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adding anova test result in box plot by ggpubr in r
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I have my data below and I would like to plot a multiple box plot with the ANOVA test result shown on the plot.
> combined
SampleID chao1 Samples Sgroup Bgroup Duration
1 BSS21 1275.1071 BSS20 BSS S 20d
2 BSS22 1575.4972 BSS20 BSS S 20d
3 BSS23 1381.2963 BSS20 BSS S 20d
4 BSS41 1090.0254 BSS40 BSS S 40d
5 BSS42 1103.6522 BSS40 BSS S 40d
6 BSS43 1065.7177 BSS40 BSS S 40d
7 BSS61 1077.8776 BSS60 BSS S 60d
8 BSS62 1123.5759 BSS60 BSS S 60d
9 BSS63 1201.3571 BSS60 BSS S 60d
10 BSW21 937.0231 BSW20 BSW W 20d
11 BSW22 970.0462 BSW20 BSW W 20d
12 BSW23 1070.1560 BSW20 BSW W 20d
13 BSW41 1894.8606 BSW40 BSW W 40d
14 BSW42 1825.0271 BSW40 BSW W 40d
15 BSW43 1869.3494 BSW40 BSW W 40d
16 BSW61 1332.4078 BSW60 BSW W 60d
17 BSW62 1354.4593 BSW60 BSW W 60d
18 BSW63 1365.2961 BSW60 BSW W 60d
19 BW21 1533.9137 BW20 BW W 20d
20 BW22 1643.1564 BW20 BW W 20d
21 BW23 1572.8900 BW20 BW W 20d
22 BW41 1678.0270 BW40 BW W 40d
23 BW42 1596.9105 BW40 BW W 40d
24 BW43 1684.8433 BW40 BW W 40d
25 BW61 1060.2059 BW60 BW W 60d
26 BW62 1127.0738 BW60 BW W 60d
27 BW63 1097.7083 BW60 BW W 60d
28 SS21 1751.0145 SS20 SS S 20d
29 SS22 1662.5932 SS20 SS S 20d
30 SS23 1806.3628 SS20 SS S 20d
31 SS41 1302.9245 SS40 SS S 40d
32 SS42 1126.5082 SS40 SS S 40d
33 SS43 1122.6136 SS40 SS S 40d
34 SS61 1429.4972 SS60 SS S 60d
35 SS62 1402.5714 SS60 SS S 60d
36 SS63 1493.1477 SS60 SS S 60d
37 SW21 1559.5000 SW20 SW W 20d
38 SW22 1387.1173 SW20 SW W 20d
39 SW23 1563.9524 SW20 SW W 20d
40 SW41 1439.0355 SW40 SW W 40d
41 SW42 1508.0054 SW40 SW W 40d
42 SW43 1425.1602 SW40 SW W 40d
43 SW61 1488.0000 SW60 SW W 60d
44 SW62 1398.9880 SW60 SW W 60d
45 SW63 1497.8553 SW60 SW W 60d
46 W011 1377.8092 W010 WCW W 10d
47 W012 1304.3725 W010 WCW W 10d
48 W013 1413.2292 W010 WCW W 10d
49 W021 1377.8092 W010 BW W 10d
50 W022 1304.3725 W010 BW W 10d
51 W023 1413.2292 W010 BW W 10d
52 W031 1377.8092 W010 SW W 10d
53 W032 1304.3725 W010 SW W 10d
54 W033 1413.2292 W010 SW W 10d
55 W041 1377.8092 W010 BSW W 10d
56 W042 1304.3725 W010 BSW W 10d
57 W043 1413.2292 W010 BSW W 10d
58 W051 1377.8092 W010 SS W 10d
59 W052 1304.3725 W010 SS W 10d
60 W053 1413.2292 W010 SS W 10d
61 W061 1377.8092 W010 BSS W 10d
62 W062 1304.3725 W010 BSS W 10d
63 W063 1413.2292 W010 BSS W 10d
64 WCW21 1246.5794 WCW20 WCW W 20d
65 WCW22 1249.2180 WCW20 WCW W 20d
66 WCW23 1134.3462 WCW20 WCW W 20d
67 WCW41 1074.9192 WCW40 WCW W 40d
68 WCW42 887.7191 WCW40 WCW W 40d
69 WCW43 990.3733 WCW40 WCW W 40d
70 WCW61 864.2727 WCW60 WCW W 60d
71 WCW62 934.5111 WCW60 WCW W 60d
72 WCW63 801.5696 WCW60 WCW W 60d
I tried the ggpubr package in r and apply the compare_means function.
