I have some data and from it I am creating a stacked barplot using ggplot2. For each sample on the x axis, additionally I have three factors per sample too. I would like to plot, between the barplot and the names of the samples, a different coloured square for each sample according to the factor. As there are three factors per sample I would like to plot these are three rows of squares, a little like a waffle chart. The data below shows the three columns of factors I have for each sample - "tissue_type", "biopsy_type", and "gleason_score". Is there any way I can plot all of these together? The stacked barplot and a kind of waffle chart?
data:
> total
aberration_type Freq sample_name tissue_type biopsy_type
1 homozygous_loss 42 160078-N_S16_L001_R1_001 Normal Normal
2 heterozygous_loss 200 160078-N_S16_L001_R1_001 Normal Normal
3 homozygous_loss 56 160078-T_S17_L001_R1_001 Tumour Repeat
4 heterozygous_loss 1917 160078-T_S17_L001_R1_001 Tumour Repeat
5 gain 666 160078-T_S17_L001_R1_001 Tumour Repeat
6 homozygous_loss 42 160079-N_S7_L001_R1_001 Normal Normal
7 heterozygous_loss 78 160079-N_S7_L001_R1_001 Normal Normal
8 homozygous_loss 193 160079-T_S8_L001_R1_001 Tumour Repeat
9 heterozygous_loss 4336 160079-T_S8_L001_R1_001 Tumour Repeat
10 gain 129 160079-T_S8_L001_R1_001 Tumour Repeat
11 homozygous_loss 42 160080-N_S20_L001_R1_001 Normal Normal
12 heterozygous_loss 78 160080-N_S20_L001_R1_001 Normal Normal
13 homozygous_loss 42 160081-N_S21_L001_R1_001 Normal Normal
14 heterozygous_loss 76 160081-N_S21_L001_R1_001 Normal Normal
15 homozygous_loss 42 160081-T_S22_L001_R1_001 Tumour Repeat
16 heterozygous_loss 1191 160081-T_S22_L001_R1_001 Tumour Repeat
17 gain 59 160081-T_S22_L001_R1_001 Tumour Repeat
18 homozygous_loss 42 160082-N_S23_L001_R1_001 Normal Normal
19 heterozygous_loss 6 160082-N_S23_L001_R1_001 Normal Normal
20 homozygous_loss 42 160083-N_S24_L001_R1_001 Normal Normal
21 heterozygous_loss 6 160083-N_S24_L001_R1_001 Normal Normal
22 homozygous_loss 42 160083-T_S25_L001_R1_001 Tumour Repeat
23 heterozygous_loss 515 160083-T_S25_L001_R1_001 Tumour Repeat
24 gain 88 160083-T_S25_L001_R1_001 Tumour Repeat
25 homozygous_loss 42 160084-N_S26_L001_R1_001 Normal Normal
26 heterozygous_loss 79 160084-N_S26_L001_R1_001 Normal Normal
27 homozygous_loss 42 160084-T_S27_L001_R1_001 Tumour Initial
28 heterozygous_loss 671 160084-T_S27_L001_R1_001 Tumour Initial
29 gain 56 160084-T_S27_L001_R1_001 Tumour Initial
30 homozygous_loss 42 160088-N_S5_L001_R1_001 Normal Normal
31 heterozygous_loss 63 160088-N_S5_L001_R1_001 Normal Normal
32 homozygous_loss 42 160088-T_S6_L001_R1_001 Tumour Initial
33 heterozygous_loss 6 160088-T_S6_L001_R1_001 Tumour Initial
34 homozygous_loss 42 160089-N_S28_L001_R1_001 Normal Normal
