Package 'RplotterPkg'

Description

Data where the base datasets::AirPassengers monthly time series of international airline passenger numbers 1949-1960 was converted to a data frame using tsbox::ts_df(datasets::AirPassengers).

Usage

air_passengers

Format

An object of class data.frame with 144 rows and 2 columns.

Source

datasets::AirPassengers

Description

Boston Marathon completion times of women of different ages. The raw tab delimited data (boston_marathon.txt) is read by data.table::fread() to produce a data frame with columns for women's "age" and marathon "time" in minutes.

Usage

boston_marathon

Format

An object of class data.table (inherits from data.frame) with 109 rows and 2 columns.

Source

LearnEDAfunctions

Description

From datasets::mtcars produces statistics for N and the means of "mpg", "hp", "wt", "disp" variables grouped by "cyl".

Usage

car_stats

Format

An object of class data.table (inherits from data.frame) with 3 rows and 6 columns.

Source

datasets::mtcars

Description

A dataframe with 4 observations of average chick weights.

Usage

chick_weights

Format

An object of class data.table (inherits from data.frame) with 4 rows and 3 columns.

Source

datasets::ChickWeight

Description

Function wraps ggplot2 geom_bar and geom_col to creates a bar plot.

Usage

create_bar_plot(
  df = NULL,
  aes_x = NULL,
  aes_y = NULL,
  aes_color = NULL,
  aes_fill = NULL,
  position = "stack",
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = NULL,
  y_title = NULL,
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  rot_x_tic_angle = 0,
  rot_y_tic_label = FALSE,
  bar_fill = NA,
  bar_color = "black",
  bar_alpha = 1,
  bar_lwd = 0.7,
  bar_width = NULL,
  x_major_breaks = NULL,
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_labels = waiver(),
  axis_text_size = 11,
  do_coord_flip = FALSE,
  order_bars = NULL,
  bar_labels = FALSE,
  bar_label_sz = 4,
  bar_label_color = "black",
  bar_label_fontface = "plain",
  bar_label_alpha = 1,
  show_major_grids = TRUE,
  show_minor_grids = TRUE,
  panel_color = "white",
  panel_border_color = "black",
  show_legend = TRUE,
  legend_pos = "right",
  legend_key_width = 0.7,
  legend_key_height = 0.7,
  legend_key_backgrd = "white",
  silent_NA_warning = FALSE,
  png_file_path = NULL,
  png_width_height = c(480, 480)
)

Arguments

Value

A ggplot class object.

Examples

library(ggplot2)
library(data.table)
library(RColorBrewer)
library(RplotterPkg)

RplotterPkg::create_bar_plot(
  df = RplotterPkg::religion,
  aes_x = "happy",
  aes_fill = "religion",
  position = "dodge",
  title = "Happy Religions",
  center_titles = TRUE,
  rot_y_tic_label = TRUE,
  bar_width = 0.8,
  order_bars = "desc",
  x_title = "Happiness",
  y_title = "Count",
  axis_text_size = 16
) +
ggplot2::scale_fill_discrete(
  type = RColorBrewer::brewer.pal(n = 9, name = "Set1")
)

Description

Function wraps around ggplot2 geom_boxplot to create a box plot.

Usage

create_box_plot(
  df = NULL,
  aes_x = NULL,
  aes_y = NULL,
  aes_color = NULL,
  aes_fill = NULL,
  aes_label = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = NULL,
  y_title = NULL,
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  rot_x_tic_angle = 0,
  rot_y_tic_label = FALSE,
  box_fill = NA,
  box_color = "black",
  box_line_width = 0.5,
  box_alpha = 1,
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_decimals = 0,
  y_scientific = FALSE,
  axis_text_size = 11,
  do_coord_flip = FALSE,
  show_legend = TRUE,
  panel_color = "white",
  panel_border_color = "black",
  legend_pos = "top",
  legend_key_width = 0.7,
  legend_key_height = 0.7,
  legend_key_backgrd = "white",
  show_major_grids = TRUE,
  show_minor_grids = TRUE,
  ol_color = "black",
  ol_fill = "black",
  ol_size = 1.5,
  ol_shape = 19,
  ol_stroke = 0.5,
  ol_alpha = NULL,
  order_by_median = NULL,
  silent_NA_warning = FALSE,
  png_file_path = NULL,
  png_width_height = c(480, 480)
)

Arguments

Value

A ggplot class object.

Examples

library(ggplot2)
library(data.table)
library(RplotterPkg)

RplotterPkg::create_box_plot(
  df = RplotterPkg::organdata,
  aes_x = "country",
  aes_y = "donors",
  aes_label = "donors",
  order_by_median = "desc",
  y_limits = c(5,35),
  y_major_breaks = seq(5, 35, 5),
  title = "Organ Donation Rate per Million",
  subtitle = "Showing outlier rates",
  x_title = "Country",
  y_title = "Donor Rate",
  do_coord_flip = TRUE,
  box_color = "purple",
  box_line_width = 0.8,
  rot_y_tic_label = TRUE,
  ol_color = "red",
  ol_size = 1.5
)

Description

Function creates a ggplot2 based density plot with options for scaling, shading probability areas, and plotting observation locations. The function's density arguments mirror most of the arguments available from density for the Kernel Density Estimation (KDE). See the density help page for more information.

