Apply the following dplyr verbs to your data
Filter rows
## # A tibble: 10 × 12
## Brand Model Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage
## <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl>
## 1 Toyo… Coro… 2018 50000 Petrol Manual First 15
## 2 Toyo… Inno… 2018 50000 Diesel Manual First 13
## 3 Toyo… Fort… 2018 38000 Diesel Automatic Second 12
## 4 Toyo… Yaris 2020 18000 Petrol Manual First 17
## 5 Toyo… Camry 2016 38000 Petrol Automatic Second 19
## 6 Toyo… Inno… 2017 38000 Diesel Manual Second 13
## 7 Toyo… Fort… 2018 38000 Diesel Automatic Second 12
## 8 Toyo… Yaris 2020 18000 Petrol Manual First 17
## 9 Toyo… Camry 2016 38000 Petrol Automatic Second 19
## 10 Toyo… Inno… 2017 38000 Diesel Manual Second 13
## # ℹ 4 more variables: Engine <dbl>, Power <dbl>, Seats <dbl>, Price <dbl>
Arrange rows
## # A tibble: 100 × 12
## Brand Model Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage
## <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl>
## 1 Mahi… Thar 2021 10000 Diesel Manual First 15
## 2 Mahi… Thar 2021 10000 Diesel Manual First 15
## 3 Ford Figo 2020 15000 Petrol Manual Third 18
## 4 Volk… Ameo 2020 15000 Petrol Automatic Third 19
## 5 Maru… S-Cr… 2020 15000 Petrol Automatic Second 18
## 6 BMW 3 Se… 2020 15000 Petrol Automatic Second 15
## 7 Volk… Ameo 2020 15000 Petrol Automatic Third 19
## 8 Maru… S-Cr… 2020 15000 Petrol Automatic Second 18
## 9 BMW 3 Se… 2020 15000 Petrol Automatic Second 15
## 10 Maru… Erti… 2020 18000 Petrol Manual First 19
## # ℹ 90 more rows
## # ℹ 4 more variables: Engine <dbl>, Power <dbl>, Seats <dbl>, Price <dbl>
Select columns
## # A tibble: 100 × 3
## Brand Model Year
## <chr> <chr> <dbl>
## 1 Toyota Corolla 2018
## 2 Honda Civic 2019
## 3 Ford Mustang 2017
## 4 Maruti Swift 2020
## 5 Hyundai Sonata 2016
## 6 Tata Nexon 2019
## 7 Mahindra Scorpio 2018
## 8 Volkswagen Polo 2020
## 9 Audi A4 2017
## 10 BMW X1 2019
## # ℹ 90 more rows
Add columns
## # A tibble: 100 × 13
## Brand Model Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage
## <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl>
## 1 Toyo… Coro… 2018 50000 Petrol Manual First 15
## 2 Honda Civic 2019 40000 Petrol Automatic Second 17
## 3 Ford Must… 2017 20000 Petrol Automatic First 10
## 4 Maru… Swift 2020 30000 Diesel Manual Third 23
## 5 Hyun… Sona… 2016 60000 Diesel Automatic Second 18
## 6 Tata Nexon 2019 35000 Petrol Manual First 17
## 7 Mahi… Scor… 2018 45000 Diesel Automatic Second 15
## 8 Volk… Polo 2020 25000 Petrol Automatic First 18
## 9 Audi A4 2017 30000 Diesel Automatic First 18
## 10 BMW X1 2019 20000 Diesel Automatic Second 20
## # ℹ 90 more rows
## # ℹ 5 more variables: Engine <dbl>, Power <dbl>, Seats <dbl>, Price <dbl>,
## # Impact_on_price <dbl>
Summarize by groups
## `summarise()` has grouped output by 'Brand', 'Transmission', 'Year'. You can
## override using the `.groups` argument.
## # A tibble: 60 × 4
## # Groups: Brand, Transmission, Year [43]
## Brand Transmission Year Price
## <chr> <chr> <dbl> <dbl>
## 1 Audi Automatic 2016 1900000
## 2 Audi Automatic 2017 2000000
## 3 Audi Automatic 2017 2200000
## 4 Audi Automatic 2017 3000000
## 5 Audi Automatic 2018 2600000
## 6 Audi Automatic 2018 3200000
## 7 BMW Automatic 2018 3200000
## 8 BMW Automatic 2019 2700000
## 9 BMW Automatic 2019 2800000
## 10 BMW Automatic 2019 3000000
## # ℹ 50 more rows