filter() for row selection in R Programming - Step-by-Step Execution

Concept Flow - filter() for row selection

Start with data frame

↓

Apply filter() with condition

↓

Check each row against condition

↓

Keep rows where condition is TRUE

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Return filtered data frame

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End

The filter() function checks each row of a data frame against a condition and keeps only the rows where the condition is true.

Execution Sample

R Programming

library(dplyr)
data <- data.frame(name=c("Anna", "Bob", "Cara"), age=c(25, 30, 22))
young <- filter(data, age < 30)
print(young)

This code selects rows from 'data' where 'age' is less than 30 and prints the filtered data.

Execution Table

Step	Row	Condition (age < 30)	Keep Row?	Output Rows So Far
1	1 (Anna, 25)	25 < 30 is TRUE	Yes	Anna, 25
2	2 (Bob, 30)	30 < 30 is FALSE	No	Anna, 25
3	3 (Cara, 22)	22 < 30 is TRUE	Yes	Anna, 25; Cara, 22
4	End	-	-	Filtering complete

💡 All rows checked; rows with age < 30 kept.

Variable Tracker

Variable	Start	After Row 1	After Row 2	After Row 3	Final
data	[Anna,25; Bob,30; Cara,22]	[Anna,25; Bob,30; Cara,22]	[Anna,25; Bob,30; Cara,22]	[Anna,25; Bob,30; Cara,22]	[Anna,25; Bob,30; Cara,22]
young	empty	[Anna,25]	[Anna,25]	[Anna,25; Cara,22]	[Anna,25; Cara,22]

Key Moments - 2 Insights

Why does the row with Bob (age 30) not appear in the output?

Because the condition 'age < 30' is FALSE for Bob (30 < 30 is FALSE), so filter() excludes that row as shown in execution_table step 2.

Does filter() change the original data frame?

No, filter() returns a new data frame with selected rows. The original 'data' remains unchanged as shown in variable_tracker.

Visual Quiz - 3 Questions

Look at the execution_table at step 3. Which rows are kept after checking Cara?

A Anna and Cara

B Anna, Bob, and Cara

C Only Anna

D Only Cara

💡 Hint

Check the 'Output Rows So Far' column at step 3 in execution_table.

At which step does the condition become FALSE for a row?

A Step 3

B Step 1

C Step 2

D No FALSE condition

💡 Hint

Look at the 'Condition (age < 30)' column in execution_table for each step.

If we change the condition to age <= 30, what happens to the output?

A Bob's row is excluded

B Bob's row is included

C Only Cara's row is included

D No rows are included

💡 Hint

Consider how the condition changes and check execution_table logic for age comparisons.

Concept Snapshot

filter(data, condition)
- Checks each row in 'data'
- Keeps rows where 'condition' is TRUE
- Returns a new filtered data frame
- Original data stays unchanged
- Useful for selecting rows by criteria

Full Transcript

The filter() function in R is used to select rows from a data frame that meet a certain condition. It checks each row one by one. If the condition is true for that row, the row is kept. If not, it is skipped. The result is a new data frame with only the rows that passed the test. The original data frame does not change. For example, filtering rows where age is less than 30 keeps only those rows with age under 30. This is shown step-by-step in the execution table and variable tracker.

Practice

1. What does the filter() function in R do when applied to a data frame?

easy

A. Selects rows that meet specific conditions

B. Selects columns based on their names

C. Sorts the data frame by a column

D. Adds new columns to the data frame

Solution

1. Step 1: Understand the purpose of filter()

   The filter() function is used to pick rows from a data frame that satisfy certain conditions.

2. Step 2: Compare with other functions

   Functions like select() pick columns, and arrange() sorts rows, so they do not match filter()'s role.

