R Dataset

R data objects are usually converted to the Clojure datastructure or tech.ml.dataset object. Here are the notes about typical use cases. Default R datasets are used as examples.

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.

BOD

No row.names available.

: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

CO2

With row.names

:$row.names	:Plant	:Type	:Treatment	:conc	:uptake
1	1	:Quebec	:nonchilled	95.0	16.0
2	1	:Quebec	:nonchilled	175.0	30.4
3	1	:Quebec	:nonchilled	250.0	34.8
4	1	:Quebec	:nonchilled	350.0	37.2
5	1	:Quebec	:nonchilled	500.0	35.3
6	1	:Quebec	:nonchilled	675.0	39.2
7	1	:Quebec	:nonchilled	1000.0	39.7
8	2	:Quebec	:nonchilled	95.0	13.6
9	2	:Quebec	:nonchilled	175.0	27.3
10	2	:Quebec	:nonchilled	250.0	37.1

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.

USBAdmissions

Dimensions with names.

:Admit	:Gender	:Dept	:$value
Admitted	Male	A	512.0
Rejected	Male	A	313.0
Admitted	Female	A	89.0
Rejected	Female	A	19.0
Admitted	Male	B	353.0
Rejected	Male	B	207.0
Admitted	Female	B	17.0
Rejected	Female	B	8.0
Admitted	Male	C	120.0
Rejected	Male	C	205.0

crimtab

Dimensions without names

:$col-0	:$col-1	:$value
9.4	142.24	0
9.5	142.24	0
9.6	142.24	0
9.7	142.24	0
9.8	142.24	0
9.9	142.24	0
10	142.24	1
10.1	142.24	0
10.2	142.24	0
10.3	142.24	0

Matrices, arrays, multidimensional arrays

The idea here is similar to R, 2d structures (matrices) are tagged using other dimensions. So for first two dimensions - matrix is created, or dimensions are added as columns. If names are missing artificial column names are added. Row names are added as :$row.names.

VADeaths

Matrix with row and column names

:$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

freeny-x

Matrix with column names

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

iris3

3d array, with names in second and third dimensions

:$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

5D array

Created with (r/r '(array ~(range 60) :dim [2 5 1 3 2]))

:$col-0	:$col-1	:$col-2	1	2	3	4	5
1	1	1	0.0	2.0	4.0	6.0	8.0
1	1	1	1.0	3.0	5.0	7.0	9.0
1	2	1	10.0	12.0	14.0	16.0	18.0
1	2	1	11.0	13.0	15.0	17.0	19.0
1	3	1	20.0	22.0	24.0	26.0	28.0
1	3	1	21.0	23.0	25.0	27.0	29.0
1	1	2	30.0	32.0	34.0	36.0	38.0
1	1	2	31.0	33.0	35.0	37.0	39.0
1	2	2	40.0	42.0	44.0	46.0	48.0
1	2	2	41.0	43.0	45.0	47.0	49.0
1	3	2	50.0	52.0	54.0	56.0	58.0
1	3	2	51.0	53.0	55.0	57.0	59.0

1D Timeseries

Timeseries are stored in two columns:

• :$time - to store time identifier as float
• :$series - to store timeseries

BJsales

:$time	:$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

Multidimensional timeseries

Is a mix of multidmentions array with added :$time column.

EuStockMarkets

:$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

Datatypes with time

(r/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)") 

:posixct	:posixlt
2008-11-01T01:20+01:00[Europe/Warsaw]	2008-11-01T01:20+01:00[Europe/Warsaw]
2008-11-01T06:00+01:00[Europe/Warsaw]	2008-11-01T06:00+01:00[Europe/Warsaw]
2008-11-01T12:20+01:00[Europe/Warsaw]	2008-11-01T12:20+01:00[Europe/Warsaw]
2008-11-01T17:30+01:00[Europe/Warsaw]	2008-11-01T17:30+01:00[Europe/Warsaw]
1808-11-01T21:45+01:24[Europe/Warsaw]	1808-11-01T21:45+01:24[Europe/Warsaw]
2008-11-02T01:15+01:00[Europe/Warsaw]	2008-11-02T01:15+01:00[Europe/Warsaw]
2008-11-02T06:30+01:00[Europe/Warsaw]	2008-11-02T06:30+01:00[Europe/Warsaw]
2008-11-02T12:50+01:00[Europe/Warsaw]	2008-11-02T12:50+01:00[Europe/Warsaw]
2008-11-02T20:00+01:00[Europe/Warsaw]	2008-11-02T20:00+01:00[Europe/Warsaw]
2008-11-03T01:05+01:00[Europe/Warsaw]	2008-11-03T01:05+01:00[Europe/Warsaw]

Other

Harman23-cor

Named list

{: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]}

Partially named list

{:a [11.0], :b [22.0], [[3]] [33.0], [[4]] [44.0], :e [55.0], :f [66.0], [[7]] [77.0], [[8]] [88.0], :i [99.0]}