mirror of
https://github.com/agdamsbo/FreesearchR.git
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109 lines
2.7 KiB
R
109 lines
2.7 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/data_plots.R, R/plot_hbar.R, R/plot_ridge.R,
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% R/plot_sankey.R, R/plot_scatter.R, R/plot_violin.R
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\name{data-plots}
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\alias{data-plots}
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\alias{data_visuals_ui}
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\alias{data_visuals_server}
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\alias{create_plot}
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\alias{plot_hbars}
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\alias{plot_ridge}
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\alias{sankey_ready}
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\alias{plot_sankey}
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\alias{plot_scatter}
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\alias{plot_violin}
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\title{Data correlations evaluation module}
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\usage{
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data_visuals_ui(id, tab_title = "Plots", ...)
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data_visuals_server(id, data, ...)
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create_plot(data, type, x, y, z = NULL, ...)
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plot_hbars(data, x, y, z = NULL)
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plot_ridge(data, x, y, z = NULL, ...)
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sankey_ready(data, x, y, numbers = "count", ...)
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plot_sankey(data, x, y, z = NULL, color.group = "x", colors = NULL)
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plot_scatter(data, x, y, z = NULL)
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plot_violin(data, x, y, z = NULL)
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}
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\arguments{
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\item{id}{Module id. (Use 'ns("id")')}
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\item{...}{ignored for now}
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\item{data}{data.frame}
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\item{type}{plot type (derived from possible_plots() and matches custom function)}
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\item{x}{primary variable}
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\item{y}{secondary variable}
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\item{z}{tertiary variable}
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}
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\value{
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Shiny ui module
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shiny server module
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ggplot2 object
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ggplot2 object
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ggplot2 object
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data.frame
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ggplot2 object
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ggplot2 object
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ggplot2 object
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}
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\description{
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Data correlations evaluation module
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Wrapper to create plot based on provided type
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Nice horizontal stacked bars (Grotta bars)
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Plot nice ridge plot
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Readying data for sankey plot
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Beautiful sankey plot with option to split by a tertiary group
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Beautiful violin plot
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Beatiful violin plot
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}
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\examples{
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create_plot(mtcars, "plot_violin", "mpg", "cyl")
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mtcars |> plot_hbars(x = "carb", y = "cyl")
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mtcars |> plot_hbars(x = "carb", y = NULL)
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mtcars |>
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default_parsing() |>
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plot_ridge(x = "mpg", y = "cyl")
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mtcars |> plot_ridge(x = "mpg", y = "cyl", z = "gear")
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ds <- data.frame(g = sample(LETTERS[1:2], 100, TRUE), first = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)), last = sample(c(letters[1:4], NA), 100, TRUE, prob = c(rep(.23, 4), .08)))
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ds |> sankey_ready("first", "last")
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ds |> sankey_ready("first", "last", numbers = "percentage")
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data.frame(
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g = sample(LETTERS[1:2], 100, TRUE),
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first = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)),
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last = sample(c(TRUE, FALSE, FALSE), 100, TRUE)
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) |>
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sankey_ready("first", "last")
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ds <- data.frame(g = sample(LETTERS[1:2], 100, TRUE), first = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)), last = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)))
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ds |> plot_sankey("first", "last")
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ds |> plot_sankey("first", "last", color.group = "y")
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ds |> plot_sankey("first", "last", z = "g", color.group = "y")
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mtcars |> plot_scatter(x = "mpg", y = "wt")
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mtcars |> plot_violin(x = "mpg", y = "cyl", z = "gear")
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}
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