Summary  
This chapter explains how to sort tabular data by one or more columns in ascending or descending order using base R’s order() function and dplyr’s arrange() (with desc()), including multi-column sorting.

General domain of usage  
Data analysis

**Sorting** is a fundamental operation in data analysis. It allows you to organize your dataset based on one or more variables - such as price, mileage, or year. This makes it easier to identify trends, outliers, or simply view the data in a meaningful order.

### Base R
You can use the `order()` function to sort a dataset by column values. By default, this returns the data in ascending order.

```
df_sorted_price_base <- df[order(df$selling_price), ]
```

### dplyr
Sorting can be done using the `arrange()` function, which also defaults to ascending order.

```
df_sorted_price_dplyr <- df %>%
  arrange(selling_price)
```

### Base R
To sort in descending order, place a negative sign (`-`) in front of the column inside the `order()` function.

```
df_sorted_price_desc <- df[order(-df$selling_price), ]
```

### dplyr
You can use the `desc()` function inside `arrange()` to reverse the order.

```
sorted_price_desc_dplyr <- df %>%
  arrange(desc(selling_price))
```

## Sorting by Multiple Columns
It is possible to sort by more than one column to create a prioritized order. For example, you might sort first by fuel type (alphabetically) and then by selling price in descending order.

### Base R
```
df_sorted <- df[order(df$fuel, -df$selling_price), ]
```

### dplyr
```
df_sorted_dplyr <- df %>%
  arrange(fuel, desc(selling_price))
```

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.

Sorting Data

Sorting in Ascending Order

Base R

dplyr

Sorting in Descending Order

Base R

dplyr

Sorting by Multiple Columns

Base R

dplyr