R 筛选4000+；中的4个完整组；图像集

我有一个4000多张图像的数据集。为了找出代码，我把一小批代码移到了另一个文件夹中

这些文件如下所示：

    folder

    r01c01f01p01-ch3.tiff
    r01c01f01p01-ch4.tiff
    r01c01f02p01-ch1.tiff
    r01c01f03p01-ch2.tiff
    r01c01f03p01-ch3.tiff
    r01c01f04p01-ch2.tiff
    r01c01f04p01-ch4.tiff
    r01c01f05p01-ch1.tiff 
    r01c01f05p01-ch2.tiff
    r01c01f06p01-ch2.tiff
    r01c01f06p01-ch4.tiff
    r01c01f09p01-ch3.tiff
    r01c01f09p01-ch4.tiff
    r01c01f10p01-ch1.tiff
    r01c01f10p01-ch4.tiff
    r01c01f11p01-ch1.tiff
    r01c01f11p01-ch2.tiff
    r01c01f11p01-ch3.tiff
    r01c01f11p01-ch4.tiff
    r01c02f10p01-ch1.tiff
    r01c02f10p01-ch2.tiff
    r01c02f10p01-ch3.tiff
    r01c02f10p01-ch4.tiff

    ch1 <- dir(path="/Desktop/cp/complete//", pattern="ch1")

    ch2 <- dir(path="/Desktop/cp/complete//", pattern="ch2")

    ch3 <- dir(path="/Desktop/cp/complete//", pattern="ch3")

    ch4 <- dir(path="/Desktop/cp/complete//", pattern="ch4")

我不能删除-ch#之前的名称，因为该信息很重要

我想做的是过滤图像文件夹，并只移动/保留所有四个ch值（ch1-4）的集合（即：r01c02f10p01）

背景：我使用不同的脚本将图像合并到一个组合中，这取决于这样一个事实，即每个图像上都有一个完整的ch1、ch2、ch3和ch4后缀，并且它们分为四个批次。（此代码用于名为Fiji的图像处理软件，而不是R。）

在尝试过滤图像时，该装置最初包括第五个通道（ch5）。我能够像这样从原始文件夹中删除带有ch5的所有图像

    ##Create folder variable which has all image files 
     setwd("/Desktop/Nov 5/")
          folder = list.files("/Desktop/Nov5/")
    folder <- list.files(getwd())

    ##Create final2 variable which has all image files ending in ch5
    final2 <- dir(path="/Desktop/Nov5/", pattern="ch5") 

    ##Remove final2 from folder
    file.remove(folder,final2) 

##创建包含所有图像文件的文件夹变量
setwd（“/Desktop/Nov 5/”）
folder=list.files（“/Desktop/Nov5/”）
文件夹%
分组依据（分组）%>%
过滤器（全部（通道集%in%ch））
要保存的文件

总结：我希望从没有完整ch值的随机组合（即：可能只有ch1和ch2，或ch3和ch4）中筛选文件，到只包含具有完整ch1、ch2、ch3和ch4图像的文件组合

注意：我对R还是很陌生，因此我可以澄清描述问题的某些语言是否已关闭。

我的解决方案：

library(tidyr)
library(dplyr)
library(magrittr)
library(stringr)

#Input data
dirnames <- data.frame(flenames = c("r01c01f01p01-ch3.tiff", "r01c01f01p01-ch4.tiff", "r01c01f02p01-ch1.tiff", "r01c01f03p01-ch2.tiff", 
                                    "r01c01f03p01-ch3.tiff", "r01c01f04p01-ch2.tiff", "r01c01f04p01-ch4.tiff", "r01c01f05p01-ch1.tiff", 
                                    "r01c01f05p01-ch2.tiff", "r01c01f06p01-ch2.tiff", "r01c01f06p01-ch4.tiff", "r01c01f09p01-ch3.tiff", 
                                    "r01c01f09p01-ch4.tiff", "r01c01f10p01-ch1.tiff", "r01c01f10p01-ch4.tiff", "r01c01f11p01-ch1.tiff", 
                                    "r01c01f11p01-ch2.tiff", "r01c01f11p01-ch3.tiff", "r01c01f11p01-ch4.tiff", "r01c02f10p01-ch1.tiff", 
                                    "r01c02f10p01-ch2.tiff", "r01c02f10p01-ch3.tiff", "r01c02f10p01-ch4.tiff"))

