Recode numeric variables into equal ranged, grouped factors,
i.e. a variable is cut into a smaller number of groups, where each group
has the same value range. group_labels() creates the related value
labels. group_var_if() and group_labels_if() are scoped
variants of group_var() and group_labels(), where grouping
will be applied only to those variables that match the logical condition
of predicate.
group_var(
x,
...,
size = 5,
as.num = TRUE,
right.interval = FALSE,
n = 30,
append = TRUE,
suffix = "_gr"
)
group_var_if(
x,
predicate,
size = 5,
as.num = TRUE,
right.interval = FALSE,
n = 30,
append = TRUE,
suffix = "_gr"
)
group_labels(x, ..., size = 5, right.interval = FALSE, n = 30)
group_labels_if(x, predicate, size = 5, right.interval = FALSE, n = 30)Arguments
- x
A vector or data frame.
- ...
Optional, unquoted names of variables that should be selected for further processing. Required, if
xis a data frame (and no vector) and only selected variables fromxshould be processed. You may also use functions like:or tidyselect's select-helpers. See 'Examples' or package-vignette.- size
Numeric; group-size, i.e. the range for grouping. By default, for each 5 categories of
xa new group is defined, i.e.size = 5. Usesize = "auto"to automatically resize a variable into a maximum of 30 groups (which is the ggplot-default grouping when plotting histograms). Usento determine the amount of groups.- as.num
Logical, if
TRUE, return value will be numeric, not a factor.- right.interval
Logical; if
TRUE, grouping starts with the lower bound ofsize. See 'Details'.- n
Sets the maximum number of groups that are defined when auto-grouping is on (
size = "auto"). Default is 30. Ifsizeis not set to"auto", this argument will be ignored.- append
Logical, if
TRUE(the default) andxis a data frame,xincluding the new variables as additional columns is returned; ifFALSE, only the new variables are returned.- suffix
Indicates which suffix will be added to each dummy variable. Use
"numeric"to number dummy variables, e.g. x_1, x_2, x_3 etc. Use"label"to add value label, e.g. x_low, x_mid, x_high. May be abbreviated.- predicate
A predicate function to be applied to the columns. The variables for which
predicatereturnsTRUEare selected.
Value
For
group_var(), a grouped variable, either as numeric or as factor (see paramteras.num). Ifxis a data frame, only the grouped variables will be returned.For
group_labels(), a string vector or a list of string vectors containing labels based on the grouped categories ofx, formatted as "from lower bound to upper bound", e.g."10-19" "20-29" "30-39"etc. See 'Examples'.
Details
If size is set to a specific value, the variable is recoded
into several groups, where each group has a maximum range of size.
Hence, the amount of groups differ depending on the range of x.
If size = "auto", the variable is recoded into a maximum of
n groups. Hence, independent from the range of
x, always the same amount of groups are created, so the range
within each group differs (depending on x's range).
right.interval determins which boundary values to include when
grouping is done. If TRUE, grouping starts with the lower
bound of size. For example, having a variable ranging from
50 to 80, groups cover the ranges from 50-54, 55-59, 60-64 etc.
If FALSE (default), grouping starts with the upper bound
of size. In this case, groups cover the ranges from
46-50, 51-55, 56-60, 61-65 etc. Note: This will cover
a range from 46-50 as first group, even if values from 46 to 49
are not present. See 'Examples'.
If you want to split a variable into a certain amount of equal
sized groups (instead of having groups where values have all the same
range), use the split_var function!
group_var() also works on grouped data frames (see group_by).
In this case, grouping is applied to the subsets of variables
in x. See 'Examples'.
Note
Variable label attributes (see, for instance,
set_label) are preserved. Usually you should use
the same values for size and right.interval in
group_labels() as used in the group_var function if you want
matching labels for the related recoded variable.
See also
split_var to split variables into equal sized groups,
group_str for grouping string vectors or
rec_pattern and rec for another convenient
way of recoding variables into smaller groups.
