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Overview ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. # The easiest way to get ggplot2 is to install the whole tidyverse: install.packages("tidyverse") # Alternatively, install just ggplot2: install.packages("ggplot

These are complete themes which control all non-data display. Use theme() if you just need to tweak the display of an existing theme. theme_grey( base_size = 11, base_family = "", header_family = NULL, base_line_size = base_size/22, base_rect_size = base_size/22, ink = "black", paper = "white", accent = "#3366FF" ) theme_gray( base_size = 11, base_family = "", header_family = NULL, base_line_size

Perform a 2D kernel density estimation using MASS::kde2d() and display the results with contours. This can be useful for dealing with overplotting. This is a 2D version of geom_density(). geom_density_2d() draws contour lines, and geom_density_2d_filled() draws filled contour bands. geom_density_2d( mapping = NULL, data = NULL, stat = "density_2d", position = "identity", ..., contour_var = "densit

Aids the eye in seeing patterns in the presence of overplotting. geom_smooth() and stat_smooth() are effectively aliases: they both use the same arguments. Use stat_smooth() if you want to display the results with a non-standard geom. geom_smooth( mapping = NULL, data = NULL, stat = "smooth", position = "identity", ..., method = NULL, formula = NULL, se = TRUE, na.rm = FALSE, orientation = NA, sho

Arguments e1 An object of class ggplot() or a theme(). e2 A plot component, as described below. What can you add? You can add any of the following types of objects: An aes() object replaces the default aesthetics. A layer created by a geom_ or stat_ function adds a new layer. A scale overrides the existing scale. A theme() modifies the current theme. A coord overrides the current coordinate system

Legend type guide shows key (i.e., geoms) mapped onto values. Legend guides for various scales are integrated if possible. guide_legend( title = waiver(), theme = NULL, position = NULL, direction = NULL, override.aes = list(), nrow = NULL, ncol = NULL, reverse = FALSE, order = 0, ... ) Arguments title A character string or expression indicating a title of guide. If NULL, the title is not shown. By

The empirical cumulative distribution function (ECDF) provides an alternative visualisation of distribution. Compared to other visualisations that rely on density (like geom_histogram()), the ECDF doesn't require any tuning parameters and handles both continuous and categorical variables. The downside is that it requires more training to accurately interpret, and the underlying visual tasks are so

geom_rect() and geom_tile() do the same thing, but are parameterised differently: geom_tile() uses the center of the tile and its size (x, y, width, height), while geom_rect() can use those or the locations of the corners (xmin, xmax, ymin and ymax). geom_raster() is a high performance special case for when all the tiles are the same size, and no pattern fills are applied. geom_raster( mapping = N

scale_x_continuous() and scale_y_continuous() are the default scales for continuous x and y aesthetics. There are three variants that set the transform argument for commonly used transformations: scale_*_log10(), scale_*_sqrt() and scale_*_reverse(). scale_x_continuous( name = waiver(), breaks = waiver(), minor_breaks = waiver(), n.breaks = NULL, labels = waiver(), limits = NULL, expand = waiver()

Themes are a powerful way to customize the non-data components of your plots: i.e. titles, labels, fonts, background, gridlines, and legends. Themes can be used to give plots a consistent customized look. Modify a single plot's theme using theme(); see theme_update() if you want modify the active theme, to affect all subsequent plots. Use the themes available in complete themes if you would like t

Text geoms are useful for labeling plots. They can be used by themselves as scatterplots or in combination with other geoms, for example, for labeling points or for annotating the height of bars. geom_text() adds only text to the plot. geom_label() draws a rectangle behind the text, making it easier to read. geom_label( mapping = NULL, data = NULL, stat = "identity", position = "nudge", ..., parse

A layer combines data, aesthetic mapping, a geom (geometric object), a stat (statistical transformation), and a position adjustment. Typically, you will create layers using a geom_ function, overriding the default position and stat if needed. layer_geoms Layer geometry display geom_abline() geom_hline() geom_vline() Reference lines: horizontal, vertical, and diagonal geom_bar() geom_col() stat_cou