#To get actual input data in the format above
#Presuming this script is being executed from the directory where the `tiff` files are stored
dirnames <- data.frame(flenames = list.files(path = getwd(), pattern = "*.tiff"))

#Creating a column for the leading string, using which we can group the channels (stored in an accompanying column)
#Then grouping by the leading string, and nesting the channel and filename columns thereunder.
#Using the length of the nested tibble to consider files for removal.
#If the nested tibble contains 4 channels (i.e. length == 4), then keep. Else discard.
dirnames %<>% 
  mutate(fc1 = str_extract(flenames, ".+(?=\\-)")) %>% #Leading string
  mutate(fc2 = str_extract(flenames, "(?<=\\-)\\w+")) %>% #Channel string
  select(-c(flenames)) %>%
  group_by(fc1) %>%  #Grouping
  nest(fc3 = fc2) %>% #Nesting
  mutate(keep_val = if(length(unlist(fc3)) == 4){"Y"} else{"N"}) %>% #Checking to keep/discard
  unnest(fc3) %>% #Unnesting to get back original data frame
  mutate(flename = paste0(fc1, "-", fc2, ".tiff")) %>% #Getting the original filename back
  ungroup(.) %>% 
  select(flename, keep_val) #Reordering data frame

#For loop to iterate through data frame, and issue file.remove() commands as necessary
for(i in 1:nrow(dirnames)){
  if(dirnames$keep_val[i] == "N"){
    cat("Removing file ", paste0(dirnames$flename[i]), "\n")
    file.remove(dirnames$flename[i])
  } else{
    cat("Keeping file ", paste0(dirnames$flename[i]), "\n")
  }
}

# Removing file  r01c01f01p01-ch3.tiff 
# Removing file  r01c01f01p01-ch4.tiff 
# Removing file  r01c01f02p01-ch1.tiff 
# Removing file  r01c01f03p01-ch2.tiff 
# Removing file  r01c01f03p01-ch3.tiff 
# Removing file  r01c01f04p01-ch2.tiff 
# Removing file  r01c01f04p01-ch4.tiff 
# Removing file  r01c01f05p01-ch1.tiff 
# Removing file  r01c01f05p01-ch2.tiff 
# Removing file  r01c01f06p01-ch2.tiff 
# Removing file  r01c01f06p01-ch4.tiff 
# Removing file  r01c01f09p01-ch3.tiff 
# Removing file  r01c01f09p01-ch4.tiff 
# Removing file  r01c01f10p01-ch1.tiff 
# Removing file  r01c01f10p01-ch4.tiff 
# Keeping file  r01c01f11p01-ch1.tiff 
# Keeping file  r01c01f11p01-ch2.tiff 
# Keeping file  r01c01f11p01-ch3.tiff 
# Keeping file  r01c01f11p01-ch4.tiff 
# Keeping file  r01c02f10p01-ch1.tiff 
# Keeping file  r01c02f10p01-ch2.tiff 
# Keeping file  r01c02f10p01-ch3.tiff 
# Keeping file  r01c02f10p01-ch4.tiff 

library（tidyr）
图书馆（dplyr）
图书馆（magrittr）
图书馆（stringr）
#输入数据
dirnames%#通道字符串
选择（-c（flenames））%>%
分组依据（fc1）%>%#分组
嵌套（fc3=fc2）%>%#嵌套
mutate（keep_val=if（length（unlist（fc3））=4）{“Y”}否则{“N”}）%>%#检查以保留/放弃
未测试（fc3）%>%#未测试以返回原始数据帧
mutate（flename=paste0（fc1，“-”，fc2，“.tiff”）%>%#恢复原始文件名
解组（%）%%>%
选择（flename，keep_val）#重新排列数据帧
#使循环遍历数据帧，并根据需要发出file.remove（）命令
对于（i in 1:nrow（dirnames））{
if（dirnames$keep_val[i]=“N”）{
cat（“删除文件”，粘贴0（dirnames$flename[i]），“\n”）
删除（dirnames$flename[i]）
}否则{
cat（“保留文件”，粘贴0（dirnames$flename[i]），“\n”）
}
}
#正在删除文件r01c01f01p01-ch3.tiff
#正在删除文件r01c01f01p01-ch4.tiff
#正在删除文件r01c01f02p01-ch1.tiff
#正在删除文件r01c01f03p01-ch2.tiff
#正在删除文件r01c01f03p01-ch3.tiff
#正在删除文件r01c01f04p01-ch2.tiff
#正在删除文件r01c01f04p01-ch4.tiff
#正在删除文件r01c01f05p01-ch1.tiff
#正在删除文件r01c01f05p01-ch2.tiff
#正在删除文件r01c01f06p01-ch2.tiff
#正在删除文件r01c01f06p01-ch4.tiff
#正在删除文件r01c01f09p01-ch3.tiff
#正在删除文件r01c01f09p01-ch4.tiff
#正在删除文件r01c01f10p01-ch1.tiff
#正在删除文件r01c01f10p01-ch4.tiff
#保存文件r01c01f11p01-ch1.tiff
#保存文件r01c01f11p01-ch2.tiff
#保存文件r01c01f11p01-ch3.tiff
#保存文件r01c01f11p01-ch4.tiff
#保存文件r01c02f10p01-ch1.tiff
#保存文件r01c02f10p01-ch2.tiff
#保存文件r01c02f10p01-ch3.tiff
#保存文件r01c02f10p01-ch4.tiff

