indexByRow

matrixStats: Benchmark report

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indexByRow() benchmarks

This report benchmark the performance of indexByRow() against alternative methods:

• indexByRow_R1() based in matrix(..., byrow=TRUE)
• indexByRow_R2() is a modified version of indexByRow_R1()

where indexByRow_R1() and indexByRow_R2() are defined as in the Appendix.

Data

> data <- rmatrices(mode = "index")

where rmatrices() is defined in the Appendix.

Results

10x10 matrix

> X <- data[["10x10"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:100] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:50] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.0077	0.0129	0.0160	0.0146	0.0166	0.0658
indexByRow_R1	0.0077	0.0142	0.0177	0.0156	0.0177	0.0893
indexByRow_R2	0.0077	0.0142	0.0188	0.0156	0.0185	0.1921

expr	min	lq	mean	median	uq	max
indexByRow	1	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	1	1.104	1.106	1.066	1.070	1.357
indexByRow_R2	1	1.104	1.171	1.066	1.116	2.918
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.0127	0.0152	0.0227	0.0177	0.0204	0.2976
indexByRow_R1	0.0162	0.0181	0.0260	0.0198	0.0239	0.2125
indexByRow_R2	0.0169	0.0196	0.0250	0.0208	0.0277	0.0781

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.0000
indexByRow_R1	1.273	1.190	1.149	1.119	1.170	0.7141
indexByRow_R2	1.333	1.291	1.102	1.174	1.359	0.2626
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
3	indexByRow_R2	0.0119	0.0141	0.0174	0.0158	0.0175	0.0404
1	indexByRow	0.0131	0.0156	0.0195	0.0181	0.0200	0.1143
2	indexByRow_R1	0.0158	0.0185	0.0227	0.0204	0.0227	0.0624

	expr	min	lq	mean	median	uq	max
3	indexByRow_R2	1.000	1.000	1.000	1.000	1.000	1.000
1	indexByRow	1.097	1.110	1.120	1.146	1.143	2.829
2	indexByRow_R1	1.323	1.315	1.302	1.293	1.297	1.543
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+odd data. Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:10000] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:5000] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	indexByRow	0.0177	0.0219	0.0302	0.0266	0.0341	0.0720
3	indexByRow_R2	0.1170	0.1190	0.1393	0.1205	0.1336	0.3033
2	indexByRow_R1	0.1170	0.1193	0.1433	0.1216	0.1447	0.2956

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
3	indexByRow_R2	6.608	5.421	4.620	4.536	3.921	4.214
2	indexByRow_R1	6.608	5.438	4.753	4.580	4.248	4.107
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.1109	0.1709	0.1917	0.1967	0.2233	0.3022
indexByRow_R1	0.1728	0.2695	0.2836	0.2947	0.3162	0.4465
indexByRow_R2	0.5801	0.7709	0.8210	0.8038	0.9216	1.2592

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	1.559	1.577	1.479	1.498	1.416	1.478
indexByRow_R2	5.233	4.510	4.282	4.086	4.128	4.167
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.1186	0.1247	0.1445	0.1324	0.1625	0.2148
indexByRow_R1	0.2729	0.2899	0.3031	0.2968	0.3105	0.4658
indexByRow_R2	0.3565	0.3830	0.3861	0.3863	0.3915	0.4123

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	2.302	2.324	2.098	2.241	1.911	2.168
indexByRow_R2	3.006	3.071	2.672	2.917	2.410	1.919
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+odd data. Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:10000] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:5000] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.0304	0.0339	0.0389	0.0364	0.0400	0.0858
indexByRow_R1	0.1971	0.1998	0.2050	0.2009	0.2092	0.2983
indexByRow_R2	0.1963	0.1990	0.2050	0.2009	0.2102	0.2776

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	6.481	5.898	5.266	5.524	5.226	3.475
indexByRow_R2	6.455	5.875	5.264	5.524	5.250	3.233
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.1113	0.1134	0.1480	0.1247	0.1719	0.2645
indexByRow_R1	0.1721	0.1763	0.2246	0.1825	0.2951	0.3892
indexByRow_R2	0.5582	0.5618	0.6793	0.5655	0.8486	1.0155

