我没有对此进行测试,因为我显然没有你的数据,但类似下面的代码应该可以工作。基本上,您创建一个包含所有文件名的向量,然后一次读取、组合和写入其中的 10 个。
library(reshape2)
library(dplyr)
# Get the names of all the csv files
files = list.files(pattern="csv$")
# Read, combine, and save ten files at a time in each iteration of the loop
for (i in (unique(1:length(files)) - 1) %/% 10)) {
# Read ten files at a time into a list
dat = lapply(files[(1:length(files) - 1) %/% 10 == i], function(f) {
d=read.csv(f, header=TRUE, stringsAsFactors=FALSE)
# Add file name as a column
d$file = gsub("(.*)\\.csv$", "\\1", f)
return(d)
})
# Combine the ten files into a single data frame
dat = bind_rows(dat)
# Reshape from long to wide format
dat = dcast(Frequency ~ file, value.var="Voltage")
# Write to csv
write.csv(dat, paste("Files_", i,".csv"), row.names=FALSE)
}
另一方面,如果您只想将它们全部合并到一个长格式文件中,这将使分析更容易(当然如果您有足够的内存):
# Read all files into a list
dat = lapply(files, function(f) {
d = read.csv(f, header=TRUE, stringsAsFactors=FALSE)
# Add file name as a column
d$file = gsub("(.*)\\.csv$", "\\1", f)
return(d)
})
# Combine into a single data frame
dat = bind_rows(dat)
# Save to csv
write.csv(dat, "All_files_combined.csv", row.names=FALSE)