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)
x | A vector or data frame. |
---|---|
... | Optional, unquoted names of variables that should be selected for
further processing. Required, if |
size | Numeric; group-size, i.e. the range for grouping. By default,
for each 5 categories of |
as.num | Logical, if |
right.interval | Logical; if |
n | Sets the maximum number of groups that are defined when auto-grouping is on
( |
append | Logical, if |
suffix | String value, will be appended to variable (column) names of
|
predicate | A predicate function to be applied to the columns. The
variables for which |
For group_var()
, a grouped variable, either as numeric or as factor (see paramter as.num
). If x
is 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 of x
, formatted as "from lower bound to upper bound", e.g. "10-19" "20-29" "30-39"
etc. See 'Examples'.
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'.
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.
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.
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 #> 2 9 10 18 21 21 11 8print(age.grpvar)#> [1] "20-29" "30-39" "40-49" "50-59" "60-69" "70-79" "80-89" "90-99"# 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()#> . #> 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#> . #> 1 2 3 4 5 6 7 8 9 10 #> 5 4 5 3 4 5 2 2 1 1