R堆叠条形图，包括；其他"；（使用ggplot2）

我想制作一个堆积条形图，描述三个不同季节两个地点的分类群的丰度。我用的是ggplot2。绘制图是可以的，但我有48个分类群，所以我在酒吧里有很多不同的颜色。只有八个分类群频繁且丰富地出现，因此我想将其他分类群归为“其他”分类群

我的数据如下所示：

SampleID     TransectID     SampleYear     Season     Location    Taxa1     Taxa2     Taxa3 .... Taxa48
BW15001              1            2015     fall        SiteA         25         0         0           0
BW15001              2            2015     fall        SiteA         32         0         0           2
BW15001              2            2015     fall        SiteA          6         0        45           0
BW15001              3            2015     fall        SiteA         78         1         2           0   

这是我尝试过的（修改自）：

y一种方法：

library(plyr)
d=data.frame(SampleID=rep('BW15001',4),
             TransectID=c(1,2,2,3),
             SampleYear=rep(2015,4),
             Taxa1=c(25,32,6,78),
             Taxa2=c(0,0,0,1),
             Taxa3=c(0,0,45,3))
#Reshape the df so that all taxa columns are melted into two
d=melt(d,id=colnames(d[,1:3]))
d$variable=as.character(d$variable)

# rename all uninteresting taxa as 'other'
`%ni%` <- Negate(`%in%`) # Here I decided to select the ones to keep, but the other way around is fine as well of course
d[d$variable %ni% c('Taxa1','Taxa2'),'variable']='Other' #here you could add a function to automatically determine which taxta you want to keep, as you already did

# aggregate all data for 'other'
d=ddply(d,colnames(d[,1:4]),summarise,value=sum(value)) 

#make your plot, this one is just a bad example
ggplot(d,aes(SampleID,value,fill=variable))+
  geom_bar(stat="identity")+
  facet_grid(. ~ Type, drop=TRUE,scale="free",space="free_x")

库（plyr）
d=数据帧（样本ID=代表（'BW15001'，4），
横切面=c（1,2,2,3），
样本年份=代表（2015,4），
Taxa1=c（25,32,6,78），
Taxa2=c（0,0,0,1），
Taxa3=c（0,0,45,3））
#重塑df，使所有分类单元列融合为两个
d=熔体（d，id=colnames（d[，1:3]））
d$variable=as.character（d$variable）
#将所有不感兴趣的分类群重命名为“其他”
`%ni%`对我的评论进行了扩展。看看这个包裹。如果没有完整的示例，很难说，但以下内容应该有效：

library(tidyverse)
library(forcats)

temp <- df %>%
  gather(taxa, amount, -c(1:5))

# Reshape the data so that that there is one record per each amount
tidy_df <- temp[rep(rownames(temp), times = temp$amount), ]

tidy_df %>%
  select(-amount) %>%
  mutate(taxa = fct_lump(taxa, n = 2)) %>%       # Check out this line
  ggplot(., aes(x = SampleID, fill = taxa)) +
    geom_bar()

你如何定义主要分类群？总体发生率？发生频率？请查看包装。具体来说，
fct\u lump（）
主要分类群占总丰度的90%以上。谢谢。你能解释一下我如何添加一个函数来自动确定要保留哪些分类群吗？例如，占总丰度90%的那些？（或两者兼而有之。）
library(plyr)
d=data.frame(SampleID=rep('BW15001',4),
             TransectID=c(1,2,2,3),
             SampleYear=rep(2015,4),
             Taxa1=c(25,32,6,78),
             Taxa2=c(0,0,0,1),
             Taxa3=c(0,0,45,3))
#Reshape the df so that all taxa columns are melted into two
d=melt(d,id=colnames(d[,1:3]))
d$variable=as.character(d$variable)

# rename all uninteresting taxa as 'other'
`%ni%` <- Negate(`%in%`) # Here I decided to select the ones to keep, but the other way around is fine as well of course
d[d$variable %ni% c('Taxa1','Taxa2'),'variable']='Other' #here you could add a function to automatically determine which taxta you want to keep, as you already did

# aggregate all data for 'other'
d=ddply(d,colnames(d[,1:4]),summarise,value=sum(value)) 

#make your plot, this one is just a bad example
ggplot(d,aes(SampleID,value,fill=variable))+
  geom_bar(stat="identity")+
  facet_grid(. ~ Type, drop=TRUE,scale="free",space="free_x")

library(tidyverse)
library(forcats)

temp <- df %>%
  gather(taxa, amount, -c(1:5))

# Reshape the data so that that there is one record per each amount
tidy_df <- temp[rep(rownames(temp), times = temp$amount), ]

tidy_df %>%
  select(-amount) %>%
  mutate(taxa = fct_lump(taxa, n = 2)) %>%       # Check out this line
  ggplot(., aes(x = SampleID, fill = taxa)) +
    geom_bar()

df %>%
  gather(taxa, amount, -c(1:5)) %>%
  mutate(amount = na_if(amount, 0)) %>%
  na.omit() %>%
  mutate(taxa = fct_lump(taxa, n = 2)) %>%
  ggplot(., aes(x = SampleID, fill = taxa)) +
   geom_bar()