visualize {radiant.data}

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

Description

Visualize data using ggplot2 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

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

Details

See https://radiant-rstats.github.io/docs/data/visualize.html for an example in Radiant

Value

Generated plots

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"
)