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	1.547	1.555	1.518	1.463	1.717	1.472
indexByRow_R2	5.017	4.956	4.590	4.534	4.937	3.840
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.1213	0.1292	0.1425	0.1355	0.1538	0.2144
indexByRow_R1	0.2656	0.2904	0.3008	0.3001	0.3091	0.3399
indexByRow_R2	0.3611	0.3828	0.3956	0.3942	0.4042	0.4797

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	2.191	2.249	2.111	2.215	2.010	1.585
indexByRow_R2	2.978	2.964	2.777	2.909	2.628	2.237
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+odd data. Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:10000] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:5000] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.0289	0.0323	0.0396	0.0364	0.0395	0.1297
indexByRow_R1	0.1790	0.1840	0.1994	0.2027	0.2073	0.2518
indexByRow_R2	0.1763	0.1829	0.1988	0.2029	0.2092	0.2264

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	6.200	5.690	5.041	5.571	5.253	1.941
indexByRow_R2	6.106	5.655	5.026	5.577	5.302	1.745
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.1744	0.2044	0.2352	0.2331	0.2510	0.4004
indexByRow_R1	0.2425	0.3078	0.3294	0.3259	0.3455	0.4877
indexByRow_R2	0.8126	0.9245	0.9443	0.9374	0.9607	1.1129

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	1.391	1.506	1.401	1.398	1.377	1.218
indexByRow_R2	4.660	4.523	4.015	4.021	3.827	2.780
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.0755	0.0824	0.1198	0.1268	0.1411	0.2017
indexByRow_R1	0.1775	0.1882	0.2555	0.2808	0.3026	0.3857
indexByRow_R2	0.2776	0.2860	0.3788	0.4107	0.4473	0.5131

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	2.352	2.285	2.133	2.214	2.145	1.912
indexByRow_R2	3.679	3.472	3.162	3.238	3.171	2.544
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+odd data. Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:100000] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:50000] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.1163	0.2215	0.2909	0.2593	0.3642	0.5532
indexByRow_R1	1.1275	1.4767	1.8302	1.7702	2.1219	2.6523
indexByRow_R2	1.1271	1.6638	2.0902	1.8353	2.2481	21.8200

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	9.699	6.665	6.292	6.828	5.827	4.795
indexByRow_R2	9.695	7.510	7.185	7.079	6.173	39.445
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	1.068	1.648	1.889	1.843	2.053	3.886
indexByRow_R1	1.779	2.751	3.454	3.054	3.545	27.781
indexByRow_R2	6.295	7.829	9.726	9.376	10.414	32.519

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	1.666	1.669	1.828	1.657	1.727	7.149
indexByRow_R2	5.896	4.751	5.149	5.088	5.074	8.368
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	1.090	1.200	1.361	1.272	1.361	3.706
indexByRow_R1	2.753	3.014	4.098	3.236	3.683	35.035
indexByRow_R2	3.332	4.046	4.641	4.257	4.728	18.979

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	2.526	2.511	3.011	2.544	2.706	9.453
indexByRow_R2	3.057	3.372	3.410	3.346	3.475	5.121
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+odd data. Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:100000] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:50000] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.1859	0.2385	0.3671	0.2514	0.4054	6.04
indexByRow_R1	1.6580	2.0123	2.3841	2.1594	2.6747	7.32
indexByRow_R2	1.6796	2.0021	2.5473	2.2491	2.6683	19.24

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	8.917	8.438	6.495	8.590	6.598	1.212
indexByRow_R2	9.033	8.395	6.940	8.947	6.583	3.185
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	1.134	1.643	1.776	1.712	1.874	5.208
indexByRow_R1	1.987	2.814	3.138	3.008	3.412	8.191
indexByRow_R2	6.961	8.099	9.872	8.717	9.719	45.456

