tests/testthat/_snaps/new-data.md
In poissonconsulting/newdata: Generate New Data Frames for Prediction
newdata
Code
chickwts
Output
weight feed
1 179 horsebean
2 160 horsebean
3 136 horsebean
4 227 horsebean
5 217 horsebean
6 168 horsebean
7 108 horsebean
8 124 horsebean
9 143 horsebean
10 140 horsebean
11 309 linseed
12 229 linseed
13 181 linseed
14 141 linseed
15 260 linseed
16 203 linseed
17 148 linseed
18 169 linseed
19 213 linseed
20 257 linseed
21 244 linseed
22 271 linseed
23 243 soybean
24 230 soybean
25 248 soybean
26 327 soybean
27 329 soybean
28 250 soybean
29 193 soybean
30 271 soybean
31 316 soybean
32 267 soybean
33 199 soybean
34 171 soybean
35 158 soybean
36 248 soybean
37 423 sunflower
38 340 sunflower
39 392 sunflower
40 339 sunflower
41 341 sunflower
42 226 sunflower
43 320 sunflower
44 295 sunflower
45 334 sunflower
46 322 sunflower
47 297 sunflower
48 318 sunflower
49 325 meatmeal
50 257 meatmeal
51 303 meatmeal
52 315 meatmeal
53 380 meatmeal
54 153 meatmeal
55 263 meatmeal
56 242 meatmeal
57 206 meatmeal
58 344 meatmeal
59 258 meatmeal
60 368 casein
61 390 casein
62 379 casein
63 260 casein
64 404 casein
65 318 casein
66 352 casein
67 359 casein
68 216 casein
69 222 casein
70 283 casein
71 332 casein
Code
new_data(chickwts)
Output
# A tibble: 1 x 2
weight feed
<dbl> <fct>
1 261. casein
Code
new_data(datasets::chickwts, "feed")
Output
# A tibble: 6 x 2
weight feed
<dbl> <fct>
1 261. casein
2 261. horsebean
3 261. linseed
4 261. meatmeal
5 261. soybean
6 261. sunflower
Code
new_data(datasets::chickwts, "weight")
Output
# A tibble: 30 x 2
weight feed
<dbl> <fct>
1 108 casein
2 119. casein
3 130. casein
4 141. casein
5 151. casein
6 162. casein
7 173. casein
8 184. casein
9 195. casein
10 206. casein
# i 20 more rows
Code
new_data(datasets::chickwts, c("weight", "feed"))
Output
# A tibble: 180 x 2
weight feed
<dbl> <fct>
1 108 casein
2 119. casein
3 130. casein
4 141. casein
5 151. casein
6 162. casein
7 173. casein
8 184. casein
9 195. casein
10 206. casein
# i 170 more rows
new_data generates data frame with correct number of rows
Code
data
Output
dlogical dinteger dnumeric dfactor ddate dhms
1 FALSE 1 1.1 1 2000-01-02 10:00:01
2 TRUE 2 2.1 2 2000-01-03 10:00:02
3 TRUE 3 3.1 3 2000-01-04 10:00:03
4 TRUE 4 4.1 4 2000-01-05 10:00:04
5 TRUE 5 5.1 5 2000-01-06 10:00:05
6 TRUE 6 6.1 6 2000-01-07 10:00:06
7 TRUE 7 7.1 7 2000-01-08 10:00:07
8 TRUE 8 8.1 8 2000-01-09 10:00:08
9 TRUE 9 9.1 9 2000-01-10 10:00:09
