In this chapter, we'll learn how to filter datasets based on specific conditions. Filtering is a powerful technique that allows you to isolate rows of data that meet certain criteria—like only selecting diesel cars, expensive cars, or vehicles with manual transmission.

Filtering helps you focus on relevant data for deeper analysis, reporting, or visualization.

Filter data using base R

• Filter rows by category: Use the $ operator to access a column;

• Apply a condition to select matching rows;

• For example, select cars where fuel type is diesel.

diesel_cars <- df[df$fuel == "Diesel", ]
head(diesel_cars)
nrow(diesel_cars)
view(diesel_cars)

Filter rows with multiple conditions:

• Combine conditions using logical operators like & (AND);

• For example, select cars that are diesel and have manual transmission.

diesel_manual_cars <- df[df$fuel == "Diesel" & df$transmission == "Manual", ]
head(diesel_manual_cars)
nrow(diesel_manual_cars)

Filter rows based on numeric values:

• You can also filter based on numeric comparisons;

• For example, select cars with a selling price above 500,000.

expensive_cars <- df[df$selling_price > 500000, ]
head(expensive_cars)
nrow(expensive_cars)

Filter data using dplyr

• Filtering becomes more readable and scalable with the dplyr package and the pipe operator (%>%):

Filter rows by category:

diesel_cars_dplyr <- df %>%
  filter(fuel == "Diesel")
head(diesel_cars_dplyr)
count(diesel_cars_dplyr)

Filter rows by numeric condition:

cheap_cars_dplyr <- df %>%
  filter(selling_price < 500000)
head(cheap_cars_dplyr)