On the contrary, I do think working with list
makes it easy to automate such things.
Here is one solution (I stored your four dataframes in folder temp/
).
filenames <- list.files("temp", pattern="*.csv", full.names=TRUE)
ldf <- lapply(filenames, read.csv)
res <- lapply(ldf, summary)
names(res) <- substr(filenames, 6, 30)
It is important to store the full path for your files (as I did with full.names
), otherwise you have to paste the working directory, e.g.
filenames <- list.files("temp", pattern="*.csv")
paste("temp", filenames, sep="https://stackoverflow.com/")
will work too. Note that I used substr
to extract file names while discarding full path.
You can access your summary tables as follows:
> res$`df4.csv`
A B
Min. :0.00 Min. : 1.00
1st Qu.:1.25 1st Qu.: 2.25
Median :3.00 Median : 6.00
Mean :3.50 Mean : 7.00
3rd Qu.:5.50 3rd Qu.:10.50
Max. :8.00 Max. :16.00
If you really want to get individual summary tables, you can extract them afterwards. E.g.,
for (i in 1:length(res))
assign(paste(paste("df", i, sep=""), "summary", sep="."), res[[i]])