10 TRUE 10 10.1 10 2000-01-11 10:00:10
Code
new_data(data)
Output
# A tibble: 1 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 5 5.6 1 2000-01-06 10:00:05
Code
new_data(data, "dlogical")
Output
# A tibble: 2 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 5 5.6 1 2000-01-06 10:00:05
2 TRUE 5 5.6 1 2000-01-06 10:00:05
Code
new_data(data, "dnumeric")
Output
# A tibble: 30 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 5 1.1 1 2000-01-06 10:00:05
2 FALSE 5 1.41 1 2000-01-06 10:00:05
3 FALSE 5 1.72 1 2000-01-06 10:00:05
4 FALSE 5 2.03 1 2000-01-06 10:00:05
5 FALSE 5 2.34 1 2000-01-06 10:00:05
6 FALSE 5 2.65 1 2000-01-06 10:00:05
7 FALSE 5 2.96 1 2000-01-06 10:00:05
8 FALSE 5 3.27 1 2000-01-06 10:00:05
9 FALSE 5 3.58 1 2000-01-06 10:00:05
10 FALSE 5 3.89 1 2000-01-06 10:00:05
# i 20 more rows
Code
new_data(data, c("dnumeric", "dinteger"))
Output
# A tibble: 300 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 1 1.1 1 2000-01-06 10:00:05
2 FALSE 2 1.1 1 2000-01-06 10:00:05
3 FALSE 3 1.1 1 2000-01-06 10:00:05
4 FALSE 4 1.1 1 2000-01-06 10:00:05
5 FALSE 5 1.1 1 2000-01-06 10:00:05
6 FALSE 6 1.1 1 2000-01-06 10:00:05
7 FALSE 7 1.1 1 2000-01-06 10:00:05
8 FALSE 8 1.1 1 2000-01-06 10:00:05
9 FALSE 9 1.1 1 2000-01-06 10:00:05
10 FALSE 10 1.1 1 2000-01-06 10:00:05
# i 290 more rows
Code
new_data(data, c("dfactor", "dinteger"), length_out = 5)
Output
# A tibble: 50 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 1 5.6 1 2000-01-06 10:00:05
2 FALSE 3 5.6 1 2000-01-06 10:00:05
3 FALSE 5 5.6 1 2000-01-06 10:00:05
4 FALSE 7 5.6 1 2000-01-06 10:00:05
5 FALSE 10 5.6 1 2000-01-06 10:00:05
6 FALSE 1 5.6 2 2000-01-06 10:00:05
7 FALSE 3 5.6 2 2000-01-06 10:00:05
8 FALSE 5 5.6 2 2000-01-06 10:00:05
9 FALSE 7 5.6 2 2000-01-06 10:00:05
10 FALSE 10 5.6 2 2000-01-06 10:00:05
# i 40 more rows
Code
new_data(data, c("dhms"), length_out = 5)
Output
# A tibble: 5 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 5 5.6 1 2000-01-06 10:00:01
2 FALSE 5 5.6 1 2000-01-06 10:00:03
3 FALSE 5 5.6 1 2000-01-06 10:00:05
4 FALSE 5 5.6 1 2000-01-06 10:00:07
5 FALSE 5 5.6 1 2000-01-06 10:00:10
new_data ref works
Code
new_data(Orange)
Output
# A tibble: 1 x 3
Tree age circumference
<ord> <dbl> <dbl>
1 5 922. 116.
Code
new_data(Orange, ref = list(age = 1))
Output
# A tibble: 1 x 3
Tree age circumference
<ord> <dbl> <dbl>
1 5 1 116.
Code
new_data(Orange, ref = list(age = c(1, 2)))
Output
# A tibble: 2 x 3
Tree age circumference
<ord> <dbl> <dbl>
1 5 1 116.
2 5 2 116.
Code
new_data(ToothGrowth, ref = list(dose = 4))
Output
# A tibble: 1 x 3
len supp dose
<dbl> <fct> <dbl>
1 18.8 OJ 4
Code
new_data(ToothGrowth, ref = list(dose = c(3, 4)))
Output
# A tibble: 2 x 3
len supp dose
<dbl> <fct> <dbl>
1 18.8 OJ 3
2 18.8 OJ 4
Code
new_data(ToothGrowth, ref = list(dose = c(3, 4), len = c(10.1, 12, 13)))
Output
# A tibble: 6 x 3
len supp dose
<dbl> <fct> <dbl>
1 10.1 OJ 3
2 10.1 OJ 4
3 12 OJ 3
4 12 OJ 4
5 13 OJ 3
6 13 OJ 4
Code
new_data(ToothGrowth, ref = list(supp = factor("VC")))
Output
# A tibble: 1 x 3
len supp dose
<dbl> <fct> <dbl>
1 18.8 VC 1.17
Code
new_data(ToothGrowth, ref = list(supp = factor("TP")))
Output
# A tibble: 1 x 3
len supp dose
<dbl> <fct> <dbl>
1 18.8 TP 1.17
Code
new_data(ToothGrowth, ref = list(supp = factor(c("VC", "OJ"))))
Output
# A tibble: 2 x 3
len supp dose
<dbl> <fct> <dbl>
1 18.8 VC 1.17
2 18.8 OJ 1.17
new_data ref overridden by seq
Code
new_data(Orange, seq = "age", ref = list(age = 118))
Output
# A tibble: 30 x 3
Tree age circumference
<ord> <dbl> <dbl>
1 5 118 116.
2 5 168. 116.
3 5 219. 116.
4 5 269. 116.
5 5 320. 116.
6 5 370. 116.
7 5 421. 116.
8 5 471. 116.
9 5 522. 116.
10 5 572. 116.
# i 20 more rows
new_data factor with 100 levels
Code
new_seq(data$fct)
Output
[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
100 Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ... 100
Code
new_data(data, "fct")
Output
# A tibble: 100 x 1
fct
<fct>
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
# i 90 more rows
new_data factor with 100 levels obs_only works
Code
new_data(data, "fct", obs_only = TRUE)
Output
# A tibble: 30 x 1
fct
<fct>
1 1
2 10
3 11
4 12
5 13
6 14
7 15
8 16
9 17
10 18
# i 20 more rows
new_data factor with 100 levels obs_only works if name
Code
new_data(data, "fct", obs_only = list("fct"))
Output
# A tibble: 30 x 1
fct
<fct>
1 1
2 10
3 11
4 12
5 13
6 14
7 15
8 16
9 17
10 18
# i 20 more rows
poissonconsulting/newdata documentation built on Jan. 18, 2024, 1:30 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
newdata
Code
chickwts
Output
weight feed
1 179 horsebean
2 160 horsebean
3 136 horsebean
4 227 horsebean
5 217 horsebean
6 168 horsebean
7 108 horsebean
8 124 horsebean
9 143 horsebean
10 140 horsebean
11 309 linseed
12 229 linseed
13 181 linseed
14 141 linseed
15 260 linseed
16 203 linseed
17 148 linseed
18 169 linseed
19 213 linseed
20 257 linseed
21 244 linseed
22 271 linseed
23 243 soybean
24 230 soybean
25 248 soybean
26 327 soybean
27 329 soybean
28 250 soybean
29 193 soybean
30 271 soybean
31 316 soybean
32 267 soybean
33 199 soybean
34 171 soybean
35 158 soybean
36 248 soybean
37 423 sunflower
38 340 sunflower
39 392 sunflower
40 339 sunflower
41 341 sunflower
42 226 sunflower
43 320 sunflower
44 295 sunflower
45 334 sunflower
46 322 sunflower
47 297 sunflower
48 318 sunflower
49 325 meatmeal
50 257 meatmeal
51 303 meatmeal
52 315 meatmeal
53 380 meatmeal
54 153 meatmeal
55 263 meatmeal
56 242 meatmeal
57 206 meatmeal
58 344 meatmeal
59 258 meatmeal
60 368 casein
61 390 casein
62 379 casein
63 260 casein
64 404 casein
65 318 casein
66 352 casein
67 359 casein
68 216 casein
69 222 casein
70 283 casein
71 332 casein
Code
new_data(chickwts)
Output
# A tibble: 1 x 2
weight feed
<dbl> <fct>
1 261. casein
Code
new_data(datasets::chickwts, "feed")
Output
# A tibble: 6 x 2
weight feed
<dbl> <fct>
1 261. casein
2 261. horsebean
3 261. linseed
4 261. meatmeal
5 261. soybean
6 261. sunflower
Code
new_data(datasets::chickwts, "weight")
Output
# A tibble: 30 x 2
weight feed
<dbl> <fct>
1 108 casein
2 119. casein
3 130. casein
4 141. casein
5 151. casein
6 162. casein
7 173. casein
8 184. casein
9 195. casein
10 206. casein
# i 20 more rows
Code
new_data(datasets::chickwts, c("weight", "feed"))
Output
# A tibble: 180 x 2
weight feed
<dbl> <fct>
1 108 casein
2 119. casein
3 130. casein
4 141. casein
5 151. casein
6 162. casein
7 173. casein
8 184. casein
9 195. casein
10 206. casein
# i 170 more rows
new_data generates data frame with correct number of rows
Code
data
Output
dlogical dinteger dnumeric dfactor ddate dhms
1 FALSE 1 1.1 1 2000-01-02 10:00:01
2 TRUE 2 2.1 2 2000-01-03 10:00:02
3 TRUE 3 3.1 3 2000-01-04 10:00:03
4 TRUE 4 4.1 4 2000-01-05 10:00:04
5 TRUE 5 5.1 5 2000-01-06 10:00:05
6 TRUE 6 6.1 6 2000-01-07 10:00:06
7 TRUE 7 7.1 7 2000-01-08 10:00:07
8 TRUE 8 8.1 8 2000-01-09 10:00:08
9 TRUE 9 9.1 9 2000-01-10 10:00:09
10 TRUE 10 10.1 10 2000-01-11 10:00:10
Code
new_data(data)
Output
# A tibble: 1 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 5 5.6 1 2000-01-06 10:00:05
Code
new_data(data, "dlogical")
Output
# A tibble: 2 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 5 5.6 1 2000-01-06 10:00:05
2 TRUE 5 5.6 1 2000-01-06 10:00:05
Code
new_data(data, "dnumeric")
Output
# A tibble: 30 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 5 1.1 1 2000-01-06 10:00:05
2 FALSE 5 1.41 1 2000-01-06 10:00:05
3 FALSE 5 1.72 1 2000-01-06 10:00:05
4 FALSE 5 2.03 1 2000-01-06 10:00:05
5 FALSE 5 2.34 1 2000-01-06 10:00:05
6 FALSE 5 2.65 1 2000-01-06 10:00:05
7 FALSE 5 2.96 1 2000-01-06 10:00:05
8 FALSE 5 3.27 1 2000-01-06 10:00:05
9 FALSE 5 3.58 1 2000-01-06 10:00:05
10 FALSE 5 3.89 1 2000-01-06 10:00:05
# i 20 more rows
Code
new_data(data, c("dnumeric", "dinteger"))
Output
# A tibble: 300 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 1 1.1 1 2000-01-06 10:00:05
2 FALSE 2 1.1 1 2000-01-06 10:00:05
3 FALSE 3 1.1 1 2000-01-06 10:00:05
4 FALSE 4 1.1 1 2000-01-06 10:00:05
5 FALSE 5 1.1 1 2000-01-06 10:00:05
6 FALSE 6 1.1 1 2000-01-06 10:00:05
7 FALSE 7 1.1 1 2000-01-06 10:00:05
8 FALSE 8 1.1 1 2000-01-06 10:00:05
9 FALSE 9 1.1 1 2000-01-06 10:00:05
10 FALSE 10 1.1 1 2000-01-06 10:00:05
# i 290 more rows
Code
new_data(data, c("dfactor", "dinteger"), length_out = 5)
Output
# A tibble: 50 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 1 5.6 1 2000-01-06 10:00:05
2 FALSE 3 5.6 1 2000-01-06 10:00:05
3 FALSE 5 5.6 1 2000-01-06 10:00:05
4 FALSE 7 5.6 1 2000-01-06 10:00:05
5 FALSE 10 5.6 1 2000-01-06 10:00:05
6 FALSE 1 5.6 2 2000-01-06 10:00:05
7 FALSE 3 5.6 2 2000-01-06 10:00:05
8 FALSE 5 5.6 2 2000-01-06 10:00:05
9 FALSE 7 5.6 2 2000-01-06 10:00:05
10 FALSE 10 5.6 2 2000-01-06 10:00:05
# i 40 more rows
Code
new_data(data, c("dhms"), length_out = 5)
Output
# A tibble: 5 x 6
dlogical dinteger dnumeric dfactor ddate dhms
<lgl> <int> <dbl> <fct> <date> <time>
1 FALSE 5 5.6 1 2000-01-06 10:00:01
2 FALSE 5 5.6 1 2000-01-06 10:00:03
3 FALSE 5 5.6 1 2000-01-06 10:00:05
4 FALSE 5 5.6 1 2000-01-06 10:00:07
5 FALSE 5 5.6 1 2000-01-06 10:00:10
new_data ref works
Code
new_data(Orange)
Output
# A tibble: 1 x 3
Tree age circumference
<ord> <dbl> <dbl>
1 5 922. 116.
Code
new_data(Orange, ref = list(age = 1))
Output
# A tibble: 1 x 3
Tree age circumference
<ord> <dbl> <dbl>
1 5 1 116.
Code
new_data(Orange, ref = list(age = c(1, 2)))
Output
# A tibble: 2 x 3
Tree age circumference
<ord> <dbl> <dbl>
1 5 1 116.
2 5 2 116.
Code
new_data(ToothGrowth, ref = list(dose = 4))
Output
# A tibble: 1 x 3
len supp dose
<dbl> <fct> <dbl>
1 18.8 OJ 4
Code
new_data(ToothGrowth, ref = list(dose = c(3, 4)))
Output
# A tibble: 2 x 3
len supp dose
<dbl> <fct> <dbl>
1 18.8 OJ 3
2 18.8 OJ 4
Code
new_data(ToothGrowth, ref = list(dose = c(3, 4), len = c(10.1, 12, 13)))
Output
# A tibble: 6 x 3
len supp dose
<dbl> <fct> <dbl>
1 10.1 OJ 3
2 10.1 OJ 4
3 12 OJ 3
4 12 OJ 4
5 13 OJ 3
6 13 OJ 4
Code
new_data(ToothGrowth, ref = list(supp = factor("VC")))
Output
# A tibble: 1 x 3
len supp dose
<dbl> <fct> <dbl>
1 18.8 VC 1.17
Code
new_data(ToothGrowth, ref = list(supp = factor("TP")))
Output
# A tibble: 1 x 3
len supp dose
<dbl> <fct> <dbl>
1 18.8 TP 1.17
Code
new_data(ToothGrowth, ref = list(supp = factor(c("VC", "OJ"))))
Output
# A tibble: 2 x 3
len supp dose
<dbl> <fct> <dbl>
1 18.8 VC 1.17
2 18.8 OJ 1.17
new_data ref overridden by seq
Code
new_data(Orange, seq = "age", ref = list(age = 118))
Output
# A tibble: 30 x 3
Tree age circumference
<ord> <dbl> <dbl>
1 5 118 116.
2 5 168. 116.
3 5 219. 116.
4 5 269. 116.
5 5 320. 116.
6 5 370. 116.
7 5 421. 116.
8 5 471. 116.
9 5 522. 116.
10 5 572. 116.
# i 20 more rows
new_data factor with 100 levels
Code
new_seq(data$fct)
Output
[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
100 Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ... 100
Code
new_data(data, "fct")
Output
# A tibble: 100 x 1
fct
<fct>
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
# i 90 more rows
new_data factor with 100 levels obs_only works
Code
new_data(data, "fct", obs_only = TRUE)
Output
# A tibble: 30 x 1
fct
<fct>
1 1
2 10
3 11
4 12
5 13
6 14
7 15
8 16
9 17
10 18
# i 20 more rows
new_data factor with 100 levels obs_only works if name
Code
new_data(data, "fct", obs_only = list("fct"))
Output
# A tibble: 30 x 1
fct
<fct>
1 1
2 10
3 11
4 12
5 13
6 14
7 15
8 16
9 17
10 18
# i 20 more rows
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.