Usage

create_density_plot(
  df = NULL,
  aes_x = NULL,
  aes_color = NULL,
  aes_fill = NULL,
  bw = "nrd0",
  adjust = 1,
  kernel = "gaussian",
  n = 512,
  position = "identity",
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = NULL,
  y_title = NULL,
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  rot_x_tic_angle = 0,
  rot_y_tic_label = FALSE,
  density_linewdth = 1,
  density_color = "black",
  density_fill = "gray",
  density_alpha = 0.4,
  x_limits = NULL,
  x_major_breaks = waiver(),
  x_minor_breaks = waiver(),
  x_labels = waiver(),
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_labels = waiver(),
  axis_text_size = 11,
  show_major_grids = TRUE,
  show_minor_grids = TRUE,
  plot_obs = FALSE,
  plot_obs_len = 0.02,
  plot_obs_color = "black",
  bold_x = NULL,
  bold_x_color = "black",
  bold_x_linetype = "dashed",
  panel_color = "white",
  panel_border_color = "black",
  show_legend = TRUE,
  legend_pos = "right",
  legend_key_width = 0.7,
  legend_key_height = 0.7,
  legend_key_backgrd = "white",
  silent_NA_warning = FALSE,
  cum_prob = NULL,
  area_colors = c("gray", "green", "gray"),
  area_quantile_line_color = "red",
  png_file_path = NULL,
  png_width_height = c(480, 480)
)

Arguments

Value

A ggplot class object.

Examples

library(ggplot2)
library(rlang)
library(data.table)
library(RplotterPkg)

RplotterPkg::create_density_plot(
  df = datasets::airquality,
  aes_x = "Ozone",
  rot_y_tic_label = TRUE,
  x_limits = c(-40,200),
  x_major_breaks = seq(-40,200,20),
  title = "Ozone Air Quality",
  x_title = "Ozone",
  y_title = "Density",
  plot_obs = TRUE,
  density_fill = "green",
  density_alpha = 0.5
)

Description

Function creates ggplot2 based plots stacked vertically (also known as ridge or raincloud plots). The function's density arguments mirror most of the arguments available from density for the Kernel Density Estimation (KDE). See the density help page for more information.

Usage

create_density_ridge_plot(
  df = NULL,
  variables = NULL,
  plot_heights = 3.5,
  plot_widths = 24,
  bw = "nrd0",
  adjust = 1,
  kernel = "gaussian",
  n = 512,
  na.rm = TRUE,
  title = NULL,
  title_fontsz = 14,
  x_title = NULL,
  rot_x_tic_angle = 0,
  density_linewdth = 1,
  density_color = "black",
  density_fill = "gray",
  density_alpha = 0.4,
  x_limits = NULL,
  x_major_breaks = waiver(),
  x_minor_breaks = waiver(),
  x_labels = waiver(),
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_show_axis = FALSE,
  axis_text_size = 11
)

Arguments

Value

A ggplot class object.

References

Micah Allen,Davide Poggiali,Kirstie Whitaker,Tom Rhys Marshall, Jordy van Langen,Rogier A. Kievit (2021) Raincloud plots: a multi-platform tool for robust data visualization: version 2

Joachim Schork (2021) Ridgeline Plots in R (3 Examples)

Examples

library(ggplot2)
library(data.table)
library(grid)
library(gtable)
library(ggplotify)
library(RplotterPkg)

RplotterPkg::create_density_ridge_plot(
  df = RplotterPkg::midwest,
  bw = "sj",
  variables = c("HS_Diploma", "College_Edu", "Prof_Deg", "White", "Black", "Asian"),
  title = "Percent Distribution Among Midwest Counties",
  x_limits = c(0, 100),
  x_major_breaks = seq(0, 100, 10),
  density_fill = "blue",
  density_alpha = 0.5,
  plot_heights = 2.5,
  plot_widths = 18
)

Description

Function returns a ggplot2 x/y plot of a pair of discrete variables. Instead of a point, a rectangle/cell/tile is located for each x/y value with the cell itself being colored or sized depending on the value of a third associated discrete or continuous variable. Function wraps ggplot2 geom_tile to produce a heatmap that shows magnitude as an array of cells in two dimensions.

Usage

create_heatmap(
  df,
  aes_x = NULL,
  aes_y = NULL,
  aes_label = NULL,
  aes_color = NULL,
  aes_fill = NULL,
  aes_size = NULL,
  aes_width = NULL,
  aes_height = NULL,
  tile_sz = 1.1,
  tile_color = "white",
  tile_fill = "white",
  label_sz = 6,
  label_color = "black",
  label_fontface = "plain",
  label_alpha = 1,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = NULL,
  y_title = NULL,
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  rot_x_tic_angle = 0,
  rot_y_tic_label = FALSE,
  x_limits = NULL,
  x_major_breaks = waiver(),
  x_minor_breaks = waiver(),
  x_labels = waiver(),
  x_major_date_breaks = waiver(),
  x_minor_date_breaks = waiver(),
  x_date_labels = waiver(),
  x_log10 = FALSE,
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_labels = waiver(),
  y_log10 = FALSE,
  x_y_decimals = NULL,
  x_y_scientific = NULL,
  axis_text_size = 11,
  panel_color = "white",
  panel_border_color = "black",
  show_legend = TRUE,
  legend_pos = "right",
  legend_key_width = 0.5,
  legend_key_height = 0.7,
  legend_key_backgrd = "white",
  silent_NA_warning = FALSE,
  png_file_path = NULL,
  png_width_height = c(480, 480)
)

Arguments

Value

A ggplot class object.