Examples
age <- abs(round(rnorm(100, 65, 20)))
age.grp <- group_var(age, size = 10)
hist(age)
hist(age.grp)
age.grpvar <- group_labels(age, size = 10)
table(age.grp)
#> age.grp
#> 1 2 3 4 5 6 7 8 9
#> 1 4 10 21 22 19 10 9 4
print(age.grpvar)
#> [1] "10-19" "30-39" "40-49" "50-59" "60-69" "70-79" "80-89"
#> [8] "90-99" "100-109"
# histogram with EUROFAMCARE sample dataset
# variable not grouped
library(sjlabelled)
data(efc)
hist(efc$e17age, main = get_label(efc$e17age))
# bar plot with EUROFAMCARE sample dataset
# grouped variable
ageGrp <- group_var(efc$e17age)
ageGrpLab <- group_labels(efc$e17age)
barplot(table(ageGrp), main = get_label(efc$e17age), names.arg = ageGrpLab)
# within a pipe-chain
library(dplyr)
efc %>%
select(e17age, c12hour, c160age) %>%
group_var(size = 20)
#> e17age c12hour c160age e17age_gr c12hour_gr c160age_gr
#> 1 83 16 56 2 1 3
#> 2 88 148 54 2 8 3
#> 3 82 70 80 2 4 5
#> 4 67 168 69 1 9 4
#> 5 84 168 47 2 9 3
#> 6 85 16 56 2 1 3
#> 7 74 161 61 1 9 4
#> 8 87 110 67 2 6 4
#> 9 79 28 59 1 2 3
#> 10 83 40 49 2 3 3
#> 11 68 100 66 1 6 4
#> 12 97 25 47 2 2 3
#> 13 80 25 58 2 2 3
#> 14 75 24 75 1 2 4
#> 15 82 56 49 2 3 3
#> 16 89 20 56 2 2 3
#> 17 80 25 75 2 2 4
#> 18 72 126 70 1 7 4
#> 19 94 168 52 2 9 3
#> 20 79 118 48 1 6 3
#> 21 89 150 58 2 8 3
#> 22 67 50 65 1 3 4
#> 23 94 18 49 2 1 3
#> 24 83 168 60 2 9 4
#> 25 85 15 55 2 1 3
#> 26 80 168 62 2 9 4
#> 27 88 12 68 2 1 4
#> 28 76 7 76 1 1 4
#> 29 84 35 58 2 2 3
#> 30 95 168 65 2 9 4
#> 31 88 150 63 2 8 4
#> 32 87 168 79 2 9 4
#> 33 89 168 65 2 9 4
#> 34 80 119 74 2 6 4
#> 35 75 168 76 1 9 4
#> 36 82 168 73 2 9 4
#> 37 69 168 67 1 9 4
#> 38 91 28 62 2 2 4
#> 39 86 168 80 2 9 5
#> 40 86 30 49 2 2 3
#> 41 84 14 46 2 1 3
#> 42 69 168 68 1 9 4
#> 43 67 168 62 1 9 4
#> 44 67 50 65 1 3 4
#> 45 66 168 63 1 9 4
#> 46 79 24 81 1 2 5
#> 47 72 168 72 1 9 4
#> 48 65 42 64 1 3 4
#> 49 75 154 73 1 8 4
#> 50 87 60 73 2 4 4
#> 51 68 168 64 1 9 4
#> 52 75 168 67 1 9 4
#> 53 65 24 60 1 2 4
#> 54 67 168 64 1 9 4
#> 55 81 150 85 2 8 5
#> 56 83 168 55 2 9 3
#> 57 NA 168 72 NA 9 4
#> 58 82 168 52 2 9 3
#> 59 79 168 63 1 9 4
#> 60 72 168 69 1 9 4
#> 61 87 128 61 2 7 4
#> 62 85 168 79 2 9 4
#> 63 88 11 64 2 1 4
#> 64 74 50 44 1 3 3
#> 65 87 80 59 2 5 3
#> 66 88 15 52 2 1 3
#> 67 76 7 46 1 1 3
#> 68 81 21 59 2 2 3
#> 69 80 168 83 2 9 5
#> 70 68 24 76 1 2 4
#> 71 82 6 31 2 1 2
#> 72 NA 30 60 NA 2 4
#> 73 91 168 57 2 9 3
#> 74 66 42 63 1 3 4
#> 75 89 30 59 2 2 3
#> 76 79 85 72 1 5 4
#> 77 69 35 47 1 2 3
#> 78 92 70 56 2 4 3
#> 79 75 9 47 1 1 3
#> 80 76 168 69 1 9 4
#> 81 77 77 74 1 4 4
#> 82 NA 24 67 NA 2 4
#> 83 76 91 72 1 5 4
#> 84 76 6 48 1 1 3
#> 85 79 22 55 1 2 3
#> 86 77 168 57 1 9 3
#> 87 67 168 62 1 9 4
#> 88 66 168 70 1 9 4
#> 89 78 168 34 1 9 2
#> 90 84 50 55 2 3 3
#> 91 79 40 69 1 3 4
#> 92 77 9 69 1 1 4
#> 93 66 25 60 1 2 4
#> 94 94 14 66 2 1 4
#> 95 65 49 60 1 3 4
#> 96 90 5 54 2 1 3
#> 97 66 7 64 1 1 4
#> 98 90 21 75 2 2 4
#> 99 85 24 80 2 2 5
#> 100 77 7 49 1 1 3
#> 101 76 168 68 1 9 4
#> 102 66 168 64 1 9 4
#> 103 76 4 72 1 1 4
#> 104 80 168 54 2 9 3
#> 105 89 168 62 2 9 4
#> 106 69 15 65 1 1 4
#> 107 75 168 75 1 9 4
#> 108 75 168 77 1 9 4
#> 109 80 20 50 2 2 3
#> 110 80 59 54 2 3 3
#> 111 74 24 72 1 2 4
#> 112 82 7 46 2 1 3
#> 113 91 100 61 2 6 4
#> 114 80 168 83 2 9 5
#> 115 74 168 44 1 9 3
#> 116 90 28 67 2 2 4
#> 117 83 20 61 2 2 4
#> 118 82 50 78 2 3 4
#> 119 73 89 77 1 5 4
#> 120 84 168 75 2 9 4
#> 121 90 12 61 2 1 4
#> 122 65 24 65 1 2 4
#> 123 84 91 74 2 5 4
#> 124 72 168 63 1 9 4
#> 125 95 168 66 2 9 4
#> 126 67 168 62 1 9 4
#> 127 81 11 49 2 1 3
#> 128 92 35 55 2 2 3
#> 129 83 10 45 2 1 3
#> 130 74 125 76 1 7 4
#> 131 91 140 65 2 8 4
#> 132 78 28 56 1 2 3
#> 133 81 84 75 2 5 4
#> 134 76 15 45 1 1 3
#> 135 93 15 59 2 1 3
#> 136 97 14 66 2 1 4
#> 137 93 24 61 2 2 4
#> 138 81 4 50 2 1 3
#> 139 75 140 75 1 8 4
#> 140 84 168 76 2 9 4
#> 141 78 6 35 1 1 2
#> 142 84 10 42 2 1 3
#> 143 87 20 65 2 2 4
#> 144 83 15 81 2 1 5
#> 145 94 65 43 2 4 3
#> 146 85 35 76 2 2 4
#> 147 70 105 67 1 6 4
#> 148 69 50 63 1 3 4
#> 149 76 168 56 1 9 3
#> 150 83 77 49 2 4 3
#> 151 91 168 55 2 9 3
#> 152 90 42 60 2 3 4
#> 153 87 4 59 2 1 3
#> 154 91 168 62 2 9 4
#> 155 72 168 62 1 9 4
#> 156 73 42 79 1 3 4
#> 157 79 6 54 1 1 3
#> 158 84 168 57 2 9 3
#> 159 82 70 74 2 4 4
#> 160 65 15 67 1 1 4
#> 161 78 30 51 1 2 3
#> 162 94 9 55 2 1 3
#> 163 91 16 56 2 1 3
#> 164 NA 35 58 NA 2 3
#> 165 80 28 43 2 2 3
#> 166 70 168 71 1 9 4
#> 167 84 9 50 2 1 3
#> 168 82 168 57 2 9 3
#> 169 76 15 52 1 1 3
#> 170 77 6 53 1 1 3
#> 171 66 14 60 1 1 4
#> 172 90 35 65 2 2 4
#> 173 76 7 72 1 1 4
#> 174 69 12 64 1 1 4
#> 175 88 50 57 2 3 3
#> 176 71 6 47 1 1 3
#> 177 93 22 62 2 2 4
#> 178 97 35 67 2 2 4
#> 179 84 6 63 2 1 4
#> 180 79 168 77 1 9 4
#> 181 91 28 60 2 2 4
#> 182 84 10 48 2 1 3
#> 183 75 91 73 1 5 4
#> 184 81 8 25 2 1 2
#> 185 77 168 58 1 9 3
#> 186 75 24 50 1 2 3
#> 187 74 168 45 1 9 3
#> 188 92 168 53 2 9 3
#> 189 79 14 59 1 1 3
#> 190 74 28 80 1 2 5
#> 191 76 16 57 1 1 3
#> 192 NA 168 66 NA 9 4
#> 193 84 6 63 2 1 4
#> 194 65 168 56 1 9 3
#> 195 85 28 61 2 2 4
#> 196 75 160 70 1 9 4
#> 197 90 39 62 2 2 4
#> 198 83 15 47 2 1 3
#> 199 87 10 67 2 1 4
#> 200 86 10 64 2 1 4
#> 201 93 17 65 2 1 4
#> 202 92 168 55 2 9 3
#> 203 92 70 66 2 4 4
#> 204 84 50 52 2 3 3
#> 205 83 12 64 2 1 4
#> 206 82 10 49 2 1 3
#> 207 87 168 42 2 9 3
#> 208 89 168 58 2 9 3
#> 209 91 168 87 2 9 5
#> 210 84 28 65 2 2 4
#> 211 93 12 65 2 1 4
#> 212 85 8 63 2 1 4
#> 213 79 21 43 1 2 3
#> 214 84 24 54 2 2 3
#> 215 65 15 42 1 1 3
#> 216 89 8 61 2 1 4
#> 217 84 16 54 2 1 3
#> 218 81 56 47 2 3 3
#> 219 77 42 46 1 3 3
#> 220 65 4 54 1 1 3
#> 221 93 25 58 2 2 3
#> 222 96 35 64 2 2 4
#> 223 90 30 51 2 2 3
#> 224 86 6 57 2 1 3
#> 225 88 110 63 2 6 4
#> 226 79 30 49 1 2 3
#> 227 91 24 66 2 2 4
#> 228 84 14 56 2 1 3
#> 229 71 4 62 1 1 4
#> 230 67 20 29 1 2 2
#> 231 87 10 55 2 1 3
#> 232 84 7 55 2 1 3
#> 233 87 161 81 2 9 5
#> 234 84 28 62 2 2 4
#> 235 86 14 49 2 1 3
#> 236 83 4 43 2 1 3
#> 237 69 50 60 1 3 4
#> 238 75 10 39 1 1 2
#> 239 79 18 41 1 1 3
#> 240 76 15 25 1 1 2
#> 241 76 6 27 1 1 2
#> 242 82 17 26 2 1 2
#> 243 88 14 52 2 1 3
#> 244 90 5 56 2 1 3
#> 245 87 10 29 2 1 2
#> 246 76 30 45 1 2 3
#> 247 76 10 64 1 1 4
#> 248 86 10 47 2 1 3
#> 249 83 84 62 2 5 4
#> 250 89 10 55 2 1 3
#> 251 82 6 70 2 1 4
#> 252 79 15 51 1 1 3
#> 253 67 40 67 1 3 4
#> 254 73 21 23 1 2 2
#> 255 89 11 52 2 1 3
#> 256 95 8 62 2 1 4
#> 257 65 5 33 1 1 2
#> 258 89 12 83 2 1 5
#> 259 83 10 75 2 1 4
#> 260 86 20 60 2 2 4
#> 261 82 12 63 2 1 4
#> 262 93 24 65 2 2 4
#> 263 90 27 60 2 2 4
#> 264 79 14 54 1 1 3
#> 265 97 18 63 2 1 4
#> 266 78 168 58 1 9 3
#> 267 74 14 42 1 1 3
#> 268 86 10 58 2 1 3
#> 269 88 6 55 2 1 3
#> 270 77 21 44 1 2 3
#> 271 76 16 46 1 1 3
#> 272 74 15 38 1 1 2
#> 273 76 20 44 1 2 3
#> 274 71 10 43 1 1 3
#> 275 74 22 34 1 2 2
#> 276 82 35 43 2 2 3
#> 277 82 30 66 2 2 4
#> 278 74 16 46 1 1 3
#> 279 73 24 68 1 2 4
#> 280 67 168 60 1 9 4
#> 281 70 168 35 1 9 2
#> 282 70 40 38 1 3 2
#> 283 70 40 41 1 3 3
#> 284 67 50 40 1 3 3
#> 285 80 20 39 2 2 2
#> 286 69 20 44 1 2 3
#> 287 89 6 57 2 1 3
#> 288 83 5 48 2 1 3
#> 289 83 48 56 2 3 3
#> 290 67 168 40 1 9 3
#> 291 84 6 61 2 1 4
#> 292 90 8 63 2 1 4
#> 293 80 43 59 2 3 3
#> 294 81 90 69 2 5 4
#> 295 87 100 60 2 6 4
#> 296 73 8 49 1 1 3
#> 297 71 15 41 1 1 3
#> 298 80 5 72 2 1 4
#> 299 69 8 64 1 1 4
#> 300 73 10 46 1 1 3
#> 301 88 30 64 2 2 4
#> 302 73 168 79 1 9 4
#> 303 78 20 39 1 2 2
#> 304 86 30 42 2 2 3
#> 305 75 30 39 1 2 2
#> 306 89 12 42 2 1 3
#> 307 83 30 54 2 2 3
#> 308 78 30 76 1 2 4
#> 309 69 14 69 1 1 4
#> 310 70 15 68 1 1 4
#> 311 86 50 66 2 3 4
#> 312 67 20 35 1 2 2
#> 313 74 25 30 1 2 2
#> 314 87 40 54 2 3 3
#> 315 66 9 56 1 1 3
#> 316 68 18 60 1 1 4
#> 317 72 25 26 1 2 2
#> 318 80 100 68 2 6 4
#> 319 89 6 51 2 1 3
#> 320 84 25 54 2 2 3
#> 321 93 15 52 2 1 3
#> 322 86 20 53 2 2 3
#> 323 84 10 54 2 1 3
#> 324 86 28 52 2 2 3
#> 325 83 10 48 2 1 3
#> 326 86 30 46 2 2 3
#> 327 88 6 68 2 1 4
#> 328 75 40 49 1 3 3
#> 329 76 7 19 1 1 1
#> 330 NA 8 49 NA 1 3
#> 331 81 15 33 2 1 2
#> 332 78 4 42 1 1 3
#> 333 83 4 34 2 1 2
#> 334 94 168 66 2 9 4
#> 335 84 7 27 2 1 2
#> 336 71 22 37 1 2 2
#> 337 86 30 54 2 2 3
#> 338 89 15 65 2 1 4
#> 339 93 30 72 2 2 4
#> 340 75 20 40 1 2 3
#> 341 73 25 26 1 2 2
#> 342 70 20 29 1 2 2
#> 343 75 20 30 1 2 2
#> 344 NA 25 30 NA 2 2
#> 345 75 14 33 1 1 2
#> 346 103 30 42 3 2 3
#> 347 70 20 51 1 2 3
#> 348 84 15 54 2 1 3
#> 349 78 6 38 1 1 2
#> 350 85 5 40 2 1 3
#> 351 81 50 50 2 3 3
#> 352 90 8 66 2 1 4
#> 353 76 20 44 1 2 3
#> 354 75 14 42 1 1 3
#> 355 74 18 43 1 1 3
#> 356 75 16 55 1 1 3
#> 357 89 20 67 2 2 4
#> 358 73 15 23 1 1 2
#> 359 79 25 70 1 2 4
#> 360 83 18 62 2 1 4
#> 361 91 22 66 2 2 4
#> 362 81 18 59 2 1 3
#> 363 77 48 76 1 3 4
#> 364 65 60 44 1 4 3
#> 365 73 10 42 1 1 3
#> 366 73 7 53 1 1 3
#> 367 69 5 44 1 1 3
#> 368 79 35 62 1 2 4
#> 369 82 28 53 2 2 3
#> 370 76 5 58 1 1 3
#> 371 84 25 38 2 2 2
#> 372 86 40 48 2 3 3
#> 373 88 49 52 2 3 3
#> 374 76 4 53 1 1 3
#> 375 94 70 67 2 4 4
#> 376 78 6 54 1 1 3
#> 377 67 7 31 1 1 2
#> 378 81 40 51 2 3 3
#> 379 69 50 48 1 3 3
#> 380 77 30 52 1 2 3
#> 381 73 70 45 1 4 3
#> 382 70 14 47 1 1 3
#> 383 82 70 42 2 4 3
#> 384 93 7 65 2 1 4
#> 385 81 6 49 2 1 3
#> 386 70 14 46 1 1 3
#> 387 78 10 65 1 1 4
#> 388 84 25 47 2 2 3
#> 389 83 5 54 2 1 3
#> 390 82 11 49 2 1 3
#> 391 85 120 47 2 7 3
#> 392 83 18 59 2 1 3
#> 393 68 6 69 1 1 4
#> 394 70 20 34 1 2 2
#> 395 76 15 30 1 1 2
#> 396 73 35 70 1 2 4
#> 397 76 15 32 1 1 2
#> 398 80 15 41 2 1 3
#> 399 67 120 63 1 7 4
#> 400 77 28 83 1 2 5
#> 401 79 10 44 1 1 3
#> 402 72 120 70 1 7 4
#> 403 90 100 56 2 6 3
#> 404 90 18 64 2 1 4
#> 405 66 8 43 1 1 3
#> 406 80 40 53 2 3 3
#> 407 81 140 55 2 8 3
#> 408 85 6 46 2 1 3
#> 409 83 11 64 2 1 4
#> 410 85 15 57 2 1 3
#> 411 84 80 60 2 5 4
#> 412 65 5 42 1 1 3
#> 413 71 6 63 1 1 4
#> 414 68 40 68 1 3 4
#> 415 65 5 67 1 1 4
#> 416 73 7 69 1 1 4
#> 417 68 4 52 1 1 3
#> 418 67 24 60 1 2 4
#> 419 73 28 48 1 2 3
#> 420 84 25 60 2 2 4
#> 421 98 140 67 2 8 4
#> 422 78 14 36 1 1 2
#> 423 82 40 75 2 3 4
#> 424 89 5 29 2 1 2
#> 425 84 20 47 2 2 3
#> 426 70 168 62 1 9 4
#> 427 78 40 59 1 3 3
#> 428 82 80 51 2 5 3
#> 429 94 120 34 2 7 2
#> 430 89 160 59 2 9 3
#> 431 73 7 31 1 1 2
#> 432 80 7 37 2 1 2
#> 433 84 4 61 2 1 4
#> 434 78 6 53 1 1 3
#> 435 83 14 89 2 1 5
#> 436 77 7 53 1 1 3
#> 437 82 28 46 2 2 3
#> 438 81 25 59 2 2 3
#> 439 72 28 74 1 2 4
#> 440 88 7 50 2 1 3
#> 441 80 21 50 2 2 3
#> 442 79 30 53 1 2 3
#> 443 66 25 55 1 2 3
#> 444 86 65 51 2 4 3
#> 445 85 18 43 2 1 3
#> 446 79 40 43 1 3 3
#> 447 84 20 59 2 2 3
#> 448 NA 80 72 NA 5 4
#> 449 87 7 56 2 1 3
#> 450 71 50 66 1 3 4
#> 451 71 30 47 1 2 3
#> 452 76 10 45 1 1 3
#> 453 80 8 NA 2 1 NA
#> 454 81 6 23 2 1 2
#> 455 90 6 25 2 1 2
#> 456 82 42 57 2 3 3
#> 457 77 10 53 1 1 3
#> 458 95 30 64 2 2 4
#> 459 86 15 56 2 1 3
#> 460 82 10 41 2 1 3
#> 461 90 10 58 2 1 3
#> 462 79 130 76 1 7 4
#> 463 95 160 69 2 9 4
#> 464 87 168 56 2 9 3
#> 465 78 24 75 1 2 4
#> 466 83 24 53 2 2 3
#> 467 75 30 53 1 2 3
#> 468 80 20 58 2 2 3
#> 469 72 10 48 1 1 3
#> 470 76 7 35 1 1 2
#> 471 84 120 59 2 7 3
#> 472 66 6 34 1 1 2
#> 473 77 6 39 1 1 2
#> 474 84 10 56 2 1 3
#> 475 68 20 60 1 2 4
#> 476 78 5 43 1 1 3
#> 477 65 60 56 1 4 3
#> 478 72 20 38 1 2 2
#> 479 67 6 54 1 1 3
#> 480 78 10 25 1 1 2
#> 481 67 84 66 1 5 4
#> 482 82 8 58 2 1 3
#> 483 75 168 75 1 9 4
#> 484 82 40 30 2 3 2
#> 485 79 40 42 1 3 3
#> 486 76 20 38 1 2 2
#> 487 89 56 63 2 3 4
#> 488 92 6 57 2 1 3
#> 489 66 7 41 1 1 3
#> 490 89 12 64 2 1 4
#> 491 79 10 32 1 1 2
#> 492 73 14 38 1 1 2
#> 493 89 28 65 2 2 4
#> 494 79 12 31 1 1 2
#> 495 NA 28 43 NA 2 3
#> 496 81 11 29 2 1 2
#> 497 84 16 62 2 1 4
#> 498 77 12 46 1 1 3
#> 499 73 22 43 1 2 3
#> 500 79 17 44 1 1 3
#> 501 73 16 47 1 1 3
#> 502 80 50 49 2 3 3
#> 503 73 6 46 1 1 3
#> 504 88 5 46 2 1 3
#> 505 82 45 60 2 3 4
#> 506 93 16 65 2 1 4
#> 507 70 15 32 1 1 2
#> 508 69 20 72 1 2 4
#> 509 70 20 35 1 2 2
#> 510 86 8 61 2 1 4
#> 511 75 8 66 1 1 4
#> 512 81 15 53 2 1 3
#> 513 65 10 35 1 1 2
#> 514 86 15 57 2 1 3
#> 515 91 27 62 2 2 4
#> 516 72 14 70 1 1 4
#> 517 87 28 53 2 2 3
#> 518 77 168 78 1 9 4
#> 519 65 168 65 1 9 4
#> 520 81 21 58 2 2 3
#> 521 72 12 62 1 1 4
