Pipe-able version of the qplot() — pipe

The qplot() function is traditionally used when your data is not stored in a data.frame/tibble and are "loose" within your environment and can be convenient if you are familiar with the base plot(), but haven't quite learned the grammar of ggplot2. It is a wrapper that creates a ggplot() style plot.

However, creating a ggplot() object can be complex and inconvenient, especially if you want a simple plot (e.g. a basic histogram). It also requires you to either break out of a pipeline, or ensure your ggplot() functions are at the end of a pipe (or introduce curly braces {} to your pipeline).

Therefore, the pipe_qplot() function will run a qplot() function for it's side effects and return your original input unchanged. It can also save your plot if needed.

pipe_qplot(
  data,
  x,
  y,
  ...,
  facets = NULL,
  margins = FALSE,
  geom = "auto",
  xlim = c(NA, NA),
  ylim = c(NA, NA),
  log = "",
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  asp = NA,
  stat = stat,
  position = position,
  theme = NULL,
  save.options = NULL,
  print.plot = T
)

Arguments

data	the data being passed through the pipeline
x, y, ...	aesthetic arguments to be passed to the `qplot()` function
facets, margins, geom, xlim, ylim, log, main, xlab, ylab, asp, stat, position	see the `qplot()` documentation for more information regarding these arguments.
theme	a character string that links to a function of the form `theme_*()`, e.g. the agrument `theme="bw"` will apply `theme_bw()`
save.options	list of values to be passed to `ggsave()` (if NULL, plot won't be saved)
print.plot	should the plot be displayed? Should only be used if `save.options` is not NULL

Examples


palmerpenguins::penguins %>%
  dplyr::group_by(species) %>%
  pipe_qplot(bill_length_mm,
    fill = species,
    theme = "light",
    geom = "density",
    alpha = 0.5,
    binwidth = 0.1
  ) %>%
  dplyr::summarise(mean = mean(bill_length_mm))
#> Warning: Ignoring unknown parameters: binwidth
#> Warning: Removed 2 rows containing non-finite values (stat_density).
#> `summarise()` ungrouping output (override with `.groups` argument)
#> # A tibble: 3 x 2
#>   species    mean
#>   <fct>     <dbl>
#> 1 Adelie     NA  
#> 2 Chinstrap  48.8
#> 3 Gentoo     NA