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Wei Liu

2013-Aug-31 09:29 UTC

[R] How to plot a pretty heatmap with uneven distributed data?

Dear All,

I want to plot a heatmap with R, but my data  distributed unevenly, for
example, my data range from 10 to 1500, but most of the data smaller than
200, when I plot a heatmap, the colour is also distributed unevenly, most
part of the heatmap is one colour, so the heatmap is ugly and meaningless.

So can anybody help me plot a pretty heatmap for me with the attached data.
I am looking forward your reply! Thansk very much.


Best Regards.
Wei Liu.
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OTUID	CKN.1	CKP.1	A1.1	A1.2	A1.3	B1.1	B1.2	B1.3	CKN.2	CKP.2	A2.1	A2.2	A2.3	B2.1
B2.2	B2.3	CKN.3	CKP.3	A3.1	A3.2	A3.3	B3.1	B3.2	B3.3
280	4.0	0.0	2.0	0.0	0.0	7.0	2.0	0.0	0.0	0.0	0.0	32.0	3.0	1.0	7.0	0.0	5.0	6.0	1.0
0.0	6.0	2.0	1.0	23.0
631	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	6.0	8.0	3.0	12.0	1.0	16.0	4.0
1.0	10.0	17.0	1.0	48.0	23.0
710	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0
527.0	0.0	0.0	0.0	0.0	0.0
1144	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	9.0	0.0	124.0	11.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	0.0	0.0	0.0	0.0	0.0
1459	0.0	0.0	2.0	0.0	1.0	0.0	75.0	0.0	1.0	13.0	0.0	1.0	2.0	0.0	0.0	0.0	0.0	0.0
0.0	1.0	5.0	0.0	0.0	0.0
1644	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	7.0	0.0	0.0	0.0
17.0	1.0	530.0	5.0	1.0
1809	0.0	0.0	32.0	0.0	2.0	1.0	429.0	1.0	1.0	102.0	2.0	137.0	0.0	0.0	43.0	0.0	0.0
0.0	0.0	0.0	2.0	0.0	0.0	0.0
1886	56.0	2.0	16.0	9.0	3.0	2.0	66.0	25.0	16.0	121.0	9.0	253.0	19.0	3.0	14.0	4.0
45.0	13.0	9.0	7.0	3.0	16.0	1.0	26.0
2000	5.0	11.0	18.0	19.0	1.0	8.0	1.0	4.0	0.0	1.0	0.0	0.0	4.0	0.0	1.0	0.0	0.0	11.0
0.0	0.0	1.0	17.0	0.0	2.0
2105	2.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	13.0	115.0	462.0	13.0	196.0
24.0	35.0	19.0	55.0	13.0	9.0	6.0	2.0
2127	0.0	0.0	0.0	0.0	0.0	0.0	8.0	0.0	0.0	109.0	0.0	40.0	0.0	0.0	1.0	0.0	0.0	0.0
0.0	0.0	1.0	0.0	0.0	5.0
2195	11.0	1.0	3.0	0.0	0.0	2.0	2.0	2.0	1.0	11.0	1.0	6.0	7.0	3.0	7.0	8.0	17.0	0.0
1.0	13.0	17.0	29.0	3.0	99.0
2200	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	181.0	0.0	0.0	0.0	0.0	1.0	5.0
0.0	0.0	1.0	1.0	1.0	0.0
2588	0.0	0.0	0.0	0.0	3.0	0.0	4.0	8.0	19.0	0.0	73.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	0.0	0.0	0.0	0.0	0.0
2676	1.0	0.0	0.0	0.0	0.0	1.0	3.0	0.0	0.0	4.0	0.0	85.0	5.0	2.0	4.0	0.0	6.0	10.0
1.0	0.0	2.0	0.0	1.0	2.0
2972	0.0	0.0	5.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	67.0	55.0	2.0	81.0	0.0	3.0
635.0	2.0	1.0	0.0	0.0	0.0	6.0
3121	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0	9.0	14.0	20.0	11.0	9.0	11.0
21.0	0.0	5.0	1.0	7.0	0.0
3135	1.0	0.0	5.0	3.0	0.0	1.0	7.0	2.0	2.0	2.0	0.0	2.0	9.0	6.0	34.0	19.0	1.0	9.0
15.0	8.0	9.0	2.0	1.0	1.0
3488	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
351.0	0.0	0.0	0.0	0.0	0.0
