Package 'ezplot' reference manual

Title:	Functions for Common Chart Types
Description:	Wrapper for the 'ggplot2' package that creates a variety of common charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce typing.
Authors:	Wojtek Kostelecki
Maintainer:	Wojtek Kostelecki <[email protected]>
License:	MIT + file LICENSE
Version:	0.8.1
Built:	2024-11-20 05:22:49 UTC
Source:	https://github.com/wkostelecki/ezplot

Aggregates data

Description

Aggregates data

Usage

agg_data(
  data,
  cols = names(data),
  group_by = NULL,
  agg_fun = function(x) sum(x, na.rm = TRUE),
  group_by2 = NULL,
  env = parent.frame()
)
agg_data(
  data,
  cols = names(data),
  group_by = NULL,
  agg_fun = function(x) sum(x, na.rm = TRUE),
  group_by2 = NULL,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`cols`	Named character vector of column names.
`group_by`	Vector of grouping columns.
`agg_fun`	Function to use for aggregating.
`group_by2`	Vector of grouping column names to use for delayed (post aggregation) calculation.
`env`	Environment for extra variables.

Value

An aggregated data.frame.

Examples

suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
agg_data(ansett, c("Passengers", count = "1"))
agg_data(ansett["Class"])
agg_data(ansett[c("Class", "Passengers")])
agg_data(ansett, "Passengers", "Class")
agg_data(ansett, "Passengers", c("Class", "Airports"))
agg_data(ansett, c(x = "Airports", y = "Passengers"), c(x = "Airports"))
agg_data(ansett, c(x = "Class", y = "1", group = "Airports"), c(x = "Class", group = "Airports"))
suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
agg_data(ansett, c("Passengers", count = "1"))
agg_data(ansett["Class"])
agg_data(ansett[c("Class", "Passengers")])
agg_data(ansett, "Passengers", "Class")
agg_data(ansett, "Passengers", c("Class", "Airports"))
agg_data(ansett, c(x = "Airports", y = "Passengers"), c(x = "Airports"))
agg_data(ansett, c(x = "Class", y = "1", group = "Airports"), c(x = "Class", group = "Airports"))

area_plot

Description

Aggregates a data.frame and creates a stacked area chart.

Usage

area_plot(
  data,
  x,
  y = "1",
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  size = 11,
  reorder = c("group", "facet_x", "facet_y"),
  palette = ez_col,
  labels_y = if (position == "fill") {
     function(x) ez_labels(100 * x, append =
    "%")
 } else {
     ez_labels
 },
  labels_x = NULL,
  use_theme = theme_ez,
  position = c("stack", "fill"),
  facet_scales = "fixed",
  facet_ncol = NULL,
  legend_ncol = NULL,
  env = parent.frame()
)
area_plot(
  data,
  x,
  y = "1",
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  size = 11,
  reorder = c("group", "facet_x", "facet_y"),
  palette = ez_col,
  labels_y = if (position == "fill") {
     function(x) ez_labels(100 * x, append =
    "%")
 } else {
     ez_labels
 },
  labels_x = NULL,
  use_theme = theme_ez,
  position = c("stack", "fill"),
  facet_scales = "fixed",
  facet_ncol = NULL,
  legend_ncol = NULL,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y`	A named character value. Evaluates to a column.
`group`	A character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`size`	theme size for `use_theme()`. Default is 14.
`reorder`	A character vector specifying the group variables to reorder. Default is `c("group", "facet_x", "facet_y")`.
`palette`	Colour function.
`labels_y`	label formatting function
`labels_x`	label formatting function
`use_theme`	ggplot theme function
`position`	Either `"stack"` (default) or `"fill"`
`facet_scales`	Option passed to scales argument in `facet_wrap` or `facet_grid`. Default is `"fixed"`.
`facet_ncol`	Option passed to ncol argument in `facet_wrap` or `facet_grid`. Default is `NULL`.
`legend_ncol`	Number of columns in legend.
`env`	environment for evaluating expressions.

Value

A ggplot object.

Examples


library(tsibble)
library(tsibbledata)
area_plot(ansett, x = "as.Date(Week)", y = "Passengers")
area_plot(ansett,
          x = "as.Date(Week)", y = c("Weekly Passengers" = "Passengers"), "Class")
area_plot(ansett, "as.Date(Week)",
          y = c("Weekly Passengers" = "Passengers"),
          group = "substr(Airports, 5, 7)",
          facet_x = "substr(Airports, 1, 3)",
          facet_y = "Class",
          facet_scales = "free_y")

library(tsibble)
library(tsibbledata)
area_plot(ansett, x = "as.Date(Week)", y = "Passengers")
area_plot(ansett,
          x = "as.Date(Week)", y = c("Weekly Passengers" = "Passengers"), "Class")
area_plot(ansett, "as.Date(Week)",
          y = c("Weekly Passengers" = "Passengers"),
          group = "substr(Airports, 5, 7)",
          facet_x = "substr(Airports, 1, 3)",
          facet_y = "Class",
          facet_scales = "free_y")

bar_plot

Description

bar_plot

Usage

bar_plot(
  data,
  x,
  y = "1",
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  size = 11,
  width = NULL,
  reorder = c("group", "facet_x", "facet_y"),
  palette = ez_col,
  labels_y = if (position == "fill") {
     function(x) ez_labels(100 * x, append =
    "%")
 } else {
     ez_labels
 },
  labels_x = identity,
  label_pos = c("auto", "inside", "top", "both", "none"),
  label_inside = c("y", "absolute", "share", "percent", "both"),
  rescale_y = 1.1,
  label_cutoff = 0.12,
  use_theme = theme_ez,
  position = "stack",
  facet_scales = "fixed",
  legend_ncol = NULL,
  coord_flip = FALSE,
  angle = 0,
  repel = FALSE
)
bar_plot(
  data,
  x,
  y = "1",
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  size = 11,
  width = NULL,
  reorder = c("group", "facet_x", "facet_y"),
  palette = ez_col,
  labels_y = if (position == "fill") {
     function(x) ez_labels(100 * x, append =
    "%")
 } else {
     ez_labels
 },
  labels_x = identity,
  label_pos = c("auto", "inside", "top", "both", "none"),
  label_inside = c("y", "absolute", "share", "percent", "both"),
  rescale_y = 1.1,
  label_cutoff = 0.12,
  use_theme = theme_ez,
  position = "stack",
  facet_scales = "fixed",
  legend_ncol = NULL,
  coord_flip = FALSE,
  angle = 0,
  repel = FALSE
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y`	A named character value. Evaluates to a column.
`group`	A character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`size`	theme size for `use_theme()`. Default is 14.
`width`	Width of bar.
`reorder`	A character vector specifying the group variables to reorder. Default is `c("group", "facet_x", "facet_y")`.
`palette`	Colour function.
`labels_y`	label formatting function
`labels_x`	label formatting function
`label_pos`	Position of labels. Can be "auto", "inside", "top", "both" or "none".
`label_inside`	Value to display inside bar segments. Options are "y", "absolute", "percent", "share" or "both".
`rescale_y`	Rescaling factor for y-axis limits
`label_cutoff`	Cutoff size (proportion of y data range) for excluding labels
`use_theme`	ggplot theme function
`position`	Either `"stack"` (default), `"fill"` or `"dodge"`
`facet_scales`	Option passed to scales argument in `facet_wrap` or `facet_grid`. Default is `"fixed"`.
`legend_ncol`	Number of columns in legend.
`coord_flip`	logical (default is FALSE). If TRUE, flips the x and y coordinate using ggplot2::coord_flip()
`angle`	angle for geom_text(_repel)
`repel`	logical (default if FALSE). If TRUE, uses ggrepel for geom_text

Value

A ggplot object.

