Learn the basics of modifying data frames in R with dplyr.

Modifying Data Frames in R

Since dplyr functions operate on data frames using column names, it is often useful to update the column names of a data frame so they are as clear and meaningful as possible. dplyr's `rename()` function allows you to easily do this.

Say you have a data frame of books, as shown in the table below:

<div class="narrative-table-container">

|name|written_by|
|-|-|
|The Lord of the Rings|J. R. R. Tolkien|
|Le Petit Prince|Antoine de Saint-Exupéry|
|Harry Potter and the Philosopher's Stone|J. K. Rowling|
|紅樓夢/红楼梦|Cao Xueqin|

</div>
<br>

`rename()` can take any number of arguments, where each new column name is assigned to replace an old column name in the format `new_column_name = old_column_name`. `rename()` returns a new data frame with the updated column names.

To update the `name` column to `book_title` and the `written_by` column to `author`:

```r
df %>%
  rename(book_title = name,
         author = written_by)
```
 
You can confirm the names of the columns have been updated using either of the base R functions `names()` or `colnames()`, which take a data frame as an argument and return a vector containing the column names.


Rename Columns

Sometimes you might want to add a new column to a data frame. This new column could be a calculation based on the data that you already have.

Suppose you own a hardware store called The Handy Woman and have a data frame containing inventory information:

<div class="narrative-table-container">

|product_id|product_description|cost_to_manufacture|price|
|--------------|---------------------------|----------------------------|-|
|1                  | 3 inch screw            | 0.50                            | 0.75 |
|2                  | 2 inch nail               | 0.10                            | 0.25 |
|3                 | hammer                    | 3.00                            | 5.50 |
|4                  | screwdriver            | 2.50                            | 3.00|

</div>
<br>

You can add a new column to the data frame using the `mutate()` function. `mutate()` takes a name-value pair as an argument. The name will be the name of the new column you are adding, and the value is an expression defining the values of the new column in terms of the existing columns. `mutate()` returns a new data frame with the added column.

Maybe you want to add a column to your inventory table with the amount of sales tax that is charged for each item.  The following code multiplies each `price` by `0.075`, the sales tax in your state:

```r
df %>%
  mutate(sales_tax = price * 0.075)
```
Now the inventory table has a column called `sales_tax`, where the value is `0.075 * price`:

<div class="narrative-table-container">

|product_id|product_description|cost_to_manufacture|price|sales_tax|
|--------------|---------------------------|----------------------------|--------|-|
|1                  | 3 inch screw            | 0.50                            | 0.75 | 0.06|
|2                  | 2 inch nail               | 0.10                            | 0.25 | 0.02|
|3                 | hammer                    | 3.00                            | 5.50 | 0.41|
|4                  | screwdriver            | 2.50                            | 3.00| 0.22|

</div>
<br>


Adding a Column

When creating new columns from a data frame, sometimes you are interested in only keeping the new columns you add, and removing the ones you do not need. dplyr's `transmute()` function will add new columns while dropping the existing columns that may no longer be useful for your analysis. Let's go back to the original inventory data frame for your store, The Handy Woman.

<div class="narrative-table-container">

|product_id|product_description|cost_to_manufacture|price|
|--------------|---------------------------|----------------------------|-|
|1                  | 3 inch screw            | 0.50                            | 0.75 |
|2                  | 2 inch nail               | 0.10                            | 0.25 |
|3                 | hammer                    | 3.00                            | 5.50 |
|4                  | screwdriver            | 2.50                            | 3.00|

</div>
<br>

Like `mutate()`, `transmute()` takes name-value pairs as arguments. The names will be the names of the new columns you are adding, and the values are expressions defining the values of the new columns. The difference, however, is that `transmute()` returns a data frame with _only_ the new columns.

To add `sales_tax` and `profit` columns while dropping all other columns from the data frame:

```r
df %>%
  transmute(sales_tax = price * 0.075,
            profit = price - cost_to_manufacture)
```

This inventory table will now look like this:

<div class="narrative-table-container">

|sales_tax|profit|
|-|-|
|0.06|0.25|
|0.02|0.15|
|0.41|2.5|
|0.22|0.5|

</div>
<br>


Transmute Columns

Let's refer back to the inventory table for your store, The Handy Woman.

<div class="narrative-table-container">

|product_id|product_description|cost_to_manufacture|price|sales_tax|
|--------------|---------------------------|----------------------------|--|--|
|1| 3 inch screw            | 0.50                            | 0.75 |0.06|
|2                  | 2 inch nail               | 0.10         | 0.25 |0.02|
|3                 | hammer                    | 3.00            | 5.50 |0.41|
|4                  | screwdriver            | 2.50               | 3.00|0.22|

</div>
<br>

You want to add two more new columns to your table. One column will contain the profit made from selling each item (`price - cost_to_manufacture`), and the other will state whether the item is currently in stock (suppose every item is currently in stock).

`mutate()` can take multiple arguments to add any number of new columns to a data frame:

```r
df %>%
  mutate(profit = price - cost_to_manufacture,
         in_stock = TRUE)
```
* `mutate()` takes two arguments, defining new columns `profit` and `in_stock`
* `profit` is equal to `price` minus `cost_to_manufacture`
* `in_stock`, rather than be derived from values in existing columns, is given the value `TRUE` for all rows

The inventory table will now look like this:

<div class="narrative-table-container">

|product_id|product_description|cost_to_manufacture|price|sales_tax|profit|in_stock|
|--------------|---------------------------|----------------------------|-|-|-|--|
|1                  | 3 inch screw            | 0.50                            | 0.75 |0.06|0.25|TRUE|
|2                  | 2 inch nail               | 0.10                            | 0.25 |0.02|0.15|TRUE|
|3                 | hammer                    | 3.00                            | 5.50 |0.41|2.5|TRUE|
|4                  | screwdriver            | 2.50                            | 3.00|0.22|0.5|TRUE|

</div>
<br>


Adding Multiple Columns

When working with data frames, you often need to modify the columns for your analysis at hand. With the help of the dplyr package, data frame modifications are easily performed.

In this lesson, you'll learn how to modify an existing data frame using dplyr. Some of the skills you'll learn include:
* adding columns to an existing data frame
* adding new columns and dropping existing columns from a data frame
* renaming columns

Introduction

With an understanding of how to manipulate data frames with dplyr, you are well on your way to becoming a data analysis expert! In this lesson you learned how to:
* add new columns to a data frame using `mutate()`
* add new columns to a data frame and drop existing columns using `transmute()`
* change the column names of a data frame using `rename()`

![Cute puppy that loves dplyr](https://content.codecademy.com/courses/Learn-R/dog_2.jpg)