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

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 = "",
  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")
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")