将r应用于csv文件的一列

Question

I have a folder with several hundred csv files. I want to use lappply to calculate the mean of one column within each csv file and save that value into a new csv file that would have two columns: Column 1 would be the name of the original file. Column 2 would be the mean value for the chosen field from the original file. Here's what I have so far:

setwd("C:/~~~~")
list.files()
filenames <- list.files()
read_csv <- lapply(filenames, read.csv, header = TRUE)
dataset <- lapply(filenames[1], mean)
write.csv(dataset, file = "Expected_Value.csv")

Which gives the error message:

Warning message: In mean.default("2pt.csv"[[1L]], ...) : argument is not numeric or logical: returning NA

So I think I have 2(at least) problems that I cannot figure out.

First, why doesn't r recognize that column 1 is numeric? I double, triple checked the csv files and I'm sure this column is numeric.

Second, how do I get the output file to return two columns the way I described above? I haven't gotten far with the second part yet.

I wanted to get the first part to work first. Any help is appreciated.

Answer 1

I didn't use lapply but have done something similar. Hope this helps!

    i= 1:2 ##modify as per need

    ##create empty dataframe
    df <- NULL 

    ##list directory from where all files are to be read
    directory <- ("C:/mydir/")

    ##read all file names from directory
    x <- as.character(list.files(directory,,pattern='csv'))
    xpath <- paste(directory, x, sep="")

    ##For loop to read each file and save metric and file name 
    for(i in i) 
    {
    file <- read.csv(xpath[i], header=T, sep=",")
    first_col <- file[,1]
    d<-NULL
   d$mean <- mean(first_col)
   d$filename=x[i]
   df <- rbind(df,d)
    }

   ###write all output to csv
   write.csv(df, file = "C:/mydir/final.csv")

   CSV file looks like below 

    mean        filename
   1999.000661  hist_03082015.csv
   1999.035121  hist_03092015.csv

Answer 2

Thanks for the two answers. After much review, it turns out that there was a much easier way to accomplish my goal. The csv files that I had were originally in one file. I split them into multiple files by location. At the time, I thought this was necessary to calculate mean on each type. Clearly, that was a mistake. I went to the original file and used aggregate . Code:

setwd("C:/~~")
allshots <- read.csv("All_Shots.csv", header=TRUE)
EV <- aggregate(allshots$points, list(Location = allshots$Loc), mean)
write.csv(EV, file= "EV_location.csv")

This was a simple solution. Thanks again or the answers. I'll need to get better at lapply for future projects so they were not a waste of time.