Summary  
This chapter explains how to subset data frames in R using base indexing—selecting specific rows by their numeric position and extracting columns by index or by name with the $ operator.  

General domain of usage  
Exploratory data analysis

Once your dataset is loaded into **R**, you need to learn how to work with specific parts of it. This means selecting particular rows and columns that you want to focus on. Whether you're cleaning data or analyzing specific trends, being able to subset your data efficiently is essential.

## Loading Your Dataset
Before working with any data, it needs to be loaded and viewed:

```
library(tidyverse) # load the tidyverse package
df <- read_csv("car_details.csv")  # read the dataset
View(df) # open the dataset in a spreadsheet-style viewer
```

## Selecting Rows
In R, you can select rows by their numeric position. Since indexing starts from 1, writing `df[3, ]` will return the third row from the dataset.

```
df[3, ]
```

## Selecting a Column by Position
Similarly to rows, you can select a column using its numeric position. By leaving the row index blank and specifying the column index, `df[, 5]` returns the fifth column of the dataset.

```
df[, 5]
```


## Selecting a Column by Name
You can also access a column directly by its name using the `$` operator. This provides a quick and readable way to extract a single column. For example, `df$km_driven` selects the column named **km\_driven**.

```
view(df$km_driven)
```


Which symbol is used to access a column by name in base R?

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Data Selection - Basics

Loading Your Dataset

Selecting Rows

Selecting a Column by Position

Selecting a Column by Name