3578	17.0	0.0	16.0	1.0	2.0	3.0	5.0	0.0	0.0	0.0	0.0	95.0	73.0	145.0	561.0	56.0
637.0	401.0	2.0	95.0	2179.0	56.0	483.0	231.0
3594	0.0	3.0	0.0	3.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	3.0	2.0	0.0	0.0
46.0	4.0	182.0	6.0	0.0
3945	0.0	0.0	1.0	2.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0	24.0	26.0	7.0	5.0	4.0	22.0	8.0
6.0	8.0	11.0	3.0	7.0	7.0
4034	5.0	0.0	1.0	1.0	0.0	2.0	6.0	0.0	0.0	3.0	0.0	1347.0	72.0	22.0	11.0	3.0	130.0
85.0	1.0	2.0	22.0	4.0	13.0	36.0
4098	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	26.0	0.0	79.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	0.0	0.0	0.0	0.0	0.0
4343	2.0	1.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	1.0	0.0	705.0	47.0	6.0	17.0	0.0	14.0
74.0	0.0	1.0	14.0	0.0	1.0	4.0
4352	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	1.0	0.0	21.0	53.0	3.0	2.0	0.0	167.0	8.0
0.0	0.0	13.0	1.0	7.0	314.0
4553	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	2.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0
0.0	3.0	1.0	1.0	567.0
4591	0.0	0.0	0.0	0.0	0.0	1.0	9.0	1.0	9.0	30.0	20.0	27.0	0.0	0.0	0.0	3.0	1.0	0.0
0.0	5.0	0.0	0.0	0.0	2.0
4623	0.0	0.0	31.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	75.0	0.0	6.0	0.0
0.0	4.0	303.0	20.0	1.0	39.0
4798	0.0	0.0	240.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0
0.0	0.0	2.0	0.0	0.0	0.0
4853	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	32.0	61.0	25.0	26.0	3.0	18.0
46.0	2.0	3.0	6.0	1.0	2.0	10.0
5068	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
0.0	100.0	0.0	0.0	1.0
5230	1.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	1.0	0.0	0.0	3.0	79.0	18.0	11.0	0.0	10.0
19.0	3.0	2.0	4.0	1.0	0.0	0.0
5263	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	2.0	0.0	105.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	0.0	0.0	0.0	0.0	0.0
5563	0.0	0.0	15.0	0.0	0.0	0.0	1368.0	7.0	4.0	524.0	12.0	489.0	0.0	0.0	26.0	0.0
0.0	1.0	0.0	0.0	25.0	1.0	0.0	11.0
6084	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	13.0	1.0	68.0	5.0	2.0	17.0
1.0	1.0	7.0	0.0	0.0	0.0
6537	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	0.0	2.0	0.0	97.0	0.0
0.0	0.0	0.0	0.0	0.0	12.0
6791	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	117.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	0.0	0.0	0.0	0.0	1.0
7077	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	2.0	4.0	0.0	0.0
9.0	5.0	117.0	13.0	11.0
7242	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0
0.0	2.0	1.0	0.0	106.0
7344	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	108.0	0.0	0.0	0.0
0.0	0.0	0.0	0.0	0.0	0.0
7423	32.0	8.0	30.0	67.0	2.0	2.0	0.0	12.0	0.0	0.0	1.0	1.0	2.0	0.0	1.0	5.0	1.0	0.0
1.0	3.0	2.0	22.0	1.0	3.0
7505	6.0	0.0	4.0	0.0	0.0	0.0	3.0	0.0	0.0	11.0	0.0	90.0	0.0	0.0	0.0	0.0	0.0	1.0
0.0	1.0	0.0	0.0	0.0	5.0
7570	0.0	0.0	0.0	0.0	0.0	0.0	1.0	136.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	0.0	0.0	0.0	0.0	0.0