For the anova test, I got:
> compare_means(chao1~Duration,data=combined,method="anova",group.by = "Sgroup")
# A tibble: 6 x 7
Sgroup .y. p p.adj p.format p.signif method
<fct> <chr> <dbl> <dbl> <chr> <chr> <chr>
1 BSS chao1 0.00394 0.0079 0.00394 ** Anova
2 BSW chao1 0.000000164 0.00000098 1.6e-07 **** Anova
3 BW chao1 0.00000283 0.000014 2.8e-06 **** Anova
4 SS chao1 0.0000996 0.0004 1.0e-04 **** Anova
5 SW chao1 0.160 0.16 0.16005 ns Anova
6 WCW chao1 0.000118 0.0004 0.00012 *** Anova
Which is exactly what I wanted. Then I make the plot:
ggboxplot(combined,x="Samples", y="chao1",palette = "jco", add = "jitter",short.panel.labs = FALSE)+facet_wrap(~Sgroup,scales = "free")
and got:
I have tried so hard by the stat_compare_means function but I still cannot add my anova test result on the plot
Any suggestion will be helped!
r boxplot ggpubr
asked 2 days ago
Lennon Lee
404
add a comment |
up vote
0
down vote
favorite
I have my data below and I would like to plot a multiple box plot with the ANOVA test result shown on the plot.
> combined
SampleID chao1 Samples Sgroup Bgroup Duration
1 BSS21 1275.1071 BSS20 BSS S 20d
2 BSS22 1575.4972 BSS20 BSS S 20d
3 BSS23 1381.2963 BSS20 BSS S 20d
4 BSS41 1090.0254 BSS40 BSS S 40d
5 BSS42 1103.6522 BSS40 BSS S 40d
6 BSS43 1065.7177 BSS40 BSS S 40d
7 BSS61 1077.8776 BSS60 BSS S 60d
8 BSS62 1123.5759 BSS60 BSS S 60d
9 BSS63 1201.3571 BSS60 BSS S 60d
10 BSW21 937.0231 BSW20 BSW W 20d
11 BSW22 970.0462 BSW20 BSW W 20d
12 BSW23 1070.1560 BSW20 BSW W 20d
13 BSW41 1894.8606 BSW40 BSW W 40d
14 BSW42 1825.0271 BSW40 BSW W 40d
15 BSW43 1869.3494 BSW40 BSW W 40d
16 BSW61 1332.4078 BSW60 BSW W 60d
17 BSW62 1354.4593 BSW60 BSW W 60d
18 BSW63 1365.2961 BSW60 BSW W 60d
19 BW21 1533.9137 BW20 BW W 20d
20 BW22 1643.1564 BW20 BW W 20d
21 BW23 1572.8900 BW20 BW W 20d
22 BW41 1678.0270 BW40 BW W 40d
23 BW42 1596.9105 BW40 BW W 40d
24 BW43 1684.8433 BW40 BW W 40d
25 BW61 1060.2059 BW60 BW W 60d
26 BW62 1127.0738 BW60 BW W 60d
27 BW63 1097.7083 BW60 BW W 60d
28 SS21 1751.0145 SS20 SS S 20d
29 SS22 1662.5932 SS20 SS S 20d
30 SS23 1806.3628 SS20 SS S 20d
31 SS41 1302.9245 SS40 SS S 40d
32 SS42 1126.5082 SS40 SS S 40d
33 SS43 1122.6136 SS40 SS S 40d
34 SS61 1429.4972 SS60 SS S 60d
35 SS62 1402.5714 SS60 SS S 60d
36 SS63 1493.1477 SS60 SS S 60d
37 SW21 1559.5000 SW20 SW W 20d
38 SW22 1387.1173 SW20 SW W 20d
39 SW23 1563.9524 SW20 SW W 20d
40 SW41 1439.0355 SW40 SW W 40d
41 SW42 1508.0054 SW40 SW W 40d
42 SW43 1425.1602 SW40 SW W 40d
43 SW61 1488.0000 SW60 SW W 60d
44 SW62 1398.9880 SW60 SW W 60d
45 SW63 1497.8553 SW60 SW W 60d
46 W011 1377.8092 W010 WCW W 10d
47 W012 1304.3725 W010 WCW W 10d
48 W013 1413.2292 W010 WCW W 10d
49 W021 1377.8092 W010 BW W 10d
50 W022 1304.3725 W010 BW W 10d
51 W023 1413.2292 W010 BW W 10d
52 W031 1377.8092 W010 SW W 10d
53 W032 1304.3725 W010 SW W 10d
54 W033 1413.2292 W010 SW W 10d
55 W041 1377.8092 W010 BSW W 10d
56 W042 1304.3725 W010 BSW W 10d
57 W043 1413.2292 W010 BSW W 10d
58 W051 1377.8092 W010 SS W 10d
59 W052 1304.3725 W010 SS W 10d
60 W053 1413.2292 W010 SS W 10d
61 W061 1377.8092 W010 BSS W 10d
62 W062 1304.3725 W010 BSS W 10d
63 W063 1413.2292 W010 BSS W 10d
64 WCW21 1246.5794 WCW20 WCW W 20d
65 WCW22 1249.2180 WCW20 WCW W 20d
66 WCW23 1134.3462 WCW20 WCW W 20d
67 WCW41 1074.9192 WCW40 WCW W 40d
68 WCW42 887.7191 WCW40 WCW W 40d
69 WCW43 990.3733 WCW40 WCW W 40d
70 WCW61 864.2727 WCW60 WCW W 60d
71 WCW62 934.5111 WCW60 WCW W 60d
72 WCW63 801.5696 WCW60 WCW W 60d
I tried the ggpubr package in r and apply the compare_means function.
For the anova test, I got:
> compare_means(chao1~Duration,data=combined,method="anova",group.by = "Sgroup")
# A tibble: 6 x 7
Sgroup .y. p p.adj p.format p.signif method
<fct> <chr> <dbl> <dbl> <chr> <chr> <chr>
1 BSS chao1 0.00394 0.0079 0.00394 ** Anova
2 BSW chao1 0.000000164 0.00000098 1.6e-07 **** Anova
3 BW chao1 0.00000283 0.000014 2.8e-06 **** Anova
4 SS chao1 0.0000996 0.0004 1.0e-04 **** Anova
5 SW chao1 0.160 0.16 0.16005 ns Anova
6 WCW chao1 0.000118 0.0004 0.00012 *** Anova
Which is exactly what I wanted. Then I make the plot:
ggboxplot(combined,x="Samples", y="chao1",palette = "jco", add = "jitter",short.panel.labs = FALSE)+facet_wrap(~Sgroup,scales = "free")
and got:
I have tried so hard by the stat_compare_means function but I still cannot add my anova test result on the plot
Any suggestion will be helped!
r boxplot ggpubr
asked 2 days ago
Lennon Lee
404
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I have my data below and I would like to plot a multiple box plot with the ANOVA test result shown on the plot.