#> 522 93 62 48 2 4 3
#> 523 65 20 51 1 2 3
#> 524 77 7 35 1 1 2
#> 525 77 10 59 1 1 3
#> 526 76 20 42 1 2 3
#> 527 85 8 52 2 1 3
#> 528 79 9 49 1 1 3
#> 529 82 12 46 2 1 3
#> 530 83 10 56 2 1 3
#> 531 86 35 51 2 2 3
#> 532 71 5 45 1 1 3
#> 533 75 18 50 1 1 3
#> 534 83 15 48 2 1 3
#> 535 78 6 41 1 1 3
#> 536 74 8 48 1 1 3
#> 537 88 12 40 2 1 3
#> 538 86 25 29 2 2 2
#> 539 82 10 50 2 1 3
#> 540 89 40 48 2 3 3
#> 541 91 20 63 2 2 4
#> 542 90 6 54 2 1 3
#> 543 77 168 65 1 9 4
#> 544 65 4 52 1 1 3
#> 545 69 168 41 1 9 3
#> 546 68 24 44 1 2 3
#> 547 70 25 53 1 2 3
#> 548 82 40 62 2 3 4
#> 549 77 18 42 1 1 3
#> 550 82 20 31 2 2 2
#> 551 69 40 40 1 3 3
#> 552 81 6 44 2 1 3
#> 553 86 5 50 2 1 3
#> 554 85 30 49 2 2 3
#> 555 67 14 24 1 1 2
#> 556 70 8 43 1 1 3
#> 557 78 28 61 1 2 4
#> 558 83 45 35 2 3 2
#> 559 81 40 40 2 3 3
#> 560 74 30 53 1 2 3
#> 561 78 28 58 1 2 3
#> 562 83 50 48 2 3 3
#> 563 69 84 65 1 5 4
#> 564 97 12 61 2 1 4
#> 565 70 40 31 1 3 2
#> 566 92 10 58 2 1 3
#> 567 91 15 53 2 1 3
#> 568 65 40 63 1 3 4
#> 569 NA 24 80 NA 2 5
#> 570 94 168 61 2 9 4
#> 571 69 160 46 1 9 3
#> 572 78 162 75 1 9 4
#> 573 87 100 49 2 6 3
#> 574 82 110 48 2 6 3
#> 575 84 140 62 2 8 4
#> 576 79 25 49 1 2 3
#> 577 85 15 49 2 1 3
#> 578 65 35 60 1 2 4
#> 579 76 30 27 1 2 2
#> 580 65 40 63 1 3 4
#> 581 78 10 57 1 1 3
#> 582 91 25 56 2 2 3
#> 583 88 12 63 2 1 4
#> 584 83 35 78 2 2 4
#> 585 73 10 36 1 1 2
#> 586 79 30 59 1 2 3
#> 587 86 10 62 2 1 4
#> 588 82 100 42 2 6 3
#> 589 75 12 54 1 1 3
#> 590 66 56 64 1 3 4
#> 591 93 40 65 2 3 4
#> 592 86 30 52 2 2 3
#> 593 86 60 78 2 4 4
#> 594 69 100 47 1 6 3
#> 595 66 20 67 1 2 4
#> 596 83 35 28 2 2 2
#> 597 65 168 43 1 9 3
#> 598 71 22 40 1 2 3
#> 599 83 60 53 2 4 3
#> 600 92 24 28 2 2 2
#> 601 83 20 78 2 2 4
#> 602 95 70 58 2 4 3
#> 603 76 25 43 1 2 3
#> 604 81 6 54 2 1 3
#> 605 79 18 28 1 1 2
#> 606 87 10 57 2 1 3
#> 607 74 15 47 1 1 3
#> 608 70 20 35 1 2 2
#> 609 80 20 30 2 2 2
#> 610 75 10 50 1 1 3
#> 611 68 22 36 1 2 2
#> 612 69 15 38 1 1 2
#> 613 68 48 58 1 3 3
#> 614 70 80 33 1 5 2
#> 615 89 77 59 2 4 3
#> 616 NA 6 30 NA 1 2
#> 617 83 6 24 2 1 2
#> 618 70 8 30 1 1 2
#> 619 82 20 39 2 2 2
#> 620 84 20 40 2 2 3
#> 621 82 8 51 2 1 3
#> 622 80 6 49 2 1 3
#> 623 86 8 60 2 1 4
#> 624 69 15 35 1 1 2
#> 625 87 10 53 2 1 3
#> 626 71 10 35 1 1 2
#> 627 66 15 39 1 1 2
#> 628 85 8 54 2 1 3
#> 629 85 6 65 2 1 4
#> 630 95 56 63 2 3 4
#> 631 76 6 53 1 1 3
#> 632 69 8 64 1 1 4
#> 633 68 28 43 1 2 3
#> 634 82 20 58 2 2 3
#> 635 92 70 67 2 4 4
#> 636 66 8 30 1 1 2
#> 637 74 7 24 1 1 2
#> 638 75 10 56 1 1 3
#> 639 90 10 44 2 1 3
#> 640 72 10 69 1 1 4
#> 641 92 168 31 2 9 2
#> 642 70 40 63 1 3 4
#> 643 79 40 44 1 3 3
#> 644 79 30 42 1 2 3
#> 645 69 30 42 1 2 3
#> 646 70 28 41 1 2 3
#> 647 82 14 77 2 1 4
#> 648 79 40 47 1 3 3
#> 649 72 10 70 1 1 4
#> 650 76 4 22 1 1 2
#> 651 65 14 37 1 1 2
#> 652 92 42 64 2 3 4
#> 653 70 42 50 1 3 3
#> 654 89 65 69 2 4 4
#> 655 72 5 44 1 1 3
#> 656 75 10 47 1 1 3
#> 657 73 45 41 1 3 3
#> 658 82 55 55 2 3 3
#> 659 80 50 60 2 3 4
#> 660 82 35 56 2 2 3
#> 661 82 40 66 2 3 4
