Summary  
This chapter covers how to filter data frames by applying basic categorical, numeric, and multiple logical conditions using both Base R and dplyr approaches.  

General domain of usage  
Data analysis

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

### Base R
You can filter rows by applying a condition to a specific column. For example, to select only the cars where the fuel type is **Diesel**, use the `$` operator to reference the column and apply a logical condition.

```
diesel_cars <- df[df$fuel == "Diesel", ]
```

### dplyr
You can use the `filter()` function and pass the condition directly.
```
diesel_cars_dplyr <- df %>%    
  filter(fuel == "Diesel")
```

## Filtering Based on Numeric Value
You can also filter data using numeric comparisons.

### Base R
```
expensive_cars <- df[df$selling_price > 500000, ]
```

### dplyr
```
cheap_cars_dplyr <- df %>%
  filter(selling_price < 500000)
```

### Base R
You can combine conditions using logical operators such as `&` for **AND**.
```
diesel_manual_cars <- df[df$fuel == "Diesel" & df$transmission == "Manual", ]
```

### dplyr
You can pass multiple conditions to `filter()` function, separated by comma.
```
diesel_manual_cars_dplyr <- df %>%
  filter(fuel == "Diesel", transmission == "Manual")
```

Gain practical experience in data analysis with R by learning how to clean, transform, and visualize datasets. Explore essential workflows such as selecting and filtering data, handling missing values, and summarizing results. Build confidence in preparing data for insights, reporting, and deeper statistical exploration.

Explore the foundations of data analysis with R. Learn how to install the tools, load and inspect datasets, select and filter information, sort and transform data, handle missing values, and summarize results for deeper insights.

Learn to create compelling visualizations with ggplot2. Build bar charts, histograms, density plots, and scatter plots, then customize and refine them with styling options and faceting to reveal deeper insights in your data.

Strengthen your understanding of statistics for data analysis. Apply descriptive measures, identify and treat outliers, and use correlation techniques with visual tools like heatmaps and scatter plots to uncover meaningful relationships.

Filtering Data - Basic Conditions

Filtering by Category

Base R

dplyr

Filtering Based on Numeric Value

Base R

dplyr

Multiple Conditions

Base R

dplyr