感谢谁能帮助我。
我正在处理从QIIME获得的一些输出,这些文本是我想要操纵以获取箱形图的文本。每个输入的格式都相同,因此操作始终相同,但是会更改源名称。对于每个输入,我要提取最后5行,为每个列/样本取一个平均值,将值与从映射文件中获取的样本实验标签(组)相关联,并按照它们用于制作所有箱形图的顺序排列获得的6个数据。
[在bash中,我执行类似[for i in GG97 GG100 SILVA97 SILVA100 NCBI RDP; do cp ${i}/alpha/collated_alpha/chao1.txt alpha_tot/${i}_chao1.txt; done
]的操作来多次执行命令,以通过${i}
以真实的方式更改代码中的名称。我正在努力寻找一种与R相同的方法。我想创建一个包含名称的向量,然后通过将for
与i
等移动来使用[1], [2]
循环,但这种方法不起作用,它停在read.delim行中,但未在wd中找到文件。
这是我编写的操作代码。发表评论后,它将对我正在使用的6个数据库(GG97 GG100 SILVA97 SILVA100 NCBI RDP)重复6次。
另外,由于要使用4个指标,因此我重复了4次此过程(这里我显示的是shannon,但我也有chao1,observed_species和PD_whole_tree的代码副本。]
library(tidyverse)
library(labelled)
mapfile <- read.delim(file="mapfile_HC+BV.txt", check.names=FALSE);
mapfile <- mapfile[,c(1,4)]
colnames(mapfile) <- c("SampleID","Pathology_group")
#GG97
collated <- read.delim(file="alpha_diversity/GG97_shannon.txt", check.names=FALSE);
collated <- tail(collated,5); collated <- collated[,-c(1:3)]
collated_reorder <- collated[,match(mapfile[,1], colnames(collated))]
labels <- t(mapfile)
colnames(collated_reorder) <- labels[2,]
mean <- colMeans(collated_reorder, na.rm = FALSE, dims = 1)
mean = as.matrix(mean); mean <- t(mean)
GG97_shannon <- as.data.frame(rbind(labels[2,],mean))
GG97_shannon <- t(GG97_shannon);
DB_type <- list(DB = "GG97"); DB_type <- rep(DB_type, 41)
GG97_shannon <- as.data.frame(cbind(DB_type,GG97_shannon))
colnames(GG97_shannon) <- c("DB","Group","value")
rm(collated,collated_reorder,DB_type,labels,mean)
在这里,我将所有输出粘贴在一起,冻结订单并制作箱线图。
alpha_shannon <- as.data.frame(rbind(GG97_shannon,GG100_shannon,SILVA97_shannon,SILVA100_shannon,NCBI_shannon,RDP_shannon))
rownames(alpha_shannon) <- NULL
rm(GG97_shannon,GG100_shannon,SILVA97_shannon,SILVA100_shannon,NCBI_shannon,RDP_shannon)
alpha_shannon$Group = factor(alpha_shannon$Group, unique(alpha_shannon$Group))
alpha_shannon$DB = factor(alpha_shannon$DB, unique(alpha_shannon$DB))
library(ggplot2)
ggplot(data = alpha_shannon) +
aes(x = DB, y = value, colour = Group) +
geom_boxplot()+
labs(title = 'Shannon',
x = 'Database',
y = 'Diversity') +
theme(legend.position = 'bottom')+
theme_grey(base_size = 16)
我如何保持此代码为“ DRY”,并且不需要146行代码来重复一遍又一遍?谢谢!
您未提供Minimal reproducible example,因此此答案不能保证正确性。
要注意的重要一点是您使用rm(...)
,因此这意味着某些变量仅在特定范围内相关。因此,将此范围封装到一个函数中。这使您的代码可重复使用,并省去了手动删除变量的麻烦:
process <- function(file, DB){
# -> Use the function parameter `file` instead of a hardcoded filename
collated <- read.delim(file=file, check.names=FALSE);
collated <- tail(collated,5); collated <- collated[,-c(1:3)]
collated_reorder <- collated[,match(mapfile[,1], colnames(collated))]
labels <- t(mapfile)
colnames(collated_reorder) <- labels[2,]
mean <- colMeans(collated_reorder, na.rm = FALSE, dims = 1)
mean = as.matrix(mean); mean <- t(mean)
# -> rename this variable to a more general name, e.g. `result`
result <- as.data.frame(rbind(labels[2,],mean))
result <- t(result);
# -> Use the function parameter `DB` instead of a hardcoded string
DB_type <- list(DB = DB); DB_type <- rep(DB_type, 41)
result <- as.data.frame(cbind(DB_type,result))
colnames(result) <- c("DB","Group","value")
# -> After the end of this function, the variables defined in this function
# vanish automatically, you just need to specify the result
return(result)
}
现在您可以重复使用该块:
GG97_shannon <- process(file = "alpha_diversity/GG97_shannon.txt", DB = "GG97)
GG100_shannon <- process(file =...., DB = ....)
SILVA97_shannon <- ...
SILVA100_shannon <- ...
NCBI_shannon <- ...
RDP_shannon <- ...
或者,您可以使用循环结构:
通用for
:
datasets <- c("GG97_shannon", "GG100_shannon", "SILVA97_shannon",
"SILVA100_shannon", "NCBI_shannon", "RDP_shannon")
files <- c("alpha_diversity/GG97_shannon.txt", .....)
DBs <- c("GG97", ....)
result <- list()
for(i in seq_along(datasets)){
result[[datasets[i]]] <- process(files[i], DBs[i])
}
mapply
,一个“特殊的for
”,用于并行循环多个向量:
# the first argument is the function from above, the other ones are given as arguments
# to our process(.) function
results <- mapply(process, files, DBs)