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.000
indexByRow_R1	1.752	1.713	1.766	1.758	1.820	1.573
indexByRow_R2	6.138	4.930	5.557	5.093	5.186	8.727
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr	min	lq	mean	median	uq	max
indexByRow	0.681	1.115	1.372	1.214	1.296	17.532
indexByRow_R1	1.797	2.836	2.982	2.972	3.187	6.456
indexByRow_R2	2.896	3.826	4.104	4.007	4.485	7.450

expr	min	lq	mean	median	uq	max
indexByRow	1.000	1.000	1.000	1.000	1.000	1.0000
indexByRow_R1	2.639	2.545	2.174	2.448	2.459	0.3683
indexByRow_R2	4.253	3.433	2.992	3.300	3.461	0.4250
Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+odd data. Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R Under development (unstable) (2015-02-27 r67909)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] markdown_0.7.7          microbenchmark_1.4-2    matrixStats_0.14.0-9000
[4] ggplot2_1.0.0           knitr_1.9.3             R.devices_2.13.0       
[7] R.utils_2.0.0           R.oo_1.19.0             R.methodsS3_1.7.0      

loaded via a namespace (and not attached):
 [1] Rcpp_0.11.4           GenomeInfoDb_1.3.13   formatR_1.0.3        
 [4] plyr_1.8.1            base64enc_0.1-3       tools_3.2.0          
 [7] digest_0.6.8          RSQLite_1.0.0         annotate_1.45.2      
[10] evaluate_0.5.7        gtable_0.1.2          R.cache_0.11.1-9000  
[13] lattice_0.20-30       DBI_0.3.1             parallel_3.2.0       
[16] mvtnorm_1.0-2         proto_0.3-10          R.rsp_0.20.0         
[19] genefilter_1.49.2     stringr_0.6.2         IRanges_2.1.41       
[22] S4Vectors_0.5.21      stats4_3.2.0          grid_3.2.0           
[25] Biobase_2.27.2        AnnotationDbi_1.29.17 XML_3.98-1.1         
[28] survival_2.38-1       multcomp_1.3-9        TH.data_1.0-6        
[31] reshape2_1.4.1        scales_0.2.4          MASS_7.3-39          
[34] splines_3.2.0         BiocGenerics_0.13.6   xtable_1.8-0         
[37] mime_0.2.1            colorspace_1.2-4      labeling_0.3         
[40] sandwich_2.3-2        munsell_0.4.2         Cairo_1.5-6          
[43] zoo_1.7-12           

Total processing time was 33.31 secs.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('indexByRow')

---

Copyright Henrik Bengtsson. Last updated on 2015-03-02 17:27:44 (-0800 UTC). Powered by RSP.

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Local functions

> indexByRow_R1 <- function(dim, idxs = NULL, ...) {
+     n <- prod(dim)
+     x <- matrix(seq_len(n), nrow = dim[2L], ncol = dim[1L], byrow = TRUE)
+     if (!is.null(idxs)) 
+         x <- x[idxs]
+     as.vector(x)
+ }

> indexByRow_R2 <- function(dim, idxs = NULL, ...) {
+     n <- prod(dim)
+     if (is.null(idxs)) {
+         x <- matrix(seq_len(n), nrow = dim[2L], ncol = dim[1L], byrow = TRUE)
+         as.vector(x)
+     }     else {
+         idxs <- idxs - 1
+         cols <- idxs%/%dim[2L]
+         rows <- idxs%%dim[2L]
+         cols + dim[1L] * rows + 1L
+     }
+ }

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), naProb = 0) {
+     mode <- match.arg(mode)
+     n <- nrow * ncol
+     if (mode == "logical") {
+         X <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else if (mode == "index") {
+         X <- seq_len(n)
+         mode <- "integer"
+     }     else {
+         X <- runif(n, min = range[1], max = range[2])
+     }
+     storage.mode(X) <- mode
+     if (naProb > 0) 
+         X[sample(n, size = naProb * n)] <- NA
+     dim(X) <- c(nrow, ncol)
+     X
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+     data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+     data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+     data[[4]] <- t(data[[3]])
+     data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+     data[[6]] <- t(data[[5]])
+     names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+     data
+ }