Reading/Writing Data

Question 1

What are two functions used to read tabular data?

Answer 1

read.table() and read.csv()

Question 2

readLines

Answer 2

reads lines of a text file into an object

Question 3

source

Answer 3

reads R code files
inverse of dump

Question 4

dget

Answer 4

reading R code files
inverse of dput

Question 5

load

Answer 5

reading in saved workspaces

Question 6

unserialize

Answer 6

reading in single R objects in binary form

Question 7

read.table, file arg

Answer 7

name of the file or connection to be read

Question 8

read.table, header arg

Answer 8

logical argument, indicates whether the first line of the file is a header line

Question 9

read.table, sep arg

Answer 9

string indicating the column delimeter

Question 10

read.table, colClasses arg

Answer 10

character vector indicating the class of each column of the dataset

Question 11

What’s the default separator for read.table?

Answer 11

space

Question 12

If there are no comment lines in your file, what’s an easy optimization to make when passing args to read.table?

Answer 12

comment.char = “”

Question 13

Why is it good to utilize the colClasses argument for large datasets?

Answer 13

Takes a long time for R to read in these data and figure out what the classes for each column are
If you pass in the character vector initially, R can load the dataset ~twice as fast!

Question 14

What’s the quick and dirty way to figure out the classes for your columns and pass that into the colClasses arg?

Answer 14

initial <- read.table(“test”, nrows=100)
classes <- sapply(initial, class)
tabAll <- read.table(“test”, colClasses = classes)

Question 15

How do you use the sapply() function, and what is its purpose?

Answer 15

sapply lets you apply a function over a list or vector
sapply(vector, function)

Question 16

How much memory will you need to read in a given file in R?

Answer 16

twice the amount of memory for the actual dataset, because of overhead

Question 17

What are the two main functions used for writing R data to file?

Answer 17

dump() and dput()

Question 18

What are two of the benefits (and one detriment) of using dump and dput?

Answer 18

preserve metadata for data frames, so you don’t have to read the data in from CSV and specify classes, etc.
text == UNIX awesomeness
One downside: not space-efficient, as a binary file would be

Question 19

How do you retrieve an object you’ve written to file using dput?

Answer 19

dget()

Question 20

What’s a major distinction between dump and dput?

Answer 20

dput can be used on a single R object, where dump can write multiple R objects to file

Question 21

How do you remove objects from a given environment?

Answer 21

rm(object)

Question 22

How do you print the first ten lines of a gzipped file called text.gz?

Answer 22

con <- gzfile(“text.gz”)
x <- readLines(con, 10)
x