Summary  
This chapter demonstrates how to compute and aggregate descriptive statistics—such as mean, median, standard deviation, variance, range, and summary—using base functions and dplyr’s `summarise()` and `group_by()` workflows.  

General domain of usage  
Exploratory data analysis

Understanding your data begins with descriptive statistics - these provide essential summaries about the distribution, central tendency, and spread of variables.

## Basic Descriptive Statistics
The most common statistical measures are:
- **Mean**: average value;
- **Standard deviation**: how much values deviate from the mean;
- **Median**: middle value;
- **Min / max**: smallest and largest values.

These give a quick overview of how your variables are distributed.

### Base R
Base R provides simple functions for calculating descriptive statistics. The `summary()` function also produces a quick statistical overview of all numeric columns.

```
mean(df$max_power, na.rm = TRUE)
median(df$selling_price, na.rm = TRUE)
min(df$mileage, na.rm = TRUE)
max(df$mileage, na.rm = TRUE)
summary(df)
```

### dplyr
With `dplyr`, you can use `summarise()` to calculate multiple statistics at once in a clean and readable format.

```
df %>%
  summarise(
    avg_power = mean(max_power, na.rm = TRUE),
    sd_power = sd(max_power, na.rm = TRUE),
    median_power = median(max_power, na.rm = TRUE)
  )
```

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.