Thin wrappers around ggplot2::cut_interval() and friends.
Useful for defining fixed x values for fitting functions.
Usage
cutter_interval(df, n, ...)
cutter_number(df, n, ...)
cutter_width(df, width, ...)
cutter_to_seq(x)Arguments
- df
a
tibble()- n
number of intervals to create or the approximate number of observations
- ...
colnames
- width
width of intervals to create
- x
a cutter on which to extract breaks
Details
Here, columns of interest may be passed directly.
If you want to cut directly on numeric vectors,
use base::cut() or ggplot2::cut_interval() and friends.
Functions
cutter_interval: makes groups with equal rangecutter_number: makes n groups with approximately equal numbers of observationscutter_width: makes groups of a certain widthcutter_to_seq: extract breaks from a cutter factor
Examples
cutter_interval(animals, 30, tpq, taq) %>% table()
#> .
#> [-175,-146] (-146,-117] (-117,-87.5] (-87.5,-58.3] (-58.3,-29.2]
#> 4 0 125 25 571
#> (-29.2,0] (0,29.2] (29.2,58.3] (58.3,87.5] (87.5,117]
#> 544 389 131 536 364
#> (117,146] (146,175] (175,204] (204,233] (233,262]
#> 79 460 383 482 215
#> (262,292] (292,321] (321,350] (350,379] (379,408]
#> 357 544 67 18 164
#> (408,438] (438,467] (467,496] (496,525] (525,554]
#> 36 0 0 30 0
#> (554,583] (583,612] (612,642] (642,671] (671,700]
#> 0 6 0 0 3
cutter_number(animals, 10, tpq, taq,) %>% table()
#> .
#> [-175,-50] (-50,0] (0,30] (30,70] (70,125] (125,200] (200,220]
#> 633 636 412 572 515 843 436
#> (220,265] (265,300] (300,700]
#> 491 671 324
cutter_width(animals, 30, tpq, taq) %>% table()
#> .
#> [-195,-165] (-165,-135] (-135,-105] (-105,-75] (-75,-45] (-45,-15]
#> 4 0 0 150 479 162
#> (-15,15] (15,45] (45,75] (75,105] (105,135] (135,165]
#> 759 136 632 365 81 411
#> (165,195] (195,225] (225,255] (255,285] (285,315] (315,345]
#> 51 859 179 397 544 0
#> (345,375] (375,405] (405,435] (435,465] (465,495] (495,525]
#> 85 164 36 0 0 30
#> (525,555] (555,585] (585,615] (615,645] (645,675] (675,705]
#> 0 0 6 0 0 3
# # note that in x_cut_number above, we have many observations yet
# the number of _different_ levels is not so diverse,
# so that n=30 would fail with "Insufficient data values"
# unlist(animals[, c("tpq", "taq")]) %>% table()
# cutter_to_seq is helpful to pass x_bin/x_pred to bin/fit_* respectively
# there is an example in ?bin that can be used to fit_* too
# cutter_to_seq go back to numeric from factor levels:
cutter_width(animals, 30, tpq, taq) %>% cutter_to_seq()
#> [1] -195 -165 -135 -105 -75 -45 -15 15 45 75 105 135 165 195 225
#> [16] 255 285 315 345 375 405 435 465 495 525 555 585 615 645 675
#> [31] 705