#> 662 71 15 46 1 1 3
#> 663 77 8 55 1 1 3
#> 664 73 25 40 1 2 3
#> 665 80 5 51 2 1 3
#> 666 71 5 37 1 1 2
#> 667 65 4 58 1 1 3
#> 668 83 14 57 2 1 3
#> 669 67 20 25 1 2 2
#> 670 68 10 43 1 1 3
#> 671 71 50 69 1 3 4
#> 672 77 16 35 1 1 2
#> 673 72 22 48 1 2 3
#> 674 68 100 66 1 6 4
#> 675 77 100 44 1 6 3
#> 676 70 30 74 1 2 4
#> 677 84 120 77 2 7 4
#> 678 89 100 61 2 6 4
#> 679 68 10 41 1 1 3
#> 680 89 120 64 2 7 4
#> 681 68 6 20 1 1 2
#> 682 72 6 27 1 1 2
#> 683 78 5 53 1 1 3
#> 684 84 10 51 2 1 3
#> 685 92 9 54 2 1 3
#> 686 68 9 69 1 1 4
#> 687 66 12 43 1 1 3
#> 688 71 20 69 1 2 4
#> 689 65 10 70 1 1 4
#> 690 81 160 60 2 9 4
#> 691 82 100 48 2 6 3
#> 692 75 20 33 1 2 2
#> 693 80 56 57 2 3 3
#> 694 70 7 37 1 1 2
#> 695 91 35 43 2 2 3
#> 696 94 120 73 2 7 4
#> 697 66 60 61 1 4 4
#> 698 87 10 54 2 1 3
#> 699 86 14 57 2 1 3
#> 700 83 21 46 2 2 3
#> 701 73 10 54 1 1 3
#> 702 70 7 48 1 1 3
#> 703 73 18 70 1 1 4
#> 704 83 4 49 2 1 3
#> 705 76 8 47 1 1 3
#> 706 90 10 64 2 1 4
#> 707 72 6 48 1 1 3
#> 708 78 10 71 1 1 4
#> 709 71 30 66 1 2 4
#> 710 72 10 56 1 1 3
#> 711 86 9 46 2 1 3
#> 712 70 36 62 1 2 4
#> 713 67 15 49 1 1 3
#> 714 65 35 55 1 2 3
#> 715 74 21 47 1 2 3
#> 716 76 168 47 1 9 3
#> 717 79 168 43 1 9 3
#> 718 66 10 61 1 1 4
#> 719 73 6 45 1 1 3
#> 720 90 20 55 2 2 3
#> 721 68 6 33 1 1 2
#> 722 67 8 46 1 1 3
#> 723 86 15 51 2 1 3
#> 724 79 84 49 1 5 3
#> 725 75 45 48 1 3 3
#> 726 85 40 63 2 3 4
#> 727 77 90 68 1 5 4
#> 728 79 35 52 1 2 3
#> 729 93 70 68 2 4 4
#> 730 80 5 18 2 1 1
#> 731 77 20 52 1 2 3
#> 732 81 35 58 2 2 3
#> 733 80 8 56 2 1 3
#> 734 86 30 47 2 2 3
#> 735 67 4 60 1 1 4
#> 736 91 84 55 2 5 3
#> 737 72 10 68 1 1 4
#> 738 82 25 59 2 2 3
#> 739 94 60 65 2 4 4
#> 740 89 6 64 2 1 4
#> 741 83 26 52 2 2 3
#> 742 75 12 54 1 1 3
#> 743 89 25 50 2 2 3
#> 744 80 15 49 2 1 3
#> 745 84 50 73 2 3 4
#> 746 77 18 44 1 1 3
#> 747 75 12 35 1 1 2
#> 748 79 22 44 1 2 3
#> 749 85 21 50 2 2 3
#> 750 87 70 58 2 4 3
#> 751 82 168 71 2 9 4
#> 752 90 5 60 2 1 4
#> 753 83 30 49 2 2 3
#> 754 85 70 62 2 4 4
#> 755 80 168 59 2 9 3
#> 756 66 9 57 1 1 3
#> 757 79 6 50 1 1 3
#> 758 91 4 64 2 1 4
#> 759 89 20 48 2 2 3
#> 760 69 8 56 1 1 3
#> 761 73 14 52 1 1 3
#> 762 83 9 58 2 1 3
#> 763 79 6 69 1 1 4
#> 764 85 14 44 2 1 3
#> 765 81 7 60 2 1 4
#> 766 85 6 19 2 1 1
#> 767 66 20 63 1 2 4
#> 768 74 20 35 1 2 2
#> 769 73 48 69 1 3 4
#> 770 76 10 44 1 1 3
#> 771 83 5 58 2 1 3
#> 772 78 6 42 1 1 3
#> 773 88 8 65 2 1 4
#> 774 89 8 53 2 1 3
#> 775 84 8 53 2 1 3
#> 776 73 8 41 1 1 3
#> 777 92 4 67 2 1 4
#> 778 65 4 33 1 1 2
#> 779 69 8 39 1 1 2
#> 780 77 25 73 1 2 4
#> 781 79 20 56 1 2 3
#> 782 73 6 50 1 1 3
#> 783 68 21 45 1 2 3
#> 784 69 28 51 1 2 3
#> 785 80 70 49 2 4 3
#> 786 81 8 34 2 1 2
#> 787 67 21 47 1 2 3
#> 788 74 28 50 1 2 3
#> 789 88 14 49 2 1 3
#> 790 70 4 37 1 1 2
#> 791 73 5 46 1 1 3
#> 792 67 6 52 1 1 3
#> 793 74 7 70 1 1 4
#> 794 87 140 62 2 8 4
#> 795 91 4 23 2 1 2
#> 796 76 8 22 1 1 2
#> 797 87 7 21 2 1 2
#> 798 72 8 48 1 1 3
#> 799 77 7 52 1 1 3
#> 800 89 30 41 2 2 3
#> 801 84 40 68 2 3 4