7610	120.0	3.0	19.0	4.0	2.0	7.0	78.0	4.0	3.0	147.0	0.0	157.0	73.0	13.0	69.0	4.0
118.0	20.0	22.0	137.0	206.0	172.0	18.0	1132.0
7618	5.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	25.0	388.0	9.0	1.0	2.0	1217.0
5.0	0.0	11.0	5.0	2.0	23.0	93.0
7650	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	2.0	1.0	1.0	1.0	0.0	131.0	0.0	0.0	2.0
0.0	0.0	3.0	0.0	0.0	0.0
7663	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	14.0	1.0	4.0	1.0	2.0	176.0
0.0	0.0	2.0	0.0	0.0	0.0
8127	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	9.0	104.0	3.0	6.0	1.0	4.0
0.0	8.0	0.0	0.0	0.0	0.0
8638	0.0	0.0	0.0	2.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	2.0	1.0	0.0	7.0	4.0	0.0	1.0
29.0	0.0	78.0	3.0	0.0
8651	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	1.0	0.0	0.0	129.0	0.0
0.0	2.0	2.0	1.0	3.0	1.0
8883	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
0.0	116.0	0.0	0.0	0.0
9017	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	15.0	30.0	4.0	6.0	1.0	25.0	7.0
3.0	5.0	5.0	5.0	6.0	7.0
9467	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	4.0	10.0	0.0
5.0	59.0	9.0	5.0	0.0	4.0
9916	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	32.0	27.0	9.0	5.0	1.0	20.0	23.0
0.0	1.0	8.0	1.0	3.0	7.0
10151	0.0	0.0	0.0	0.0	0.0	0.0	5.0	0.0	0.0	48.0	0.0	125.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	0.0	0.0	0.0	0.0	1.0
10162	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	3.0	0.0	1.0	0.0	2.0	0.0
0.0	1.0	0.0	0.0	115.0	0.0
10277	3.0	13.0	15.0	29.0	2.0	2.0	0.0	7.0	0.0	0.0	0.0	3.0	1.0	1.0	1.0	2.0	2.0	2.0
0.0	7.0	0.0	17.0	0.0	5.0
10753	0.0	0.0	0.0	3.0	1.0	7.0	0.0	40.0	0.0	19.0	5.0	0.0	6.0	302.0	424.0	1597.0
1.0	3.0	247.0	87.0	0.0	481.0	0.0	1.0
10770	1.0	2.0	2.0	1.0	0.0	1.0	4.0	0.0	2.0	1.0	0.0	29.0	6.0	2.0	5.0	1.0	8.0	5.0
3.0	4.0	12.0	4.0	3.0	9.0
11273	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	12.0	1.0	0.0	11.0	0.0
0.0	61.0	0.0	13.0	34.0	1.0
11274	8.0	1.0	38.0	2.0	0.0	1.0	302.0	12.0	16.0	108.0	5.0	155.0	2.0	1.0	50.0	1.0
1.0	3.0	10.0	3.0	31.0	7.0	1.0	3.0
11470	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	5.0	6.0	7.0	4.0	0.0	24.0	2.0
1.0	14.0	33.0	9.0	151.0	10.0
11520	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	10.0	0.0
1.0	0.0	3.0	3.0	2.0	82.0
11660	7.0	1.0	3.0	0.0	0.0	19.0	0.0	0.0	0.0	1.0	0.0	5.0	0.0	0.0	0.0	0.0	18.0	24.0
0.0	1.0	238.0	16.0	5.0	1264.0
11685	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	45.0	1.0
0.0	1.0	6.0	0.0	8.0	397.0
11940	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	1.0	4.0	0.0	0.0	0.0	0.0	0.0
0.0	0.0	0.0	3.0	0.0	209.0
12042	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0
0.0	0.0	1.0	1.0	0.0	159.0
12100	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	4.0	2.0	204.0	0.0	0.0	5.0
0.0	0.0	22.0	0.0	0.0	0.0
12150	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	922.0	0.0	0.0	0.0	0.0	1.0	2.0
0.0	0.0	1.0	0.0	0.0	1.0
12768	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	1.0	0.0	1.0	1.0	1.0	6.0	0.0
