Lab 05: Handling Files

Files?

We can use R in isolation…

But it’s much more convenient if we can save our results (data) and share them with other applications.

Or use our results (data) from other applications, and manipulate them in R.

For this, we need files.

File Formats

What is a file?

For our purposes, just think of a file as a blob of saved text.

Suppose we wanted to represent a complex structure in a file.

An Agreed on Format

To do this, we need to agree on some convention (format) of representing the structure in plain text.

File Formats: Table Representation

What if we wanted to represent a table?

In markdown:

| Some Column | Another Column | Yet Another |
|-------------|----------------|-------------|
|     3       |    "hello"     |    TRUE     |
|     17      |   "goodbye"    |   FALSE     |

Any issues with this?

File Formats: A Popular Choice, CSV

One common format for representing tables is .csv – “comma separated values”.

name,position,student
Gaston,Professor,FALSE
Jakob,uGSI,TRUE
Joy,Tutor,TRUE

Each row is represented by a line, with the values in each column separated by commas.

The first row optionally includes the column names.

Reading Tables (General)

The general function for importing tables from files is read.table().

read.table(file, header = FALSE, sep = "", quote = "\"'",
           dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"),
           row.names, col.names, as.is = !stringsAsFactors,
           na.strings = "NA", colClasses = NA, nrows = -1,
           skip = 0, check.names = TRUE, fill = !blank.lines.skip,
           strip.white = FALSE, blank.lines.skip = TRUE,
           comment.char = "#",
           allowEscapes = FALSE, flush = FALSE,
           stringsAsFactors = FALSE,
           fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)

file: The file to read from
header: Does the file have a header for the column names?
sep: What separates the data (e.g., ',' for .csv files)?

Reading CSV Files

To avoid writing passing read.table() arguments to correctly parse .csv files, we have a convenience function:

read.csv(file, header = TRUE, sep = ",", quote = "\"",
         dec = ".", fill = TRUE, comment.char = "", ...)

And for other common formats:

read.csv2(file, header = TRUE, sep = ";", quote = "\"",
          dec = ",", fill = TRUE, comment.char = "", ...)

read.delim(file, header = TRUE, sep = "\t", quote = "\"",
           dec = ".", fill = TRUE, comment.char = "", ...)

read.delim2(file, header = TRUE, sep = "\t", quote = "\"",
            dec = ",", fill = TRUE, comment.char = "", ...)

Making Sense of `read.table()`

Suppose we have a file starwars.csv including the following contents.

"name","height","mass"
"Luke Skywalker",172,77
"C-3PO",167,75
"R2-D2",96,32
"Darth Vader",202,136
...

read.table(
  # What arguments do we pass?
)

`read.table()` Solution

Suppose we have a file starwars.csv including the following contents.

"name","height","mass"
"Luke Skywalker",172,77
"C-3PO",167,75
"R2-D2",96,32
"Darth Vader",202,136
...

read.table(
  file = "starwars.csv",
  header = TRUE,
  sep = ",",
  colClasses = c("character", "double", "double")
)

What if our values were separated with ';' instead?

Writing to Files

We have write.table() for writing tables to files.

write.table(x, file = "", append = FALSE, quote = TRUE, sep = " ",
            eol = "\n", na = "NA", dec = ".", row.names = TRUE,
            col.names = TRUE, qmethod = c("escape", "double"),
            fileEncoding = "")

And for convenience,

write.csv(...)
write.csv2(...)

Saving R Output

If we want to save the output of our program to a file, we use sink().

sink("my-output.txt")  # We specify the file to redirect our output to
print("Hello World!")  
3 + 2
sink()                 # After this call, our output will be printed as usual

The output of our program was not printed to the console, but redirected to the file my-output.txt, which now contains:

[1] "Hello World!"
[1] 5