#> 802 78 50 32 1 3 2
#> 803 81 35 50 2 2 3
#> 804 74 42 68 1 3 4
#> 805 80 40 55 2 3 3
#> 806 77 168 70 1 9 4
#> 807 79 150 69 1 8 4
#> 808 89 168 63 2 9 4
#> 809 90 168 77 2 9 4
#> 810 87 168 77 2 9 4
#> 811 81 14 29 2 1 2
#> 812 82 6 33 2 1 2
#> 813 71 8 43 1 1 3
#> 814 75 22 54 1 2 3
#> 815 69 10 38 1 1 2
#> 816 77 6 45 1 1 3
#> 817 81 30 70 2 2 4
#> 818 87 8 53 2 1 3
#> 819 73 10 53 1 1 3
#> 820 65 10 35 1 1 2
#> 821 68 12 42 1 1 3
#> 822 72 20 40 1 2 3
#> 823 70 15 36 1 1 2
#> 824 68 20 32 1 2 2
#> 825 70 15 39 1 1 2
#> 826 68 20 40 1 2 3
#> 827 69 25 35 1 2 2
#> 828 78 7 49 1 1 3
#> 829 74 4 47 1 1 3
#> 830 68 20 44 1 2 3
#> 831 86 10 38 2 1 2
#> 832 79 50 70 1 3 4
#> 833 75 20 55 1 2 3
#> 834 85 25 52 2 2 3
#> 835 71 5 48 1 1 3
#> 836 74 7 47 1 1 3
#> 837 74 5 62 1 1 4
#> 838 86 12 57 2 1 3
#> 839 66 35 20 1 2 2
#> 840 70 35 29 1 2 2
#> 841 86 30 33 2 2 2
#> 842 85 40 60 2 3 4
#> 843 82 15 58 2 1 3
#> 844 79 10 44 1 1 3
#> 845 69 8 43 1 1 3
#> 846 67 5 41 1 1 3
#> 847 94 14 56 2 1 3
#> 848 75 14 48 1 1 3
#> 849 79 6 47 1 1 3
#> 850 78 14 44 1 1 3
#> 851 66 7 39 1 1 2
#> 852 65 20 31 1 2 2
#> 853 88 6 47 2 1 3
#> 854 78 10 35 1 1 2
#> 855 83 30 46 2 2 3
#> 856 87 14 31 2 1 2
#> 857 99 10 44 2 1 3
#> 858 66 10 34 1 1 2
#> 859 89 28 58 2 2 3
#> 860 71 8 37 1 1 2
#> 861 82 6 43 2 1 3
#> 862 69 20 45 1 2 3
#> 863 81 5 54 2 1 3
#> 864 84 7 57 2 1 3
#> 865 90 20 57 2 2 3
#> 866 80 18 28 2 1 2
#> 867 79 12 62 1 1 4
#> 868 67 17 64 1 1 4
#> 869 71 14 48 1 1 3
#> 870 75 5 38 1 1 2
#> 871 68 6 38 1 1 2
#> 872 75 4 33 1 1 2
#> 873 73 4 49 1 1 3
#> 874 81 6 42 2 1 3
#> 875 76 10 55 1 1 3
#> 876 65 6 45 1 1 3
#> 877 83 14 68 2 1 4
#> 878 79 30 39 1 2 2
#> 879 73 35 79 1 2 4
#> 880 78 10 48 1 1 3
#> 881 83 100 49 2 6 3
#> 882 74 8 34 1 1 2
#> 883 81 10 53 2 1 3
#> 884 87 20 65 2 2 4
#> 885 67 40 39 1 3 2
#> 886 79 40 74 1 3 4
#> 887 81 40 76 2 3 4
#> 888 70 6 41 1 1 3
#> 889 65 30 61 1 2 4
#> 890 93 28 61 2 2 4
#> 891 74 20 40 1 2 3
#> 892 77 25 45 1 2 3
#> 893 75 30 36 1 2 2
#> 894 90 50 60 2 3 4
#> 895 91 20 50 2 2 3
#> 896 86 15 55 2 1 3
#> 897 65 110 38 1 6 2
#> 898 82 28 82 2 2 5
#> 899 80 85 36 2 5 2
#> 900 74 160 71 1 9 4
#> 901 65 10 44 1 1 3
#> 902 67 8 45 1 1 3
#> 903 NA NA NA NA NA NA
#> 904 NA NA NA NA NA NA
#> 905 NA NA NA NA NA NA
#> 906 NA NA NA NA NA NA
#> 907 NA NA NA NA NA NA
#> 908 NA NA NA NA NA NA
# create vector with values from 50 to 80
dummy <- round(runif(200, 50, 80))
# labels with grouping starting at lower bound
group_labels(dummy)
#> [1] "50-54" "55-59" "60-64" "65-69" "70-74" "75-79" "80-84"
# labels with grouping startint at upper bound
group_labels(dummy, right.interval = TRUE)
#> [1] "46-50" "51-55" "56-60" "61-65" "66-70" "71-75" "76-80"
# works also with gouped data frames
mtcars %>%
group_var(disp, size = 4, append = FALSE) %>%
table()
#> disp_gr
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
#> 1 1 2 1 1 3 1 2 2 2 1 1 3 1 1 1 2 2 1 1 1 1
mtcars %>%
group_by(cyl) %>%
group_var(disp, size = 4, append = FALSE) %>%
table()
#> disp_gr
#> 1 2 3 4 5 6 7 8 9 10
#> 5 4 5 3 4 5 2 2 1 1