read.table and delimiters in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a file named 'data.txt' using read.table with default delimiter.

R Programming

data <- read.table('data.txt', sep=[1])

Drag options to blanks, or click blank then click option'

A" "

B"\t"

C","

D";"

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma or tab as separator when the file uses spaces.

Leaving sep empty which causes errors if file is not space-separated.

The default delimiter for read.table is a space, so sep=" " is correct.

2fill in blank

medium

Complete the code to read a CSV file named 'data.csv' using read.table with the correct delimiter.

R Programming

data <- read.table('data.csv', sep=[1], header=TRUE)

Drag options to blanks, or click blank then click option'

A"\t"

B","

C" "

D";"

Attempts:

3 left

💡 Hint

Common Mistakes

Using space or tab as separator for CSV files.

Not setting header=TRUE when the file has column names.

CSV files use commas to separate values, so sep="," is correct.

3fill in blank

hard

Fix the error in the code to read a semicolon-delimited file named 'data.txt'.

R Programming

data <- read.table('data.txt', sep=[1], header=TRUE)

Drag options to blanks, or click blank then click option'

A","

B"\t"

C" "

D";"

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma or tab as separator for semicolon-delimited files.

Not specifying sep parameter when delimiter is not space.

Semicolon-delimited files require sep=";" to separate columns correctly.

4fill in blank

hard

Fill both blanks to read a tab-delimited file 'data.tsv' with headers.

R Programming

data <- read.table('data.tsv', sep=[1], header=[2])

Drag options to blanks, or click blank then click option'

A"\t"

BTRUE

CFALSE

D","

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma as separator for tab-delimited files.

Setting header=FALSE when the file has headers.

Tab-delimited files use sep="\t" and header=TRUE if the file has column names.

5fill in blank

hard

Fill all three blanks to read a file 'data.txt' with pipe '|' delimiter and no headers.

R Programming

data <- read.table('data.txt', sep=[1], header=[2], stringsAsFactors=[3])

Drag options to blanks, or click blank then click option'

A"|"

BFALSE

CTRUE

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma or tab as separator for pipe-delimited files.

Setting header=TRUE when no headers exist.

Not setting stringsAsFactors=FALSE causing unwanted factor columns.

Pipe-delimited files use sep="|". If no headers, header=FALSE. stringsAsFactors=FALSE prevents automatic factor conversion.

Practice

1. What does the sep argument in read.table specify?

easy

A. The character that separates columns in the data file

B. The number of rows to read from the file

C. The name of the file to read

D. Whether the file has a header row or not

Solution

1. Step 1: Understand the purpose of sep in read.table

   The sep argument tells R what character is used to separate columns in the text file, such as a comma or tab.

2. Step 2: Compare with other arguments

   Other arguments like header specify if the first row is column names, but sep specifically defines the delimiter.

3. Final Answer:

   The character that separates columns in the data file -> Option A

4. Quick Check:

   sep = delimiter character [OK]

Hint: Remember: sep means separator character [OK]

Common Mistakes:

• Confusing sep with header argument
• Thinking sep controls number of rows
• Using sep to specify file name

2. Which of the following is the correct syntax to read a tab-separated file named data.txt with headers using read.table?

easy

A. read.table("data.txt", sep="\t", header=TRUE)

B. read.table("data.txt", sep=";", header=FALSE)

C. read.table("data.txt", sep=",", header=TRUE)

D. read.table("data.txt", header=TRUE)

Solution

1. Step 1: Identify the correct separator for tab

   Tabs are represented as "\t" in R strings, so sep="\t" is needed for tab-separated files.

2. Step 2: Check header argument

   The question says the file has headers, so header=TRUE must be set.

3. Final Answer:

   read.table("data.txt", sep="\t", header=TRUE) -> Option A

4. Quick Check:

   Tab separator + header TRUE = read.table("data.txt", sep="\t", header=TRUE) [OK]

Hint: Use sep="\t" for tab-separated files [OK]

Common Mistakes:

• Using comma instead of tab for sep
• Forgetting to set header=TRUE
• Using header=FALSE when headers exist

3. Given a file scores.csv with content:

Name,Score
Alice,90
Bob,85
Carol,88

What will be the output of read.table("scores.csv", sep=",", header=TRUE)?

medium

A. An error because sep="," is incorrect for CSV files

B. A data frame with columns Name and Score and 3 rows

C. A data frame with one column containing all text

D. A data frame with no rows because header=TRUE skips all data

Solution

1. Step 1: Understand the file format and arguments

   The file is comma-separated with a header row. Using sep="," and header=TRUE matches this format.

2. Step 2: Predict the output

   The function will read the file into a data frame with columns "Name" and "Score" and three rows for Alice, Bob, and Carol.

3. Final Answer:

   A data frame with columns Name and Score and 3 rows -> Option B

4. Quick Check:

   CSV + sep="," + header=TRUE = correct data frame [OK]

Hint: CSV files use sep="," and header=TRUE if first row has names [OK]

Common Mistakes:

• Assuming sep="," causes error
• Expecting one column instead of two
• Thinking header=TRUE skips all data

4. You try to read a semicolon-separated file with read.table("data.txt", sep=",", header=TRUE) but get wrong columns. What is the likely problem?

medium

A. The file path is incorrect

B. The file does not have headers, so header=TRUE is wrong

C. The separator is incorrect; should be sep=";"

D. The file is empty

Solution

1. Step 1: Analyze the separator mismatch

   The file uses semicolons to separate columns, but the code uses comma separator, causing wrong parsing.

2. Step 2: Correct the separator

   Changing sep="," to sep=";" matches the file format and fixes the issue.

3. Final Answer:

   The separator is incorrect; should be sep=";" -> Option C

4. Quick Check:

   Separator mismatch causes wrong columns [OK]

Hint: Match sep to actual file delimiter [OK]

Common Mistakes:

• Changing header instead of sep
• Assuming file path error without checking sep
• Ignoring file content format

5. You have a file data.txt where columns are separated by a pipe character (|) and the first row is not a header. Which read.table call correctly reads this file?

hard

A. read.table("data.txt", sep="\\|", header=FALSE)

B. read.table("data.txt", sep="\|", header=TRUE)

C. read.table("data.txt", sep="|", header=TRUE)

D. read.table("data.txt", sep="|", header=FALSE)

Solution

1. Step 1: Identify the correct separator for pipe

   The pipe character is used as is in sep without escaping in R strings, so sep="|" is correct.

2. Step 2: Set header argument correctly

   The first row is not a header, so header=FALSE must be set to treat all rows as data.

3. Final Answer:

   read.table("data.txt", sep="|", header=FALSE) -> Option D

4. Quick Check:

   Pipe separator + no header = read.table("data.txt", sep="|", header=FALSE) [OK]

Hint: Use sep="|" and header=FALSE if no header row [OK]

Common Mistakes:

• Escaping pipe incorrectly in sep
• Setting header=TRUE when no header exists
• Using double backslashes unnecessarily