35 heterozygous_loss 114 160089-N_S28_L001_R1_001 Normal Normal
36 homozygous_loss 113 160089-T_S29_L001_R1_001 Tumour Repeat
37 heterozygous_loss 4196 160089-T_S29_L001_R1_001 Tumour Repeat
38 gain 8 160089-T_S29_L001_R1_001 Tumour Repeat
39 homozygous_loss 42 160090-N_S13_L001_R1_001 Normal Normal
40 heterozygous_loss 75 160090-N_S13_L001_R1_001 Normal Normal
41 homozygous_loss 42 160091-N_S14_L001_R1_001 Normal Normal
42 heterozygous_loss 74 160091-N_S14_L001_R1_001 Normal Normal
43 homozygous_loss 42 160091-T_S15_L001_R1_001 Tumour Repeat
44 heterozygous_loss 194 160091-T_S15_L001_R1_001 Tumour Repeat
45 homozygous_loss 41 160093-N_S9_L001_R1_001 Normal Normal
46 heterozygous_loss 6 160093-N_S9_L001_R1_001 Normal Normal
47 homozygous_loss 42 160093-T_S10_L001_R1_001 Tumour Initial
48 heterozygous_loss 1034 160093-T_S10_L001_R1_001 Tumour Initial
49 homozygous_loss 42 160094-N_S11_L001_R1_001 Normal Normal
50 heterozygous_loss 77 160094-N_S11_L001_R1_001 Normal Normal
51 homozygous_loss 42 160094-T_S12_L001_R1_001 Tumour Repeat
52 heterozygous_loss 2192 160094-T_S12_L001_R1_001 Tumour Repeat
53 gain 10 160094-T_S12_L001_R1_001 Tumour Repeat
54 homozygous_loss 42 160095-N_S1_L001_R1_001 Normal Normal
55 heterozygous_loss 76 160095-N_S1_L001_R1_001 Normal Normal
56 homozygous_loss 41 160095-T_S2_L001_R1_001 Tumour Initial
57 heterozygous_loss 442 160095-T_S2_L001_R1_001 Tumour Initial
58 homozygous_loss 42 160096-N_S4_L001_R1_001 Normal Normal
59 heterozygous_loss 6 160096-N_S4_L001_R1_001 Normal Normal
60 homozygous_loss 42 160096-T_S4_L001_R1_001 Tumour Repeat
61 heterozygous_loss 484 160096-T_S4_L001_R1_001 Tumour Repeat
62 homozygous_loss 42 160098-N_S4_L001_R1_001 Normal Normal
63 heterozygous_loss 68 160098-N_S4_L001_R1_001 Normal Normal
64 homozygous_loss 42 160098-T_S4_L001_R1_001 Tumour Initial
65 heterozygous_loss 598 160098-T_S4_L001_R1_001 Tumour Initial
gleason_score
1 Normal
2 Normal
3 3_4
4 3_4
5 3_4
6 Normal
7 Normal
8 3_3
9 3_3
10 3_3
11 Normal
12 Normal
13 Normal
14 Normal
15 3_3
16 3_3
17 3_3
18 Normal
19 Normal
20 Normal
21 Normal
22 3_3
23 3_3
24 3_3
25 Normal
26 Normal
27 3_3
28 3_3
29 3_3
30 Normal
31 Normal
32 3_3
33 3_3
34 Normal
35 Normal
36 3_4
37 3_4
38 3_4
39 Normal
40 Normal
41 Normal
42 Normal
43 3_3
44 3_3
45 Normal
46 Normal
47 3_3
48 3_3
49 Normal
50 Normal
51 3_4
52 3_4
53 3_4
54 Normal
55 Normal
56 3_3
57 3_3
58 Normal
59 Normal
60 3_4
61 3_4
62 Normal
63 Normal
64 3_3
65 3_3
how i am currently making a stacked barplot using ggplot
ggplot(data = total, aes(x = reorder(sample_name, -Freq), y = Freq, fill = aberration_type)) +
geom_bar(stat="identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 5)) +
ggtitle("Frequency aberrant bins") +
xlab("Sample Name") +
ylab("Frequency")
