Nothing
tests/testthat/_snaps/format.md
In prt: Tabular Data Backed by Partitioned 'fst' Files
format_dt output matches known output
Code
print(prt_cars, n = 8L, width = 30L)
Output
# A prt: 32 × 11
# Partitioning: [16, 16] rows
mpg cyl disp hp
<dbl> <dbl> <dbl> <dbl>
1 21 6 160 110
2 21 6 160 110
3 22.8 4 108 93
4 21.4 6 258 110
5 18.7 8 360 175
6 18.1 6 225 105
7 14.3 8 360 245
8 24.4 4 147. 62
…
25 19.2 8 400 175
26 27.3 4 79 66
27 26 4 120. 91
28 30.4 4 95.1 113
29 15.8 8 351 264
30 19.7 6 145 175
31 15 8 301 335
32 21.4 4 121 109
# ℹ 24 more rows
# ℹ 7 more variables:
# drat <dbl>, wt <dbl>,
# qsec <dbl>, vs <dbl>,
# am <dbl>, gear <dbl>,
# carb <dbl>
Code
print(prt_iris, n = 5L, width = 30L)
Output
# A prt: 150 × 5
# Partitioning: [75, 75] rows
Sepal.Length Sepal.Width
<dbl> <dbl>
1 5.1 3.5
2 4.9 3
3 4.7 3.2
4 4.6 3.1
5 5 3.6
…
146 6.7 3
147 6.3 2.5
148 6.5 3
149 6.2 3.4
150 5.9 3
# ℹ 145 more rows
# ℹ 3 more variables:
# Petal.Length <dbl>,
# Petal.Width <dbl>,
# Species <fct>
Code
print(prt_iris, n = -1L, width = 30L)
Output
# A prt: 150 × 5
# Partitioning: [75, 75] rows
Sepal.Length Sepal.Width
<dbl> <dbl>
1 5.1 3.5
2 4.9 3
3 4.7 3.2
4 4.6 3.1
5 5 3.6
…
146 6.7 3
147 6.3 2.5
148 6.5 3
149 6.2 3.4
150 5.9 3
# ℹ 145 more rows
# ℹ 3 more variables:
# Petal.Length <dbl>,
# Petal.Width <dbl>,
# Species <fct>
Code
print(prt_iris, n = Inf, width = 30L)
Output
# A prt: 150 × 5
# Partitioning: [75, 75] rows
Sepal.Length Sepal.Width
<dbl> <dbl>
1 5.1 3.5
2 4.9 3
3 4.7 3.2
4 4.6 3.1
5 5 3.6
6 5.4 3.9
7 4.6 3.4
8 5 3.4
9 4.4 2.9
10 4.9 3.1
11 5.4 3.7
12 4.8 3.4
13 4.8 3
14 4.3 3
15 5.8 4
16 5.7 4.4
17 5.4 3.9
18 5.1 3.5
19 5.7 3.8
20 5.1 3.8
21 5.4 3.4
22 5.1 3.7
23 4.6 3.6
24 5.1 3.3
25 4.8 3.4
26 5 3
27 5 3.4
28 5.2 3.5
29 5.2 3.4
30 4.7 3.2
31 4.8 3.1
32 5.4 3.4
33 5.2 4.1
34 5.5 4.2
35 4.9 3.1
36 5 3.2
37 5.5 3.5
38 4.9 3.6
39 4.4 3
40 5.1 3.4
41 5 3.5
42 4.5 2.3
43 4.4 3.2
44 5 3.5
45 5.1 3.8
46 4.8 3
47 5.1 3.8
48 4.6 3.2
49 5.3 3.7
50 5 3.3
51 7 3.2
52 6.4 3.2
53 6.9 3.1
54 5.5 2.3
55 6.5 2.8
56 5.7 2.8
57 6.3 3.3
58 4.9 2.4
59 6.6 2.9
60 5.2 2.7
61 5 2
62 5.9 3
63 6 2.2
64 6.1 2.9
65 5.6 2.9
66 6.7 3.1
67 5.6 3
68 5.8 2.7
69 6.2 2.2
70 5.6 2.5
71 5.9 3.2
72 6.1 2.8
73 6.3 2.5
74 6.1 2.8
75 6.4 2.9
76 6.6 3
77 6.8 2.8
78 6.7 3
79 6 2.9
80 5.7 2.6
81 5.5 2.4
82 5.5 2.4
83 5.8 2.7
84 6 2.7
85 5.4 3
86 6 3.4
87 6.7 3.1
88 6.3 2.3
89 5.6 3
90 5.5 2.5
91 5.5 2.6
92 6.1 3
93 5.8 2.6
94 5 2.3
95 5.6 2.7
96 5.7 3
97 5.7 2.9
98 6.2 2.9
99 5.1 2.5
100 5.7 2.8
101 6.3 3.3
102 5.8 2.7
103 7.1 3
104 6.3 2.9
105 6.5 3
106 7.6 3
107 4.9 2.5
108 7.3 2.9
109 6.7 2.5
110 7.2 3.6
111 6.5 3.2
112 6.4 2.7
113 6.8 3
114 5.7 2.5
115 5.8 2.8
116 6.4 3.2
117 6.5 3
118 7.7 3.8
119 7.7 2.6
120 6 2.2
121 6.9 3.2
122 5.6 2.8
123 7.7 2.8
124 6.3 2.7
125 6.7 3.3
126 7.2 3.2
127 6.2 2.8
128 6.1 3
129 6.4 2.8
130 7.2 3
131 7.4 2.8
132 7.9 3.8
133 6.4 2.8
134 6.3 2.8
135 6.1 2.6
136 7.7 3
137 6.3 3.4
138 6.4 3.1
139 6 3
140 6.9 3.1
141 6.7 3.1
142 6.9 3.1
143 5.8 2.7
144 6.8 3.2
145 6.7 3.3
146 6.7 3
147 6.3 2.5
148 6.5 3
149 6.2 3.4
150 5.9 3
# ℹ 3 more variables:
# Petal.Length <dbl>,
# Petal.Width <dbl>,
# Species <fct>
Code
print(prt_iris, n = 3L, width = 5L)
Output
# A
# prt:
# 150
# ×
# 5
# Partitioning:
# [75,
# 75]
# rows
# ℹ 147
# more
# rows
# ℹ 5
# more
# variables:
# Sepal.Length <dbl>, …
Code
print(prt_iris, n = NULL, width = 70L)
Output
# A prt: 150 × 5
# Partitioning: [75, 75] rows
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
…
146 6.7 3 5.2 2.3 virginica
147 6.3 2.5 5 1.9 virginica
148 6.5 3 5.2 2 virginica
149 6.2 3.4 5.4 2.3 virginica
150 5.9 3 5.1 1.8 virginica
# ℹ 145 more rows
Code
print(prt_all, n = NULL, width = 30L)
Output
# A prt: 3 × 7
# Partitioning: [3] rows
a b c d
<dbl> <int> <lgl> <chr>
1 1 1 TRUE a
2 2.5 2 FALSE b
3 NA NA NA <NA>
# ℹ 3 more variables:
# e <fct>, f <date>,
# g <dttm>
Code
print(prt_all, n = NULL, width = 300L)
Output
# A prt: 3 × 7
# Partitioning: [3] rows
a b c d e f g
<dbl> <int> <lgl> <chr> <fct> <date> <dttm>
1 1 1 TRUE a a 2015-12-10 2015-12-09 10:51:35
2 2.5 2 FALSE b b 2015-12-11 2015-12-09 10:51:36
3 NA NA NA <NA> <NA> NA NA
Code
print(create_prt(tibble::tibble(a = seq.int(10000)), dir = tmp), n = 5L, width = 30L)
Output
# A prt: 10,000 × 1
# Partitioning: [10,000] rows
a
<int>
1 1
2 2
3 3
4 4
5 5
…
9,996 9996
9,997 9997
9,998 9998
9,999 9999
10,000 10000
# ℹ 9,995 more rows
Code
print(format_dt(prt_all, n = 1L, max_extra_cols = 2L, width = 30L))
Output
[1] "# A prt: 3 × 7" "# Partitioning: [3] rows"
[3] " a b c d " " <dbl> <int> <lgl> <chr>"
[5] "1 1 1 TRUE a " "…"
[7] "3 NA NA NA <NA> " "# ℹ 2 more rows"
[9] "# ℹ 3 more variables:" "# e <fct>, f <date>, …"
Code
print(format_dt(prt_all, n = 1L, max_extra_cols = 0L, width = 30L))
Output
[1] "# A prt: 3 × 7" "# Partitioning: [3] rows"
[3] " a b c d " " <dbl> <int> <lgl> <chr>"
[5] "1 1 1 TRUE a " "…"
[7] "3 NA NA NA <NA> " "# ℹ 2 more rows"
Code
print(format_dt(create_prt(tibble::tibble(`mean(x)` = 5, `var(x)` = 3), dir = tmp),
width = 28))
Output
[1] "# A prt: 1 × 2" "# Partitioning: [1] rows"
[3] " `mean(x)` `var(x)`" " <dbl> <dbl>"
[5] "1 5 3"
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prt documentation built on April 9, 2023, 5:07 p.m.
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format_dt output matches known output
Code
print(prt_cars, n = 8L, width = 30L)
Output
# A prt: 32 × 11
# Partitioning: [16, 16] rows
mpg cyl disp hp
<dbl> <dbl> <dbl> <dbl>
1 21 6 160 110
2 21 6 160 110
3 22.8 4 108 93
4 21.4 6 258 110
5 18.7 8 360 175
6 18.1 6 225 105
7 14.3 8 360 245
8 24.4 4 147. 62
…
25 19.2 8 400 175
26 27.3 4 79 66
27 26 4 120. 91
28 30.4 4 95.1 113
29 15.8 8 351 264
30 19.7 6 145 175
31 15 8 301 335
32 21.4 4 121 109
# ℹ 24 more rows
# ℹ 7 more variables:
# drat <dbl>, wt <dbl>,
# qsec <dbl>, vs <dbl>,
# am <dbl>, gear <dbl>,
# carb <dbl>
Code
print(prt_iris, n = 5L, width = 30L)
Output
# A prt: 150 × 5
# Partitioning: [75, 75] rows
Sepal.Length Sepal.Width
<dbl> <dbl>
1 5.1 3.5
2 4.9 3
3 4.7 3.2
4 4.6 3.1
5 5 3.6
…
146 6.7 3
147 6.3 2.5
148 6.5 3
149 6.2 3.4
150 5.9 3
# ℹ 145 more rows
# ℹ 3 more variables:
# Petal.Length <dbl>,
# Petal.Width <dbl>,
# Species <fct>
Code
print(prt_iris, n = -1L, width = 30L)
Output
# A prt: 150 × 5
# Partitioning: [75, 75] rows
Sepal.Length Sepal.Width
<dbl> <dbl>
1 5.1 3.5
2 4.9 3
3 4.7 3.2
4 4.6 3.1
5 5 3.6
…
146 6.7 3
147 6.3 2.5
148 6.5 3
149 6.2 3.4
150 5.9 3
# ℹ 145 more rows
# ℹ 3 more variables:
# Petal.Length <dbl>,
# Petal.Width <dbl>,
# Species <fct>
Code
print(prt_iris, n = Inf, width = 30L)
Output
# A prt: 150 × 5
# Partitioning: [75, 75] rows
Sepal.Length Sepal.Width
<dbl> <dbl>
1 5.1 3.5
2 4.9 3
3 4.7 3.2
4 4.6 3.1
5 5 3.6
6 5.4 3.9
7 4.6 3.4
8 5 3.4
9 4.4 2.9
10 4.9 3.1
11 5.4 3.7
12 4.8 3.4
13 4.8 3
14 4.3 3
15 5.8 4
16 5.7 4.4
17 5.4 3.9
18 5.1 3.5
19 5.7 3.8
20 5.1 3.8
21 5.4 3.4
22 5.1 3.7
23 4.6 3.6
24 5.1 3.3
25 4.8 3.4
26 5 3
27 5 3.4
28 5.2 3.5
29 5.2 3.4
30 4.7 3.2
31 4.8 3.1
32 5.4 3.4
33 5.2 4.1
34 5.5 4.2
35 4.9 3.1
36 5 3.2
37 5.5 3.5
38 4.9 3.6
39 4.4 3
40 5.1 3.4
41 5 3.5
42 4.5 2.3
43 4.4 3.2
44 5 3.5
45 5.1 3.8
46 4.8 3
47 5.1 3.8
48 4.6 3.2
49 5.3 3.7
50 5 3.3
51 7 3.2
52 6.4 3.2
53 6.9 3.1
54 5.5 2.3
55 6.5 2.8
56 5.7 2.8
57 6.3 3.3
58 4.9 2.4
59 6.6 2.9
60 5.2 2.7
61 5 2
62 5.9 3
63 6 2.2
64 6.1 2.9
65 5.6 2.9
66 6.7 3.1
67 5.6 3
68 5.8 2.7
69 6.2 2.2
70 5.6 2.5
71 5.9 3.2
72 6.1 2.8
73 6.3 2.5
74 6.1 2.8
75 6.4 2.9
76 6.6 3
77 6.8 2.8
78 6.7 3
79 6 2.9
80 5.7 2.6
81 5.5 2.4
82 5.5 2.4
83 5.8 2.7
84 6 2.7
85 5.4 3
86 6 3.4
87 6.7 3.1
88 6.3 2.3
89 5.6 3
90 5.5 2.5
91 5.5 2.6
92 6.1 3
93 5.8 2.6
94 5 2.3
95 5.6 2.7
96 5.7 3
97 5.7 2.9
98 6.2 2.9
99 5.1 2.5
100 5.7 2.8
101 6.3 3.3
102 5.8 2.7
103 7.1 3
104 6.3 2.9
105 6.5 3
106 7.6 3
107 4.9 2.5
108 7.3 2.9
109 6.7 2.5
110 7.2 3.6
111 6.5 3.2
112 6.4 2.7
113 6.8 3
114 5.7 2.5
115 5.8 2.8
116 6.4 3.2
117 6.5 3
118 7.7 3.8
119 7.7 2.6
120 6 2.2
121 6.9 3.2
122 5.6 2.8
123 7.7 2.8
124 6.3 2.7
125 6.7 3.3
126 7.2 3.2
127 6.2 2.8
128 6.1 3
129 6.4 2.8
130 7.2 3
131 7.4 2.8
132 7.9 3.8
133 6.4 2.8
134 6.3 2.8
135 6.1 2.6
136 7.7 3
137 6.3 3.4
138 6.4 3.1
139 6 3
140 6.9 3.1
141 6.7 3.1
142 6.9 3.1
143 5.8 2.7
144 6.8 3.2
145 6.7 3.3
146 6.7 3
147 6.3 2.5
148 6.5 3
149 6.2 3.4
150 5.9 3
# ℹ 3 more variables:
# Petal.Length <dbl>,
# Petal.Width <dbl>,
# Species <fct>
Code
print(prt_iris, n = 3L, width = 5L)
Output
# A
# prt:
# 150
# ×
# 5
# Partitioning:
# [75,
# 75]
# rows
# ℹ 147
# more
# rows
# ℹ 5
# more
# variables:
# Sepal.Length <dbl>, …
Code
print(prt_iris, n = NULL, width = 70L)
Output
# A prt: 150 × 5
# Partitioning: [75, 75] rows
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
…
146 6.7 3 5.2 2.3 virginica
147 6.3 2.5 5 1.9 virginica
148 6.5 3 5.2 2 virginica
149 6.2 3.4 5.4 2.3 virginica
150 5.9 3 5.1 1.8 virginica
# ℹ 145 more rows
Code
print(prt_all, n = NULL, width = 30L)
Output
# A prt: 3 × 7
# Partitioning: [3] rows
a b c d
<dbl> <int> <lgl> <chr>
1 1 1 TRUE a
2 2.5 2 FALSE b
3 NA NA NA <NA>
# ℹ 3 more variables:
# e <fct>, f <date>,
# g <dttm>
Code
print(prt_all, n = NULL, width = 300L)
Output
# A prt: 3 × 7
# Partitioning: [3] rows
a b c d e f g
<dbl> <int> <lgl> <chr> <fct> <date> <dttm>
1 1 1 TRUE a a 2015-12-10 2015-12-09 10:51:35
2 2.5 2 FALSE b b 2015-12-11 2015-12-09 10:51:36
3 NA NA NA <NA> <NA> NA NA
Code
print(create_prt(tibble::tibble(a = seq.int(10000)), dir = tmp), n = 5L, width = 30L)
Output
# A prt: 10,000 × 1
# Partitioning: [10,000] rows
a
<int>
1 1
2 2
3 3
4 4
5 5
…
9,996 9996
9,997 9997
9,998 9998
9,999 9999
10,000 10000
# ℹ 9,995 more rows
Code
print(format_dt(prt_all, n = 1L, max_extra_cols = 2L, width = 30L))
Output
[1] "# A prt: 3 × 7" "# Partitioning: [3] rows"
[3] " a b c d " " <dbl> <int> <lgl> <chr>"
[5] "1 1 1 TRUE a " "…"
[7] "3 NA NA NA <NA> " "# ℹ 2 more rows"
[9] "# ℹ 3 more variables:" "# e <fct>, f <date>, …"
Code
print(format_dt(prt_all, n = 1L, max_extra_cols = 0L, width = 30L))
Output
[1] "# A prt: 3 × 7" "# Partitioning: [3] rows"
[3] " a b c d " " <dbl> <int> <lgl> <chr>"
[5] "1 1 1 TRUE a " "…"
[7] "3 NA NA NA <NA> " "# ℹ 2 more rows"
Code
print(format_dt(create_prt(tibble::tibble(`mean(x)` = 5, `var(x)` = 3), dir = tmp),
width = 28))
Output
[1] "# A prt: 1 × 2" "# Partitioning: [1] rows"
[3] " `mean(x)` `var(x)`" " <dbl> <dbl>"
[5] "1 5 3"
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