude NA ----
excl <- self$options$excl
if (excl) {mydata <- jmvcore::naOmit(mydata)}
# define main arguments
# mydep <- mydata[[self$options$dep]]
# mygroup <- mydata[[self$options$group]]
dep <- self$options$dep
group <- self$options$group
dep <- jmvcore::composeTerms(listOfComponents = dep)
group <- jmvcore::composeTerm(components = group)
# if ( ! is.null(self$options$grvar) ) {
# mygrvar <- mydata[[self$options$grvar]]
# }
if ( ! is.null(self$options$grvar) ) {
grvar <- self$options$grvar
}
# define variable types ----
contin <- c("integer", "numeric", "double")
categ <- c("factor")
# independent ----
# independent, factor, continuous ----
if (direction == "independent") {
if (inherits(mygroup, "factor") && inherits(mydep, contin)) {
# ggbetweenstats violin plots for comparisons between groups/conditions
# plotData <- data.frame(gr = mygroup,
# dp = jmvcore::toNumeric(mydep))
plot <- ggstatsplot::ggbetweenstats(
data = mydata,
x = !!group,
y = !!dep,
type = distribution
)
# independent, continuous, continuous ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, contin)) {
# ggscatterstats scatterplots for correlations between two variables
# plotData <-
# data.frame(gr = jmvcore::toNumeric(mygroup),
# dp = jmvcore::toNumeric(mydep))
plot <- ggstatsplot::ggscatterstats(
data = mydata,
x = !!group,
y = !!dep,
type = distribution)
# independent, factor, factor ----
} else if (inherits(mygroup, "factor") &&
inherits(mydep, "factor")) {
# ggbarstats bar charts for categorical data
# plotData <- data.frame(gr = mygroup,
# dp = mydep)
plot <- ggstatsplot::ggbarstats(
data = mydata,
main = !!dep,
condition = !!group)
# independent, continuous, factor ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, "factor")) {
plot <- ggstatsplot::ggdotplotstats(
data = mydata,
x = !!dep,
y = !!group)
}
# repeated ----
} else if (direction == "repeated") {
# repeated, factor, continuous ----
if (inherits(mygroup, "factor") &&
inherits(mydep, contin)) {
# ggwithinstats violin plots for comparisons within groups/conditions
# plotData <- data.frame(gr = mygroup,
# dp = jmvcore::toNumeric(mydep))
plot <- ggstatsplot::ggwithinstats(
data = mydata,
x = !!group,
y = !!dep,
type = distribution,
pairwise.comparisons = TRUE
# pairwise.comparisons = pairw
)
# repeated, continuous, continuous ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, contin)) {
plot <- c("Not Available")
# my.rmc <- rmcorr::rmcorr(participant = Subject,
# measure1 = PacO2,
# measure2 = pH,
# dataset = rmcorr::bland1995)
#
# plot(my.rmc, overall = TRUE)
#
# ggplot2::ggplot(rmcorr::bland1995,
# ggplot2::aes(x = PacO2,
# y = pH,
# group = factor(Subject),
# color = factor(Subject)
# )
# ) +
# ggplot2::geom_point(ggplot2::aes(colour = factor(Subject))) +
# ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1)
# repeated, factor, factor ----
} else if (inherits(mygroup, "factor") &&
inherits(mydep, "factor")) {
# Select alluvial style ----
alluvsty <- self$options$alluvsty
if (alluvsty == "t1") {
# ggalluvial
# http://corybrunson.github.io/ggalluvial/
plotData <- data.frame(gr = mygroup,
dp = mydep)
mydata_changes <- plotData %>%
dplyr::group_by(gr, dp) %>%
dplyr::tally(x = .)