Examples

library(tsibble)
library(tsibbledata)
library(lubridate)

bar_plot(ansett, "year(Week)", "Passengers")
bar_plot(ansett, "year(Week)", "Passengers", "Class", label_pos = "both")
bar_plot(ansett, "year(Week)", "Passengers", "Class", label_pos = "both", label_inside = "both")
bar_plot(ansett, "year(Week)", "Passengers", "Class", coord_flip = TRUE)
library(tsibble)
library(tsibbledata)
library(lubridate)

bar_plot(ansett, "year(Week)", "Passengers")
bar_plot(ansett, "year(Week)", "Passengers", "Class", label_pos = "both")
bar_plot(ansett, "year(Week)", "Passengers", "Class", label_pos = "both", label_inside = "both")
bar_plot(ansett, "year(Week)", "Passengers", "Class", coord_flip = TRUE)

calendar_plot

Description

calendar_plot

Usage

calendar_plot(data, x, y, ...)
calendar_plot(data, x, y, ...)

Arguments

`data`	A data.frame.
`x`	date column
`y`	A named character value. Evaluates to a column.
`...`	additional arguments for tile_plot

Examples

library(tsibbledata)
calendar_plot(vic_elec, "Time", "Demand", zlim = c(NA, NA))
library(tsibbledata)
calendar_plot(vic_elec, "Time", "Demand", zlim = c(NA, NA))

density_plot

Description

creates a density plot

Usage

density_plot(
  data,
  x,
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  palette = ez_col,
  adjust = 1,
  alpha = 0.5,
  facet_scales = "fixed",
  facet_ncol = NULL,
  legend_ncol = NULL,
  env = parent.frame()
)
density_plot(
  data,
  x,
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  palette = ez_col,
  adjust = 1,
  alpha = 0.5,
  facet_scales = "fixed",
  facet_ncol = NULL,
  legend_ncol = NULL,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`group`	A character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`palette`	Colour function.
`adjust`	multiplicate bandwidth adjustment
`alpha`	alpha
`facet_scales`	Option passed to scales argument in `facet_wrap` or `facet_grid`. Default is `"fixed"`.
`facet_ncol`	Option passed to ncol argument in `facet_wrap` or `facet_grid`. Default is `NULL`.
`legend_ncol`	Number of columns in legend.
`env`	environment for evaluating expressions.

Examples

library(tsibbledata)
density_plot(mtcars, "wt", "cyl")
density_plot(subset(tsibbledata::olympic_running, Length == 100 & Year >= 1980),
             "Time", "Year - Year %% 10", "Sex", facet_scales = "free", facet_ncol = 1, adjust = 2)
library(tsibbledata)
density_plot(mtcars, "wt", "cyl")
density_plot(subset(tsibbledata::olympic_running, Length == 100 & Year >= 1980),
             "Time", "Year - Year %% 10", "Sex", facet_scales = "free", facet_ncol = 1, adjust = 2)

distribution_plot

Description

distribution_plot

Usage

distribution_plot(
  data,
  x,
  facet_x = NULL,
  nbins = 20,
  use_theme = theme_ez,
  size = 11,
  env = parent.frame()
)
distribution_plot(
  data,
  x,
  facet_x = NULL,
  nbins = 20,
  use_theme = theme_ez,
  size = 11,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`nbins`	Number of bins for histogram. Default is 20.
`use_theme`	ggplot theme function
`size`	theme size for `use_theme()`. Default is 14.
`env`	environment for evaluating expressions.

Examples

n = 100
df = data.frame(residuals = rnorm(n),
                group1 = sample(c("a", "b"), n, replace = TRUE))
distribution_plot(df, "residuals")
distribution_plot(df, "residuals", "group1")
n = 100
df = data.frame(residuals = rnorm(n),
                group1 = sample(c("a", "b"), n, replace = TRUE))
distribution_plot(df, "residuals")
distribution_plot(df, "residuals", "group1")

ez_app

Description

ez_app

Usage

ez_app(data = NULL)
ez_app(data = NULL)

Arguments

data

A data frame

Examples

## Not run: 
library(tsibble)
library(tsibbledata)
ez_app(ansett)

## End(Not run)
## Not run: 
library(tsibble)
library(tsibbledata)
ez_app(ansett)

## End(Not run)

Color palette interpolation

Description

Color palette interpolation

Usage

ez_col(n = 50, palette = NULL)
ez_col(n = 50, palette = NULL)

Arguments

`n`	number of colours
`palette`	palette to interpolate from

Value

rgb

Examples

ez_col(15)
ez_col(2, c("blue", "red"))
ez_col(3, c("blue", "red"))
ez_col(15)
ez_col(2, c("blue", "red"))
ez_col(3, c("blue", "red"))

ez_jet

Description

color palette for

Usage

ez_jet(
  n = 100,
  palette = c("dodgerblue4", "steelblue2", "olivedrab3", "darkgoldenrod1", "brown")
)
ez_jet(
  n = 100,
  palette = c("dodgerblue4", "steelblue2", "olivedrab3", "darkgoldenrod1", "brown")
)

Arguments

`n`	Number of colours to return.
`palette`	Vector of colours.

Examples

ez_jet(100)
ez_jet(1)
ez_jet(100)
ez_jet(1)

Function for formatting numeric labels

Description

Function for formatting numeric labels

Usage

ez_labels(
  x,
  prepend = "",
  append = "",
  as_factor = FALSE,
  round = Inf,
  signif = Inf,
  sign = FALSE
)
ez_labels(
  x,
  prepend = "",
  append = "",
  as_factor = FALSE,
  round = Inf,
  signif = Inf,
  sign = FALSE
)

Arguments

`x`	numeric
`prepend`	character
`append`	character
`as_factor`	logical
`round`	numeric passed to `round()`
`signif`	numeric passed to `signif()`
`sign`	logical. Adds a plus sign to positive numbers

Value

Examples

ez_labels(10^(0:10))
ez_labels(2000, append = " apples")
ez_labels(0:10, append = " apples", as_factor = TRUE)
ez_labels(c(0, 0.1, 0.01, 0.001, 0.0001))
ez_labels(c(-1, -0.0001, 0, 0.0001, 1), round = 2, sign = FALSE)
ez_labels(c(-1, -0.0001, 0, 0.0001, 1), round = 2, sign = TRUE)
ez_labels(10^(0:10))
ez_labels(2000, append = " apples")
ez_labels(0:10, append = " apples", as_factor = TRUE)
ez_labels(c(0, 0.1, 0.01, 0.001, 0.0001))
ez_labels(c(-1, -0.0001, 0, 0.0001, 1), round = 2, sign = FALSE)
ez_labels(c(-1, -0.0001, 0, 0.0001, 1), round = 2, sign = TRUE)

ez_png

Description

Saves ggplot or ezplot objects to png (with useful defaults).

Usage

ez_png(
  g,
  file,
  width = 1200,
  height = 600,
  res = 72,
  resx = 1,
  ...,
  vp = NULL,
  dir.create = FALSE,
  check = TRUE
)
ez_png(
  g,
  file,
  width = 1200,
  height = 600,
  res = 72,
  resx = 1,
  ...,
  vp = NULL,
  dir.create = FALSE,
  check = TRUE
)

Arguments

`g`	A ggplot or ezplot object.
`file`	A png file path.
`width`	Image width (in pixels). Default is 1200.
`height`	Image height (in pixels). Default is 600.
`res`	Resolution (PPI) of output image. Default is 72.
`resx`	Resolution multiplier. Default is 1.
`...`	Further arguments to pass to `png()`.
`vp`	A viewport object created with grid::viewport.
`dir.create`	Logical. If `TRUE`, creates the directory to save into. Default is `FALSE`.
`check`	Logical. If `TRUE`, opens png file after saving. Default is `TRUE`.

ez_server

Description

ez_server

Usage

ez_server(data)
ez_server(data)

Arguments

data

A data frame

ez_ui

Description

ez_ui

Usage

ez_ui(data)
ez_ui(data)

Arguments

data

A data frame

get_incr

Description

returns the minimum increment between sorted unique values of a vector

Usage

get_incr(x)
get_incr(x)

Arguments

`x`	A numeric or date vector

histogram_plot

Description

creates a histogram plot

Usage

histogram_plot(
  data,
  x,
  y = "count",
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  palette = ez_col,
  position = "stack",
  bins = 30,
  alpha = 0.5,
  facet_scales = "fixed",
  facet_ncol = NULL,
  legend_ncol = NULL,
  env = parent.frame()
)
histogram_plot(
  data,
  x,
  y = "count",
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  palette = ez_col,
  position = "stack",
  bins = 30,
  alpha = 0.5,
  facet_scales = "fixed",
  facet_ncol = NULL,
  legend_ncol = NULL,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y`	A named character value. Evaluates to a column.
`group`	A character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`palette`	Colour function.
`position`	Either `"stack"` (default) or `"fill"`
`bins`	number of bins
`alpha`	fill alpha
`facet_scales`	Option passed to scales argument in `facet_wrap` or `facet_grid`. Default is `"fixed"`.
`facet_ncol`	Option passed to ncol argument in `facet_wrap` or `facet_grid`. Default is `NULL`.
`legend_ncol`	Number of columns in legend.
`env`	environment for evaluating expressions.

Examples

histogram_plot(airquality, "Wind", group = "Month")
histogram_plot(airquality, "Wind", "density", facet_x = "Month")
histogram_plot(airquality, "Wind", group = "Month")
histogram_plot(airquality, "Wind", "density", facet_x = "Month")

ks_plot

Description

ks plot

Usage

ks_plot(
  data,
  fitted,
  actual,
  palette = ez_col,
  size_line = 1,
  size = 11,
  env = parent.frame()
)
ks_plot(
  data,
  fitted,
  actual,
  palette = ez_col,
  size_line = 1,
  size = 11,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`fitted`	Vector of fitted values
`actual`	Vector of actual values
`palette`	Colour function.
`size_line`	width of line for `geom_line()`. Default is 1.
`size`	theme size for `use_theme()`. Default is 14.
`env`	environment for evaluating expressions.

Examples

ks_plot(mtcars, "-disp", "am")
x = c(rnorm(100), rnorm(100) + 2)
label = c(rep('low', 100), rep('high', 100))
ks_plot(data.frame(x, label), "x", "label")
ks_plot(data.frame(x, label = factor(label, c('low', 'high'))), "x", "label")
ks_plot(mtcars, "-disp", "am")
x = c(rnorm(100), rnorm(100) + 2)
label = c(rep('low', 100), rep('high', 100))
ks_plot(data.frame(x, label), "x", "label")
ks_plot(data.frame(x, label = factor(label, c('low', 'high'))), "x", "label")

lift_plot

Description

precision-recall plot

Usage

lift_plot(
  data,
  fitted,
  actual,
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  size_line = 1,
  size = 11,
  env = parent.frame()
)
lift_plot(
  data,
  fitted,
  actual,
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  size_line = 1,
  size = 11,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`fitted`	Vector of fitted values
`actual`	Vector of actual values
`group`	A character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`size_line`	width of line for `geom_line()`. Default is 1.
`size`	theme size for `use_theme()`. Default is 14.
`env`	environment for evaluating expressions.

Examples

library(ggplot2)
n = 1000
df = data.frame(actual = sample(c(FALSE, TRUE), n, replace = TRUE),
                runif = runif(n))
df[["fitted"]] = runif(n) ^ ifelse(df[["actual"]] == 1, 0.5, 2)

density_plot(df, "fitted", "actual")

lift_plot(df, "fitted", "actual")
lift_plot(df, "fitted", "actual") + scale_y_log10()
lift_plot(df, "runif", "actual", size_line = 0.5)


library(dplyr, warn.conflicts = FALSE)
lift_plot(df, "fitted", "actual", "sample(c(1, 2), n(), TRUE)")

lift_plot(df, "fitted", "actual",
        "sample(c(1, 2), n(), TRUE)",
        "sample(c(3, 4), n(), TRUE)")

lift_plot(df, "fitted", "actual",
        "sample(c(1, 2), n(), TRUE)",
        "sample(c(3, 4), n(), TRUE)",
        "sample(c(5, 6), n(), TRUE)")

library(ggplot2)
n = 1000
df = data.frame(actual = sample(c(FALSE, TRUE), n, replace = TRUE),
                runif = runif(n))
df[["fitted"]] = runif(n) ^ ifelse(df[["actual"]] == 1, 0.5, 2)

density_plot(df, "fitted", "actual")

lift_plot(df, "fitted", "actual")
lift_plot(df, "fitted", "actual") + scale_y_log10()
lift_plot(df, "runif", "actual", size_line = 0.5)


library(dplyr, warn.conflicts = FALSE)
lift_plot(df, "fitted", "actual", "sample(c(1, 2), n(), TRUE)")

lift_plot(df, "fitted", "actual",
        "sample(c(1, 2), n(), TRUE)",
        "sample(c(3, 4), n(), TRUE)")

lift_plot(df, "fitted", "actual",
        "sample(c(1, 2), n(), TRUE)",
        "sample(c(3, 4), n(), TRUE)",
        "sample(c(5, 6), n(), TRUE)")

line_plot

Description

Creates line plots.

