Automatically remove unused variables from the "default" data object embedded in a gg or ggplot object with drop_vars().

drop_vars(p, keep.vars = character(), guess.vars = TRUE)

mapped_vars(p, invert = FALSE)

data_vars(p)

data_attributes(p)

Arguments

p

ggplot Plot object with embedded data.

keep.vars

character Names of unused variables to be kept.

guess.vars

logical Flag indicating whether to find used variables automatically.

invert

logical If TRUE return indices for elements of data that are not mapped to any aesthetic or facet.

Value

character vector with names of mapped variables in the default data object.

character vector with names of all variables in the default data object.

list containing all attributes of the default data object.

Note

These functions are under development and not yet thoroughly tested! They are a demonstration of how one can manipulate the internals of ggplot objects in 'ggplot2' version 3.1.0. These functions may stop working after some future update to the 'ggplot2' package. Although I will maintain this package for use in some of my other packages, there is no guarantee that I will be able to achieve this transparently.

Obviously, rather than using function drop_vars() after creating the ggplot object it is usually more efficient to select the variables of interest and pass a data frame containing only these to the ggplot() constructor.

Warning!

The current implementation drops variables only from the default data object. Data objects within layers are not modified.

Examples

library(ggplot2) p <- ggplot(mpg, aes(factor(year), (cty + hwy) / 2)) + geom_boxplot() + facet_grid(. ~ class) mapped_vars(p) # those in use
#> [1] "year" "cty" "hwy" "class"
mapped_vars(p, invert = TRUE) # those not used
#> [1] "manufacturer" "model" "displ" "cyl" "trans" #> [6] "drv" "fl"
p.dp <- drop_vars(p) # we drop unused vars # number of columns in the data member ncol(p$data)
#> [1] 11
ncol(p.dp$data)
#> [1] 4
# which vars are in the data member data_vars(p)
#> [1] "manufacturer" "model" "displ" "year" "cyl" #> [6] "trans" "drv" "cty" "hwy" "fl" #> [11] "class"
data_vars(p.dp)
#> [1] "year" "cty" "hwy" "class"
# which variables in data are used in the plot mapped_vars(p)
#> [1] "year" "cty" "hwy" "class"
mapped_vars(p.dp)
#> [1] "year" "cty" "hwy" "class"
# the plots identical p
p.dp
# structure and size of p str(p, max.level = 0)
#> Object size: 30.2 kB #> List of 9
str(p.dp, max.level = 0) # smaller in size
#> Object size: 12.7 kB #> List of 9
# structure and size of p["data"] str(p, components = "data")
#> Object size: 24.1 kB #> List of 1 #> $ data:Classes 'tbl_df', 'tbl' and 'data.frame': 234 obs. of 11 variables:
str(p.dp, components = "data") # smaller in size
#> Object size: 6.6 kB #> List of 1 #> $ data:Classes 'tbl_df', 'tbl' and 'data.frame': 234 obs. of 4 variables: