使用数据ggplot的子集创建自定义图例 [英] Creating a custom legend with a subset of data ggplot
本文介绍了使用数据ggplot的子集创建自定义图例的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在创建一些堆积的条形图，其中大约有50种不同的因素.我想要一个传说，它仅绘制8个最丰富因素的颜色.
I am creating some stacked barplots in which there are about 50 different factors. I would like a legend that just maps the colors for the 8 most abundant factors.
我首先计算了平均丰度.然后，我以此命令下定了因数.并且还可以获取相应的颜色，但是我不知道是否应该独立地"创建图例，或者是否缺少ggplot的另一种简便"方式.任何帮助表示赞赏.
I first calculated the mean abundance. Then I have ordered the factor by this. And can also get the corresponding colors, but I don't know if I should 'independently' create a legend, or if I am missing another 'easy' way with ggplot. Any help appreciated.
谢谢
## calculating the OTUs mean abundance
## instead of labelling all 50 in the legend just label top 5 or 10 for label
meanAbund<-as.data.frame(colSums(dat.grouped[,grep("Otu",colnames(dat.grouped))]))
meanAbund$Name<-as.character(tax_tab1[match(rownames(meanAbund),rownames(tax_tab1)),ncol(tax_tab1)])
colnames(meanAbund)<-c("mean_abund","Name")
top8_meanAbund<-meanAbund[rev(order(meanAbund$mean_abund)),][1:8]
# use melted dataframe for plotting
## now ordered by mean abundance
dat.groupedM$Level<-factor(dat.groupedM$Level,meanAbund[rev(order(meanAbund$mean_abund)),"Name"])
#top8 (mean is greater than 0.1 %)
# top 8 colors
library(RColorBrewer)
n <- length(unique(dat.groupedM$Level))
# generate divergent color palete, cause so many colors
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
set.seed(200)
mycols<-sample(col_vector, n)
top8cols<-mycols[1:8]
#pie(rep(1,n), mycols)
ggplot(dat.groupedM, aes(x = Time, y = value, fill = Level))+
geom_bar(stat = "identity") +
facet_wrap(~Diet)+
scale_fill_manual(values = mycols) +
theme_bw()+
theme(legend.position = "none")+
ggtitle(paste(level,"composition - Cohort 1"))
谢谢，现在我有这个，请参阅下文.但是它没有使用我以前用scale_fill_manual指定的调色板"mycols"，现在我根据建议更改为"mycols".好的，现在这是正确的！
thanks getting closer now I have this, see below. But it isn't using my divergent palette "mycols" which I had specified with scale_fill_manual before now I changed to this as per suggestion. Okay now this is correct !!
scale_fill_manual(values = mycols,breaks = top10_meanAbund$Name)+
> dput(dat.groupedM)
structure(list(Diet = structure(c(2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L), .Label = c("Control", "RC", "WD"), class = "factor"), Time = structure(c(2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L), .Label = c("Control", "ZT2", "ZT14"
), class = "factor"), variable = structure(c(1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 16L, 16L,
16L, 16L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 19L, 19L, 19L,
19L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L,
23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 26L,
26L, 26L, 26L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 29L, 29L,
29L, 29L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 32L, 32L, 32L,
32L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L,