Usage

line_plot(
  data,
  x,
  y = "1",
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  yoy = FALSE,
  size_line = 1,
  points = FALSE,
  size = 11,
  reorder = c("group", "facet_x", "facet_y"),
  palette = ez_col,
  labels_y = ez_labels,
  limits_y = c(NA, NA),
  use_theme = theme_ez,
  facet_scales = "fixed",
  na.rm = FALSE,
  legend_ncol = NULL
)
line_plot(
  data,
  x,
  y = "1",
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  yoy = FALSE,
  size_line = 1,
  points = FALSE,
  size = 11,
  reorder = c("group", "facet_x", "facet_y"),
  palette = ez_col,
  labels_y = ez_labels,
  limits_y = c(NA, NA),
  use_theme = theme_ez,
  facet_scales = "fixed",
  na.rm = FALSE,
  legend_ncol = NULL
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y`	A named character value. Evaluates to a column.
`group`	A character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`yoy`	Logical used to indicate whether a YOY grouping should be created. Default is `FALSE`.
`size_line`	width of line for `geom_line()`. Default is 1.
`points`	logical. Option to include points
`size`	theme size for `use_theme()`. Default is 14.
`reorder`	A character vector specifying the group variables to reorder. Default is `c("group", "facet_x", "facet_y")`.
`palette`	Colour function.
`labels_y`	label formatting function
`limits_y`	vector of c(min, max) y-axis limits
`use_theme`	ggplot theme function
`facet_scales`	Option passed to scales argument in `facet_wrap` or `facet_grid`. Default is `"fixed"`.
`na.rm`	logical. Option to exclude NAs
`legend_ncol`	Number of columns in legend.

Value

A ggplot object.

Examples

suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
line_plot(pelt, "Year", c("Hare", "Lynx"), points = TRUE, limits_y = c(0, NA))
suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
line_plot(pelt, "Year", c("Hare", "Lynx"), points = TRUE, limits_y = c(0, NA))

model_plot

Description

model_plot

Usage

model_plot(
  data,
  x,
  actual,
  fitted,
  facet_x = NULL,
  point_size = 2,
  res_bins = NA_real_,
  size = 11
)
model_plot(
  data,
  x,
  actual,
  fitted,
  facet_x = NULL,
  point_size = 2,
  res_bins = NA_real_,
  size = 11
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`actual`	A character value. Evaluates to a logical or binary column.
`fitted`	A character value. Evaluates to a numeric column.
`facet_x`	A character value. Evaluates to a column.
`point_size`	Numeric. Default is 2.
`res_bins`	Number of bins in the residual distribution. Default value (NA) doesn't show the distribution.
`size`	theme size for `use_theme()`. Default is 14.

Value

A ggplot object.

Examples

y = rnorm(26)
df = data.frame(ID = 1:26, actual = y + rnorm(26), fitted = y, id = letters)
model_plot(df, "ID", "actual", "fitted")
model_plot(df, "id", "actual", "fitted")
model_plot(df, "ID", "actual", "fitted", res_bins = 10)
model_plot(df, "id", "actual", "fitted", res_bins = 10)
y = rnorm(26)
df = data.frame(ID = 1:26, actual = y + rnorm(26), fitted = y, id = letters)
model_plot(df, "ID", "actual", "fitted")
model_plot(df, "id", "actual", "fitted")
model_plot(df, "ID", "actual", "fitted", res_bins = 10)
model_plot(df, "id", "actual", "fitted", res_bins = 10)

na_plot

Description

Visual representation of the NAs in a data.frame

Usage

na_plot(data, palette = ez_col)
na_plot(data, palette = ez_col)

Arguments

`data`	A data.frame.
`palette`	Colour function.

Value

A ggplot object.

Examples

na_plot(airquality)
na_plot(airquality)

nameifnot

Description

Names unnamed elements of a character vector.

Usage

nameifnot(x, make.names = FALSE)
nameifnot(x, make.names = FALSE)

Arguments

`x`	A character vector.
`make.names`	Logical. Whether to force names of x to be valid variable names. Default is FALSE.

Value

A named vector.

no_null

Description

Converts "NULL" character to NULL.

Usage

no_null(x)
no_null(x)

Arguments

`x`	A character vector.

Value

Examples

no_null(NULL)
no_null("NULL")
no_null("NOPE")
no_null(NULL)
no_null("NULL")
no_null("NOPE")

not_numeric

Description

Returns names of non-numeric columns.

Usage

not_numeric(x)
not_numeric(x)

Arguments

`x`	A data.frame.

Value

A character vector.

perf

Description

Precision recall calculation

Usage

perf(fitted, actual, x_measure, y_measure)
perf(fitted, actual, x_measure, y_measure)

Arguments

`fitted`	Vector with values between 0 and 1
`actual`	Vector with two levels
`x_measure`	metric for ROCR::performance
`y_measure`	metric for ROCR::performance

Examples

ezplot:::perf(runif(1), sample(c(TRUE, FALSE), 1, replace = TRUE), "rpp", "lift")
ezplot:::perf(runif(10), sample(c(TRUE, FALSE), 10, replace = TRUE), "rpp", "lift")
ezplot:::perf(runif(5), sample(c(TRUE, FALSE), 5, replace = TRUE), "rec", "prec")
ezplot:::perf(runif(5), sample(c(TRUE, FALSE), 5, replace = TRUE), "fpr", "tpr")
ezplot:::perf(runif(5), sample(c(TRUE, FALSE), 5, replace = TRUE), "cutoff", "tpr")
ezplot:::perf(runif(1), sample(c(TRUE, FALSE), 1, replace = TRUE), "rpp", "lift")
ezplot:::perf(runif(10), sample(c(TRUE, FALSE), 10, replace = TRUE), "rpp", "lift")
ezplot:::perf(runif(5), sample(c(TRUE, FALSE), 5, replace = TRUE), "rec", "prec")
ezplot:::perf(runif(5), sample(c(TRUE, FALSE), 5, replace = TRUE), "fpr", "tpr")
ezplot:::perf(runif(5), sample(c(TRUE, FALSE), 5, replace = TRUE), "cutoff", "tpr")

perf_df

Description

shows classification performance statistics as a table

Usage

perf_df(fitted, actual, quantiles = NULL)
perf_df(fitted, actual, quantiles = NULL)

Arguments

`fitted`	A character value. Evaluates to a numeric column.
`actual`	A character value. Evaluates to a logical or binary column.
`quantiles`	Number of quantiles to show. If `NULL`, uses distinct values of `fitted` for the cutoffs rather than showing quantiles.