0.0	79.0	0.0	21.0	1.0	2.0
13213	4.0	3.0	0.0	3.0	1.0	0.0	2.0	0.0	0.0	4.0	1.0	15.0	5.0	5.0	3.0	2.0	10.0	9.0
5.0	6.0	6.0	14.0	3.0	10.0
13515	7.0	0.0	6.0	1.0	0.0	1.0	8.0	1.0	1.0	18.0	0.0	1.0	10.0	0.0	1.0	2.0	18.0	2.0
6.0	3.0	24.0	16.0	5.0	313.0
13588	13.0	5.0	15.0	6.0	5.0	3.0	48.0	9.0	13.0	18.0	17.0	75.0	31.0	24.0	27.0	16.0
39.0	46.0	27.0	40.0	46.0	54.0	28.0	65.0
13686	2.0	0.0	41.0	1.0	1.0	7.0	14.0	2.0	0.0	2.0	0.0	3.0	7.0	1.0	2.0	0.0	1.0	2.0
0.0	0.0	13.0	3.0	1.0	3.0
13855	2.0	2.0	3.0	9.0	0.0	1.0	9.0	10.0	5.0	2.0	0.0	2.0	9.0	12.0	19.0	40.0	5.0
0.0	14.0	312.0	63.0	714.0	33.0	73.0
14660	4.0	0.0	2.0	2.0	0.0	1.0	1.0	0.0	0.0	2.0	0.0	25.0	13.0	7.0	3.0	3.0	19.0	3.0
2.0	8.0	7.0	7.0	7.0	3.0
14780	1.0	0.0	11.0	1.0	0.0	1.0	2.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	34.0	0.0	51.0	0.0
0.0	2.0	1471.0	3.0	7.0	57.0
14815	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	91.0	4.0	2.0	1.0	0.0	5.0	1.0
0.0	0.0	0.0	0.0	0.0	0.0
14880	0.0	0.0	1.0	0.0	0.0	0.0	201.0	0.0	0.0	10.0	0.0	9.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	0.0	1.0	0.0	0.0	0.0
14947	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	1.0	319.0	0.0	1.0	8.0
0.0	0.0	15.0	0.0	0.0	0.0
15124	0.0	0.0	6.0	4.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	2.0	4.0	5.0	1.0	1.0	4.0	1.0
1.0	65.0	19.0	34.0	4.0	5.0
15175	2.0	0.0	3.0	3.0	0.0	0.0	31.0	7.0	2.0	11.0	0.0	18.0	0.0	0.0	11.0	1.0	0.0
0.0	1.0	0.0	7.0	3.0	0.0	1.0
15213	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	13.0	0.0
0.0	1.0	0.0	0.0	0.0	95.0
15394	0.0	0.0	0.0	6.0	0.0	0.0	2.0	2.0	1.0	2.0	0.0	3.0	0.0	0.0	4.0	3.0	4.0	0.0
2.0	14.0	13.0	47.0	5.0	19.0
15471	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
147.0	0.0	0.0	0.0	0.0	0.0
15821	1.0	0.0	1.0	0.0	0.0	0.0	4.0	1.0	0.0	2.0	0.0	6.0	4.0	1.0	2.0	2.0	5.0	28.0
4.0	0.0	3.0	16.0	1.0	25.0
16032	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	17.0	1.0	84.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	0.0	0.0	0.0	0.0	0.0
16153	1.0	1.0	0.0	4.0	0.0	0.0	2.0	4.0	2.0	1.0	0.0	0.0	0.0	2.0	1.0	3.0	0.0	0.0
1.0	16.0	4.0	71.0	6.0	0.0
16156	0.0	5.0	3.0	3.0	0.0	1.0	11.0	3.0	2.0	4.0	3.0	11.0	6.0	6.0	5.0	4.0	10.0
11.0	2.0	24.0	13.0	230.0	13.0	45.0
16213	0.0	1.0	5.0	3.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	5.0	6.0	0.0	11.0	0.0	0.0	358.0
15.0	0.0	0.0	0.0	0.0	2.0
16363	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	12.0	0.0	1.0	0.0
0.0	0.0	122.0	0.0	0.0	4.0
16528	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	9.0	52.0	2.0	13.0	0.0	6.0	22.0
0.0	1.0	2.0	0.0	2.0	1.0
16806	15.0	6.0	12.0	3.0	1.0	2.0	43.0	7.0	10.0	21.0	10.0	76.0	25.0	18.0	45.0	6.0
55.0	509.0	40.0	19.0	44.0	128.0	13.0	170.0
16917	4.0	2.0	2.0	0.0	0.0	1.0	6.0	1.0	1.0	7.0	3.0	30.0	19.0	10.0	6.0	3.0	35.0
6.0	4.0	8.0	12.0	4.0	10.0	12.0
16952	2.0	0.0	0.0	0.0	0.0	1.0	38.0	0.0	0.0	57.0	0.0	136.0	0.0	0.0	0.0	0.0	0.0
4.0	0.0	0.0	0.0	0.0	0.0	3.0

Belmont, John W

2013-Aug-31 15:48 UTC

head link

[R] How to plot a pretty heatmap with uneven distributed data?

normalize the data by rows and use the zscores (or some similar procedure)

________________________________________
From: r-help-bounces at r-project.org [r-help-bounces at r-project.org] On
Behalf Of Wei Liu [messagetoliu at gmail.com]
Sent: Saturday, August 31, 2013 4:29 AM
To: r-help at r-project.org
Subject: [R] How to plot a pretty heatmap with uneven distributed data?

Dear All,

I want to plot a heatmap with R, but my data  distributed unevenly, for
example, my data range from 10 to 1500, but most of the data smaller than
200, when I plot a heatmap, the colour is also distributed unevenly, most
part of the heatmap is one colour, so the heatmap is ugly and meaningless.

So can anybody help me plot a pretty heatmap for me with the attached data.
I am looking forward your reply! Thansk very much.

Best Regards.
Wei Liu.

Jim Lemon

2013-Aug-31 22:04 UTC

head link

[R] How to plot a pretty heatmap with uneven distributed data?

On 08/31/2013 07:29 PM, Wei Liu wrote:> Dear All,
>
> I want to plot a heatmap with R, but my data  distributed unevenly, for
> example, my data range from 10 to 1500, but most of the data smaller than
> 200, when I plot a heatmap, the colour is also distributed unevenly, most
> part of the heatmap is one colour, so the heatmap is ugly and meaningless.
>
> So can anybody help me plot a pretty heatmap for me with the attached data.
> I am looking forward your reply! Thansk very much.
>Hi Wei Liu,
You would have to explain the transformation of your variables, but 
perhaps something like:

library(plotrix)
color2D.matplot(log(example_data+0.01),extremes=c("red","blue"))

would do what you want.

Jim

R help - Aug 2013 - How to plot a pretty heatmap with uneven distributed data?

[R] How to plot a pretty heatmap with uneven distributed data?

[R] How to plot a pretty heatmap with uneven distributed data?

[R] How to plot a pretty heatmap with uneven distributed data?