> combined
SampleID chao1 Samples Sgroup Bgroup Duration
1 BSS21 1275.1071 BSS20 BSS S 20d
2 BSS22 1575.4972 BSS20 BSS S 20d
3 BSS23 1381.2963 BSS20 BSS S 20d
4 BSS41 1090.0254 BSS40 BSS S 40d
5 BSS42 1103.6522 BSS40 BSS S 40d
6 BSS43 1065.7177 BSS40 BSS S 40d
7 BSS61 1077.8776 BSS60 BSS S 60d
8 BSS62 1123.5759 BSS60 BSS S 60d
9 BSS63 1201.3571 BSS60 BSS S 60d
10 BSW21 937.0231 BSW20 BSW W 20d
11 BSW22 970.0462 BSW20 BSW W 20d
12 BSW23 1070.1560 BSW20 BSW W 20d
13 BSW41 1894.8606 BSW40 BSW W 40d
14 BSW42 1825.0271 BSW40 BSW W 40d
15 BSW43 1869.3494 BSW40 BSW W 40d
16 BSW61 1332.4078 BSW60 BSW W 60d
17 BSW62 1354.4593 BSW60 BSW W 60d
18 BSW63 1365.2961 BSW60 BSW W 60d
19 BW21 1533.9137 BW20 BW W 20d
20 BW22 1643.1564 BW20 BW W 20d
21 BW23 1572.8900 BW20 BW W 20d
22 BW41 1678.0270 BW40 BW W 40d
23 BW42 1596.9105 BW40 BW W 40d
24 BW43 1684.8433 BW40 BW W 40d
25 BW61 1060.2059 BW60 BW W 60d
26 BW62 1127.0738 BW60 BW W 60d
27 BW63 1097.7083 BW60 BW W 60d
28 SS21 1751.0145 SS20 SS S 20d
29 SS22 1662.5932 SS20 SS S 20d
30 SS23 1806.3628 SS20 SS S 20d
31 SS41 1302.9245 SS40 SS S 40d
32 SS42 1126.5082 SS40 SS S 40d
33 SS43 1122.6136 SS40 SS S 40d
34 SS61 1429.4972 SS60 SS S 60d
35 SS62 1402.5714 SS60 SS S 60d
36 SS63 1493.1477 SS60 SS S 60d
37 SW21 1559.5000 SW20 SW W 20d
38 SW22 1387.1173 SW20 SW W 20d
39 SW23 1563.9524 SW20 SW W 20d
40 SW41 1439.0355 SW40 SW W 40d
41 SW42 1508.0054 SW40 SW W 40d
42 SW43 1425.1602 SW40 SW W 40d
43 SW61 1488.0000 SW60 SW W 60d
44 SW62 1398.9880 SW60 SW W 60d
45 SW63 1497.8553 SW60 SW W 60d
46 W011 1377.8092 W010 WCW W 10d
47 W012 1304.3725 W010 WCW W 10d
48 W013 1413.2292 W010 WCW W 10d
49 W021 1377.8092 W010 BW W 10d
50 W022 1304.3725 W010 BW W 10d
51 W023 1413.2292 W010 BW W 10d
52 W031 1377.8092 W010 SW W 10d
53 W032 1304.3725 W010 SW W 10d
54 W033 1413.2292 W010 SW W 10d
55 W041 1377.8092 W010 BSW W 10d
56 W042 1304.3725 W010 BSW W 10d
57 W043 1413.2292 W010 BSW W 10d
58 W051 1377.8092 W010 SS W 10d
59 W052 1304.3725 W010 SS W 10d
60 W053 1413.2292 W010 SS W 10d
61 W061 1377.8092 W010 BSS W 10d
62 W062 1304.3725 W010 BSS W 10d
63 W063 1413.2292 W010 BSS W 10d
64 WCW21 1246.5794 WCW20 WCW W 20d
65 WCW22 1249.2180 WCW20 WCW W 20d
66 WCW23 1134.3462 WCW20 WCW W 20d
67 WCW41 1074.9192 WCW40 WCW W 40d
68 WCW42 887.7191 WCW40 WCW W 40d
69 WCW43 990.3733 WCW40 WCW W 40d
70 WCW61 864.2727 WCW60 WCW W 60d
71 WCW62 934.5111 WCW60 WCW W 60d
72 WCW63 801.5696 WCW60 WCW W 60d
I tried the ggpubr package in r and apply the compare_means function.
