Automatically remove unused variables from the "default" data object embedded
in a gg or ggplot object with drop_vars(). Explore data
variables and their use with mapped_vars(), data_vars() and
data_attributes().
Usage
drop_vars(p, keep.vars = character(), guess.vars = TRUE)
mapped_vars(p, invert = FALSE)
data_vars(p)
data_attributes(p)Value
A "ggplot" object that is a copy of p but containing
only a subset of the variables in its default data.
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 creayed with 'ggplot2' versions 3.1.0 and later.
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"
# what are the attributes of data
data_attributes(p)
#> $class
#> [1] "tbl_df" "tbl" "data.frame"
#>
#> $row.names
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
#> [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
#> [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
#> [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
#> [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
#> [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
#>
#> $names
#> [1] "manufacturer" "model" "displ" "year" "cyl"
#> [6] "trans" "drv" "cty" "hwy" "fl"
#> [11] "class"
#>
data_attributes(p.dp)
#> $names
#> [1] "year" "cty" "hwy" "class"
#>
#> $row.names
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
#> [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
#> [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
#> [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
#> [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
#> [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
#>
#> $class
#> [1] "tbl_df" "tbl" "data.frame"
#>
# the plots identical
p
p.dp
# structure and size of p
str(p, max.level = 0)
#> Object size: 212.1 kB
#> List of 12
str(p.dp, max.level = 0) # smaller in size
#> Object size: 194.7 kB
#> List of 12
# structure and size of p["data"]
str(p, components = "data")
#> Object size: 24.1 kB
#> List of 1
#> $ data: tibble [234 × 11] (S3: tbl_df/tbl/data.frame)
str(p.dp, components = "data") # smaller in size
#> Object size: 6.6 kB
#> List of 1
#> $ data: tibble [234 × 4] (S3: tbl_df/tbl/data.frame)
# shape data
if (requireNamespace("sf", quietly = TRUE)) {
nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
p.sf <- ggplot(data = nc) +
geom_sf()
p.sf
mapped_vars(p.sf)
drop_vars(p.sf)
}
#> 'drop_vars()' does not yet support shape file 'sf' data.