Examples

library(ggplot2)
library(data.table)
library(RplotterPkg)

RplotterPkg::create_heatmap(
  df = RplotterPkg::spinrates,
  aes_x = "velocity",
  aes_y = "spinrate",
  aes_fill = "swing_miss",
  aes_label = "swing_miss",
  label_fontface = "bold",
  title = "Likelihood of swinging and missing on a fastball",
  x_title = "Velocity",
  y_title = "Spinrate",
  label_sz = 5,
  rot_y_tic_label = TRUE
) +
 ggplot2::scale_fill_gradientn(
   colors = RColorBrewer::brewer.pal(n = 9, name = "YlOrRd"),
   n.breaks = 8
 ) +
 ggplot2::guides(
   fill = ggplot2::guide_colorbar(
     ticks.colour = "black"
   )
 )

Description

Function plots a ggplot2 based histogram with options for scaling and viewing observation locations. The function offers one of four ways of setting the number of bins.

Usage

create_histogram_plot(
  df,
  aes_x = NULL,
  aes_color = NULL,
  aes_fill = NULL,
  position = "stack",
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = NULL,
  y_title = NULL,
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  rot_x_tic_angle = 0,
  rot_y_tic_label = FALSE,
  bins = 20,
  binwidth = NULL,
  bin_breaks = NULL,
  bin_class = NULL,
  bar_fill = NA,
  bar_color = "black",
  bar_alpha = 1,
  bar_lwd = 1,
  x_limits = NULL,
  x_major_breaks = waiver(),
  x_minor_breaks = waiver(),
  x_labels = waiver(),
  x_decimals = NULL,
  x_scientific = NULL,
  x_log10 = FALSE,
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_labels = waiver(),
  axis_text_size = 11,
  do_coord_flip = FALSE,
  bar_labels = FALSE,
  bar_label_size = 6,
  bar_label_color = "black",
  plot_obs = FALSE,
  plot_obs_len = 0.02,
  plot_obs_color = "black",
  bold_x = NULL,
  bold_x_color = "black",
  bold_x_linetype = "dashed",
  show_major_grids = TRUE,
  show_minor_grids = TRUE,
  show_legend = TRUE,
  panel_color = "white",
  panel_border_color = "black",
  legend_pos = "top",
  legend_key_width = 0.7,
  legend_key_height = 0.7,
  legend_key_backgrd = "white",
  silent_NA_warning = FALSE,
  png_file_path = NULL,
  png_width_height = c(480, 480)
)

Arguments

Value

A ggplot class object.

Examples

library(ggplot2)
library(data.table)
library(rlang)
library(RplotterPkg)

RplotterPkg::create_histogram_plot(
  df = RplotterPkg::midwest,
  aes_x = "Area",
  binwidth = 0.01,
  x_limits = c(0.0, 0.11),
  x_major_breaks = seq(0.0, 0.11, 0.01),
  title = "Distribution of area",
  subtitle = "437 counties from midwest dataset",
  x_title = "Area",
  y_title = "Count",
  bar_color = "white",
  bar_lwd = 2.0,
  bar_fill = "brown",
  do_coord_flip = TRUE,
  bar_labels = TRUE,
  bar_label_size = 4,
  bar_label_color = "blue",
  rot_y_tic_label = TRUE,
  silent_NA_warning = TRUE,
  plot_obs = TRUE,
  plot_obs_color = "darkorange"
)

Description

Function wraps ggplot2::geom_pointrange to produce x/y plot of numeric ranges. Function returns a ggplot2 plot object displaying the individual spread or vertical interval/range for a collection of x/y pairs of points.

Usage

create_range_plot(
  df = NULL,
  orientation = "x",
  aes_x = NULL,
  aes_y = NULL,
  aes_min = NULL,
  aes_max = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = NULL,
  y_title = NULL,
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  rot_x_tic_angle = 0,
  rot_y_tic_label = FALSE,
  x_limits = NULL,
  x_major_breaks = waiver(),
  x_minor_breaks = waiver(),
  x_labels = waiver(),
  x_log10 = FALSE,
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_labels = waiver(),
  y_log10 = FALSE,
  axis_text_size = 11,
  pts_fill = "white",
  pts_shape = 21,
  pts_stroke = 1,
  pts_size = 2,
  line_type = "solid",
  line_width = 1,
  line_pts_color = "black",
  line_pts_alpha = 1,
  panel_color = "white",
  panel_border_color = "black",
  show_major_grids = TRUE,
  show_minor_grids = TRUE,
  bold_x = NULL,
  bold_x_color = "black",
  bold_x_linetype = "dashed",
  bold_y = NULL,
  bold_y_color = "black",
  bold_y_linetype = "dashed",
  silent_NA_warning = FALSE,
  png_file_path = NULL,
  png_width_height = c(480, 480)
)

Arguments

Value

A ggplot class object.

Examples

library(ggplot2)
library(RplotterPkg)

RplotterPkg::create_range_plot(
  df = RplotterPkg::penguins_stats,
  orientation = "x",
  aes_x = "avg_body_mass",
  aes_y = "species",,
  aes_min = "min_body_mass",
  aes_max = "max_body_mass",
  title = "Average and Range of Penguins Body Mass(g) by Species",
  subtitle = "Source: palmerpenguins",
  center_titles = TRUE,
  x_title = "Body Mass(g)",
  y_title = "Species",
  pts_fill = "blue",
  pts_shape = 22,
  pts_stroke = 1.7,
  line_width = 1.5,
  line_type = "solid",
  line_pts_color = "red",
  line_pts_alpha = 0.5,
  x_limits = c(2500, 7000),
  x_major_breaks = seq(2500,7000,500),
  show_major_grids = TRUE,
  show_minor_grids = FALSE
)

Description

The function is wrapper around ggplot2::geom_raster() that plots from a data.frame with columns for x and y coordinates along with an associated numeric attribute.

