% Generated by roxygen2: do not edit by hand % Please edit documentation in R/visualize.R \name{visualize} \alias{visualize} \title{Visualize data using ggplot2 \url{https://ggplot2.tidyverse.org/}} \usage{ visualize( dataset, xvar, yvar = "", comby = FALSE, combx = FALSE, type = ifelse(is.empty(yvar), "dist", "scatter"), nrobs = -1, facet_row = ".", facet_col = ".", color = "none", fill = "none", size = "none", fillcol = "blue", linecol = "black", pointcol = "black", bins = 10, smooth = 1, fun = "mean", check = "", axes = "", alpha = 0.5, theme = "theme_gray", base_size = 11, base_family = "", labs = list(), xlim = NULL, ylim = NULL, data_filter = "", arr = "", rows = NULL, shiny = FALSE, custom = FALSE, envir = parent.frame() ) } \arguments{ \item{dataset}{Data to plot (data.frame or tibble)} \item{xvar}{One or more variables to display along the X-axis of the plot} \item{yvar}{Variable to display along the Y-axis of the plot (default = "none")} \item{comby}{Combine yvars in plot (TRUE or FALSE, FALSE is the default)} \item{combx}{Combine xvars in plot (TRUE or FALSE, FALSE is the default)} \item{type}{Type of plot to create. One of Distribution ('dist'), Density ('density'), Scatter ('scatter'), Surface ('surface'), Line ('line'), Bar ('bar'), or Box-plot ('box')} \item{nrobs}{Number of data points to show in scatter plots (-1 for all)} \item{facet_row}{Create vertically arranged subplots for each level of the selected factor variable} \item{facet_col}{Create horizontally arranged subplots for each level of the selected factor variable} \item{color}{Adds color to a scatter plot to generate a 'heat map'. For a line plot one line is created for each group and each is assigned a different color} \item{fill}{Display bar, distribution, and density plots by group, each with a different color. Also applied to surface plots to generate a 'heat map'} \item{size}{Numeric variable used to scale the size of scatter-plot points} \item{fillcol}{Color used for bars, boxes, etc. when no color or fill variable is specified} \item{linecol}{Color for lines when no color variable is specified} \item{pointcol}{Color for points when no color variable is specified} \item{bins}{Number of bins used for a histogram (1 - 50)} \item{smooth}{Adjust the flexibility of the loess line for scatter plots} \item{fun}{Set the summary measure for line and bar plots when the X-variable is a factor (default is "mean"). Also used to plot an error bar in a scatter plot when the X-variable is a factor. Options are "mean" and/or "median"} \item{check}{Add a regression line ("line"), a loess line ("loess"), or jitter ("jitter") to a scatter plot} \item{axes}{Flip the axes in a plot ("flip") or apply a log transformation (base e) to the y-axis ("log_y") or the x-axis ("log_x")} \item{alpha}{Opacity for plot elements (0 to 1)} \item{theme}{ggplot theme to use (e.g., "theme_gray" or "theme_classic")} \item{base_size}{Base font size to use (default = 11)} \item{base_family}{Base font family to use (e.g., "Times" or "Helvetica")} \item{labs}{Labels to use for plots} \item{xlim}{Set limit for x-axis (e.g., c(0, 1))} \item{ylim}{Set limit for y-axis (e.g., c(0, 1))} \item{data_filter}{Expression used to filter the dataset. This should be a string (e.g., "price > 10000")} \item{arr}{Expression used to sort the data. Likely used in combination for `rows`} \item{rows}{Rows to select from the specified dataset} \item{shiny}{Logical (TRUE, FALSE) to indicate if the function call originate inside a shiny app} \item{custom}{Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and \url{https://ggplot2.tidyverse.org} for options.} \item{envir}{Environment to extract data from} } \value{ Generated plots } \description{ Visualize data using ggplot2 \url{https://ggplot2.tidyverse.org/} } \details{ See \url{https://radiant-rstats.github.io/docs/data/visualize.html} for an example in Radiant } \examples{ visualize(diamonds, "price:cut", type = "dist", fillcol = "red") visualize(diamonds, "carat:cut", yvar = "price", type = "scatter", pointcol = "blue", fun = c("mean", "median"), linecol = c("red", "green") ) visualize(diamonds, yvar = "price", xvar = c("cut", "clarity"), type = "bar", fun = "median" ) visualize(diamonds, yvar = "price", xvar = c("cut", "clarity"), type = "line", fun = "max" ) visualize(diamonds, yvar = "price", xvar = "carat", type = "scatter", size = "table", custom = TRUE ) + scale_size(range = c(1, 10), guide = "none") visualize(diamonds, yvar = "price", xvar = "carat", type = "scatter", custom = TRUE) + labs(title = "A scatterplot", x = "price in $") visualize(diamonds, xvar = "price:carat", custom = TRUE) \%>\% wrap_plots(ncol = 2) + plot_annotation(title = "Histograms") visualize(diamonds, xvar = "cut", yvar = "price", type = "bar", facet_row = "cut", fill = "cut" ) }