3. Final Answer:

   Selects rows that meet specific conditions -> Option A

4. Quick Check:

   filter() = row selection [OK]

Hint: Remember: filter() picks rows, select() picks columns [OK]

Common Mistakes:

• Confusing filter() with select()
• Thinking filter() sorts data
• Assuming filter() adds columns

2. Which of the following is the correct syntax to filter rows where the column age is greater than 30 using dplyr?

easy

A. filter(data, age => 30)

B. filter(data, age > 30)

C. filter(data, age = 30)

D. filter(data, age < 30)

Solution

1. Step 1: Check the comparison operator

   The correct operator for 'greater than' is >, so age > 30 is valid.

2. Step 2: Validate syntax correctness

   filter(data, age > 30) correctly applies the condition inside filter(). Other options use wrong operators or conditions.

3. Final Answer:

   filter(data, age > 30) -> Option B

4. Quick Check:

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

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

Common Mistakes:

• Using => instead of >
• Using = instead of == or >
• Confusing < with >

3. Given the data frame df below:

df <- data.frame(name = c('Anna', 'Bob', 'Cara'), age = c(25, 35, 30))

What will be the output of filter(df, age >= 30)?

medium

A. All rows

B. Rows with Anna and Bob

C. Rows with Anna and Cara

D. Rows with Bob and Cara

Solution

1. Step 1: Identify rows where age >= 30

   From the data, Bob has age 35 and Cara has age 30, both satisfy age >= 30.

2. Step 2: Confirm which rows are selected

   Anna has age 25, which is less than 30, so she is excluded.

3. Final Answer:

   Rows with Bob and Cara -> Option D

4. Quick Check:

   age >= 30 selects Bob and Cara [OK]

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

Common Mistakes:

• Including rows with age less than 30
• Misreading >= as >
• Confusing row names with values

4. Consider this code snippet:

library(dplyr)
df <- data.frame(score = c(80, 90, 70))
filtered <- filter(df, score => 80)

What is the problem with this code?

medium

A. The operator => is invalid in R

B. The data frame is missing a column

C. filter() needs a pipe operator %>%

D. score should be in quotes

Solution

1. Step 1: Check the operator used in filter()

   The operator => is not valid in R; the correct operator for 'greater or equal' is >=.

2. Step 2: Validate other parts of the code

   The data frame has the correct column, filter() can be used without pipe, and column names do not need quotes inside filter().

3. Final Answer:

   The operator => is invalid in R -> Option A

4. Quick Check:

   Use >=, not =>, for comparisons [OK]

Hint: Use >= for 'greater or equal' in R, not => [OK]

Common Mistakes:

• Using => instead of >=
• Thinking filter() always needs %>%
• Quoting column names inside filter()

5. You have a data frame df with columns city and temperature. You want to select rows where city is either 'Paris' or 'London' and temperature is above 20. Which filter() command correctly does this?

hard

A. filter(df, city == 'Paris' | city == 'London' & temperature > 20)

B. filter(df, city == 'Paris' & city == 'London' & temperature > 20)

C. filter(df, (city == 'Paris' | city == 'London') & temperature > 20)

D. filter(df, city == 'Paris' || city == 'London' && temperature > 20)

Solution

1. Step 1: Understand operator precedence in R

   Logical AND (&) has higher precedence than OR (|), so parentheses are needed to group city conditions.

2. Step 2: Analyze each option's logic

   filter(df, (city == 'Paris' | city == 'London') & temperature > 20) correctly groups city conditions with parentheses and then ANDs with temperature > 20. filter(df, city == 'Paris' | city == 'London' & temperature > 20) lacks parentheses, causing wrong logic. filter(df, city == 'Paris' & city == 'London' & temperature > 20) requires city to be both Paris and London simultaneously, impossible. filter(df, city == 'Paris' || city == 'London' && temperature > 20) uses || and && which are not vectorized and cause errors in filter().

3. Final Answer:

   filter(df, (city == 'Paris' | city == 'London') & temperature > 20) -> Option C

4. Quick Check:

   Use parentheses to group OR conditions in filter() [OK]

Hint: Use parentheses to group OR conditions before AND [OK]

Common Mistakes:

• Missing parentheses causing wrong logic
• Using && and || instead of & and |
• Trying city == 'Paris' & city == 'London' together