The function accepts a data frame with columns for numeric x/y values and an attribute that will be mapped as a color or fill aesthetic. Function provides additional ggplot2 text labeling and axis scaling.

Usage

create_raster_plot(
  df,
  aes_x = NULL,
  aes_y = NULL,
  aes_color = NULL,
  aes_fill = NULL,
  interpolate = FALSE,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = NULL,
  y_title = NULL,
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  panel_color = "white",
  panel_border_color = "black",
  x_limits = NULL,
  x_major_breaks = waiver(),
  x_minor_breaks = waiver(),
  x_labels = waiver(),
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_labels = waiver(),
  scale_breaks = waiver(),
  scale_values = NULL,
  scale_limits = NULL,
  scale_labels = waiver(),
  scale_colors = heat.colors(8),
  scale_na_value = "gray50",
  own_scale = FALSE,
  show_legend = TRUE,
  legend_pos = "right",
  legend_key_width = 0.5,
  legend_key_height = 0.7,
  legend_key_backgrd = "white"
)

Arguments

Value

A ggplot class object.

Examples

library(ggplot2)
library(RplotterPkg)

RplotterPkg::create_raster_plot(
  df = RplotterPkg::kentucky_elevation,
  title = "County Elevations in Southeast Kentucky",
  aes_x = "x",
  aes_y = "y",
  aes_fill = "elevation"
) +
ggplot2::geom_sf(
  data = RplotterPkg::kentucky_counties,
  aes(x = NULL, y = NULL),
  alpha = 0,
  linewidth = 1.5
) +
ggplot2::geom_sf_text(
  data = RplotterPkg::kentucky_counties,
  aes(x = NULL, y = NULL, label = ID)
)

Description

Function is a wrapper around ggplot2 geom_point and geom_line to produce a scatter plot. Function returns a ggplot2 plot object of x/y scatter points/lines. Options are provided for axis scaling and variable/non-variable dependent aesthetics.

Usage

create_scatter_plot(
  df = NULL,
  aes_x = NULL,
  aes_y = NULL,
  aes_color = NULL,
  aes_fill = NULL,
  aes_size = NULL,
  aes_alpha = NULL,
  aes_linetype = NULL,
  aes_label = NULL,
  aes_label_color = "black",
  aes_label_size = 6,
  aes_label_nudge_x = 0,
  aes_label_nudge_y = -0.3,
  aes_CI_lwr = NULL,
  aes_CI_upr = NULL,
  position = position_jitter(width = 0, height = 0),
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = NULL,
  y_title = NULL,
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  rot_x_tic_angle = 0,
  rot_y_tic_label = FALSE,
  x_limits = NULL,
  x_major_breaks = waiver(),
  x_minor_breaks = waiver(),
  x_labels = waiver(),
  x_major_date_breaks = waiver(),
  x_minor_date_breaks = waiver(),
  x_date_labels = waiver(),
  x_log10 = FALSE,
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_labels = waiver(),
  y_log10 = FALSE,
  x_y_decimals = NULL,
  x_y_scientific = NULL,
  axis_text_size = 11,
  show_pts = TRUE,
  pts_fill = "white",
  pts_shape = 21,
  pts_stroke = 1,
  pts_color = "black",
  pts_size = 1,
  pts_line_alpha = 1,
  connect = FALSE,
  line_width = 0.6,
  line_color = "black",
  connect_linetype = "solid",
  show_major_grids = TRUE,
  show_minor_grids = TRUE,
  show_legend = TRUE,
  legend_pos = "right",
  legend_key_width = 0.7,
  legend_key_height = 0.7,
  legend_key_backgrd = "white",
  panel_color = "white",
  panel_border_color = "black",
  bold_y = NULL,
  bold_y_color = "black",
  bold_y_linetype = "dashed",
  CI_dir = "y",
  CI_show_line = FALSE,
  CI_show_errorbar = FALSE,
  CI_show_ribbon = FALSE,
  CI_line_color = "black",
  CI_line_width = 1,
  CI_linetype = "dashed",
  CI_errorbar_color = "black",
  CI_errorbar_width = 1,
  CI_ribbon_color = "gray70",
  silent_NA_warning = FALSE,
  png_file_path = NULL,
  png_width_height = c(480, 480)
)

Arguments

Value

A ggplot class object.

Examples

library(ggplot2)
library(RplotterPkg)

RplotterPkg::create_scatter_plot(
  df = ggplot2::economics,
  aes_x = "date",
  aes_y = "unemploy",
  pts_shape = 21,
  pts_fill = "black",
  line_color = "violet",
  connect = TRUE,
  title = "US Monthly Unemployment",
  subtitle = "July, 1967 to April, 2015 (in thousands)",
  x_title = "Date",
  y_title = "Unemployment",
  rot_y_tic_label = TRUE,
  x_date_labels = "%Y-%b",
  x_major_date_breaks = "5 year",
  y_limits = c(0, 16000),
  y_major_breaks = seq(0, 16000, 2000),
  show_minor_grids = FALSE,
  bold_y = 8000,
  bold_y_color = "red",
  bold_y_linetype = "dashed"
)

Description

The function is a wrapper around ggplot2::geom_sf() that plots simple feature objects. See Simple Features for R for more information on simple features.

