Question

In this activity you are asked to write a R script that generates .dat file by taking binary file as an input. The output file must contain the frequency of 1's in each row and also if the value in the cell is 1 then it should get suffixed with the column index and 0's should get ignored.
Output has to look like the following:
2 0:1 1:1
4 0:1 2:1 3:1 4:1
3 1:1 3:1 4:1

5 0:1 1:1 2:1 3:1 6:1

0, 1, 2, 3, 4, 6 are the column indexes and 1's are the values in that row and the indexed column.

Answer 1

Firstly, this post will go deeper into the ways of getting data from common sources, which is often spreadsheet-like data, into R. Just like with the previous post, the focus will be on reading data into R that is different from Excel or any other type of files.

Next, the data from other sources like statistical software, databases, web scraping, etc. will be discussed.

If you want to know more about the possible steps that you might need to undertake before importing your data, go to our first post, which explains how you can prepare your data and workspace before getting your data into R.

Reading in Flat Files Into R with scan()

Besides read.table(), which was mentioned in the first post of the R data import tutorial, the scan() function can also work when handling data that is stored in simple delimited text files. Unlike the read.table() function, the scan() function returns a list or a vector, not a dataframe.

Suppose you have the following .txt document:

24 1991
21 1993
53 1962

You can read in the data (which you can download here) with the following command:

data <- scan("birth.txt")

Note that your file can also be an online data set. In that case, you just pass the URL as the first argument of the scan() function.

Alternatively, you could also read in the data into a matrix:

data <- matrix(scan("birth.txt"),
               nrow=2,
               byrow=TRUE)

Tip: if you want to do this thoroughly, you might need to specify additional arguments to make the matrix just the way you want it to be. Go to this page for more information on the matrix() function.

You can also read the columns of the input file into separate vectors:

data <- scan("age.txt",
             what = list(Age = 0,
                         Birthyear= 0),
             skip=1,
             quiet=TRUE)

Note how you first pass the (path to the) file with its extension as an argument (depending on whether you set the working directory to the folder that holds your dataset or not) and then specify the type of data to be read in, whether or not you want to skip the first line of your dataset, which character delimits the fields and if you want to print a line that says how many items have been read.

If your data can also contain other data types, you should tweak the scan() function a little bit, just like in this example:

data <- scan("age.csv",
             what = list(Age = 0,
                         Name= "",
                         Birthyear= 0),
             skip=1,
             sep=";",
             quiet=TRUE)

Tip: you can do this yourself, too! Download the text file that was used above here.

And then you can also read in the data in a data frame:

data <- data.frame(scan("age.csv",
                        what = list(Age = 0,
                                    Name = "",
                                    Birthyear= 0),
                        skip=1,
                        sep=";",
                        quiet=TRUE)

Tip: a lot of the arguments that the scan() function can take are the same as the ones that you can use with the read.table() function. That is why it's always handy to check out the documentation! Go here if you want to read up on the scan() function's arguments.