How to read and name different CSV files in R

Question

I would like to work on several csv files to make some comparisons, so I wrote this code to read the different csv files I have:

path <- \"C:\\\\data\\\\\"
fi         


        

Answer 1

I would suggest using the data.table package - it's faster and for non-blank columns in the end, it converts those to NA (in my experience). Here's some code I wrote for a simialr task:

read_func <- function(z) {
  dat <- fread(z, stringsAsFactors = FALSE)
  names(dat) <- c("start_time", "end_time", "Total", "Diffuse", "Direct", "Reflect")
  dat$start_tme <- as.POSIXct(strptime(dat$start_tme,
                    format = "%d/%m/%y %H:%M:%S"), tz = "Pacific/Easter")
  patrn <- "([0-9][0-9][0-9])\\.csv"
  dat$type <- paste("Dataset",gsub(".csv", "", regmatches(z,regexpr(patrn, z))),sep="")
  return(as.data.table(dat))
}

path <- ".//Data/" 
file_list <- dir(path, pattern = "csv")
file_names <- unname(sapply(file_list, function(x) paste(path, x, sep = "")))
data_list <- lapply(file_names, read_func)

dat <- rbindlist(data_list, use.names = TRUE) 

rm(path, file_list, file_names)

This will give you a list with each item as the data.table from the corresponding file name. I assumed that all file names have a three digit number before the extension which I used to assign a variable type to each data.table. You can change patrn to match your specific use case. This way, when you combine all of them into a single data.table dat, you can always sort/filter based on type. For example, if youwanted to plot diffuse vs direct for Dataset158 and datase222, you could do the following:

ggplot(data = dat[type == 'Dataset158' | type == 'Dataset222'], 
       aes(x = Diffuse, y = Direct)) + geom_point()

Hope this helps!

Answer 2

You're having a problem because your csv files have a blank column at the end... which makes your data end in a comma:

04/09/14 00:01:00,04/09/14 00:01:00,2.221220E-003,5.797364E-004,0.000000E+000,1.641484E-003,

This leads R to think your data consists of 7 columns rather than 6. The correct solution is to resave all your csv files correctly. Otherwise, R will see 7 columns but only 6 columnnames, and will logically think that the first column is rownames. Here you can apply the patch we came up with @konradrudolph:

library(tibble) df %>% rownames_to_column() %>% setNames(c(colnames(.)[-1], 'DROP')) %>% select(-DROP)

where df is the data from the csv. But patches like this can lead to unexpected results... better save the csv files correctly.