For a good introduction to using ggplot2::geom_sf see Drawing beautiful maps programmatically with R, sf and ggplot2.

The function accepts objects of class sf for visualizing their points, lines, and polygon geometries. Easy plotting is possible with the function's parameters for scaling, color, size, variable aesthetic mapping, and text labeling. Overlapping of geometries is possible by chaining multiple functions via standard piping.

Usage

create_sf_plot(
  sf,
  gg = NULL,
  aes_color = NULL,
  aes_fill = NULL,
  aes_size = NULL,
  aes_text = NULL,
  text_size = 3,
  text_color = "black",
  text_fontface = "plain",
  text_check_overlap = FALSE,
  text_nudge_x = 0,
  text_nudge_y = 0,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = NULL,
  y_title = NULL,
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  xlim = NULL,
  ylim = NULL,
  grid_line_color = NULL,
  grid_line_size = 1,
  panel_color = NULL,
  panel_border_color = "black",
  panel_expand = FALSE,
  sf_color = "black",
  sf_fill = "gray",
  sf_stroke = 0.1,
  sf_shape = 21,
  sf_size = 0.1,
  sf_linewidth = 0.1,
  sf_alpha = 1,
  inherit_aes = TRUE,
  na_rm = FALSE,
  scale_breaks = waiver(),
  scale_values = NULL,
  scale_limits = NULL,
  scale_labels = waiver(),
  scale_colors = heat.colors(8),
  scale_na_value = "gray50",
  own_scale = FALSE,
  show_legend = TRUE,
  legend_pos = "right",
  legend_key_width = 0.5,
  legend_key_height = 0.7,
  legend_key_backgrd = "white"
)

Arguments

Value

A ggplot class object.

Examples

library(ggplot2)
library(RColorBrewer)
library(RplotterPkg)

# map country coffee production over country geometries
RplotterPkg::create_sf_plot(
  sf = spData::world,
  title = "World Coffee Production 2017",
  subtitle = "Source: spData::coffee_data",
  panel_color = "white",
  panel_border_color = "white",
) |>
  RplotterPkg::create_sf_plot(
    sf = RplotterPkg::world_coffee,
    aes_fill = "coffee_production_2017",
    scale_breaks = seq(0,3000,250),
    scale_labels = seq(0,3000,250),
    scale_limits = c(0,3000),
    scale_colors = RColorBrewer::brewer.pal(n = 9, name = "YlOrRd"),
    legend_key_width = 0.7,
    legend_key_height = 1.0
  )

Description

Function wraps a ggplot2 geom_linerange to produce a "stick" plot. Function returns a plot object showing vertical/horizontal lines that run from a base value to a measurement value. Options are provided for scaling.

Usage

create_stick_plot(
  df = NULL,
  base_val = 0,
  aes_x = NULL,
  aes_y = NULL,
  aes_color = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = NULL,
  y_title = NULL,
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  rot_x_tic_angle = 0,
  rot_y_tic_label = FALSE,
  x_limits = NULL,
  x_major_breaks = waiver(),
  x_minor_breaks = waiver(),
  x_labels = waiver(),
  x_major_date_breaks = waiver(),
  x_date_labels = waiver(),
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_labels = waiver(),
  axis_text_size = 11,
  line_color = "black",
  line_width = 0.8,
  line_type = "solid",
  line_alpha = 1,
  show_major_grids = TRUE,
  show_minor_grids = TRUE,
  panel_color = "white",
  panel_border_color = "black",
  show_legend = TRUE,
  legend_pos = "right",
  legend_key_width = 1.5,
  legend_key_height = 1.5,
  legend_key_backgrd = "white",
  bold_y = NULL,
  bold_y_color = "black",
  silent_NA_warning = FALSE,
  png_file_path = NULL,
  png_width_height = c(480, 480)
)

Arguments

Value

A ggplot class object.

Examples

library(ggplot2)
library(RplotterPkg)

RplotterPkg::create_stick_plot(
  df = RplotterPkg::air_passengers,
  aes_x = "time",
  aes_y = "value",
  title = "Monthly Totals of International Airline Passengers",
  subtitle = "1949 - 1960 (classic Box & Jenkins)",
  x_title = "Time",
  y_title = "Totals",
  x_major_date_breaks = "1 year",
  x_date_labels = "%Y",
  rot_y_tic_label = TRUE,
  show_minor_grids = FALSE,
  bold_y = 0.0
)

Description

Function wraps the gt package for creating tables from a data.frame object. Function incorporates the gt package to create tables primarily for R Markdown/Quarto documents. The function offers a quick alternative if just limited styling and formatting are required.

Usage

create_table(
  df = NULL,
  title = NULL,
  subtitle = NULL,
  container_width_px = NULL,
  container_height_px = NULL,
  rowname_col = NULL,
  col_label_lst = NULL,
  col_width_lst = NULL,
  header_line = FALSE,
  caption = NULL,
  source_note = NULL,
  hor_scroll_bar = FALSE,
  ver_scroll_bar = FALSE,
  decimals_lst = NULL,
  footnote_title = NULL,
  footnote_col_head_lst = NULL,
  footnote_body_lst = NULL
)

Arguments

Value

A gt object of class "gt_tbl" which can be further modified.