# head(as.data.frame(UCBAdmissions), n = 12)
# ggalluvial::is_alluvia_form(
# as.data.frame(UCBAdmissions),
# axes = 1:3, silent = TRUE)
# plot <- ggplot(as.data.frame(UCBAdmissions),
# aes(y = Freq, axis1 = Gender, axis2 = Dept)) +
# geom_alluvium(aes(fill = Admit), width = 1/12) +
# geom_stratum(width = 1/12, fill = "black", color = "grey") +
# geom_label(stat = "stratum", infer.label = TRUE) +
# scale_x_discrete(limits = c("Gender", "Dept"), expand = c(.05, .05)) +
# scale_fill_brewer(type = "qual", palette = "Set1") +
# ggtitle("UC Berkeley admissions and rejections, by sex and department")
stratum <- ggalluvial::StatStratum
plot <- ggplot2::ggplot(data = mydata_changes,
ggplot2::aes(
axis1 = gr,
axis2 = dp,
y = n
)) +
ggplot2::scale_x_discrete(
limits = c(self$options$group, self$options$dep),
expand = c(.1, .05)
) +
ggplot2::xlab(self$options$group) +
ggalluvial::geom_alluvium(ggplot2::aes(fill = gr,
colour = gr)) +
ggalluvial::geom_stratum() +
ggalluvial::stat_stratum(geom = "stratum") +
ggplot2::geom_label(stat = stratum, infer.label = TRUE) +
# ggalluvial::geom_stratum(stat = "stratum", label.strata = TRUE) +
# ggplot2::geom_text(stat = "stratum", infer.label = TRUE) +
# ggplot2::geom_text(label.strata = TRUE) +
# ggalluvial::geom_stratum()
ggplot2::theme_minimal()
# ggplot2::ggtitle(paste0("Changes in ", self$options$group))
#
#
# nodes = data.frame("name" =
# c(self$options$group,
# self$options$dep))
#
# links <- mydata_changes
#
# names(links) = c("source", "target", "value")
#
# plot <- networkD3::sankeyNetwork(Links = links, Nodes = nodes,
# Source = "source", Target = "target",
# Value = "value", NodeID = "name",
# fontSize= 12, nodeWidth = 30)
# library(networkD3)
# nodes = data.frame("name" =
# c("Node A", # Node 0
# "Node B", # Node 1
# "Node C", # Node 2
# "Node D"))# Node 3
# links = as.data.frame(matrix(c(
# 0, 1, 10, # Each row represents a link. The first number
# 0, 2, 20, # represents the node being conntected from.
# 1, 3, 30, # the second number represents the node connected to.
# 2, 3, 40),# The third number is the value of the node
# byrow = TRUE, ncol = 3))
# names(links) = c("source", "target", "value")
# sankeyNetwork(Links = links, Nodes = nodes,
# Source = "source", Target = "target",
# Value = "value", NodeID = "name",
# fontSize= 12, nodeWidth = 30)
# plot <- c("Under Construction")
# plot <- list(plot1,
# plot2)
} else if (alluvsty == "t2") {
# easyalluvial
# https://erblast.github.io/easyalluvial/
plot <-
easyalluvial::alluvial_wide( data = mydata,
max_variables = 5,
fill_by = 'first_variable'
)
}
# repeated, continuous, factor ----
} else if (inherits(mygroup, contin) &&
inherits(mydep, "factor")) {
plot <- c("Not Available")
}
}
# grouped_ functions ----
if ( ! is.null(self$options$grvar) ) {
# plotData <- data.frame(gr = mygroup,
# dp = jmvcore::toNumeric(mydep),
# grvar = mygrvar )
plot <- ggstatsplot::grouped_ggbetweenstats(
data = mydata,
x = !!group,
y = !!dep,
grouping.var = !!grvar,
pairwise.comparisons = TRUE,
# pairwise.comparisons = pairw,
p.adjust.method = "bonferroni"
)
# ,
# ggplot.component = list(ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())),
# k = 3,
# title.prefix = "Movie genre",
# caption = substitute(paste(italic("Source"), ":IMDb (Internet Movie Database)")),
# palette = "default_jama",
# package = "ggsci",
# messages = FALSE,
# plotgrid.args = list(nrow = 2),
# title.text = "Differences in movie length by mpaa ratings for different genres"
}
# Print Plot ----
print(plot)
TRUE
}
)
)
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