Value

A data.frame summarizing binary classification performance:

quantile: fitted value quantile (only if !is.null(quantile)
cutoff: fitted value cutoff
fp: false positives
tp: true positives
tn: true negatives
fn: false negatives
pp: positive predictions
np: negative predictions
ipp: group positive predictions
ifp: group false positives
itp: group true positives
rpp: rate of positive predictions
acc: accuracy
fpr: false positive rate
tpr: true positive rate
fnr: false negative rate
tnr: true negative rate
prec: precision
clift: lift
ilift: group lift
f1: f1 measure
ks: Kolmogorov-Smirnov statistic
auc: area under ROC curve
aucpr: area under PR curve

Examples

perf_df(mtcars$mpg, mtcars$am)
perf_df(mtcars$mpg, mtcars$am, quantiles = 4)
perf_df(mtcars$mpg, mtcars$am, quantiles = 8)
perf_df(mtcars$mpg, mtcars$am, quantiles = 10)
perf_df(mtcars$wt, mtcars$am==0)
perf_df(mtcars$mpg, mtcars$am)
perf_df(mtcars$mpg, mtcars$am, quantiles = 4)
perf_df(mtcars$mpg, mtcars$am, quantiles = 8)
perf_df(mtcars$mpg, mtcars$am, quantiles = 10)
perf_df(mtcars$wt, mtcars$am==0)

performance_plot

Description

plots binary classification performance metrics

Usage

performance_plot(
  data,
  fitted,
  actual,
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  x = "fpr",
  y = "tpr",
  auc = c("title", "group"),
  size_line = 1,
  size = 11,
  env = parent.frame()
)
performance_plot(
  data,
  fitted,
  actual,
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  x = "fpr",
  y = "tpr",
  auc = c("title", "group"),
  size_line = 1,
  size = 11,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`fitted`	A character value. Evaluates to a numeric column.
`actual`	A character value. Evaluates to a logical or binary column.
`group`	A character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`x`	ROCR::performance() measure
`y`	ROCR::performance() measure
`auc`	character vector indicating which AUC values should be displayed. Options are 'title' and 'group'
`size_line`	width of line for `geom_line()`. Default is 1.
`size`	theme size for `use_theme()`. Default is 14.
`env`	environment for evaluating expressions.

Examples

performance_plot(mtcars, "-disp", "am")
performance_plot(mtcars, "-disp", "am", "cyl")
performance_plot(mtcars, "-disp", "am", "cyl", x = "rec", y = "prec")
performance_plot(mtcars, "-disp", "am", x = "rpp", y = "gain")
performance_plot(mtcars, "-disp", "am", x = "rpp", y = "lift")
performance_plot(mtcars, "-disp", "am", x = "cutoff", y = "tpr")
performance_plot(mtcars, "-disp", "am")
performance_plot(mtcars, "-disp", "am", "cyl")
performance_plot(mtcars, "-disp", "am", "cyl", x = "rec", y = "prec")
performance_plot(mtcars, "-disp", "am", x = "rpp", y = "gain")
performance_plot(mtcars, "-disp", "am", x = "rpp", y = "lift")
performance_plot(mtcars, "-disp", "am", x = "cutoff", y = "tpr")

pie_plot

Description

Creates pie charts.

Usage

pie_plot(
  data,
  x,
  y = "1",
  facet_x = NULL,
  facet_y = NULL,
  labels_y = function(x) ez_labels(x * 100, append = "%", round = round, signif =
    signif),
  size = 11,
  label_cutoff = 0.04,
  round = Inf,
  signif = 3,
  palette = ez_col,
  reorder = c("x", "facet_x", "facet_y"),
  label_x = 0.8,
  legend_ncol = NULL
)
pie_plot(
  data,
  x,
  y = "1",
  facet_x = NULL,
  facet_y = NULL,
  labels_y = function(x) ez_labels(x * 100, append = "%", round = round, signif =
    signif),
  size = 11,
  label_cutoff = 0.04,
  round = Inf,
  signif = 3,
  palette = ez_col,
  reorder = c("x", "facet_x", "facet_y"),
  label_x = 0.8,
  legend_ncol = NULL
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y`	A named character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`labels_y`	label formatting function
`size`	theme size for `use_theme()`. Default is 14.
`label_cutoff`	Cutoff size (proportion of y data range) for excluding labels
`round`	Option for rounding label.
`signif`	Option for retaining significant figures in label.
`palette`	Colour function.
`reorder`	A character vector specifying the group variables to reorder. Default is `c("group", "facet_x", "facet_y")`.
`label_x`	Position of label from centre of pie. 0 is the centre of the pie and 1 is the outer edge.
`legend_ncol`	Number of columns in legend.

Value

ggplot object

Examples


suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
pie_plot(ansett, "Class", "Passengers")
pie_plot(ansett, "Class", "Passengers", reorder = NULL, label_x = 0.5)
pie_plot(ansett, "Class", "Passengers", "Airports", reorder = NULL, label_x = 0.5)

suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
pie_plot(ansett, "Class", "Passengers")
pie_plot(ansett, "Class", "Passengers", reorder = NULL, label_x = 0.5)
pie_plot(ansett, "Class", "Passengers", "Airports", reorder = NULL, label_x = 0.5)

pr_plot

Description

precision-recall plot

Usage

pr_plot(
  data,
  fitted,
  actual,
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  palette = ez_col,
  size_line = 1,
  size = 11,
  labs = "short",
  env = parent.frame()
)
pr_plot(
  data,
  fitted,
  actual,
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  palette = ez_col,
  size_line = 1,
  size = 11,
  labs = "short",
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`fitted`	Vector of fitted values
`actual`	Vector of actual values
`group`	A character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`palette`	Colour function.
`size_line`	width of line for `geom_line()`. Default is 1.
`size`	theme size for `use_theme()`. Default is 14.
`labs`	'short' or 'long'
`env`	environment for evaluating expressions.

Examples

library(ggplot2)
n = 1000
df = data.frame(actual = sample(c(FALSE, TRUE), n, replace = TRUE),
                runif = runif(n))
df[["fitted"]] = runif(n) ^ ifelse(df[["actual"]] == 1, 0.5, 2)

density_plot(df, "fitted", "actual")

pr_plot(df, "fitted", "actual")
pr_plot(df, "runif", "actual", size_line = 0.5)


library(dplyr, warn.conflicts = FALSE)
pr_plot(df, "fitted", "actual", "sample(c(1, 2), n(), TRUE)")

pr_plot(df, "fitted", "actual",
        "sample(c(1, 2), n(), TRUE)",
        "sample(c(3, 4), n(), TRUE)")

pr_plot(df, "fitted", "actual",
        "sample(c(1, 2), n(), TRUE)",
        "sample(c(3, 4), n(), TRUE)",
        "sample(c(5, 6), n(), TRUE)")

library(ggplot2)
n = 1000
df = data.frame(actual = sample(c(FALSE, TRUE), n, replace = TRUE),
                runif = runif(n))
df[["fitted"]] = runif(n) ^ ifelse(df[["actual"]] == 1, 0.5, 2)

density_plot(df, "fitted", "actual")

pr_plot(df, "fitted", "actual")
pr_plot(df, "runif", "actual", size_line = 0.5)


library(dplyr, warn.conflicts = FALSE)
pr_plot(df, "fitted", "actual", "sample(c(1, 2), n(), TRUE)")

pr_plot(df, "fitted", "actual",
        "sample(c(1, 2), n(), TRUE)",
        "sample(c(3, 4), n(), TRUE)")

pr_plot(df, "fitted", "actual",
        "sample(c(1, 2), n(), TRUE)",
        "sample(c(3, 4), n(), TRUE)",
        "sample(c(5, 6), n(), TRUE)")

prec_rec

Description

Precision recall calculation

Usage

prec_rec(fitted, actual)
prec_rec(fitted, actual)