Examples

library(gt)
library(glue)
library(purrr)
library(RplotterPkg)

RplotterPkg::create_table(
  df = RplotterPkg::people,
  container_width_px = 500,
  col_label_lst = list(
    col_0 = "ID",
    col_1 = "First Name",
    col_2 = "Last Name",
    col_3 = "Age",
    col_4 = "Address",
    col_5 = "Spouse"
  ),
  decimals_lst = list(
    cols = 4,
    decimal = 0
  ),
  rowname_col = "ID",
  source_note = "Source: *Chardon City Directory*",
  footnote_title = "Employees are current",
  footnote_col_head_lst = list(
    footnote = "As reported on their job application",
    col = c(2,3)
  ),
  footnote_body_lst = list(
    footnote = "Age is approximated",
    col = 4,
    row = c(1,2)
  )
)

Description

Function creates a ggplot2 graphic table from a data frame. Function creates a simple, non-scrollable static table to be laid out with other ggplot2 graphics. The function is based on grid along with the gtable package.

For estimating the overall height of the table consider that each row of the table is 0.2 inches high, .54 inches for heading and if a title is defined then add an additional 0.5 inches.

Usage

create_table_graphic(
  df = NULL,
  table_width = 8,
  head_color = "black",
  head_fill = "white",
  head_font_sz = 14,
  cell_color = "black",
  cell_fill = "white",
  cell_font_sz = 11,
  cell_just = "center",
  cell_hor_pos = 0.5,
  title = NULL,
  title_font_sz = 16,
  show_row_names = TRUE,
  row_names_width = 0.5,
  return_gtable = FALSE
)

Arguments

Value

A gtable or ggplot class object.

Examples

library(grid)
library(gtable)
library(ggplotify)
library(RplotterPkg)

RplotterPkg::create_table_graphic(
  df = RplotterPkg::car_stats,
  table_width = 7,
  show_row_names = TRUE,
  cell_just = "right",
  cell_hor_pos = 0.95,
  title = "Average Measures by Cylinders"
)

Description

Function creates a "waffle" chart that displays percentages for multiple variables. The chart is similar to a heatmap with a 10 by 10 array of tiles (each representing one percent) that are colored to the extent of the variable's proportion value. For more information on "waffle" charts see What is a waffle chart.

Usage

create_waffle_chart(
  x = NULL,
  name_col = NULL,
  prop_col = NULL,
  title = NULL,
  select_fill = "brown",
  default_fill = "lightgray"
)

Arguments

Value

A ggplot2 class object

Examples

library(grid)
library(gtable)
library(ggplotify)
library(ggplot2)
library(RplotterPkg)

proportions_v <- c(
var1=10,var2=40,var3=20, var4=50, var5=5,
var6=30, var7=10, var8=67, var9=42, var10=33,
var11=7, var12=35,var13=22, var14=90
)
create_waffle_chart(
  x = proportions_v,
  title = "Test For 14 Proportion Variables"
)

Description

Number of farms in the states of the U.S. The raw tab delimited data (farms.txt) is read by data.table::fread() to produce a data frame with columns for "state" and "count".

Usage

farms

Format

An object of class data.table (inherits from data.frame) with 50 rows and 2 columns.

Source

LearnEDAfunctions

Description

Based on gapminder::gapminder dataset, selects 10 year intervals and 4 continents. The "year" variable is defined as a factor and the "pop" variable is divided by 1e+6.

Usage

gapminder_data

Format

An object of class data.table (inherits from data.frame) with 560 rows and 6 columns.

Source

gapminder::gapminder

Description

Function retrieves a grob component from a ggplot2 plot object.

Usage

get_grob_component(a_plot = NULL, component_name = NULL)

Arguments

Value

A list of grob objects

Examples

library(ggplot2)
library(ggplotify)
library(grid)
library(RplotterPkg)

mtcars_plot <- ggplot2::ggplot(
  data = datasets::mtcars,
) +
ggplot2::geom_point(aes(x = mpg, y = wt, color = cyl), size = 3)

legend_right <- RplotterPkg::get_grob_component(
  a_plot = mtcars_plot,
  component_name = "guide-box-right"
)

Description

Team home run numbers for different seasons. The raw tab delimited data (homeruns_2000.txt) is read by data.table::fread() to produce a data frame with numeric columns for "YEARS" and "HOMERUNS".

Usage

homeruns_2000

Format

An object of class data.table (inherits from data.frame) with 210 rows and 2 columns.

Source

LearnEDAfunctions

Description

A simple feature object with the boundary geometries for nine southeast Kentucky counties. .

Usage

kentucky_counties

Format

An object of class sf (inherits from data.table, data.frame) with 9 rows and 2 columns.

Source

maps::map()

Description

Tiled raster elevation data(in meters) converted to a data frame, covering the southeast region of Kentucky.

Usage

kentucky_elevation

Format

An object of class data.frame with 609 rows and 3 columns.

Source

elevatr::get_elev_raster() and the Amazon Web Services Terrain Tiles service.

Description

From the ggplot2::midwest dataset, selects for midwest counties percent high school diploma, percent college educated, percentage professional degree, percent white, percent black, percent asian.

Usage

midwest

Format

An object of class data.table (inherits from data.frame) with 437 rows and 7 columns.

Source

ggplot2::midwest

Description

Arranges a group of ggplot2 plots or grid::grob's into multiple panels.