36L, 36L, 36L, 36L, 37L, 37L, 37L, 37L, 38L, 38L, 38L, 38L, 39L,
39L, 39L, 39L, 40L, 40L, 40L, 40L, 41L, 41L, 41L, 41L, 42L, 42L,
42L, 42L, 43L, 43L, 43L, 43L, 44L, 44L, 44L, 44L, 45L, 45L, 45L,
45L, 46L, 46L, 46L, 46L, 47L, 47L, 47L, 47L, 48L, 48L, 48L, 48L,
49L, 49L, 49L, 49L, 50L, 50L, 50L, 50L), .Label = c("Otu1", "Otu108",
"Otu11", "Otu112", "Otu136", "Otu14", "Otu155", "Otu157", "Otu158",
"Otu161", "Otu182", "Otu186", "Otu197", "Otu21", "Otu23", "Otu242",
"Otu244", "Otu247", "Otu27", "Otu29", "Otu30", "Otu314", "Otu35",
"Otu36", "Otu378", "Otu4", "Otu40", "Otu42", "Otu427", "Otu44",
"Otu464", "Otu49", "Otu53", "Otu535", "Otu56", "Otu585", "Otu607",
"Otu623", "Otu629", "Otu645", "Otu648", "Otu664", "Otu69", "Otu715",
"Otu730", "Otu741", "Otu76", "Otu77", "Otu86", "Otu90"), class = "factor"),
value = c(0.125797907894198, 0.236708006778536, 0.577822547980787,
0.590703405992419, 0.010616635104027, 0.0101758392492056,
0.000833768625886666, 0.00075132991625995, 3.6675879491077e-05,
4.75000300636826e-05, 1.80354502712558e-05, 7.12272349413578e-06,
0.0923297961192989, 0.0516121598003722, 0.00943059557285678,
0.0071906839719107, 0.00539229454875785, 0.00506484304043506,
0.00338347504700533, 0.0025680210124108, 0.00175591025013762,
0.00395104856215105, 0.0561412864297836, 0.0389581641347799,
0.000170273382201598, 0.00010844375756416, 2.43702775724318e-05,
2.69013349522259e-05, 0.00832912099453767, 0.010385453199374,
0.000178806881780292, 0.000192961017676232, 0.000133411286157921,
0.000160277350764036, 3.61247330219488e-05, 2.18172717187001e-05,
0.0052992653709469, 0.0157284378186556, 1.02095850472487e-05,
6.26578426928999e-06, 1.60700750910741e-05, 1.5642472562587e-05,
6.62816031320301e-06, 0.00184183762620945, 0.000976314979621793,
0.000837378482800852, 0.00119405390180218, 0.000749043101546972,
2.53563183604468e-05, 6.40671433657518e-05, 2.15845735655314e-05,
2.23193546150843e-05, 0.021152108912735, 0.0154293683430873,
0.0179088127298003, 0.0243504748685249, 0.112274814726721,
0.117619628857172, 3.02064928526444e-05, 4.05741755377207e-05,
0.00181223384695308, 0.00207625195815772, 0.000943789542460111,
0.000206467245724716, 0.00154532777776536, 0.00226885210972427,
0.000119002475328241, 0.00022113732768583, 0.000235485770787934,
0.000458323057092208, 0.000652960109256795, 0.000724862646259261,
0.0727879614169974, 0.0343552622468531, 0.00966883214135748,
0.0121723948692721, 0.0502079899784375, 0.0642059286182179,
0.0744606748095006, 0.0279356500162451, 0.0165712179668264,
0.0155282557608783, 0.0161360297824822, 0.0176198016904319,
0.0025636247608102, 0.0011990003339988, 8.03074966867612e-05,
2.33672477744522e-05, 0.082133947866647, 0.000268818474649765,
9.9394282675768e-05, 5.50800067064597e-05, 0.0740561864383357,
0.0623723308152442, 0.0122016944534841, 0.00661733731518039,
0.000206774473028281, 0.000181997559545235, 0.000504582984323445,
0.0003864059666649, 0.260050134018791, 0.262172946886686,
0.18452199172589, 0.173786000484964, 0.0269969369566207,