Arguments

`fitted`	Vector with values between 0 and 1
`actual`	Vector with two levels

Examples

ezplot:::prec_rec(runif(1), sample(c(TRUE, FALSE), 1, replace = TRUE))
ezplot:::prec_rec(runif(5), sample(c(TRUE, FALSE), 5, replace = TRUE))
ezplot:::prec_rec(runif(1), sample(c(TRUE, FALSE), 1, replace = TRUE))
ezplot:::prec_rec(runif(5), sample(c(TRUE, FALSE), 5, replace = TRUE))

Quick facet

Description

Applies faceting to ggplot objects when g[["data"]] has a facet_x or facet_y column.

Usage

quick_facet(g, ncol = NULL, ...)
quick_facet(g, ncol = NULL, ...)

Arguments

`g`	A ggplot object.
`ncol`	Number of facet columns.
`...`	Arguments to pass to `facet_grid` or `facet_wrap`.

Order levels of factor columns using fct_reorder

Description

Order levels of factor columns using fct_reorder

Usage

reorder_levels(
  data,
  cols = c("group", "facet_x", "facet_y"),
  y = "y",
  .desc = rep(TRUE, length(cols))
)
reorder_levels(
  data,
  cols = c("group", "facet_x", "facet_y"),
  y = "y",
  .desc = rep(TRUE, length(cols))
)

Arguments

`data`	A data.frame.
`cols`	Names of columns to reorder.
`y`	Numeric column for order priority.
`.desc`	A logical vector of length 1 or ncol(data). Default is TRUE for all columns in `cols`.

Value

A data.frame.

Examples

str(ezplot:::reorder_levels(mtcars, "cyl", "1"))
str(ezplot:::reorder_levels(mtcars, "cyl", "1", FALSE))
str(ezplot:::reorder_levels(mtcars, "cyl", "mpg"))
str(ezplot:::reorder_levels(mtcars, "cyl", "1"))
str(ezplot:::reorder_levels(mtcars, "cyl", "1", FALSE))
str(ezplot:::reorder_levels(mtcars, "cyl", "mpg"))

roc

Description

Calculates ROC and AUC

Usage

roc(fitted, actual)
roc(fitted, actual)

Arguments

`fitted`	Vector with values between 0 and 1
`actual`	Vector with two levels

Examples

ezplot:::roc(runif(1), sample(c(TRUE, FALSE), 1, replace = TRUE))
ezplot:::roc(runif(3), sample(c(TRUE, FALSE), 3, replace = TRUE))
ezplot:::roc(runif(1), sample(c(TRUE, FALSE), 1, replace = TRUE))
ezplot:::roc(runif(3), sample(c(TRUE, FALSE), 3, replace = TRUE))

roc_plot

Description

roc_plot

Usage

roc_plot(
  data,
  fitted,
  actual,
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  palette = ez_col,
  size_line = 1,
  size = 11,
  env = parent.frame()
)
roc_plot(
  data,
  fitted,
  actual,
  group = NULL,
  facet_x = NULL,
  facet_y = NULL,
  palette = ez_col,
  size_line = 1,
  size = 11,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`fitted`	Vector of fitted values
`actual`	Vector of actual values
`group`	A character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`palette`	Colour function.
`size_line`	width of line for `geom_line()`. Default is 1.
`size`	theme size for `use_theme()`. Default is 14.
`env`	environment for evaluating expressions.

Examples

library(ggplot2)
n = 1000
df = data.frame(actual = sample(c(FALSE, TRUE), n, replace = TRUE),
                runif = runif(n))
df[["fitted"]] = runif(n) ^ ifelse(df[["actual"]] == 1, 0.5, 2)

ggplot(df) +
  geom_density(aes(fitted, fill = actual), alpha = 0.5)

roc_plot(df, "actual", "actual")
roc_plot(df, "fitted", "actual")
roc_plot(df, "runif", "actual", size_line = 0.5)


library(dplyr, warn.conflicts = FALSE)
roc_plot(df, "fitted", "actual", "sample(c(1, 2), n(), TRUE)")

roc_plot(df, "fitted", "actual",
         "sample(c(1, 2), n(), TRUE)",
         "sample(c(3, 4), n(), TRUE)")

roc_plot(df, "fitted", "actual",
         "sample(c(1, 2), n(), TRUE)",
         "sample(c(3, 4), n(), TRUE)",
         "sample(c(5, 6), n(), TRUE)")

library(ggplot2)
n = 1000
df = data.frame(actual = sample(c(FALSE, TRUE), n, replace = TRUE),
                runif = runif(n))
df[["fitted"]] = runif(n) ^ ifelse(df[["actual"]] == 1, 0.5, 2)

ggplot(df) +
  geom_density(aes(fitted, fill = actual), alpha = 0.5)

roc_plot(df, "actual", "actual")
roc_plot(df, "fitted", "actual")
roc_plot(df, "runif", "actual", size_line = 0.5)


library(dplyr, warn.conflicts = FALSE)
roc_plot(df, "fitted", "actual", "sample(c(1, 2), n(), TRUE)")

roc_plot(df, "fitted", "actual",
         "sample(c(1, 2), n(), TRUE)",
         "sample(c(3, 4), n(), TRUE)")

roc_plot(df, "fitted", "actual",
         "sample(c(1, 2), n(), TRUE)",
         "sample(c(3, 4), n(), TRUE)",
         "sample(c(5, 6), n(), TRUE)")

save_png

Description

Saves ggplot or ezplot objects to png.

Usage

save_png(g, file, width, height, res, ..., vp = NULL)
save_png(g, file, width, height, res, ..., vp = NULL)

Arguments

`g`	A ggplot or ezplot object.
`file`	A png file path.
`width`	Width of output image.
`height`	Height or output image.
`res`	Resolution of output image.
`...`	Further arguments to pass to `png()`.
`vp`	A viewport object created with grid::viewport.

scatter plot

Description

create a scatter plot

Usage

scatter_plot(
  data,
  x,
  y,
  group = NULL,
  palette = ez_col,
  size = 11,
  point_size = 2.5,
  smooth = FALSE,
  env = parent.frame()
)
scatter_plot(
  data,
  x,
  y,
  group = NULL,
  palette = ez_col,
  size = 11,
  point_size = 2.5,
  smooth = FALSE,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y`	A named character value. Evaluates to a column.
`group`	A character value. Evaluates to a column.
`palette`	Colour function.
`size`	theme size for `use_theme()`. Default is 14.
`point_size`	Numeric. Default is 2.
`smooth`	logical. If `TRUE`, adds `geom_smooth()`.
`env`	environment for evaluating expressions.

