ggplot2 (version 3.3.3)

translate_qplot_ggplot: Translating between qplot and ggplot

Description

Within ggplot2, there are two basic methods to create plots, with qplot() and ggplot(). qplot() is designed primarily for interactive use: it makes a number of assumptions that speed most cases, but when designing multilayered plots with different data sources it can get in the way. This section describes what those defaults are, and how they map to the fuller ggplot() syntax.

Examples

# NOT RUN {
# By default, qplot() assumes that you want a scatterplot,
# i.e., you want to use geom_point()
# qplot(x, y, data = data)
# ggplot(data, aes(x, y)) + geom_point()

# Using Aesthetics

# If you map additional aesthetics, these will be added to the defaults. With
# qplot() there is no way to use different aesthetic mappings (or data) in
# different layers
# qplot(x, y, data = data, shape = shape, colour = colour)
# ggplot(data, aes(x, y, shape = shape, colour = colour)) + geom_point()
#
# Aesthetic parameters in qplot() always try to map the aesthetic to a
# variable. If the argument is not a variable but a value, effectively a new column
# is added to the original dataset with that value. To set an aesthetic to a
# value and override the default appearance, you surround the value with I() in
# qplot(), or pass it as a parameter to the layer.
# qplot(x, y, data = data, colour = I("red"))
# ggplot(data, aes(x, y)) + geom_point(colour = "red")

# Changing the geom parameter changes the geom added to the plot
# qplot(x, y, data = data, geom = "line")
# ggplot(data, aes(x, y)) + geom_line()

# Not all geoms require both x and y, e.g., geom_bar() and geom_histogram().
# For these two geoms, if the y aesthetic is not supplied, both qplot and
# ggplot commands default to "count" on the y-axis
# ggplot(data, aes(x)) + geom_bar()
# qplot(x, data = data, geom = "bar")

# If a vector of multiple geom names is supplied to the geom argument, each
# geom will be added in turn
# qplot(x, y, data = data, geom = c("point", "smooth"))
# ggplot(data, aes(x, y)) + geom_point() + geom_smooth()

# Unlike the rest of ggplot2, stats and geoms are independent
# qplot(x, y, data = data, stat = "bin")
# ggplot(data, aes(x, y)) + geom_point(stat = "bin")
#
# Any layer parameters will be passed on to all layers. Most layers will ignore
# parameters that they don't need
# qplot(x, y, data = data, geom = c("point", "smooth"), method = "lm")
# ggplot(data, aes(x, y)) + geom_point(method = "lm") + geom_smooth(method = "lm")

# Scales and axes

# You can control basic properties of the x and y scales with the xlim, ylim,
# xlab and ylab arguments
# qplot(x, y, data = data, xlim = c(1, 5), xlab = "my label")
# ggplot(data, aes(x, y)) + geom_point() +
# scale_x_continuous("my label", limits = c(1, 5))

# qplot(x, y, data = data, xlim = c(1, 5), ylim = c(10, 20))
# ggplot(data, aes(x, y)) + geom_point() +
# scale_x_continuous(limits = c(1, 5)) + scale_y_continuous(limits = c(10, 20))

# Like plot(), qplot() has a convenient way of log transforming the axes.
# qplot(x, y, data = data, log = "xy")
# ggplot(data, aes(x, y)) + geom_point() + scale_x_log10() + scale_y_log10()
# There are many other possible transformations, but not all are
# accessible from within qplot(), see ?scale_continuous for more

# Plot options

# qplot() recognises the same options as plot does, and converts them to their
# ggplot2 equivalents. See ?theme for more on ggplot options
# qplot(x, y, data = data, main="title", asp = 1)
# ggplot(data, aes(x, y)) + geom_point() + labs(title = "title") + theme(aspect.ratio = 1)
# }