内的注释中包含对代码的解释

对于实际使用，您可以使用类似于

dirnamesMy solution的内容获取输入文件列表：

library(tidyr)
library(dplyr)
library(magrittr)
library(stringr)

#Input data
dirnames <- data.frame(flenames = c("r01c01f01p01-ch3.tiff", "r01c01f01p01-ch4.tiff", "r01c01f02p01-ch1.tiff", "r01c01f03p01-ch2.tiff", 
                                    "r01c01f03p01-ch3.tiff", "r01c01f04p01-ch2.tiff", "r01c01f04p01-ch4.tiff", "r01c01f05p01-ch1.tiff", 
                                    "r01c01f05p01-ch2.tiff", "r01c01f06p01-ch2.tiff", "r01c01f06p01-ch4.tiff", "r01c01f09p01-ch3.tiff", 
                                    "r01c01f09p01-ch4.tiff", "r01c01f10p01-ch1.tiff", "r01c01f10p01-ch4.tiff", "r01c01f11p01-ch1.tiff", 
                                    "r01c01f11p01-ch2.tiff", "r01c01f11p01-ch3.tiff", "r01c01f11p01-ch4.tiff", "r01c02f10p01-ch1.tiff", 
                                    "r01c02f10p01-ch2.tiff", "r01c02f10p01-ch3.tiff", "r01c02f10p01-ch4.tiff"))

#To get actual input data in the format above
#Presuming this script is being executed from the directory where the `tiff` files are stored
dirnames <- data.frame(flenames = list.files(path = getwd(), pattern = "*.tiff"))

#Creating a column for the leading string, using which we can group the channels (stored in an accompanying column)
#Then grouping by the leading string, and nesting the channel and filename columns thereunder.
#Using the length of the nested tibble to consider files for removal.
#If the nested tibble contains 4 channels (i.e. length == 4), then keep. Else discard.
dirnames %<>% 
  mutate(fc1 = str_extract(flenames, ".+(?=\\-)")) %>% #Leading string
  mutate(fc2 = str_extract(flenames, "(?<=\\-)\\w+")) %>% #Channel string
  select(-c(flenames)) %>%
  group_by(fc1) %>%  #Grouping
  nest(fc3 = fc2) %>% #Nesting
  mutate(keep_val = if(length(unlist(fc3)) == 4){"Y"} else{"N"}) %>% #Checking to keep/discard
  unnest(fc3) %>% #Unnesting to get back original data frame
  mutate(flename = paste0(fc1, "-", fc2, ".tiff")) %>% #Getting the original filename back
  ungroup(.) %>% 
  select(flename, keep_val) #Reordering data frame

#For loop to iterate through data frame, and issue file.remove() commands as necessary
for(i in 1:nrow(dirnames)){
  if(dirnames$keep_val[i] == "N"){
    cat("Removing file ", paste0(dirnames$flename[i]), "\n")
    file.remove(dirnames$flename[i])
  } else{
    cat("Keeping file ", paste0(dirnames$flename[i]), "\n")
  }
}