Function creates a figure with title and arranges multiple plots/grobs across a given number of rows and columns. The function depends on the grid, gtable, and ggplotify packages.

Usage

multi_panel_grid(
  layout = NULL,
  title = NULL,
  title_fontsz = 20,
  plot_titles = NULL,
  y_tick_width = 0.5,
  cell_width = 8,
  cell_height = 8,
  do_grid = FALSE
)

Arguments

Details

The function has two modes of layout: a grid and a non-grid. In the grid layout, each plot submitted will be laid out "as-is" with no manipulation of labels, legends, or axis'.

For the non-grid layout, the function goes beyond just placing a group of plots across a grid of rows and columns. It is somewhat opinionated in favor of common titling/labeling and scaling that make sense for all the plots and provide the ability to compare. The function has a 'title' parameter that is assumed appropriate for all the plots–any titles/subtitles among the plots themselves will be removed. The first left y-axis scaling the function comes across will be used for all the plots. Similarly the first bottom x-axis scaling the function comes across will be assumed appropriate for all the plots. The first legend that is found is assumed right for all the plots and will be placed at the top in a horizontal direction. Of course none of these sub-elements among the plots need to exist and you are back to simply a grid of plots.

As an example of defining the 'layout' list argument, 'layout' has three named elements: "plots", "rows", and "cols". "plots" is a list of ggplot2 plot objects; "rows" defines the row number for each plot and "cols" defines the column number for each plot. If we have two plots with both plots on a single row then "rows" = c(1,1) and "cols" = c(1,2).

Value

A ggplot2 class object

Examples

library(ggplot2)
library(ggplotify)
library(rlang)
library(grid)
library(gtable)
library(ggplotify)
library(purrr)
library(RplotterPkg)

years <- levels(RplotterPkg::gapminder_data$year)
build_plot <- function(id, dt, years){
  plot_dt <- dt[year == years[[id]], ]
  aplot <- RplotterPkg::create_scatter_plot(
    df = plot_dt,
    aes_x = "lifeExp",
    aes_y = "pop",
    aes_fill = "continent",
    x_limits = c(20, 80),
    x_major_breaks = seq(from = 20, to = 80, by = 10),
    y_limits = c(0, 400),
    y_major_breaks = seq(from = 0, to = 400, by = 50),
    subtitle = years[[id]],
    title = "Life Expectancy Across Continents",
    x_title = "Life Expectancy",
    y_title = "Population(1e+6)",
    pts_size = 5,
    legend_key_width = 0.8,
    legend_key_height = 0.6,
    show_minor_grids = FALSE,
    silent_NA_warning = TRUE
  )
  return(aplot)
}
plot_lst <- purrr::map(1:4,
  build_plot,
  dt = RplotterPkg::gapminder_data,
  years = years
)
layout <- list(
  plots = plot_lst,
  rows = c(1, 1, 1, 1),
  cols = c(1, 2, 3, 4)
)
RplotterPkg::multi_panel_grid(
  layout = layout,
  title = "Life expectancy vs population(millions) across continents",
  plot_titles = c("1952","1972","1992","2002"),
  cell_height = 12
)

Description

From the socviz::organdata dataset two variables are selected: Organ Donation rate per million population and country.

Usage

organdata

Format

An object of class data.table (inherits from data.frame) with 204 rows and 2 columns.

Source

socviz::organdata

Description

The palmerpenguins:penguins dataset is grouped by the variable "species" and a data frame is produced with statistics for average, minimum and maximum body mass.

Usage

penguins_stats

Format

An object of class data.table (inherits from data.frame) with 3 rows and 4 columns.

Source

palmerpenguins:penguins

Description

A made-up data frame with generic column names and mix of numeric and character data.

Usage

people

Format

An object of class data.table (inherits from data.frame) with 4 rows and 6 columns.

Source

R coding with data.table::data.table()

Description

Function locates values corresponding to percentiles in a vector of numeric data. Function displays a table with lower/upper locations, values, average and spread between lower/upper from median to one hundred and twenty-eighth percentiles. The function returns a list with a viewable gt table along with the data.table containing the table's values.

The "label" column of the table correspond to the following percentiles:

1. "M" = Half (i.e. the median)

2. "F" = Fourth (i.e. quartiles 25/75)

3. "E" = Eighth

4. "D" = Sixteenth

5. "C" = Thirty-secondth

6. "B" = Sixty-fourth

7. "A" = One hundred and twenty-eighth

Usage

percentile_table(vals = NULL, na_rm = TRUE, title = NULL, table_width = NULL)

Arguments

Value

A named list with a gt object and a data.table with percentile values contained in the display table.

Examples

library(gt)
library(data.table)
library(RplotterPkg)

set.seed(12345)
random_vals <- stats::rnorm(n = 1000, mean = 20, sd = 4)
percentile_random_lst <- RplotterPkg::percentile_table(
  vals = random_vals
)
percentile_random_lst$table_gt

Description

From the socviz::gss_sm dataset General Social Survey, 2016, a sub-data frame was created from the "happy" and "religion" variables.

Usage

religion

Format

An object of class data.table (inherits from data.frame) with 2844 rows and 2 columns.

Source

socviz::gss_sm

Description

A dataframe with 20 observations that shows effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

Usage

sleep

Format

An object of class data.table (inherits from data.frame) with 20 rows and 4 columns.