Examples

scatter_plot(mtcars, "wt", "hp")
scatter_plot(mtcars, "wt", "hp", "factor(cyl)")
scatter_plot(mtcars, "factor(cyl)", "hp")
scatter_plot(mtcars, "wt", "hp")
scatter_plot(mtcars, "wt", "hp", "factor(cyl)")
scatter_plot(mtcars, "factor(cyl)", "hp")

secondary_plot creates a plot with a secondary y-axis

Description

secondary_plot creates a plot with a secondary y-axis

Usage

secondary_plot(
  data,
  x,
  y1 = "1",
  y2 = "1",
  facet_x = NULL,
  facet_y = NULL,
  palette = ez_col,
  size_line = 1,
  labels_y1 = ez_labels,
  labels_y2 = ez_labels,
  ylim1 = NULL,
  ylim2 = NULL,
  reorder = c("facet_x", "facet_y"),
  size = 11
)
secondary_plot(
  data,
  x,
  y1 = "1",
  y2 = "1",
  facet_x = NULL,
  facet_y = NULL,
  palette = ez_col,
  size_line = 1,
  labels_y1 = ez_labels,
  labels_y2 = ez_labels,
  ylim1 = NULL,
  ylim2 = NULL,
  reorder = c("facet_x", "facet_y"),
  size = 11
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y1`	Variable to plot on the left-hand axis
`y2`	Variable to plot on the right-hand axis
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`palette`	Colour function.
`size_line`	line size
`labels_y1`	label formatting function
`labels_y2`	label formatting function
`ylim1`	(optional) left axis limits
`ylim2`	(optional) right axis limits
`reorder`	A character vector specifying the group variables to reorder. Default is `c("group", "facet_x", "facet_y")`.
`size`	theme size for `use_theme()`. Default is 14.

Value

A ggplot object.

Examples

suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
secondary_plot(pelt, "Year", "Hare", "Lynx")
secondary_plot(pelt, "Year", c("Hare Population" = "Hare"), c("Lynx Population" = "Lynx"))
secondary_plot(aus_production, "Quarter",
               c("Quarterly Beer Production (megalitres)" = "Beer"),
               c("Quarterly Cement Production (tonnes)" = "Cement"),
               "lubridate::quarter(Quarter)",
               ylim1 = c(0, 600), ylim2 = c(0, 3000),
               size = 10)
suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
secondary_plot(pelt, "Year", "Hare", "Lynx")
secondary_plot(pelt, "Year", c("Hare Population" = "Hare"), c("Lynx Population" = "Lynx"))
secondary_plot(aus_production, "Quarter",
               c("Quarterly Beer Production (megalitres)" = "Beer"),
               c("Quarterly Cement Production (tonnes)" = "Cement"),
               "lubridate::quarter(Quarter)",
               ylim1 = c(0, 600), ylim2 = c(0, 3000),
               size = 10)

side_plot

Description

side_plot

Usage

side_plot(
  data,
  x,
  y = "1",
  labels_y = ez_labels,
  size = 11,
  palette = ez_col,
  signif = 3,
  reorder = TRUE,
  rescale_y = 1.25
)
side_plot(
  data,
  x,
  y = "1",
  labels_y = ez_labels,
  size = 11,
  palette = ez_col,
  signif = 3,
  reorder = TRUE,
  rescale_y = 1.25
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y`	A named character value. Evaluates to a column.
`labels_y`	label formatting function
`size`	theme size for `use_theme()`. Default is 14.
`palette`	Colour function.
`signif`	Number of significant digits.
`reorder`	A character vector specifying the group variables to reorder. Default is `c("group", "facet_x", "facet_y")`.
`rescale_y`	Rescaling factor for y-axis limits

Examples

side_plot(mtcars, "gear", "1", rescale_y = 4/3)
side_plot(mtcars, "cyl", c("Cars with <120 HP" = "hp < 120"))
side_plot(mtcars, "cyl", c(count = "ifelse(cyl == 4, 1, -1)", "hp <= 120"))
side_plot(mtcars, "cyl", c("hp <= 120", "~ - wt / cyl"), rescale_y = 1.5)
side_plot(mtcars, "cyl", c("1", "-1"))
side_plot(mtcars, "gear", "1", rescale_y = 4/3)
side_plot(mtcars, "cyl", c("Cars with <120 HP" = "hp < 120"))
side_plot(mtcars, "cyl", c(count = "ifelse(cyl == 4, 1, -1)", "hp <= 120"))
side_plot(mtcars, "cyl", c("hp <= 120", "~ - wt / cyl"), rescale_y = 1.5)
side_plot(mtcars, "cyl", c("1", "-1"))

text_contrast

Description

text_contrast

Usage

text_contrast(x)
text_contrast(x)

Arguments

`x`	Vector of colours.

Value

Vector indicating whether black or white should be used for text overlayed on x.

Examples

text_contrast("#000000")
text_contrast("black")
text_contrast("#000000")
text_contrast("black")

Default theme

Description

Default theme

Usage

theme_ez(base_size = 11, base_family = "")
theme_ez(base_size = 11, base_family = "")

Arguments

`base_size`	base font size
`base_family`	base fond family

Value

theme

Examples

library(ggplot2)
ggplot(mtcars) + geom_point(aes(cyl, mpg)) + theme_ez()
library(ggplot2)
ggplot(mtcars) + geom_point(aes(cyl, mpg)) + theme_ez()

tile_plot

Description

Creates tile plots.

Usage

tile_plot(
  data,
  x,
  y,
  z = c(Count = "1"),
  facet_x = NULL,
  facet_y = NULL,
  size = 11,
  facet_ncol = NULL,
  labels_x = NULL,
  labels_y = NULL,
  labels_z = ez_labels,
  zlim = function(x) c(pmin(0, x[1]), pmax(0, x[2])),
  palette = ez_jet,
  reorder = c("facet_x", "facet_y")
)
tile_plot(
  data,
  x,
  y,
  z = c(Count = "1"),
  facet_x = NULL,
  facet_y = NULL,
  size = 11,
  facet_ncol = NULL,
  labels_x = NULL,
  labels_y = NULL,
  labels_z = ez_labels,
  zlim = function(x) c(pmin(0, x[1]), pmax(0, x[2])),
  palette = ez_jet,
  reorder = c("facet_x", "facet_y")
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y`	A named character value. Evaluates to a column.
`z`	A named character. Evaluates to a column and is mapped to the fill colour of the tiles.
`facet_x`	A character value. Evaluates to a column.
`facet_y`	A character. Evaluates to a column.
`size`	theme size for `use_theme()`. Default is 14.
`facet_ncol`	Option passed to ncol argument in `facet_wrap` or `facet_grid`. Default is `NULL`.
`labels_x`	label formatting function
`labels_y`	label formatting function
`labels_z`	label formatting function
`zlim`	argument for `scale_fill_grandientn(limits = zlim)`
`palette`	Colour function.
`reorder`	A character vector specifying the group variables to reorder. Default is `c("group", "facet_x", "facet_y")`.