# Removing file  r01c01f01p01-ch3.tiff 
# Removing file  r01c01f01p01-ch4.tiff 
# Removing file  r01c01f02p01-ch1.tiff 
# Removing file  r01c01f03p01-ch2.tiff 
# Removing file  r01c01f03p01-ch3.tiff 
# Removing file  r01c01f04p01-ch2.tiff 
# Removing file  r01c01f04p01-ch4.tiff 
# Removing file  r01c01f05p01-ch1.tiff 
# Removing file  r01c01f05p01-ch2.tiff 
# Removing file  r01c01f06p01-ch2.tiff 
# Removing file  r01c01f06p01-ch4.tiff 
# Removing file  r01c01f09p01-ch3.tiff 
# Removing file  r01c01f09p01-ch4.tiff 
# Removing file  r01c01f10p01-ch1.tiff 
# Removing file  r01c01f10p01-ch4.tiff 
# Keeping file  r01c01f11p01-ch1.tiff 
# Keeping file  r01c01f11p01-ch2.tiff 
# Keeping file  r01c01f11p01-ch3.tiff 
# Keeping file  r01c01f11p01-ch4.tiff 
# Keeping file  r01c02f10p01-ch1.tiff 
# Keeping file  r01c02f10p01-ch2.tiff 
# Keeping file  r01c02f10p01-ch3.tiff 
# Keeping file  r01c02f10p01-ch4.tiff 

library（tidyr）
图书馆（dplyr）
图书馆（magrittr）
图书馆（stringr）
#输入数据
dirnames%#通道字符串
选择（-c（flenames））%>%
分组依据（fc1）%>%#分组
嵌套（fc3=fc2）%>%#嵌套
mutate（keep_val=if（length（unlist（fc3））=4）{“Y”}否则{“N”}）%>%#检查以保留/放弃
未测试（fc3）%>%#未测试以返回原始数据帧
mutate（flename=paste0（fc1，“-”，fc2，“.tiff”）%>%#恢复原始文件名
解组（%）%%>%
选择（flename，keep_val）#重新排列数据帧
#使循环遍历数据帧，并根据需要发出file.remove（）命令
对于（i in 1:nrow（dirnames））{
if（dirnames$keep_val[i]=“N”）{
cat（“删除文件”，粘贴0（dirnames$flename[i]），“\n”）
删除（dirnames$flename[i]）
}否则{
cat（“保留文件”，粘贴0（dirnames$flename[i]），“\n”）
}
}
#正在删除文件r01c01f01p01-ch3.tiff
#正在删除文件r01c01f01p01-ch4.tiff
#正在删除文件r01c01f02p01-ch1.tiff
#正在删除文件r01c01f03p01-ch2.tiff
#正在删除文件r01c01f03p01-ch3.tiff
#正在删除文件r01c01f04p01-ch2.tiff
#正在删除文件r01c01f04p01-ch4.tiff
#正在删除文件r01c01f05p01-ch1.tiff
#正在删除文件r01c01f05p01-ch2.tiff
#正在删除文件r01c01f06p01-ch2.tiff
#正在删除文件r01c01f06p01-ch4.tiff
#正在删除文件r01c01f09p01-ch3.tiff
#正在删除文件r01c01f09p01-ch4.tiff
#正在删除文件r01c01f10p01-ch1.tiff
#正在删除文件r01c01f10p01-ch4.tiff
#保存文件r01c01f11p01-ch1.tiff
#保存文件r01c01f11p01-ch2.tiff
#保存文件r01c01f11p01-ch3.tiff
#保存文件r01c01f11p01-ch4.tiff
#保存文件r01c02f10p01-ch1.tiff
#保存文件r01c02f10p01-ch2.tiff
#保存文件r01c02f10p01-ch3.tiff
#保存文件r01c02f10p01-ch4.tiff

内的注释中包含对代码的解释

实际使用时，你可以得到一个输入文件的列表，比如“代码> DrimeNo”，以使事物保持最小（参见）和两个问题之间的独立关注点，你可以编辑这一个，仅仅是开始有一个需要移动的文件名列表的任务，这样你就可以专注于我编辑的任务，试图使它平缓下来。但从功能上讲，这两项任务是紧密交织在一起的。我更关心的是如何正确地筛选内容，我进行了编辑以反映这一点。为了使内容最小化（请参阅）并将两个问题之间的关注点分开，您可以将此问题编辑为仅包含需要移动的文件名列表的任务，这样您就可以专注于此
library(tidyr)
library(dplyr)
library(magrittr)
library(stringr)