0.0314904830827211, 0.00193643447967831, 0.00224489247051206,
3.93537896032742e-05, 1.28428312370815e-05, 0.0088546261393493,
0.0167354308377304, 1.21169704358824e-05, 2.12185497965253e-05,
6.62816031320301e-06, 1.43770979648094e-05, 1.55017664132774e-05,
4.91659313365037e-05, 0.000125001966670021, 6.22422609777641e-05,
2.67226640566305e-05, 3.7715397236691e-05, 0.000156595002030591,
0.000177342276671081, 9.9289511018095e-05, 3.26074531511788e-05,
0.0133283089983016, 0.016339336685079, 0.000374046362264717,
0.000314832095026693, 0.000101840989635774, 0.0499536313744285,
3.07770376000906e-05, 2.61927046816827e-05, 6.62816031320301e-06,
6.26578426928999e-06, 0.0190857963424119, 0.033561743200736,
0.00670074972877479, 0.00537788085712994, 1.18971385403729e-05,
1.11445994651961e-05, 6.62816031320301e-06, 6.26578426928999e-06,
1.14712208113288e-05, 2.05354865936513e-05, 7.60426873897666e-06,
6.26578426928999e-06, 1.20062319150955e-05, 1.36539983715138e-05,
6.62816031320301e-06, 7.2222843532018e-06, 9.9140609239925e-06,
9.94926827158573e-06, 7.53483093323745e-06, 8.69321376638133e-06,
1.21169704358824e-05, 1.19091870744721e-05, 6.62816031320301e-06,
7.23507396088365e-06, 2.46118496456816e-05, 2.17587837491135e-05,
6.62816031320301e-06, 6.26578426928999e-06, 1.82559375344064e-05,
1.89730194735652e-05, 6.62816031320301e-06, 6.26578426928999e-06,
0.00660850850197291, 0.0212222385456563, 0.00211118341459913,
0.00141420689575609, 9.26797200368037e-06, 8.90032999685065e-06,
7.54855398500617e-06, 6.26578426928999e-06, 2.23089983289096e-05,
2.17587837491135e-05, 6.62816031320301e-06, 6.26578426928999e-06,
5.45457596965578e-06, 5.45457596965578e-06, 6.62816031320301e-06,
5.98284905868666e-06, 3.2907497931271e-05, 2.32160410324433e-05,
6.62816031320301e-06, 1.51092819784857e-05, 1.21169704358824e-05,
1.19091870744721e-05, 7.91517227728171e-06, 7.23507396088365e-06,
4.18664545392701e-05, 4.14341191547126e-05, 0.000151073775168509,
0.000399628089283355, 9.9140609239925e-06, 1.4300121286468e-05,
7.53483093323745e-06, 6.26578426928999e-06), Level = structure(c(1L,
1L, 1L, 1L, 25L, 25L, 25L, 25L, 3L, 3L, 3L, 3L, 7L, 7L, 7L,
7L, 28L, 28L, 28L, 28L, 9L, 9L, 9L, 9L, 31L, 31L, 31L, 31L,
27L, 27L, 27L, 27L, 30L, 30L, 30L, 30L, 26L, 26L, 26L, 26L,
42L, 42L, 42L, 42L, 39L, 39L, 39L, 39L, 11L, 11L, 11L, 11L,
13L, 13L, 13L, 13L, 4L, 4L, 4L, 4L, 33L, 33L, 33L, 33L, 36L,
36L, 36L, 36L, 43L, 43L, 43L, 43L, 6L, 6L, 6L, 6L, 5L, 5L,
5L, 5L, 15L, 15L, 15L, 15L, 38L, 38L, 38L, 38L, 10L, 10L,
10L, 10L, 8L, 8L, 8L, 8L, 45L, 45L, 45L, 45L, 2L, 2L, 2L,
2L, 16L, 16L, 16L, 16L, 24L, 24L, 24L, 24L, 44L, 44L, 44L,
44L, 18L, 18L, 18L, 18L, 46L, 46L, 46L, 46L, 20L, 20L, 20L,
20L, 12L, 12L, 12L, 12L, 47L, 47L, 47L, 47L, 17L, 17L, 17L,
17L, 32L, 32L, 32L, 32L, 48L, 48L, 48L, 48L, 49L, 49L, 49L,
49L, 50L, 50L, 50L, 50L, 34L, 34L, 34L, 34L, 29L, 29L, 29L,
29L, 40L, 40L, 40L, 40L, 19L, 19L, 19L, 19L, 35L, 35L, 35L,
35L, 37L, 37L, 37L, 37L, 41L, 41L, 41L, 41L, 14L, 14L, 14L,