Examples

## Not run: 
library(tsibbledata)
library(dplyr)
nyc_bikes %>%
  mutate(duration = as.numeric(stop_time - start_time)) %>%
  filter(between(duration, 0, 16)) %>%
  tile_plot(c("Hour of Day" = "lubridate::hour(start_time) + 0.5"),
            c("Ride Duration (min)" = "duration - duration %% 2 + 1"))

## End(Not run)
## Not run: 
library(tsibbledata)
library(dplyr)
nyc_bikes %>%
  mutate(duration = as.numeric(stop_time - start_time)) %>%
  filter(between(duration, 0, 16)) %>%
  tile_plot(c("Hour of Day" = "lubridate::hour(start_time) + 0.5"),
            c("Ride Duration (min)" = "duration - duration %% 2 + 1"))

## End(Not run)

Unpack cols argument to agg_data

Description

Unpack cols argument to agg_data

Usage

unpack_cols(x)
unpack_cols(x)

Arguments

x

cols

Value

list

Examples

ezplot:::unpack_cols("x")
ezplot:::unpack_cols(c(x = "x", y = "x + y", expr = "~ x + y"))
ezplot:::unpack_cols("x")
ezplot:::unpack_cols(c(x = "x", y = "x + y", expr = "~ x + y"))

variable_plot

Description

Plots variables (multiple "y" values) broken out as vertical facets.

Usage

variable_plot(
  data,
  x,
  y,
  group = NULL,
  facet_x = NULL,
  palette = ez_col,
  size = 14,
  labels_y = ez_labels,
  geom = "line",
  size_line = 1,
  legend_ncol = NULL,
  ylab = NULL,
  yoy = FALSE,
  switch = "y",
  rescale_y = 1
)
variable_plot(
  data,
  x,
  y,
  group = NULL,
  facet_x = NULL,
  palette = ez_col,
  size = 14,
  labels_y = ez_labels,
  geom = "line",
  size_line = 1,
  legend_ncol = NULL,
  ylab = NULL,
  yoy = FALSE,
  switch = "y",
  rescale_y = 1
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y`	A named character value. Evaluates to a column.
`group`	A character value. Evaluates to a column.
`facet_x`	A character value. Evaluates to a column.
`palette`	Colour function.
`size`	theme size for `use_theme()`. Default is 14.
`labels_y`	label formatting function
`geom`	Either "line", "col" or "bar". Default is "line"
`size_line`	width of line for `geom_line()`. Default is 1.
`legend_ncol`	Number of columns in legend.
`ylab`	y label text
`yoy`	Logical used to indicate whether a YOY grouping should be created. Default is `FALSE`.
`switch`	Option to switch location of variable (facet) labels. Default is 'y' (yes) which shows facet strips on left side of panels.
`rescale_y`	Rescaling factor for y-axis limits

Examples

suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
variable_plot(ansett, "Week", "Passengers", facet_x = "Class", yoy = TRUE)
variable_plot(pelt, "Year", c("Lynx", "Hare"), "round(Year, -1)")
suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
variable_plot(ansett, "Week", "Passengers", facet_x = "Class", yoy = TRUE)
variable_plot(pelt, "Year", c("Lynx", "Hare"), "round(Year, -1)")

waterfall_plot

Description

function for creating waterfall charts

Usage

waterfall_plot(
  data,
  x,
  y,
  group,
  size = 11,
  labels = ez_labels,
  label_rescale = 1,
  y_min = "auto",
  rescale_y = 1.1,
  n_signif = 3,
  rotate_xlabel = FALSE,
  bottom_label = TRUE,
  ingroup_label = FALSE,
  n_x = 2,
  env = parent.frame()
)
waterfall_plot(
  data,
  x,
  y,
  group,
  size = 11,
  labels = ez_labels,
  label_rescale = 1,
  y_min = "auto",
  rescale_y = 1.1,
  n_signif = 3,
  rotate_xlabel = FALSE,
  bottom_label = TRUE,
  ingroup_label = FALSE,
  n_x = 2,
  env = parent.frame()
)

Arguments

`data`	A data.frame.
`x`	A named character value. Evaluates to a column.
`y`	A named character value. Evaluates to a column.
`group`	A character value. Evaluates to a column.
`size`	theme size for `use_theme()`. Default is 14.
`labels`	Function for formatting labels.
`label_rescale`	Scaling factor for chart labels (relative to axis labels).
`y_min`	Minimum limit of y axis.
`rescale_y`	Rescaling factor for y-axis limits
`n_signif`	Number of significant figures in labels.
`rotate_xlabel`	Logical.
`bottom_label`	Logical.
`ingroup_label`	Logical. Shows in-group percentage change.
`n_x`	Number of x levels to show in chart.
`env`	environment for evaluating expressions.

Examples

library(tsibbledata)
waterfall_plot(aus_retail,
               "lubridate::year(Month)",
               "Turnover",
               "sub(' Territory', '\nTerritory', State)",
               rotate_xlabel = TRUE)
waterfall_plot(aus_retail,
               "lubridate::year(Month)",
               "Turnover",
               "sub(' Territory', '\nTerritory', State)",
               rotate_xlabel = TRUE,
               label_rescale = 0.5,
               ingroup_label = TRUE,
               bottom_label = FALSE,
               n_x = 3,
               size = 20,
               y_min = 0)
library(tsibbledata)
waterfall_plot(aus_retail,
               "lubridate::year(Month)",
               "Turnover",
               "sub(' Territory', '\nTerritory', State)",
               rotate_xlabel = TRUE)
waterfall_plot(aus_retail,
               "lubridate::year(Month)",
               "Turnover",
               "sub(' Territory', '\nTerritory', State)",
               rotate_xlabel = TRUE,
               label_rescale = 0.5,
               ingroup_label = TRUE,
               bottom_label = FALSE,
               n_x = 3,
               size = 20,
               y_min = 0)

Package 'ezplot'

Help Index

Aggregates data

Description

Usage

Arguments

Value

Examples

area_plot

Description

Usage

Arguments

Value

Examples

bar_plot

Description

Usage

Arguments

Value

Examples

calendar_plot

Description

Usage

Arguments

Examples

density_plot

Description

Usage

Arguments

Examples

distribution_plot

Description

Usage

Arguments

Examples

ez_app

Description

Usage

Arguments

Examples

Color palette interpolation

Description

Usage

Arguments

Value

Examples

ez_jet

Description

Usage

Arguments

Examples

Function for formatting numeric labels

Description

Usage

Arguments

Value

Examples

ez_png

Description

Usage

Arguments

ez_server

Description

Usage

Arguments

ez_ui

Description

Usage

Arguments

get_incr

Description

Usage

Arguments

histogram_plot

Description

Usage

Arguments

Examples

ks_plot

Description