Source

datasets::sleep

Description

From the raw data file spinrates.csv the comma delimited data was converted to a data frame using data.table::fread(). The data are baseball percent "swing and miss" with their respective average ball velocity and levels of spinrate.

Usage

spinrates

Format

An object of class data.table (inherits from data.frame) with 168 rows and 4 columns.

Source

Fangraphs

Description

Function produces a scatter plot of median versus spread across a variable's factor levels.

The spread is defined as the difference between the 75th and 25th quartiles. Function returns a named list with a data.table of each level's median, quartile values, and a ggplot2 scatter plot with medians along the x axis and spreads along the y axis.

Usage

spread_level_plot(
  df,
  meas_var,
  factor_var,
  plot_line_fit = TRUE,
  plot_log10 = TRUE,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = "Median",
  y_title = "Spread",
  x_limits = NULL,
  x_major_breaks = waiver(),
  y_limits = NULL,
  y_major_breaks = waiver(),
  label_pts = TRUE
)

Arguments

Value

A list object with a data.table and ggplot2 scatter plot.

Examples

library(here)
library(data.table)
library(ggplot2)
library(RplotterPkg)

spread_level_lst <- RplotterPkg::spread_level_plot(
  df = RplotterPkg::homeruns_2000,
  meas_var = "HOMERUNS",
  factor_var = "YEARS",
  x_title = "Log Median",
  y_title = "Log Spread"
)
spread_level_lst$scatter_plot

Description

Consolidation of data into one dataframe for the "South" region.

Usage

states

Format

An object of class data.table (inherits from data.frame) with 16 rows and 3 columns.

Source

datasets::state_name, state_region, state_x77

Description

Function is a wrapper around aplpack::stem.leaf that provides one or more stem and leaf display(s).

Function accepts a named list of numeric vectors from which stem and leaf displays are provided.

Usage

stem_leaf_display(
  x = NULL,
  title = NULL,
  unit = 1,
  m = 1,
  min_val = NULL,
  max_val = NULL,
  outliers = TRUE,
  depths = FALSE,
  col_width = 4,
  row_height = 0.5,
  font_sz = 11,
  heading_color = "black"
)

Arguments

Value

A ggplot2 class object

Examples

library(grid)
library(gtable)
library(ggplotify)
library(ggplot2)
library(aplpack)
library(RplotterPkg)

# stem and leaf for marathon times of women across ages
marathon_times_lst <- list(
  "age_20" = RplotterPkg::boston_marathon[age == 20,]$time,
  "age_30" = RplotterPkg::boston_marathon[age == 30,]$time,
  "age_40" = RplotterPkg::boston_marathon[age == 40,]$time,
  "age_50" = RplotterPkg::boston_marathon[age == 50,]$time,
  "age_60" = RplotterPkg::boston_marathon[age == 60,]$time
)
# display stem and leaf of women times across ages
RplotterPkg::stem_leaf_display(
  x = marathon_times_lst,
  title = "Women times(min) in Boston marathon",
  heading_color = "#FF5500"
)

Description

Function creates a ggplot2 based symmetry plot from a vector of numeric values.

Function orders the numeric vector from low to high. It then divides the data into two groups: values above the median(u group) and values below the median(v group). The distances from the median for values in each group are computed. A scatter plot is created where distances in the v group are plotted along the x axis and distances from the v group are plotted along the y axis. If the upper and lower distance data forms a straight line then the original data is perfectly symmetrical.

A reference symmetry line is also drawn. If points fall close to the line then the data is nearly symmetric. If the data is left-skewed, then the points fall below the line. If the data is right skewed, the points fall above the line.

Function returns a ggplot2 plot object of x/y scatter point distances from the median. Options are provided for axis scaling and point labeling.

Usage

symmetry_plot(
  df,
  var_name,
  position = position_jitter(width = 0, height = 0),
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  center_titles = FALSE,
  x_title = "V",
  y_title = "U",
  hide_x_tics = FALSE,
  hide_y_tics = FALSE,
  rot_x_tic_angle = 0,
  rot_y_tic_label = FALSE,
  x_limits = NULL,
  x_major_breaks = waiver(),
  x_minor_breaks = waiver(),
  x_labels = waiver(),
  y_limits = NULL,
  y_major_breaks = waiver(),
  y_minor_breaks = waiver(),
  y_labels = waiver(),
  x_y_decimals = NULL,
  x_y_scientific = NULL,
  axis_text_size = 11,
  pts_fill = "black",
  pts_shape = 21,
  pts_stroke = 1,
  pts_color = "black",
  pts_size = 1,
  pts_alpha = 1,
  line_width = 0.6,
  line_color = "blue",
  line_alpha = 0.7,
  line_linetype = "solid",
  show_major_grids = TRUE,
  show_minor_grids = TRUE,
  panel_color = "white",
  panel_border_color = "black"
)

Arguments

Value

A ggplot class plot object

Examples

library(ggplot2)
library(RplotterPkg)

RplotterPkg::symmetry_plot(
  df = RplotterPkg::farms,
  var_name = "count",
  title = "Symmetry in farm counts across US states",
  rot_y_tic_label = TRUE,
  line_linetype = "dotted"
)

Description

World coffee production by country (in thousands of 60 kg bags) is joined with a simple feature of country geometries via their common parameter 'name_long'.

Usage

world_coffee

Format

An object of class sf (inherits from data.table, data.frame) with 34 rows and 3 columns.

Source

spData::coffee_data and spData::world