14L, 21L, 21L, 21L, 21L, 23L, 23L, 23L, 23L, 22L, 22L, 22L,
22L), .Label = c("Akkermansia", "Bacteroides", "Pseudomonas",
"Anaeroplasma", "Acetatifactor", "Lactobacillus", "Clostridium_XlVa",
"Oscillibacter", "Eubacterium", "Alistipes", "Acinetobacter",
"Parabacteroides", "Parasutterella", "Bacillus", "Clostridium_sensu_stricto",
"Turicibacter", "Clostridium_IV", "Staphylococcus", "Bifidobacterium",
"Romboutsia", "Escherichia/Shigella", "Listeria", "Enterococcus",
"Lactococcus", "Flavonifractor", "Ruminococcus", "Lachnospiracea_incertae_sedis",
"Clostridium_XlVb", "Stenotrophomonas", "Eisenbergiella",
"Hungatella", "Adhaeribacter", "Butyricicoccus", "Rhizobium",
"Prevotella", "Intestinimonas", "Sphingobium", "Dorea", "Enterorhabdus",
"Shewanella", "Bradyrhizobium", "Desulfovibrio", "Clostridium_XVIII",
"Anaerostipes", "Asaccharobacter", "Anaerotruncus", "Eggerthella",
"Butyricimonas", "Bilophila", "Anaerococcus"), class = "factor")), row.names = c(2L,
3L, 4L, 5L, 7L, 8L, 9L, 10L, 12L, 13L, 14L, 15L, 17L, 18L, 19L,
20L, 22L, 23L, 24L, 25L, 27L, 28L, 29L, 30L, 32L, 33L, 34L, 35L,
37L, 38L, 39L, 40L, 42L, 43L, 44L, 45L, 47L, 48L, 49L, 50L, 52L,
53L, 54L, 55L, 57L, 58L, 59L, 60L, 62L, 63L, 64L, 65L, 67L, 68L,
69L, 70L, 72L, 73L, 74L, 75L, 77L, 78L, 79L, 80L, 82L, 83L, 84L,
85L, 87L, 88L, 89L, 90L, 92L, 93L, 94L, 95L, 97L, 98L, 99L, 100L,
102L, 103L, 104L, 105L, 107L, 108L, 109L, 110L, 112L, 113L, 114L,
115L, 117L, 118L, 119L, 120L, 122L, 123L, 124L, 125L, 127L, 128L,
129L, 130L, 132L, 133L, 134L, 135L, 137L, 138L, 139L, 140L, 142L,
143L, 144L, 145L, 147L, 148L, 149L, 150L, 152L, 153L, 154L, 155L,
157L, 158L, 159L, 160L, 162L, 163L, 164L, 165L, 167L, 168L, 169L,
170L, 172L, 173L, 174L, 175L, 177L, 178L, 179L, 180L, 182L, 183L,
184L, 185L, 187L, 188L, 189L, 190L, 192L, 193L, 194L, 195L, 197L,
198L, 199L, 200L, 202L, 203L, 204L, 205L, 207L, 208L, 209L, 210L,
212L, 213L, 214L, 215L, 217L, 218L, 219L, 220L, 222L, 223L, 224L,
225L, 227L, 228L, 229L, 230L, 232L, 233L, 234L, 235L, 237L, 238L,
239L, 240L, 242L, 243L, 244L, 245L, 247L, 248L, 249L, 250L), class = "data.frame")
推荐答案
我无法运行您的代码，因此我将使用虚拟数据回答您的问题.
I couldn't get your code to run, so I'll answer your question with dummy data instead.
让我们生成一些不均匀分布的数据，在这种情况下为字母.
Let's generate some unequally distributed data, letters in this case.
set.seed(0)
df <- data.frame(x = LETTERS[c(rpois(100, 1) + 1, rpois(100, 8) + 1)])
我们可以确定8个最频繁出现的字母:
We can determine the 8 most frequently occuring letters:
most_freq <- names(tail(sort(table(df$x)), 8))
然后我们可以将其作为 breaks 参数放入任何离散的比例尺函数(包括手动比例尺)中:
Which we can then put into any of the discrete scales functions (including the manual one) as the breaks argument:
ggplot(df, aes(x, fill = x)) +
geom_bar() +
scale_fill_discrete(breaks = most_freq)
并且您可以看到，不在休息时间中的类别仍然带有颜色，但在图例中没有提及.
And as you can see, the categories not in the breaks still get a colour but don't get mentioned in the legend.
这篇关于使用数据ggplot的子集创建自定义图例的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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