For the anova test, I got:
> compare_means(chao1~Duration,data=combined,method="anova",group.by = "Sgroup")
# A tibble: 6 x 7
Sgroup .y. p p.adj p.format p.signif method
<fct> <chr> <dbl> <dbl> <chr> <chr> <chr>
1 BSS chao1 0.00394 0.0079 0.00394 ** Anova
2 BSW chao1 0.000000164 0.00000098 1.6e-07 **** Anova
3 BW chao1 0.00000283 0.000014 2.8e-06 **** Anova
4 SS chao1 0.0000996 0.0004 1.0e-04 **** Anova
5 SW chao1 0.160 0.16 0.16005 ns Anova
6 WCW chao1 0.000118 0.0004 0.00012 *** Anova
Which is exactly what I wanted. Then I make the plot:
ggboxplot(combined,x="Samples", y="chao1",palette = "jco", add = "jitter",short.panel.labs = FALSE)+facet_wrap(~Sgroup,scales = "free")
and got:
I have tried so hard by the stat_compare_means function but I still cannot add my anova test result on the plot
Any suggestion will be helped!
r boxplot ggpubr
asked 2 days ago
Lennon Lee
404
I have my data below and I would like to plot a multiple box plot with the ANOVA test result shown on the plot.
> combined
SampleID chao1 Samples Sgroup Bgroup Duration
1 BSS21 1275.1071 BSS20 BSS S 20d
2 BSS22 1575.4972 BSS20 BSS S 20d
3 BSS23 1381.2963 BSS20 BSS S 20d
4 BSS41 1090.0254 BSS40 BSS S 40d
5 BSS42 1103.6522 BSS40 BSS S 40d
6 BSS43 1065.7177 BSS40 BSS S 40d
7 BSS61 1077.8776 BSS60 BSS S 60d
8 BSS62 1123.5759 BSS60 BSS S 60d
9 BSS63 1201.3571 BSS60 BSS S 60d
10 BSW21 937.0231 BSW20 BSW W 20d
11 BSW22 970.0462 BSW20 BSW W 20d
12 BSW23 1070.1560 BSW20 BSW W 20d
13 BSW41 1894.8606 BSW40 BSW W 40d
14 BSW42 1825.0271 BSW40 BSW W 40d
15 BSW43 1869.3494 BSW40 BSW W 40d
16 BSW61 1332.4078 BSW60 BSW W 60d
17 BSW62 1354.4593 BSW60 BSW W 60d
18 BSW63 1365.2961 BSW60 BSW W 60d
19 BW21 1533.9137 BW20 BW W 20d
20 BW22 1643.1564 BW20 BW W 20d
21 BW23 1572.8900 BW20 BW W 20d
22 BW41 1678.0270 BW40 BW W 40d
23 BW42 1596.9105 BW40 BW W 40d
24 BW43 1684.8433 BW40 BW W 40d
25 BW61 1060.2059 BW60 BW W 60d
26 BW62 1127.0738 BW60 BW W 60d
27 BW63 1097.7083 BW60 BW W 60d
28 SS21 1751.0145 SS20 SS S 20d
29 SS22 1662.5932 SS20 SS S 20d
30 SS23 1806.3628 SS20 SS S 20d
31 SS41 1302.9245 SS40 SS S 40d
32 SS42 1126.5082 SS40 SS S 40d
33 SS43 1122.6136 SS40 SS S 40d
