teal.modules.general (version 0.3.0)

tm_a_pca: teal module: Principal component analysis

Description

Module conducts principal component analysis (PCA) on a given dataset and offers different ways of visualizing the outcomes, including elbow plot, circle plot, biplot, and eigenvector plot. Additionally, it enables dynamic customization of plot aesthetics, such as opacity, size, and font size, through UI inputs.

Usage

tm_a_pca(
  label = "Principal Component Analysis",
  dat,
  plot_height = c(600, 200, 2000),
  plot_width = NULL,
  ggtheme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"),
  ggplot2_args = teal.widgets::ggplot2_args(),
  rotate_xaxis_labels = FALSE,
  font_size = c(12, 8, 20),
  alpha = c(1, 0, 1),
  size = c(2, 1, 8),
  pre_output = NULL,
  post_output = NULL
)

Value

Object of class teal_module to be used in teal applications.

Arguments

label

(character(1)) Label shown in the navigation item for the module or module group. For modules() defaults to "root". See Details.

dat

(data_extract_spec or list of multiple data_extract_spec) specifying columns used to compute PCA.

plot_height

(numeric) optional, specifies the plot height as a three-element vector of value, min, and max intended for use with a slider UI element.

plot_width

(numeric) optional, specifies the plot width as a three-element vector of value, min, and max for a slider encoding the plot width.

ggtheme

(character) optional, ggplot2 theme to be used by default. Defaults to "gray".

ggplot2_args

(ggplot2_args) optional, object created by teal.widgets::ggplot2_args() with settings for all the plots or named list of ggplot2_args objects for plot-specific settings. The argument is merged with options variable teal.ggplot2_args and default module setup.

List names should match the following: c("default", "Elbow plot", "Circle plot", "Biplot", "Eigenvector plot").

For more details see the vignette: vignette("custom-ggplot2-arguments", package = "teal.widgets").

rotate_xaxis_labels

(logical) optional, whether to rotate plot X axis labels. Does not rotate by default (FALSE).

font_size

(numeric) optional, specifies font size. It controls the font size for plot titles, axis labels, and legends.

• If vector of length == 1 then the font sizes will have a fixed size.

• while vector of value, min, and max allows dynamic adjustment.

alpha

(integer(1) or integer(3)) optional, specifies point opacity.

• When the length of alpha is one: the plot points will have a fixed opacity.

• When the length of alpha is three: the plot points opacity are dynamically adjusted based on vector of value, min, and max.

size

(integer(1) or integer(3)) optional, specifies point size.

• When the length of size is one: the plot point sizes will have a fixed size.

• When the length of size is three: the plot points size are dynamically adjusted based on vector of value, min, and max.

pre_output

(shiny.tag) optional, text or UI element to be displayed before the module's output, providing context or a title. with text placed before the output to put the output into context. For example a title.

post_output

(shiny.tag) optional, text or UI element to be displayed after the module's output, adding context or further instructions. Elements like shiny::helpText() are useful.

Examples

library(teal.widgets)

# general data example
data <- teal_data()
data <- within(data, {
  require(nestcolor)
  USArrests <- USArrests
})

datanames(data) <- "USArrests"

app <- init(
  data = data,
  modules = modules(
    tm_a_pca(
      "PCA",
      dat = data_extract_spec(
        dataname = "USArrests",
        select = select_spec(
          choices = variable_choices(
            data = data[["USArrests"]], c("Murder", "Assault", "UrbanPop", "Rape")
          ),
          selected = c("Murder", "Assault"),
          multiple = TRUE
        ),
        filter = NULL
      ),
      ggplot2_args = ggplot2_args(
        labs = list(subtitle = "Plot generated by PCA Module")
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# CDISC data example
data <- teal_data()
data <- within(data, {
  require(nestcolor)
  ADSL <- rADSL
})
datanames(data) <- "ADSL"
join_keys(data) <- default_cdisc_join_keys[datanames(data)]

app <- init(
  data = data,
  modules = modules(
    tm_a_pca(
      "PCA",
      dat = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          choices = variable_choices(
            data = data[["ADSL"]], c("BMRKR1", "AGE", "EOSDY")
          ),
          selected = c("BMRKR1", "AGE"),
          multiple = TRUE
        ),
        filter = NULL
      ),
      ggplot2_args = ggplot2_args(
        labs = list(subtitle = "Plot generated by PCA Module")
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}