Filtering rows in R Programming - Step-by-Step Execution

Concept Flow - Filtering rows

Start with data frame

↓

Apply filter condition

↓

Check each row against condition

↓

Keep rows where condition is TRUE

↓

Return filtered data frame

↓

End

Filtering rows means checking each row in a data frame and keeping only those that meet a condition.

Execution Sample

R Programming

df <- data.frame(x = 1:5, y = c(10, 20, 15, 5, 30))
filtered_df <- df[df$x > 2, ]
print(filtered_df)

This code keeps rows where the value in column x is greater than 2.

Execution Table

Step	Row Index	x Value	Condition (x > 2)	Keep Row?	Output Row
1	1	1	FALSE	No
2	2	2	FALSE	No
3	3	3	TRUE	Yes	3, 15
4	4	4	TRUE	Yes	4, 5
5	5	5	TRUE	Yes	5, 30
6	End				Filtering complete

💡 All rows checked; only rows with x > 2 are kept.

Variable Tracker

Variable	Start	After Step 3	After Step 4	After Step 5	Final
df$x	1,2,3,4,5	1,2,3,4,5	1,2,3,4,5	1,2,3,4,5	1,2,3,4,5
Condition (x > 2)	NA	FALSE, FALSE, TRUE, FALSE, FALSE	FALSE, FALSE, TRUE, TRUE, FALSE	FALSE, FALSE, TRUE, TRUE, TRUE	FALSE, FALSE, TRUE, TRUE, TRUE
filtered_df	NA	3, 15	3, 15; 4, 5	3, 15; 4, 5; 5, 30	3, 15; 4, 5; 5, 30

Key Moments - 2 Insights

Why do rows with x = 1 and x = 2 not appear in the filtered data?

Because their condition x > 2 is FALSE (see execution_table rows 1 and 2), so they are not kept.

What does the comma after df[df$x > 2, ] mean in R?

It means we are selecting rows where the condition is TRUE and keeping all columns. Omitting the comma (df[df$x > 2]) does the same and keeps all columns.

Visual Quiz - 3 Questions

Look at the execution_table, what is the condition result for row index 4?

A FALSE

B NA

C TRUE

D Error

💡 Hint

Check the 'Condition (x > 2)' column for row 4 in the execution_table.

At which step does the filtered data frame first include a row?

A Step 3

B Step 2

C Step 1

D Step 5

💡 Hint

Look at the 'Output Row' column in execution_table to see when rows start appearing.

If the condition changed to df$x >= 4, which row index would be kept first?

A 3

B 4

C 2

D 5

💡 Hint

Check variable_tracker for values of x and think which satisfy x >= 4.

Concept Snapshot

Filtering rows in R:
Use df[condition, ] to keep rows where condition is TRUE.
Condition checks each row's values.
Rows failing condition are dropped.
Comma after condition means keep all columns.
Result is a smaller data frame.

Full Transcript

Filtering rows means selecting only those rows in a data frame that meet a certain condition. In R, you write df[condition, ] where condition is a logical test on rows. Each row is checked: if the condition is TRUE, the row is kept; if FALSE, it is dropped. For example, df[df$x > 2, ] keeps rows where the x column is greater than 2. The comma after the condition means all columns are kept. The output is a new data frame with only the filtered rows.

Practice

1. What does the subset() function do in R when working with data frames?

easy

A. It sorts the data frame by a column.

B. It changes the column names.

C. It adds new columns to the data frame.

D. It selects rows based on a condition.

Solution

1. Step 1: Understand the purpose of subset() and compare with other options

   The subset() function is used to pick rows from a data frame that meet a specific condition. Changing column names, adding columns, or sorting are done by other functions, not subset().

2. Final Answer:

   It selects rows based on a condition. -> Option D

3. Quick Check:

   subset() filters rows [OK]

Hint: Remember: subset() picks rows that match conditions [OK]

Common Mistakes:

• Thinking subset() changes columns
• Confusing subset() with sorting functions
• Using subset() to add data

2. Which of the following is the correct syntax to filter rows where column age is greater than 30 in a data frame df?

easy

A. subset(df, age > 30)

B. subset(df, age => 30)

C. subset(df, age = 30)

D. subset(df, age < 30)

Solution

1. Step 1: Check the condition syntax and validate the subset syntax

   The correct comparison operator for 'greater than' is >, so age > 30 is correct. subset(df, age > 30) correctly filters rows where age is greater than 30.

2. Final Answer:

   subset(df, age > 30) -> Option A

3. Quick Check:

   Use > for greater than in subset() [OK]

Hint: Use >, <, == for conditions inside subset() [OK]

Common Mistakes:

• Using => instead of >
• Using = instead of == or >
• Filtering with wrong comparison signs

3. Given the data frame df <- data.frame(name=c('Anna','Bob','Cara'), age=c(25, 35, 30)), what is the output of subset(df, age >= 30)?

medium

A. Rows with Bob and Cara

B. All rows

C. Rows with Anna and Bob

D. Rows with Anna and Cara

Solution

1. Step 1: Identify rows where age >= 30 and check which are selected

   Bob has age 35 and Cara has age 30, both satisfy age >= 30. Anna has age 25, which does not meet the condition, so only Bob and Cara rows remain.

2. Final Answer:

   Rows with Bob and Cara -> Option A

3. Quick Check:

   Filter keeps rows with age 30 or more [OK]

Hint: Check each row's value against condition carefully [OK]

Common Mistakes:

• Including rows with age less than 30
• Confusing >= with >
• Assuming all rows are returned

4. What is wrong with this code to filter rows where score is less than 50 in data frame df?

subset(df, score < 50 &)

medium

A. The subset function cannot filter numeric columns.

B. The condition ends with an incomplete operator '&'.

C. The comparison operator '<' is invalid in subset.

D. The data frame name is missing.

Solution

1. Step 1: Analyze the condition syntax and check other options

   The condition ends with '&' which expects another condition after it, but none is provided. Subset can filter numeric columns, '<' is valid, and data frame name is present as 'df'.

2. Final Answer:

   The condition ends with an incomplete operator '&'. -> Option B

3. Quick Check:

   Conditions must be complete without dangling operators [OK]

Hint: Avoid ending conditions with & or | without second part [OK]

Common Mistakes:

• Leaving dangling & or | operators
• Thinking subset can't filter numbers
• Misusing comparison operators

5. You have a data frame df with columns age and status. How do you filter rows where age is over 25 and status is 'active'?

hard

A. subset(df, age > 25, status == 'active')

B. subset(df, age > 25 | status == 'active')

C. subset(df, age > 25 & status == 'active')

D. subset(df, age > 25 && status = 'active')

Solution

1. Step 1: Use correct logical operator for AND and check comparison operators and syntax

   The operator for AND in R is a single ampersand '&', so use age > 25 & status == 'active'. Use '==' to compare values, not '=', and combine conditions inside one subset call.

2. Final Answer:

   subset(df, age > 25 & status == 'active') -> Option C

3. Quick Check:

   Use & and == for combined conditions [OK]

Hint: Use & for AND, | for OR, and == for equality in subset() [OK]

Common Mistakes:

• Using | instead of & for AND
• Using = instead of == for comparison
• Separating conditions with commas