34 SS61 1429.4972 SS60 SS S 60d
35 SS62 1402.5714 SS60 SS S 60d
36 SS63 1493.1477 SS60 SS S 60d
37 SW21 1559.5000 SW20 SW W 20d
38 SW22 1387.1173 SW20 SW W 20d
39 SW23 1563.9524 SW20 SW W 20d
40 SW41 1439.0355 SW40 SW W 40d
41 SW42 1508.0054 SW40 SW W 40d
42 SW43 1425.1602 SW40 SW W 40d
43 SW61 1488.0000 SW60 SW W 60d
44 SW62 1398.9880 SW60 SW W 60d
45 SW63 1497.8553 SW60 SW W 60d
46 W011 1377.8092 W010 WCW W 10d
47 W012 1304.3725 W010 WCW W 10d
48 W013 1413.2292 W010 WCW W 10d
49 W021 1377.8092 W010 BW W 10d
50 W022 1304.3725 W010 BW W 10d
51 W023 1413.2292 W010 BW W 10d
52 W031 1377.8092 W010 SW W 10d
53 W032 1304.3725 W010 SW W 10d
54 W033 1413.2292 W010 SW W 10d
55 W041 1377.8092 W010 BSW W 10d
56 W042 1304.3725 W010 BSW W 10d
57 W043 1413.2292 W010 BSW W 10d
58 W051 1377.8092 W010 SS W 10d
59 W052 1304.3725 W010 SS W 10d
60 W053 1413.2292 W010 SS W 10d
61 W061 1377.8092 W010 BSS W 10d
62 W062 1304.3725 W010 BSS W 10d
63 W063 1413.2292 W010 BSS W 10d
64 WCW21 1246.5794 WCW20 WCW W 20d
65 WCW22 1249.2180 WCW20 WCW W 20d
66 WCW23 1134.3462 WCW20 WCW W 20d
67 WCW41 1074.9192 WCW40 WCW W 40d
68 WCW42 887.7191 WCW40 WCW W 40d
69 WCW43 990.3733 WCW40 WCW W 40d
70 WCW61 864.2727 WCW60 WCW W 60d
71 WCW62 934.5111 WCW60 WCW W 60d
72 WCW63 801.5696 WCW60 WCW W 60d
I tried the ggpubr package in r and apply the compare_means function.
For the anova test, I got:
> compare_means(chao1~Duration,data=combined,method="anova",group.by = "Sgroup")
# A tibble: 6 x 7
Sgroup .y. p p.adj p.format p.signif method
<fct> <chr> <dbl> <dbl> <chr> <chr> <chr>
1 BSS chao1 0.00394 0.0079 0.00394 ** Anova
2 BSW chao1 0.000000164 0.00000098 1.6e-07 **** Anova
3 BW chao1 0.00000283 0.000014 2.8e-06 **** Anova
4 SS chao1 0.0000996 0.0004 1.0e-04 **** Anova
5 SW chao1 0.160 0.16 0.16005 ns Anova
6 WCW chao1 0.000118 0.0004 0.00012 *** Anova
Which is exactly what I wanted. Then I make the plot:
ggboxplot(combined,x="Samples", y="chao1",palette = "jco", add = "jitter",short.panel.labs = FALSE)+facet_wrap(~Sgroup,scales = "free")
and got:
I have tried so hard by the stat_compare_means function but I still cannot add my anova test result on the plot
Any suggestion will be helped!
