Sorting data by multiple columns is an essential skill for data analysts and scientists. This not only helps in analyzing the data but also makes it more readable and understandable. In R, you can sort a data frame by multiple columns using various techniques. This article provides an exhaustive guide on how to achieve multi-column sorting in R.

## Introduction

Sorting by multiple columns means arranging the data frame based on the values of two or more columns, with a hierarchy between them. For example, if you have a data frame with columns `A`

, `B`

, and `C`

, you may want to sort it by column `A`

first and then by column `B`

.

Let’s start with a simple example:

```
df <- data.frame(
A = c(1, 3, 2, 4, 1),
B = c('a', 'd', 'c', 'b', 'b'),
C = c(5, 1, 3, 4, 2)
)
```

## Sorting Basics in R

Before diving into multiple column sorting, it’s good to know the basics of single-column sorting. In R, you can sort a data frame using the `order()`

function or the `arrange()`

function from the dplyr package.

### Sorting with order( )

To sort this data frame by the `A`

column in ascending order, you can use the `order()`

function in base R as follows:

```
# Sort the data frame by the A column using the order() function
sorted_df_order <- df[order(df$A), ]
# Display the sorted data frame
print(sorted_df_order)
```

### Sorting with arrange( ) from dplyr

Alternatively, you can use the `arrange()`

function from the dplyr package to achieve the same result.

```
library(dplyr)
# Sort the data frame by the A column using the arrange() function
sorted_df_arrange <- df %>% arrange(A)
# Display the sorted data frame
print(sorted_df_arrange)
```

## Sort by Multiple Columns in R

Let’s see how to sort by multiple columns in R

## Using the order( ) Function in Base R

The `order()`

function is the base R function for sorting. You can use it to sort by multiple columns by providing additional arguments:

`sorted_df <- df[order(df$A, df$B), ]`

### Descending Sort with order( )

To sort in descending order using `order()`

, you can negate the column if it contains numeric data:

`sorted_df <- df[order(-df$A, -df$C), ]`

For character or factor columns, you can use the `decreasing = TRUE`

parameter inside the `order()`

function.

## Leveraging arrange( ) in dplyr

The `dplyr`

package provides the `arrange()`

function, which is more user-friendly than `order()`

:

```
library(dplyr)
sorted_df <- df %>% arrange(A, B)
```

### Descending Sort with arrange( )

For sorting in descending order, you can use the `desc()`

function:

`sorted_df <- df %>% arrange(desc(A), desc(C))`

## Dealing with Different Data Types

When sorting by multiple columns, the data types of the columns matter. Numeric, character, and date types are straightforward, but for factors, the level order is used for sorting.

## Handling Missing Values

Both `order()`

and `arrange()`

handle missing values (`NA`

) by default by placing them at the end. If you want to remove the rows with `NA`

before sorting, you can use `na.omit()`

or `drop_na()`

from `tidyr`

.

`sorted_df <- na.omit(df)[order(na.omit(df)$A, na.omit(df)$B), ]`

Or with dplyr:

```
sorted_df <- df %>%
filter(!is.na(A) & !is.na(B)) %>% # Filter out NA values
arrange(A, B) # Sort by columns A and B
```

## Sorting with Factors

When one of your columns is a factor, R will use the level order to sort that column. If you want to sort based on the actual values, you need to convert it to a character vector:

```
df$B <- as.character(df$B)
sorted_df <- df[order(df$A, df$B), ]
```

## Conclusion

Sorting by multiple columns is often crucial for data analysis and visualization. In R, this can be efficiently performed using either the `order()`

function in base R or the `arrange()`

function from the `dplyr`

package. While `order()`

offers a more basic approach, `arrange()`

comes with a more readable syntax and additional features. Understanding how to sort by multiple columns effectively allows you to manage your data in a way that facilitates more advanced analyses and creates more insightful visualizations.