Visualize data using ggplot2 https://ggplot2.tidyverse.org/

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

dataset: Data to plot (data.frame or tibble)
xvar: One or more variables to display along the X-axis of the plot
yvar: Variable to display along the Y-axis of the plot (default = "none")
comby: Combine yvars in plot (TRUE or FALSE, FALSE is the default)
combx: Combine xvars in plot (TRUE or FALSE, FALSE is the default)
type: Type of plot to create. One of Distribution ('dist'), Density ('density'), Scatter ('scatter'), Surface ('surface'), Line ('line'), Bar ('bar'), or Box-plot ('box')
nrobs: Number of data points to show in scatter plots (-1 for all)
facet_row: Create vertically arranged subplots for each level of the selected factor variable
facet_col: Create horizontally arranged subplots for each level of the selected factor variable
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
fill: Display bar, distribution, and density plots by group, each with a different color. Also applied to surface plots to generate a 'heat map'
size: Numeric variable used to scale the size of scatter-plot points
fillcol: Color used for bars, boxes, etc. when no color or fill variable is specified
linecol: Color for lines when no color variable is specified
pointcol: Color for points when no color variable is specified
bins: Number of bins used for a histogram (1 - 50)
smooth: Adjust the flexibility of the loess line for scatter plots
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"
check: Add a regression line ("line"), a loess line ("loess"), or jitter ("jitter") to a scatter plot
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")
alpha: Opacity for plot elements (0 to 1)
theme: ggplot theme to use (e.g., "theme_gray" or "theme_classic")
base_size: Base font size to use (default = 11)
base_family: Base font family to use (e.g., "Times" or "Helvetica")
labs: Labels to use for plots
xlim: Set limit for x-axis (e.g., c(0, 1))
ylim: Set limit for y-axis (e.g., c(0, 1))
data_filter: Expression used to filter the dataset. This should be a string (e.g., "price > 10000")
arr: Expression used to sort the data. Likely used in combination for `rows`
rows: Rows to select from the specified dataset
shiny: Logical (TRUE, FALSE) to indicate if the function call originate inside a shiny app
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 https://ggplot2.tidyverse.org for options.
envir: Environment to extract data from

Value

Generated plots

Details

See 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"
)