tidyr Package in R Programming

Introduction

Before diving into the details of the tidyr package, let us start with an introduction to data tidying and transformation. It is essential to understand tidying data and data transformation as they serve as the foundation of data manipulation in R. The concept behind tidyr is to promote the principles of tidy data providing a structured and organized mechanism for working with datasets.

Understanding Data Tidying and Transformation

Data tidying is the process of transforming a raw dataset into a structured format. It involves reshaping data, rearranging columns and rows, and addressing missing or inconsistent values, ensuring the data is standardized for extracting meaningful insights.

Importance of Tidy Data

Tidy data is essential for effective data analysis as it simplifies data handling and visualization. When data is organized in a tidy format, applying data analysis techniques, such as aggregation, filtering, and merging, becomes easier. Clean data also improves data sharing and collaboration with individuals or teams since they can quickly understand the data and its intended purpose.

Principles of Tidy Data

The principles of tidy data, proposed by Hadley Wickham, revolve around three key concepts:

• Every column is a variable:
  Each variable has its column, which makes it clear what each data point represents.
• Every row is an observation:
  Each row represents a single observation providing a clear difference between different data points.
• Every cell is a single value:
  Each cell has only one piece of information, removing ambiguity and allowing quick access to specific data points.

Data Tidying Challenges

Tidying data can be complex, especially when working with real-world datasets. Common challenges include:

• Dealing with missing values.
• Converting data from wide-format data to long format.
• Handling data with inconsistent variable naming conventions.
• Merging or splitting columns.
• Dealing with duplicate entries.

To deal with these challenges, we can use the powerful tool - the tidyr package.

Introduction to Tidyr Package

The tidyr package in R is developed by Hadley Wickham. This package makes it easier to work with data by transforming and reshaping it. The tidyr package is a core component of the tidyverse package focused on data manipulation and visualization.

Installation and Loading

To start using the tidyr package, ensure it is installed in the R environment. We can install it using the following code:

Once installed, we can load the package into our current R session using the following command:

With the package installed and loaded in R, we can now easily access all the functions tidyr provides.

Common Functions in Tidyr

The tidyr package offers several common functions that simplify data tidying and reshaping processes. Some of the most widely used functions include: pivot_longer() , pivot_wider(), replace_na(), separate(), unite() etc.

Data Tidying Functions in Tidyr

Several useful data tidying functions are available in the tidyr package. These functions are frequently used to handle missing data, split and merge variables, and transform data between wide and long formats.

Before exploring the functions, let's create a simple data frame named "fruits". In this dataset, we recorded the number of apples, kiwis, oranges, and strawberries counted on two days.

Output:

With this "fruits" dataset, we will explore and use various functions the tidyr package provides.

1. gather()

It is used for converting data from wide format to long format. It gathers multiple columns into key-value pairs, generating a new column for the key and another for the values.

For example:

Output:

Here we used the gather() function to gather the names of the fruits into a new column called "fruit", and their corresponding values into a new column called "count".

2. spread()

It performs the opposite operation of the gather() function. It transforms data from long to wide format, creating new columns based on unique values in a key column.

For example:

Output:

Here we used the spread() function to spread the fruits into separate columns, with the corresponding counts as their values.

3. separate()

It splits a single column into multiple columns based on a separator.

Let us add a new column called color to the gathr data frame.

Output:

Now, we will use the separate() function to split the color column.

Output:

Here we have specified the sep argument as - which separates the two values in the color column into two separate columns, color, and price.

4. unite()

It is used to concatenate multiple columns into a single column.

For example:

Output:

Here, we combined the color and price columns of the sepr data frame into a single column named color-price, using - as the separator.

5. fill()

It fills missing values in a dataset with the previous non-missing value.

First, we will modify our gathr data frame by replacing the last four values of the count column with NA values.

Output:

Let's use the fill() function to fill the missing values.

Output:

Here we replaced the missing values (NA) in the count column with the previous non-missing value in the same column.

6. drop_na()

It is used to remove rows with missing values.

For example:

Output:

Here we removed the rows with missing values (NA) from the gathr data frame.

Data Transformation Functions in Tidyr

Next, we will discuss the available data transformation functions in tidyr:

1. pivot_longer()

It is used to transform data from wide format to long format. It is a more powerful version of the gather() function, which can handle multiple columns and column headers.

For example:

Output:

2. pivot_wider()

It converts data from long to wide format, a more versatile version of the spread() function.

For example:

Output:

3. separate_rows()

It is employed to split a single row containing a character vector into multiple rows.

First, we will update the remv data frame by adding a row of values as shown below:

Output:

Now we will use the seprate_rows() functions to split the data row having multiple values.

Output:

4. complete()

The complete() function in R transforms implicit missing values into explicit missing values. It is accomplished by ensuring a data frame contains all possible combinations of specified columns. If any combinations are missing, the function uses NA or the supplied default values to fill them.

For example:

Output:

5. replace_na()

It replaces NA (missing) values in a data frame with specified replacement values.

For example:

Output:

Here we replaced all the NA (missing) values in the count column with the value 1.

6. crossing()

It is used to create a new data frame that represents all possible combinations of variables. It takes individual vectors or data frames as inputs and generates a data frame with all possible combinations of their values.

First, we will create two separate data frames from our rpna data frame used above. The df1 will have the first four rows, while the df2 will have the last four rows of data from the rpna data frame. Also, we will rename the columns using the colnames() function.

Output:

Next, we will use the crossing() function to create all possible combinations of rows from df1 and df2, resulting in a total of 16 rows.

For example:

Output: