4 Dataset transfer from R to Clojure
(ns clojisr.v1.tutorials.dataset
(:require [clojisr.v1.r :as r :refer [r r->clj clj->r require-r]]
[scicloj.kindly.v4.kind :as kind]
[scicloj.kindly.v4.api :as kindly]))(r/set-default-session-type! :rserve){:session-type :rserve}(r/discard-all-sessions){}(require-r '[datasets])nil4.1 Data Frame
Any data.frame, also tribble and data.table are treated the same. If row.names are available they are converted to the additional column :$row.names.
r.datasets/BOD Time demand
1 1 8.3
2 2 10.3
3 3 19.0
4 4 16.0
5 5 15.6
6 7 19.8
(r->clj '(attributes BOD)){:names ["Time" "demand"],
:class ["data.frame"],
:row.names [1 2 3 4 5 6],
:reference ["A1.4, p. 270"]}(r->clj r.datasets/BOD)_unnamed [6 2]:
| :Time | :demand |
|---|---|
| 1.0 | 8.3 |
| 2.0 | 10.3 |
| 3.0 | 19.0 |
| 4.0 | 16.0 |
| 5.0 | 15.6 |
| 7.0 | 19.8 |
r.datasets/CO2 Plant Type Treatment conc uptake
1 Qn1 Quebec nonchilled 95 16.0
2 Qn1 Quebec nonchilled 175 30.4
3 Qn1 Quebec nonchilled 250 34.8
4 Qn1 Quebec nonchilled 350 37.2
5 Qn1 Quebec nonchilled 500 35.3
6 Qn1 Quebec nonchilled 675 39.2
7 Qn1 Quebec nonchilled 1000 39.7
8 Qn2 Quebec nonchilled 95 13.6
9 Qn2 Quebec nonchilled 175 27.3
10 Qn2 Quebec nonchilled 250 37.1
11 Qn2 Quebec nonchilled 350 41.8
12 Qn2 Quebec nonchilled 500 40.6
13 Qn2 Quebec nonchilled 675 41.4
14 Qn2 Quebec nonchilled 1000 44.3
15 Qn3 Quebec nonchilled 95 16.2
16 Qn3 Quebec nonchilled 175 32.4
17 Qn3 Quebec nonchilled 250 40.3
18 Qn3 Quebec nonchilled 350 42.1
19 Qn3 Quebec nonchilled 500 42.9
20 Qn3 Quebec nonchilled 675 43.9
21 Qn3 Quebec nonchilled 1000 45.5
22 Qc1 Quebec chilled 95 14.2
23 Qc1 Quebec chilled 175 24.1
24 Qc1 Quebec chilled 250 30.3
25 Qc1 Quebec chilled 350 34.6
26 Qc1 Quebec chilled 500 32.5
27 Qc1 Quebec chilled 675 35.4
28 Qc1 Quebec chilled 1000 38.7
29 Qc2 Quebec chilled 95 9.3
30 Qc2 Quebec chilled 175 27.3
31 Qc2 Quebec chilled 250 35.0
32 Qc2 Quebec chilled 350 38.8
33 Qc2 Quebec chilled 500 38.6
34 Qc2 Quebec chilled 675 37.5
35 Qc2 Quebec chilled 1000 42.4
36 Qc3 Quebec chilled 95 15.1
37 Qc3 Quebec chilled 175 21.0
38 Qc3 Quebec chilled 250 38.1
39 Qc3 Quebec chilled 350 34.0
40 Qc3 Quebec chilled 500 38.9
41 Qc3 Quebec chilled 675 39.6
42 Qc3 Quebec chilled 1000 41.4
43 Mn1 Mississippi nonchilled 95 10.6
44 Mn1 Mississippi nonchilled 175 19.2
45 Mn1 Mississippi nonchilled 250 26.2
46 Mn1 Mississippi nonchilled 350 30.0
47 Mn1 Mississippi nonchilled 500 30.9
48 Mn1 Mississippi nonchilled 675 32.4
49 Mn1 Mississippi nonchilled 1000 35.5
50 Mn2 Mississippi nonchilled 95 12.0
51 Mn2 Mississippi nonchilled 175 22.0
52 Mn2 Mississippi nonchilled 250 30.6
53 Mn2 Mississippi nonchilled 350 31.8
54 Mn2 Mississippi nonchilled 500 32.4
55 Mn2 Mississippi nonchilled 675 31.1
56 Mn2 Mississippi nonchilled 1000 31.5
57 Mn3 Mississippi nonchilled 95 11.3
58 Mn3 Mississippi nonchilled 175 19.4
59 Mn3 Mississippi nonchilled 250 25.8
60 Mn3 Mississippi nonchilled 350 27.9
61 Mn3 Mississippi nonchilled 500 28.5
62 Mn3 Mississippi nonchilled 675 28.1
63 Mn3 Mississippi nonchilled 1000 27.8
64 Mc1 Mississippi chilled 95 10.5
65 Mc1 Mississippi chilled 175 14.9
66 Mc1 Mississippi chilled 250 18.1
67 Mc1 Mississippi chilled 350 18.9
68 Mc1 Mississippi chilled 500 19.5
69 Mc1 Mississippi chilled 675 22.2
70 Mc1 Mississippi chilled 1000 21.9
71 Mc2 Mississippi chilled 95 7.7
72 Mc2 Mississippi chilled 175 11.4
73 Mc2 Mississippi chilled 250 12.3
74 Mc2 Mississippi chilled 350 13.0
75 Mc2 Mississippi chilled 500 12.5
76 Mc2 Mississippi chilled 675 13.7
77 Mc2 Mississippi chilled 1000 14.4
78 Mc3 Mississippi chilled 95 10.6
79 Mc3 Mississippi chilled 175 18.0
80 Mc3 Mississippi chilled 250 17.9
81 Mc3 Mississippi chilled 350 17.9
82 Mc3 Mississippi chilled 500 17.9
83 Mc3 Mississippi chilled 675 18.9
84 Mc3 Mississippi chilled 1000 19.9
(r->clj '(attributes CO2)){:names ["Plant" "Type" "Treatment" "conc" "uptake"],
:row.names
[1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84],
:class ["nfnGroupedData" "nfGroupedData" "groupedData" "data.frame"],
:formula [~ uptake [| conc Plant]],
:outer [~ [* Treatment Type]],
:labels
{:x ["Ambient carbon dioxide concentration"], :y ["CO2 uptake rate"]},
:units {:x ["(uL/L)"], :y ["(umol/m^2 s)"]}}(r->clj r.datasets/CO2)_unnamed [84 6]:
| :$row.names | :Plant | :Type | :Treatment | :conc | :uptake |
|---|---|---|---|---|---|
| 1 | :Qn1 | :Quebec | :nonchilled | 95.0 | 16.0 |
| 2 | :Qn1 | :Quebec | :nonchilled | 175.0 | 30.4 |
| 3 | :Qn1 | :Quebec | :nonchilled | 250.0 | 34.8 |
| 4 | :Qn1 | :Quebec | :nonchilled | 350.0 | 37.2 |
| 5 | :Qn1 | :Quebec | :nonchilled | 500.0 | 35.3 |
| 6 | :Qn1 | :Quebec | :nonchilled | 675.0 | 39.2 |
| 7 | :Qn1 | :Quebec | :nonchilled | 1000.0 | 39.7 |
| 8 | :Qn2 | :Quebec | :nonchilled | 95.0 | 13.6 |
| 9 | :Qn2 | :Quebec | :nonchilled | 175.0 | 27.3 |
| 10 | :Qn2 | :Quebec | :nonchilled | 250.0 | 37.1 |
| … | … | … | … | … | … |
| 74 | :Mc2 | :Mississippi | :chilled | 350.0 | 13.0 |
| 75 | :Mc2 | :Mississippi | :chilled | 500.0 | 12.5 |
| 76 | :Mc2 | :Mississippi | :chilled | 675.0 | 13.7 |
| 77 | :Mc2 | :Mississippi | :chilled | 1000.0 | 14.4 |
| 78 | :Mc3 | :Mississippi | :chilled | 95.0 | 10.6 |
| 79 | :Mc3 | :Mississippi | :chilled | 175.0 | 18.0 |
| 80 | :Mc3 | :Mississippi | :chilled | 250.0 | 17.9 |
| 81 | :Mc3 | :Mississippi | :chilled | 350.0 | 17.9 |
| 82 | :Mc3 | :Mississippi | :chilled | 500.0 | 17.9 |
| 83 | :Mc3 | :Mississippi | :chilled | 675.0 | 18.9 |
| 84 | :Mc3 | :Mississippi | :chilled | 1000.0 | 19.9 |
4.2 Table
Table is converted to a long form where each dimension has it’s own column. If column names are not available, column id is prefixed with :$col. Values are stored in the last, :$value column.
r.datasets/UCBAdmissions, , Dept = A
Gender
Admit Male Female
Admitted 512 89
Rejected 313 19
, , Dept = B
Gender
Admit Male Female
Admitted 353 17
Rejected 207 8
, , Dept = C
Gender
Admit Male Female
Admitted 120 202
Rejected 205 391
, , Dept = D
Gender
Admit Male Female
Admitted 138 131
Rejected 279 244
, , Dept = E
Gender
Admit Male Female
Admitted 53 94
Rejected 138 299
, , Dept = F
Gender
Admit Male Female
Admitted 22 24
Rejected 351 317
(r->clj '(attributes UCBAdmissions)){:dim [2 2 6],
:dimnames
{:Admit ["Admitted" "Rejected"],
:Gender ["Male" "Female"],
:Dept ["A" "B" "C" "D" "E" "F"]},
:class ["table"]}(r->clj r.datasets/UCBAdmissions)_unnamed [24 4]:
| :Admit | :Gender | :Dept | :$value |
|---|---|---|---|
| Admitted | Male | A | 512.0 |
| Admitted | Male | B | 313.0 |
| Admitted | Male | C | 89.0 |
| Admitted | Male | D | 19.0 |
| Admitted | Male | E | 353.0 |
| Admitted | Male | F | 207.0 |
| Admitted | Female | A | 17.0 |
| Admitted | Female | B | 8.0 |
| Admitted | Female | C | 120.0 |
| Admitted | Female | D | 205.0 |
| … | … | … | … |
| Rejected | Male | B | 279.0 |
| Rejected | Male | C | 131.0 |
| Rejected | Male | D | 244.0 |
| Rejected | Male | E | 53.0 |
| Rejected | Male | F | 138.0 |
| Rejected | Female | A | 94.0 |
| Rejected | Female | B | 299.0 |
| Rejected | Female | C | 22.0 |
| Rejected | Female | D | 351.0 |
| Rejected | Female | E | 24.0 |
| Rejected | Female | F | 317.0 |
r.datasets/crimtab 142.24 144.78 147.32 149.86 152.4 154.94 157.48 160.02 162.56 165.1 167.64
9.4 0 0 0 0 0 0 0 0 0 0 0
9.5 0 0 0 0 0 1 0 0 0 0 0
9.6 0 0 0 0 0 0 0 0 0 0 0
9.7 0 0 0 0 0 0 0 0 0 0 0
9.8 0 0 0 0 0 0 1 0 0 0 0
9.9 0 0 1 0 1 0 1 0 0 0 0
10 1 0 0 1 2 0 2 0 0 1 0
10.1 0 0 0 1 3 1 0 1 1 0 0
10.2 0 0 2 2 2 1 0 2 0 1 0
10.3 0 1 1 3 2 2 3 5 0 0 0
10.4 0 0 1 1 2 3 3 4 3 3 0
10.5 0 0 0 1 3 7 6 4 3 1 3
10.6 0 0 0 1 4 5 9 14 6 3 1
10.7 0 0 1 2 4 9 14 16 15 7 3
10.8 0 0 0 2 5 6 14 27 10 7 1
10.9 0 0 0 0 2 6 14 24 27 14 10
11 0 0 0 2 6 12 15 31 37 27 17
11.1 0 0 0 3 3 12 22 26 24 26 24
11.2 0 0 0 3 2 7 21 30 38 29 27
11.3 0 0 0 1 0 5 10 24 26 39 26
11.4 0 0 0 0 3 4 9 29 56 58 26
11.5 0 0 0 0 0 5 11 17 33 57 38
11.6 0 0 0 0 2 1 4 13 37 39 48
11.7 0 0 0 0 0 2 9 17 30 37 48
11.8 0 0 0 0 1 0 2 11 15 35 41
11.9 0 0 0 0 1 1 2 12 10 27 32
12 0 0 0 0 0 0 1 4 8 19 42
12.1 0 0 0 0 0 0 0 2 4 13 22
12.2 0 0 0 0 0 0 1 2 5 6 23
12.3 0 0 0 0 0 0 0 0 4 8 10
12.4 0 0 0 0 0 0 1 1 1 2 7
12.5 0 0 0 0 0 0 0 1 0 1 3
12.6 0 0 0 0 0 0 0 0 0 1 0
12.7 0 0 0 0 0 0 0 0 0 1 1
12.8 0 0 0 0 0 0 0 0 0 0 1
12.9 0 0 0 0 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0 0 0 0 3
13.1 0 0 0 0 0 0 0 0 0 0 0
13.2 0 0 0 0 0 0 0 0 0 0 1
13.3 0 0 0 0 0 0 0 0 0 0 0
13.4 0 0 0 0 0 0 0 0 0 0 0
13.5 0 0 0 0 0 0 0 0 0 0 0
170.18 172.72 175.26 177.8 180.34 182.88 185.42 187.96 190.5 193.04 195.58
9.4 0 0 0 0 0 0 0 0 0 0 0
9.5 0 0 0 0 0 0 0 0 0 0 0
9.6 0 0 0 0 0 0 0 0 0 0 0
9.7 0 0 0 0 0 0 0 0 0 0 0
9.8 0 0 0 0 0 0 0 0 0 0 0
9.9 0 0 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0 0
10.1 0 0 0 0 0 0 0 0 0 0 0
10.2 0 0 0 0 0 0 0 0 0 0 0
10.3 0 0 0 0 0 0 0 0 0 0 0
10.4 0 0 0 0 0 0 0 0 0 0 0
10.5 1 0 1 0 0 0 0 0 0 0 0
10.6 0 0 1 0 0 0 0 0 0 0 0
10.7 1 2 0 0 0 0 0 0 0 0 0
10.8 2 1 0 0 0 0 0 0 0 0 0
10.9 4 1 0 0 0 0 0 0 0 0 0
11 10 6 0 0 0 0 0 0 0 0 0
11.1 7 4 1 0 0 0 0 0 0 0 0
11.2 20 4 1 0 0 0 0 0 0 0 1
11.3 24 7 2 0 0 0 0 0 0 0 0
11.4 22 10 11 0 0 0 0 0 0 0 0
11.5 34 25 11 2 0 0 0 0 0 0 0
11.6 38 27 12 2 2 0 1 0 0 0 0
11.7 45 24 9 9 2 0 0 0 0 0 0
11.8 34 29 10 5 1 0 0 0 0 0 0
11.9 35 19 10 9 3 1 0 0 0 0 0
12 39 22 16 8 2 2 0 0 0 0 0
12.1 28 15 27 10 4 1 0 0 0 0 0
12.2 17 16 11 8 1 1 0 0 0 0 0
12.3 13 20 23 6 5 0 0 0 0 0 0
12.4 12 4 7 7 1 0 0 1 0 0 0
12.5 12 11 8 6 8 0 2 0 0 0 0
12.6 3 5 7 8 6 3 1 1 0 0 0
12.7 7 5 5 8 2 2 0 0 0 0 0
12.8 2 3 1 8 5 3 1 1 0 0 0
12.9 1 2 2 0 1 1 0 0 0 0 0
13 0 1 0 1 0 2 1 0 0 0 0
13.1 1 1 0 0 0 0 0 0 0 0 0
13.2 1 0 1 0 3 0 0 0 0 0 0
13.3 0 0 0 0 0 1 0 1 0 0 0
13.4 0 0 0 0 0 0 0 0 0 0 0
13.5 0 0 0 0 0 0 1 0 0 0 0
(r->clj '(attributes crimtab)){:dim [42 22],
:dimnames
[["9.4"
"9.5"
"9.6"
"9.7"
"9.8"
"9.9"
"10"
"10.1"
"10.2"
"10.3"
"10.4"
"10.5"
"10.6"
"10.7"
"10.8"
"10.9"
"11"
"11.1"
"11.2"
"11.3"
"11.4"
"11.5"
"11.6"
"11.7"
"11.8"
"11.9"
"12"
"12.1"
"12.2"
"12.3"
"12.4"
"12.5"
"12.6"
"12.7"
"12.8"
"12.9"
"13"
"13.1"
"13.2"
"13.3"
"13.4"
"13.5"]
["142.24"
"144.78"
"147.32"
"149.86"
"152.4"
"154.94"
"157.48"
"160.02"
"162.56"
"165.1"
"167.64"
"170.18"
"172.72"
"175.26"
"177.8"
"180.34"
"182.88"
"185.42"
"187.96"
"190.5"
"193.04"
"195.58"]],
:class ["table"]}(r->clj r.datasets/crimtab)_unnamed [924 3]:
| :\(col-0 | :\)col-1 | :$value | |
|---|---|---|
| 9.4 | 142.24 | 0 |
| 9.4 | 144.78 | 0 |
| 9.4 | 147.32 | 0 |
| 9.4 | 149.86 | 0 |
| 9.4 | 152.4 | 0 |
| 9.4 | 154.94 | 0 |
| 9.4 | 157.48 | 1 |
| 9.4 | 160.02 | 0 |
| 9.4 | 162.56 | 0 |
| 9.4 | 165.1 | 0 |
| … | … | … |
| 13.5 | 170.18 | 0 |
| 13.5 | 172.72 | 0 |
| 13.5 | 175.26 | 0 |
| 13.5 | 177.8 | 0 |
| 13.5 | 180.34 | 0 |
| 13.5 | 182.88 | 0 |
| 13.5 | 185.42 | 0 |
| 13.5 | 187.96 | 0 |
| 13.5 | 190.5 | 0 |
| 13.5 | 193.04 | 0 |
| 13.5 | 195.58 | 0 |
4.3 Matrices, arrays, multidimensional arrays
First two dimensions creates dataset, all additional dimensions are added as columns
r.datasets/VADeaths Rural Male Rural Female Urban Male Urban Female
50-54 11.7 8.7 15.4 8.4
55-59 18.1 11.7 24.3 13.6
60-64 26.9 20.3 37.0 19.3
65-69 41.0 30.9 54.6 35.1
70-74 66.0 54.3 71.1 50.0
(r->clj '(attributes VADeaths)){:dim [5 4],
:dimnames
[["50-54" "55-59" "60-64" "65-69" "70-74"]
["Rural Male" "Rural Female" "Urban Male" "Urban Female"]]}(r->clj r.datasets/VADeaths)_unnamed [5 5]:
| :$row.names | Rural Male | Rural Female | Urban Male | Urban Female |
|---|---|---|---|---|
| 50-54 | 11.7 | 8.7 | 15.4 | 8.4 |
| 55-59 | 18.1 | 11.7 | 24.3 | 13.6 |
| 60-64 | 26.9 | 20.3 | 37.0 | 19.3 |
| 65-69 | 41.0 | 30.9 | 54.6 | 35.1 |
| 70-74 | 66.0 | 54.3 | 71.1 | 50.0 |
r.datasets/freeny-x lag quarterly revenue price index income level market potential
[1,] 8.79636 4.70997 5.82110 12.9699
[2,] 8.79236 4.70217 5.82558 12.9733
[3,] 8.79137 4.68944 5.83112 12.9774
[4,] 8.81486 4.68558 5.84046 12.9806
[5,] 8.81301 4.64019 5.85036 12.9831
[6,] 8.90751 4.62553 5.86464 12.9854
[7,] 8.93673 4.61991 5.87769 12.9900
[8,] 8.96161 4.61654 5.89763 12.9943
[9,] 8.96044 4.61407 5.92574 12.9992
[10,] 9.00868 4.60766 5.94232 13.0033
[11,] 9.03049 4.60227 5.95365 13.0099
[12,] 9.06906 4.58960 5.96120 13.0159
[13,] 9.05871 4.57592 5.97805 13.0212
[14,] 9.10698 4.58661 6.00377 13.0265
[15,] 9.12685 4.57997 6.02829 13.0351
[16,] 9.17096 4.57176 6.03475 13.0429
[17,] 9.18665 4.56104 6.03906 13.0497
[18,] 9.23823 4.54906 6.05046 13.0551
[19,] 9.26487 4.53957 6.05563 13.0634
[20,] 9.28436 4.51018 6.06093 13.0693
[21,] 9.31378 4.50352 6.07103 13.0737
[22,] 9.35025 4.49360 6.08018 13.0770
[23,] 9.35835 4.46505 6.08858 13.0849
[24,] 9.39767 4.44924 6.10199 13.0918
[25,] 9.42150 4.43966 6.11207 13.0950
[26,] 9.44223 4.42025 6.11596 13.0984
[27,] 9.48721 4.41060 6.12129 13.1089
[28,] 9.52374 4.41151 6.12200 13.1169
[29,] 9.53980 4.39810 6.13119 13.1222
[30,] 9.58123 4.38513 6.14705 13.1266
[31,] 9.60048 4.37320 6.15336 13.1356
[32,] 9.64496 4.32770 6.15627 13.1415
[33,] 9.64390 4.32023 6.16274 13.1444
[34,] 9.69405 4.30909 6.17369 13.1459
[35,] 9.69958 4.30909 6.16135 13.1520
[36,] 9.68683 4.30552 6.18231 13.1593
[37,] 9.71774 4.29627 6.18768 13.1579
[38,] 9.74924 4.27839 6.19377 13.1625
[39,] 9.77536 4.27789 6.20030 13.1664
(r->clj '(attributes freeny.x)){:dim [39 4],
:dimnames
[nil
["lag quarterly revenue"
"price index"
"income level"
"market potential"]]}(r->clj r.datasets/freeny-x)_unnamed [39 4]:
| lag quarterly revenue | price index | income level | market potential |
|---|---|---|---|
| 8.79636 | 4.70997 | 5.82110 | 12.9699 |
| 8.79236 | 4.70217 | 5.82558 | 12.9733 |
| 8.79137 | 4.68944 | 5.83112 | 12.9774 |
| 8.81486 | 4.68558 | 5.84046 | 12.9806 |
| 8.81301 | 4.64019 | 5.85036 | 12.9831 |
| 8.90751 | 4.62553 | 5.86464 | 12.9854 |
| 8.93673 | 4.61991 | 5.87769 | 12.9900 |
| 8.96161 | 4.61654 | 5.89763 | 12.9943 |
| 8.96044 | 4.61407 | 5.92574 | 12.9992 |
| 9.00868 | 4.60766 | 5.94232 | 13.0033 |
| … | … | … | … |
| 9.53980 | 4.39810 | 6.13119 | 13.1222 |
| 9.58123 | 4.38513 | 6.14705 | 13.1266 |
| 9.60048 | 4.37320 | 6.15336 | 13.1356 |
| 9.64496 | 4.32770 | 6.15627 | 13.1415 |
| 9.64390 | 4.32023 | 6.16274 | 13.1444 |
| 9.69405 | 4.30909 | 6.17369 | 13.1459 |
| 9.69958 | 4.30909 | 6.16135 | 13.1520 |
| 9.68683 | 4.30552 | 6.18231 | 13.1593 |
| 9.71774 | 4.29627 | 6.18768 | 13.1579 |
| 9.74924 | 4.27839 | 6.19377 | 13.1625 |
| 9.77536 | 4.27789 | 6.20030 | 13.1664 |
r.datasets/iris3, , Setosa
Sepal L. Sepal W. Petal L. Petal W.
[1,] 5.1 3.5 1.4 0.2
[2,] 4.9 3.0 1.4 0.2
[3,] 4.7 3.2 1.3 0.2
[4,] 4.6 3.1 1.5 0.2
[5,] 5.0 3.6 1.4 0.2
[6,] 5.4 3.9 1.7 0.4
[7,] 4.6 3.4 1.4 0.3
[8,] 5.0 3.4 1.5 0.2
[9,] 4.4 2.9 1.4 0.2
[10,] 4.9 3.1 1.5 0.1
[11,] 5.4 3.7 1.5 0.2
[12,] 4.8 3.4 1.6 0.2
[13,] 4.8 3.0 1.4 0.1
[14,] 4.3 3.0 1.1 0.1
[15,] 5.8 4.0 1.2 0.2
[16,] 5.7 4.4 1.5 0.4
[17,] 5.4 3.9 1.3 0.4
[18,] 5.1 3.5 1.4 0.3
[19,] 5.7 3.8 1.7 0.3
[20,] 5.1 3.8 1.5 0.3
[21,] 5.4 3.4 1.7 0.2
[22,] 5.1 3.7 1.5 0.4
[23,] 4.6 3.6 1.0 0.2
[24,] 5.1 3.3 1.7 0.5
[25,] 4.8 3.4 1.9 0.2
[26,] 5.0 3.0 1.6 0.2
[27,] 5.0 3.4 1.6 0.4
[28,] 5.2 3.5 1.5 0.2
[29,] 5.2 3.4 1.4 0.2
[30,] 4.7 3.2 1.6 0.2
[31,] 4.8 3.1 1.6 0.2
[32,] 5.4 3.4 1.5 0.4
[33,] 5.2 4.1 1.5 0.1
[34,] 5.5 4.2 1.4 0.2
[35,] 4.9 3.1 1.5 0.2
[36,] 5.0 3.2 1.2 0.2
[37,] 5.5 3.5 1.3 0.2
[38,] 4.9 3.6 1.4 0.1
[39,] 4.4 3.0 1.3 0.2
[40,] 5.1 3.4 1.5 0.2
[41,] 5.0 3.5 1.3 0.3
[42,] 4.5 2.3 1.3 0.3
[43,] 4.4 3.2 1.3 0.2
[44,] 5.0 3.5 1.6 0.6
[45,] 5.1 3.8 1.9 0.4
[46,] 4.8 3.0 1.4 0.3
[47,] 5.1 3.8 1.6 0.2
[48,] 4.6 3.2 1.4 0.2
[49,] 5.3 3.7 1.5 0.2
[50,] 5.0 3.3 1.4 0.2
, , Versicolor
Sepal L. Sepal W. Petal L. Petal W.
[1,] 7.0 3.2 4.7 1.4
[2,] 6.4 3.2 4.5 1.5
[3,] 6.9 3.1 4.9 1.5
[4,] 5.5 2.3 4.0 1.3
[5,] 6.5 2.8 4.6 1.5
[6,] 5.7 2.8 4.5 1.3
[7,] 6.3 3.3 4.7 1.6
[8,] 4.9 2.4 3.3 1.0
[9,] 6.6 2.9 4.6 1.3
[10,] 5.2 2.7 3.9 1.4
[11,] 5.0 2.0 3.5 1.0
[12,] 5.9 3.0 4.2 1.5
[13,] 6.0 2.2 4.0 1.0
[14,] 6.1 2.9 4.7 1.4
[15,] 5.6 2.9 3.6 1.3
[16,] 6.7 3.1 4.4 1.4
[17,] 5.6 3.0 4.5 1.5
[18,] 5.8 2.7 4.1 1.0
[19,] 6.2 2.2 4.5 1.5
[20,] 5.6 2.5 3.9 1.1
[21,] 5.9 3.2 4.8 1.8
[22,] 6.1 2.8 4.0 1.3
[23,] 6.3 2.5 4.9 1.5
[24,] 6.1 2.8 4.7 1.2
[25,] 6.4 2.9 4.3 1.3
[26,] 6.6 3.0 4.4 1.4
[27,] 6.8 2.8 4.8 1.4
[28,] 6.7 3.0 5.0 1.7
[29,] 6.0 2.9 4.5 1.5
[30,] 5.7 2.6 3.5 1.0
[31,] 5.5 2.4 3.8 1.1
[32,] 5.5 2.4 3.7 1.0
[33,] 5.8 2.7 3.9 1.2
[34,] 6.0 2.7 5.1 1.6
[35,] 5.4 3.0 4.5 1.5
[36,] 6.0 3.4 4.5 1.6
[37,] 6.7 3.1 4.7 1.5
[38,] 6.3 2.3 4.4 1.3
[39,] 5.6 3.0 4.1 1.3
[40,] 5.5 2.5 4.0 1.3
[41,] 5.5 2.6 4.4 1.2
[42,] 6.1 3.0 4.6 1.4
[43,] 5.8 2.6 4.0 1.2
[44,] 5.0 2.3 3.3 1.0
[45,] 5.6 2.7 4.2 1.3
[46,] 5.7 3.0 4.2 1.2
[47,] 5.7 2.9 4.2 1.3
[48,] 6.2 2.9 4.3 1.3
[49,] 5.1 2.5 3.0 1.1
[50,] 5.7 2.8 4.1 1.3
, , Virginica
Sepal L. Sepal W. Petal L. Petal W.
[1,] 6.3 3.3 6.0 2.5
[2,] 5.8 2.7 5.1 1.9
[3,] 7.1 3.0 5.9 2.1
[4,] 6.3 2.9 5.6 1.8
[5,] 6.5 3.0 5.8 2.2
[6,] 7.6 3.0 6.6 2.1
[7,] 4.9 2.5 4.5 1.7
[8,] 7.3 2.9 6.3 1.8
[9,] 6.7 2.5 5.8 1.8
[10,] 7.2 3.6 6.1 2.5
[11,] 6.5 3.2 5.1 2.0
[12,] 6.4 2.7 5.3 1.9
[13,] 6.8 3.0 5.5 2.1
[14,] 5.7 2.5 5.0 2.0
[15,] 5.8 2.8 5.1 2.4
[16,] 6.4 3.2 5.3 2.3
[17,] 6.5 3.0 5.5 1.8
[18,] 7.7 3.8 6.7 2.2
[19,] 7.7 2.6 6.9 2.3
[20,] 6.0 2.2 5.0 1.5
[21,] 6.9 3.2 5.7 2.3
[22,] 5.6 2.8 4.9 2.0
[23,] 7.7 2.8 6.7 2.0
[24,] 6.3 2.7 4.9 1.8
[25,] 6.7 3.3 5.7 2.1
[26,] 7.2 3.2 6.0 1.8
[27,] 6.2 2.8 4.8 1.8
[28,] 6.1 3.0 4.9 1.8
[29,] 6.4 2.8 5.6 2.1
[30,] 7.2 3.0 5.8 1.6
[31,] 7.4 2.8 6.1 1.9
[32,] 7.9 3.8 6.4 2.0
[33,] 6.4 2.8 5.6 2.2
[34,] 6.3 2.8 5.1 1.5
[35,] 6.1 2.6 5.6 1.4
[36,] 7.7 3.0 6.1 2.3
[37,] 6.3 3.4 5.6 2.4
[38,] 6.4 3.1 5.5 1.8
[39,] 6.0 3.0 4.8 1.8
[40,] 6.9 3.1 5.4 2.1
[41,] 6.7 3.1 5.6 2.4
[42,] 6.9 3.1 5.1 2.3
[43,] 5.8 2.7 5.1 1.9
[44,] 6.8 3.2 5.9 2.3
[45,] 6.7 3.3 5.7 2.5
[46,] 6.7 3.0 5.2 2.3
[47,] 6.3 2.5 5.0 1.9
[48,] 6.5 3.0 5.2 2.0
[49,] 6.2 3.4 5.4 2.3
[50,] 5.9 3.0 5.1 1.8
(r->clj '(attributes iris3)){:dim [50 4 3],
:dimnames
[nil
["Sepal L." "Sepal W." "Petal L." "Petal W."]
["Setosa" "Versicolor" "Virginica"]]}(r->clj r.datasets/iris3)_unnamed [150 5]:
| :$col-0 | Sepal L. | Sepal W. | Petal L. | Petal W. |
|---|---|---|---|---|
| Setosa | 5.1 | 3.5 | 1.4 | 0.2 |
| Setosa | 4.9 | 3.0 | 1.4 | 0.2 |
| Setosa | 4.7 | 3.2 | 1.3 | 0.2 |
| Setosa | 4.6 | 3.1 | 1.5 | 0.2 |
| Setosa | 5.0 | 3.6 | 1.4 | 0.2 |
| Setosa | 5.4 | 3.9 | 1.7 | 0.4 |
| Setosa | 4.6 | 3.4 | 1.4 | 0.3 |
| Setosa | 5.0 | 3.4 | 1.5 | 0.2 |
| Setosa | 4.4 | 2.9 | 1.4 | 0.2 |
| Setosa | 4.9 | 3.1 | 1.5 | 0.1 |
| … | … | … | … | … |
| Virginica | 6.9 | 3.1 | 5.4 | 2.1 |
| Virginica | 6.7 | 3.1 | 5.6 | 2.4 |
| Virginica | 6.9 | 3.1 | 5.1 | 2.3 |
| Virginica | 5.8 | 2.7 | 5.1 | 1.9 |
| Virginica | 6.8 | 3.2 | 5.9 | 2.3 |
| Virginica | 6.7 | 3.3 | 5.7 | 2.5 |
| Virginica | 6.7 | 3.0 | 5.2 | 2.3 |
| Virginica | 6.3 | 2.5 | 5.0 | 1.9 |
| Virginica | 6.5 | 3.0 | 5.2 | 2.0 |
| Virginica | 6.2 | 3.4 | 5.4 | 2.3 |
| Virginica | 5.9 | 3.0 | 5.1 | 1.8 |
(def array-5d (r '(array ~(range 60) :dim [2 5 1 3 2])))array-5d, , 1, 1, 1
[,1] [,2] [,3] [,4] [,5]
[1,] 0 2 4 6 8
[2,] 1 3 5 7 9
, , 1, 2, 1
[,1] [,2] [,3] [,4] [,5]
[1,] 10 12 14 16 18
[2,] 11 13 15 17 19
, , 1, 3, 1
[,1] [,2] [,3] [,4] [,5]
[1,] 20 22 24 26 28
[2,] 21 23 25 27 29
, , 1, 1, 2
[,1] [,2] [,3] [,4] [,5]
[1,] 30 32 34 36 38
[2,] 31 33 35 37 39
, , 1, 2, 2
[,1] [,2] [,3] [,4] [,5]
[1,] 40 42 44 46 48
[2,] 41 43 45 47 49
, , 1, 3, 2
[,1] [,2] [,3] [,4] [,5]
[1,] 50 52 54 56 58
[2,] 51 53 55 57 59
(r->clj '(attributes ~array-5d)){:dim [2 5 1 3 2]}(r->clj array-5d)_unnamed [12 8]:
| :\(col-0 | :\)col-1 | :$col-2 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 0 | 2 | 4 | 6 | 8 |
| 1 | 1 | 1 | 1 | 3 | 5 | 7 | 9 |
| 1 | 1 | 2 | 10 | 12 | 14 | 16 | 18 |
| 1 | 1 | 2 | 11 | 13 | 15 | 17 | 19 |
| 1 | 2 | 1 | 20 | 22 | 24 | 26 | 28 |
| 1 | 2 | 1 | 21 | 23 | 25 | 27 | 29 |
| 1 | 2 | 2 | 30 | 32 | 34 | 36 | 38 |
| 1 | 2 | 2 | 31 | 33 | 35 | 37 | 39 |
| 1 | 3 | 1 | 40 | 42 | 44 | 46 | 48 |
| 1 | 3 | 1 | 41 | 43 | 45 | 47 | 49 |
| 1 | 3 | 2 | 50 | 52 | 54 | 56 | 58 |
| 1 | 3 | 2 | 51 | 53 | 55 | 57 | 59 |
4.4 1D timeseries
Timeseries are stored in two columns:
:$time- to store time identifier as double *:$series- to store timeseries
r.datasets/BJsalesTime Series:
Start = 1
End = 150
Frequency = 1
[1] 200.1 199.5 199.4 198.9 199.0 200.2 198.6 200.0 200.3 201.2 201.6 201.5
[13] 201.5 203.5 204.9 207.1 210.5 210.5 209.8 208.8 209.5 213.2 213.7 215.1
[25] 218.7 219.8 220.5 223.8 222.8 223.8 221.7 222.3 220.8 219.4 220.1 220.6
[37] 218.9 217.8 217.7 215.0 215.3 215.9 216.7 216.7 217.7 218.7 222.9 224.9
[49] 222.2 220.7 220.0 218.7 217.0 215.9 215.8 214.1 212.3 213.9 214.6 213.6
[61] 212.1 211.4 213.1 212.9 213.3 211.5 212.3 213.0 211.0 210.7 210.1 211.4
[73] 210.0 209.7 208.8 208.8 208.8 210.6 211.9 212.8 212.5 214.8 215.3 217.5
[85] 218.8 220.7 222.2 226.7 228.4 233.2 235.7 237.1 240.6 243.8 245.3 246.0
[97] 246.3 247.7 247.6 247.8 249.4 249.0 249.9 250.5 251.5 249.0 247.6 248.8
[109] 250.4 250.7 253.0 253.7 255.0 256.2 256.0 257.4 260.4 260.0 261.3 260.4
[121] 261.6 260.8 259.8 259.0 258.9 257.4 257.7 257.9 257.4 257.3 257.6 258.9
[133] 257.8 257.7 257.2 257.5 256.8 257.5 257.0 257.6 257.3 257.5 259.6 261.1
[145] 262.9 263.3 262.8 261.8 262.2 262.7
(r->clj '(attributes BJsales)){:tsp [1.0 150.0 1.0], :class ["ts"]}(r->clj r.datasets/BJsales)_unnamed [149 2]:
| :\(time | :\)series | |
|---|---|
| 1.0 | 200.1 |
| 2.0 | 199.5 |
| 3.0 | 199.4 |
| 4.0 | 198.9 |
| 5.0 | 199.0 |
| 6.0 | 200.2 |
| 7.0 | 198.6 |
| 8.0 | 200.0 |
| 9.0 | 200.3 |
| 10.0 | 201.2 |
| … | … |
| 139.0 | 257.0 |
| 140.0 | 257.6 |
| 141.0 | 257.3 |
| 142.0 | 257.5 |
| 143.0 | 259.6 |
| 144.0 | 261.1 |
| 145.0 | 262.9 |
| 146.0 | 263.3 |
| 147.0 | 262.8 |
| 148.0 | 261.8 |
| 149.0 | 262.2 |
4.5 Multidimensional timeseries
(r '(window EuStockMarkets :end [1991,155]))Time Series:
Start = c(1991, 130)
End = c(1991, 155)
Frequency = 260
DAX SMI CAC FTSE
1991.496 1628.75 1678.1 1772.8 2443.6
1991.500 1613.63 1688.5 1750.5 2460.2
1991.504 1606.51 1678.6 1718.0 2448.2
1991.508 1621.04 1684.1 1708.1 2470.4
1991.512 1618.16 1686.6 1723.1 2484.7
1991.515 1610.61 1671.6 1714.3 2466.8
1991.519 1630.75 1682.9 1734.5 2487.9
1991.523 1640.17 1703.6 1757.4 2508.4
1991.527 1635.47 1697.5 1754.0 2510.5
1991.531 1645.89 1716.3 1754.3 2497.4
1991.535 1647.84 1723.8 1759.8 2532.5
1991.538 1638.35 1730.5 1755.5 2556.8
1991.542 1629.93 1727.4 1758.1 2561.0
1991.546 1621.49 1733.3 1757.5 2547.3
1991.550 1624.74 1734.0 1763.5 2541.5
1991.554 1627.63 1728.3 1762.8 2558.5
1991.558 1631.99 1737.1 1768.9 2587.9
1991.562 1621.18 1723.1 1778.1 2580.5
1991.565 1613.42 1723.6 1780.1 2579.6
1991.569 1604.95 1719.0 1767.7 2589.3
1991.573 1605.75 1721.2 1757.9 2595.0
1991.577 1616.67 1725.3 1756.6 2595.6
1991.581 1619.29 1727.2 1754.7 2588.8
1991.585 1620.49 1727.2 1766.8 2591.7
1991.588 1619.67 1731.6 1766.5 2601.7
1991.592 1623.07 1724.1 1762.2 2585.4
(r->clj '(attributes EuStockMarkets)){:dim [1860 4],
:dimnames [nil ["DAX" "SMI" "CAC" "FTSE"]],
:tsp [1991.496153846154 1998.646153846154 260.0],
:class ["mts" "ts" "matrix"]}(r->clj r.datasets/EuStockMarkets)_unnamed [1860 5]:
| :$time | DAX | SMI | CAC | FTSE |
|---|---|---|---|---|
| 1991.49615385 | 1628.75 | 1678.1 | 1772.8 | 2443.6 |
| 1991.50000000 | 1613.63 | 1688.5 | 1750.5 | 2460.2 |
| 1991.50384615 | 1606.51 | 1678.6 | 1718.0 | 2448.2 |
| 1991.50769231 | 1621.04 | 1684.1 | 1708.1 | 2470.4 |
| 1991.51153846 | 1618.16 | 1686.6 | 1723.1 | 2484.7 |
| 1991.51538462 | 1610.61 | 1671.6 | 1714.3 | 2466.8 |
| 1991.51923077 | 1630.75 | 1682.9 | 1734.5 | 2487.9 |
| 1991.52307692 | 1640.17 | 1703.6 | 1757.4 | 2508.4 |
| 1991.52692308 | 1635.47 | 1697.5 | 1754.0 | 2510.5 |
| 1991.53076923 | 1645.89 | 1716.3 | 1754.3 | 2497.4 |
| … | … | … | … | … |
| 1998.60769231 | 5861.19 | 8239.5 | 4177.3 | 5837.0 |
| 1998.61153846 | 5774.38 | 8139.2 | 4095.0 | 5809.7 |
| 1998.61538462 | 5718.70 | 8170.2 | 4047.9 | 5736.1 |
| 1998.61923077 | 5614.77 | 7943.2 | 3976.4 | 5632.5 |
| 1998.62307692 | 5528.12 | 7846.2 | 3968.6 | 5594.1 |
| 1998.62692308 | 5598.32 | 7952.9 | 4041.9 | 5680.4 |
| 1998.63076923 | 5460.43 | 7721.3 | 3939.5 | 5587.6 |
| 1998.63461538 | 5285.78 | 7447.9 | 3846.0 | 5432.8 |
| 1998.63846154 | 5386.94 | 7607.5 | 3945.7 | 5462.2 |
| 1998.64230769 | 5355.03 | 7552.6 | 3951.7 | 5399.5 |
| 1998.64615385 | 5473.72 | 7676.3 | 3995.0 | 5455.0 |
4.6 Datetime columns
(def dt (r "
day <- c(\"20081101\", \"20081101\", \"20081101\", \"20081101\", \"18081101\", \"20081102\", \"20081102\", \"20081102\", \"20081102\", \"20081103\")
time <- c(\"01:20:00\", \"06:00:00\", \"12:20:00\", \"17:30:00\", \"21:45:00\", \"01:15:00\", \"06:30:00\", \"12:50:00\", \"20:00:00\", \"01:05:00\")
dts1 <- paste(day, time)
dts2 <- as.POSIXct(dts1, format = \"%Y%m%d %H:%M:%S\")
dts3 <- as.POSIXlt(dts1, format = \"%Y%m%d %H:%M:%S\")
dts <- data.frame(posixct=dts2, posixlt=dts3)"))dt posixct posixlt
1 2008-11-01 01:20:00 2008-11-01 01:20:00
2 2008-11-01 06:00:00 2008-11-01 06:00:00
3 2008-11-01 12:20:00 2008-11-01 12:20:00
4 2008-11-01 17:30:00 2008-11-01 17:30:00
5 1808-11-01 21:45:00 1808-11-01 21:45:00
6 2008-11-02 01:15:00 2008-11-02 01:15:00
7 2008-11-02 06:30:00 2008-11-02 06:30:00
8 2008-11-02 12:50:00 2008-11-02 12:50:00
9 2008-11-02 20:00:00 2008-11-02 20:00:00
10 2008-11-03 01:05:00 2008-11-03 01:05:00
(r->clj '(attributes ~dt)){:names ["posixct" "posixlt"],
:class ["data.frame"],
:row.names [1 2 3 4 5 6 7 8 9 10]}(r->clj dt)_unnamed [10 2]:
| :posixct | :posixlt |
|---|---|
| 2008-11-01T01:20 | 2008-11-01T01:20 |
| 2008-11-01T06:00 | 2008-11-01T06:00 |
| 2008-11-01T12:20 | 2008-11-01T12:20 |
| 2008-11-01T17:30 | 2008-11-01T17:30 |
| 1808-11-01T21:45 | 1808-11-01T21:45 |
| 2008-11-02T01:15 | 2008-11-02T01:15 |
| 2008-11-02T06:30 | 2008-11-02T06:30 |
| 2008-11-02T12:50 | 2008-11-02T12:50 |
| 2008-11-02T20:00 | 2008-11-02T20:00 |
| 2008-11-03T01:05 | 2008-11-03T01:05 |
4.7 Distances
r.datasets/UScitiesD Atlanta Chicago Denver Houston LosAngeles Miami NewYork
Chicago 587
Denver 1212 920
Houston 701 940 879
LosAngeles 1936 1745 831 1374
Miami 604 1188 1726 968 2339
NewYork 748 713 1631 1420 2451 1092
SanFrancisco 2139 1858 949 1645 347 2594 2571
Seattle 2182 1737 1021 1891 959 2734 2408
Washington.DC 543 597 1494 1220 2300 923 205
SanFrancisco Seattle
Chicago
Denver
Houston
LosAngeles
Miami
NewYork
SanFrancisco
Seattle 678
Washington.DC 2442 2329
(r->clj '(attributes UScitiesD)){:Labels
["Atlanta"
"Chicago"
"Denver"
"Houston"
"LosAngeles"
"Miami"
"NewYork"
"SanFrancisco"
"Seattle"
"Washington.DC"],
:Size [10],
:call {0 as.dist.default, :m [t cities.mat]},
:class ["dist"],
:Diag [false],
:Upper [false]}(r->clj r.datasets/UScitiesD)_unnamed [10 11]:
| :$row.names | Atlanta | Chicago | Denver | Houston | LosAngeles | Miami | NewYork | SanFrancisco | Seattle | Washington.DC |
|---|---|---|---|---|---|---|---|---|---|---|
| Atlanta | 0 | 587 | 1212 | 701 | 1936 | 604 | 748 | 2139 | 2182 | 543 |
| Chicago | 587 | 0 | 920 | 940 | 1745 | 1188 | 713 | 1858 | 1737 | 597 |
| Denver | 1212 | 920 | 0 | 879 | 831 | 1726 | 1631 | 949 | 1021 | 1494 |
| Houston | 701 | 940 | 879 | 0 | 1374 | 968 | 1420 | 1645 | 1891 | 1220 |
| LosAngeles | 1936 | 1745 | 831 | 1374 | 0 | 2339 | 2451 | 347 | 959 | 2300 |
| Miami | 604 | 1188 | 1726 | 968 | 2339 | 0 | 1092 | 2594 | 2734 | 923 |
| NewYork | 748 | 713 | 1631 | 1420 | 2451 | 1092 | 0 | 2571 | 2408 | 205 |
| SanFrancisco | 2139 | 1858 | 949 | 1645 | 347 | 2594 | 2571 | 0 | 678 | 2442 |
| Seattle | 2182 | 1737 | 1021 | 1891 | 959 | 2734 | 2408 | 678 | 0 | 2329 |
| Washington.DC | 543 | 597 | 1494 | 1220 | 2300 | 923 | 205 | 2442 | 2329 | 0 |
4.8 Other
4.8.1 List
r.datasets/Harman23-cor$cov
height arm.span forearm lower.leg weight bitro.diameter
height 1.000 0.846 0.805 0.859 0.473 0.398
arm.span 0.846 1.000 0.881 0.826 0.376 0.326
forearm 0.805 0.881 1.000 0.801 0.380 0.319
lower.leg 0.859 0.826 0.801 1.000 0.436 0.329
weight 0.473 0.376 0.380 0.436 1.000 0.762
bitro.diameter 0.398 0.326 0.319 0.329 0.762 1.000
chest.girth 0.301 0.277 0.237 0.327 0.730 0.583
chest.width 0.382 0.415 0.345 0.365 0.629 0.577
chest.girth chest.width
height 0.301 0.382
arm.span 0.277 0.415
forearm 0.237 0.345
lower.leg 0.327 0.365
weight 0.730 0.629
bitro.diameter 0.583 0.577
chest.girth 1.000 0.539
chest.width 0.539 1.000
$center
[1] 0 0 0 0 0 0 0 0
$n.obs
[1] 305
(r->clj '(attributes Harman23.cor)){:names ["cov" "center" "n.obs"]}(r->clj r.datasets/Harman23-cor){:cov
[1.0
0.846
0.805
0.859
0.473
0.398
0.301
0.382
0.846
1.0
0.881
0.826
0.376
0.326
0.277
0.415
0.805
0.881
1.0
0.801
0.38
0.319
0.237
0.345
0.859
0.826
0.801
1.0
0.436
0.329
0.327
0.365
0.473
0.376
0.38
0.436
1.0
0.762
0.73
0.629
0.398
0.326
0.319
0.329
0.762
1.0
0.583
0.577
0.301
0.277
0.237
0.327
0.73
0.583
1.0
0.539
0.382
0.415
0.345
0.365
0.629
0.577
0.539
1.0],
:center [0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0],
:n.obs [305.0]}4.8.2 Partially named list
(def pnl (r '[:!list :a 112 "abc" "cde" :b "qwe"]))pnl$a
[1] 112
[[2]]
[1] "abc"
[[3]]
[1] "cde"
$b
[1] "qwe"
(r->clj '(attributes ~pnl)){:names ["a" "" "" "b"]}(r->clj pnl){:a [112], 1 ["abc"], 2 ["cde"], :b ["qwe"]}4.9 Dataset -> R
Every dataset is converted to data.frame object.
(clj->r (r->clj r.datasets/UScitiesD)) Atlanta Chicago Denver Houston LosAngeles Miami NewYork
Atlanta 0 587 1212 701 1936 604 748
Chicago 587 0 920 940 1745 1188 713
Denver 1212 920 0 879 831 1726 1631
Houston 701 940 879 0 1374 968 1420
LosAngeles 1936 1745 831 1374 0 2339 2451
Miami 604 1188 1726 968 2339 0 1092
NewYork 748 713 1631 1420 2451 1092 0
SanFrancisco 2139 1858 949 1645 347 2594 2571
Seattle 2182 1737 1021 1891 959 2734 2408
Washington.DC 543 597 1494 1220 2300 923 205
SanFrancisco Seattle Washington.DC
Atlanta 2139 2182 543
Chicago 1858 1737 597
Denver 949 1021 1494
Houston 1645 1891 1220
LosAngeles 347 959 2300
Miami 2594 2734 923
NewYork 2571 2408 205
SanFrancisco 0 678 2442
Seattle 678 0 2329
Washington.DC 2442 2329 0