r boxplot ggpubr
r boxplot ggpubr
asked 2 days ago
Lennon Lee
404
asked 2 days ago
Lennon Lee
404
asked 2 days ago
Lennon Lee
404
asked 2 days ago
Lennon Lee
404
asked 2 days ago
Lennon Lee
404
404
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
up vote
0
down vote
I've had problems with facet_wrap in the past, it maybe that its not supported. Hopefully I'm proven wrong! If you want an alternative, you can use the cowplot package to build the grids yourself. It's more work, but allows more flexibility perhaps. Here is an example with the data used when you run ?stat_compare_means
# packages
library(cowplot)
library(ggpubr)
# data from ggpubr
data("ToothGrowth")
head(ToothGrowth)
# define groups, build plots in a list and subset
groups <- unique(ToothGrowth$supp)
pl <- lapply(groups, function(g){
p <- ggboxplot(ToothGrowth[ToothGrowth$supp==g,], x = "dose", y = "len",
color = "dose", palette = "npg") +
stat_compare_means()
})
# use cowplot to bring together
cowplot::plot_grid(plotlist = pl, ncol = 2)
answered 2 days ago
Jonny Phelps
66327
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
I've had problems with facet_wrap in the past, it maybe that its not supported. Hopefully I'm proven wrong! If you want an alternative, you can use the cowplot package to build the grids yourself. It's more work, but allows more flexibility perhaps. Here is an example with the data used when you run ?stat_compare_means
# packages
library(cowplot)
library(ggpubr)
# data from ggpubr
data("ToothGrowth")
head(ToothGrowth)
# define groups, build plots in a list and subset
groups <- unique(ToothGrowth$supp)
pl <- lapply(groups, function(g){
p <- ggboxplot(ToothGrowth[ToothGrowth$supp==g,], x = "dose", y = "len",
color = "dose", palette = "npg") +
stat_compare_means()
})
# use cowplot to bring together
cowplot::plot_grid(plotlist = pl, ncol = 2)
answered 2 days ago
Jonny Phelps
66327
add a comment |
up vote
0
down vote
I've had problems with facet_wrap in the past, it maybe that its not supported. Hopefully I'm proven wrong! If you want an alternative, you can use the cowplot package to build the grids yourself. It's more work, but allows more flexibility perhaps. Here is an example with the data used when you run ?stat_compare_means
# packages
library(cowplot)
library(ggpubr)
# data from ggpubr
data("ToothGrowth")
head(ToothGrowth)
# define groups, build plots in a list and subset
groups <- unique(ToothGrowth$supp)
pl <- lapply(groups, function(g){
p <- ggboxplot(ToothGrowth[ToothGrowth$supp==g,], x = "dose", y = "len",
color = "dose", palette = "npg") +
stat_compare_means()
})
# use cowplot to bring together
cowplot::plot_grid(plotlist = pl, ncol = 2)
answered 2 days ago
Jonny Phelps
66327
add a comment |
up vote
0
down vote
up vote
0
down vote
I've had problems with facet_wrap in the past, it maybe that its not supported. Hopefully I'm proven wrong! If you want an alternative, you can use the cowplot package to build the grids yourself. It's more work, but allows more flexibility perhaps. Here is an example with the data used when you run ?stat_compare_means
# packages
library(cowplot)
library(ggpubr)
# data from ggpubr
data("ToothGrowth")
head(ToothGrowth)
# define groups, build plots in a list and subset
groups <- unique(ToothGrowth$supp)
pl <- lapply(groups, function(g){
p <- ggboxplot(ToothGrowth[ToothGrowth$supp==g,], x = "dose", y = "len",
color = "dose", palette = "npg") +
stat_compare_means()
})
# use cowplot to bring together
cowplot::plot_grid(plotlist = pl, ncol = 2)
answered 2 days ago
Jonny Phelps
66327
I've had problems with facet_wrap in the past, it maybe that its not supported. Hopefully I'm proven wrong! If you want an alternative, you can use the cowplot package to build the grids yourself. It's more work, but allows more flexibility perhaps. Here is an example with the data used when you run ?stat_compare_means
# packages
library(cowplot)
library(ggpubr)
# data from ggpubr
data("ToothGrowth")
head(ToothGrowth)
# define groups, build plots in a list and subset
groups <- unique(ToothGrowth$supp)
pl <- lapply(groups, function(g){
p <- ggboxplot(ToothGrowth[ToothGrowth$supp==g,], x = "dose", y = "len",
color = "dose", palette = "npg") +
stat_compare_means()
})
# use cowplot to bring together
cowplot::plot_grid(plotlist = pl, ncol = 2)
answered 2 days ago
Jonny Phelps
66327
answered 2 days ago
Jonny Phelps
66327
answered 2 days ago
Jonny Phelps
66327
answered 2 days ago
Jonny Phelps
66327
66327
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