#Input data
dirnames <- data.frame(flenames = c("r01c01f01p01-ch3.tiff", "r01c01f01p01-ch4.tiff", "r01c01f02p01-ch1.tiff", "r01c01f03p01-ch2.tiff", 
                                    "r01c01f03p01-ch3.tiff", "r01c01f04p01-ch2.tiff", "r01c01f04p01-ch4.tiff", "r01c01f05p01-ch1.tiff", 
                                    "r01c01f05p01-ch2.tiff", "r01c01f06p01-ch2.tiff", "r01c01f06p01-ch4.tiff", "r01c01f09p01-ch3.tiff", 
                                    "r01c01f09p01-ch4.tiff", "r01c01f10p01-ch1.tiff", "r01c01f10p01-ch4.tiff", "r01c01f11p01-ch1.tiff", 
                                    "r01c01f11p01-ch2.tiff", "r01c01f11p01-ch3.tiff", "r01c01f11p01-ch4.tiff", "r01c02f10p01-ch1.tiff", 
                                    "r01c02f10p01-ch2.tiff", "r01c02f10p01-ch3.tiff", "r01c02f10p01-ch4.tiff"))

#To get actual input data in the format above
#Presuming this script is being executed from the directory where the `tiff` files are stored
dirnames <- data.frame(flenames = list.files(path = getwd(), pattern = "*.tiff"))

#Creating a column for the leading string, using which we can group the channels (stored in an accompanying column)
#Then grouping by the leading string, and nesting the channel and filename columns thereunder.
#Using the length of the nested tibble to consider files for removal.
#If the nested tibble contains 4 channels (i.e. length == 4), then keep. Else discard.
dirnames %<>% 
  mutate(fc1 = str_extract(flenames, ".+(?=\\-)")) %>% #Leading string
  mutate(fc2 = str_extract(flenames, "(?<=\\-)\\w+")) %>% #Channel string
  select(-c(flenames)) %>%
  group_by(fc1) %>%  #Grouping
  nest(fc3 = fc2) %>% #Nesting
  mutate(keep_val = if(length(unlist(fc3)) == 4){"Y"} else{"N"}) %>% #Checking to keep/discard
  unnest(fc3) %>% #Unnesting to get back original data frame
  mutate(flename = paste0(fc1, "-", fc2, ".tiff")) %>% #Getting the original filename back
  ungroup(.) %>% 
  select(flename, keep_val) #Reordering data frame

#For loop to iterate through data frame, and issue file.remove() commands as necessary
for(i in 1:nrow(dirnames)){
  if(dirnames$keep_val[i] == "N"){
    cat("Removing file ", paste0(dirnames$flename[i]), "\n")
    file.remove(dirnames$flename[i])
  } else{
    cat("Keeping file ", paste0(dirnames$flename[i]), "\n")
  }
}

# Removing file  r01c01f01p01-ch3.tiff 
# Removing file  r01c01f01p01-ch4.tiff 
# Removing file  r01c01f02p01-ch1.tiff 
# Removing file  r01c01f03p01-ch2.tiff 
# Removing file  r01c01f03p01-ch3.tiff 
# Removing file  r01c01f04p01-ch2.tiff 
# Removing file  r01c01f04p01-ch4.tiff 
# Removing file  r01c01f05p01-ch1.tiff 
# Removing file  r01c01f05p01-ch2.tiff 
# Removing file  r01c01f06p01-ch2.tiff 
# Removing file  r01c01f06p01-ch4.tiff 
# Removing file  r01c01f09p01-ch3.tiff 
# Removing file  r01c01f09p01-ch4.tiff 
# Removing file  r01c01f10p01-ch1.tiff 
# Removing file  r01c01f10p01-ch4.tiff 
# Keeping file  r01c01f11p01-ch1.tiff 
# Keeping file  r01c01f11p01-ch2.tiff 
# Keeping file  r01c01f11p01-ch3.tiff 
# Keeping file  r01c01f11p01-ch4.tiff 
# Keeping file  r01c02f10p01-ch1.tiff 
# Keeping file  r01c02f10p01-ch2.tiff 
# Keeping file  r01c02f10p01-ch3.tiff 
# Keeping file  r01c02f10p01-ch4.tiff