**Course Title:** Mastering R Programming: Data Analysis, Visualization, and Beyond **Section Title:** Data Manipulation with dplyr and tidyr **Topic:** Data reshaping with `tidyr`: Pivoting and unpivoting data using `gather()` and `spread()`. **Overview** In the world of data analysis, data is often not in the ideal format for analysis. This is where data reshaping comes in – the process of transforming data from a wide format to a long format, or vice versa. In this topic, we will explore the `tidyr` package, which provides a set of tools for reshaping and manipulating data. Specifically, we will focus on the `gather()` and `spread()` functions, which enable us to pivot and unpivot data with ease. **What is Data Reshaping?** Data reshaping is the process of transforming data from one format to another. There are two main types of data reshaping: 1. **Wide to Long**: This involves transforming data from a wide format, where each row represents a single observation and each column represents a variable, to a long format, where each row represents a single variable and each column represents an observation. 2. **Long to Wide**: This involves transforming data from a long format to a wide format. **The gather() Function** The `gather()` function is used to transform data from a wide format to a long format. It is part of the `tidyr` package and can be used to unpivot data. The basic syntax of the `gather()` function is as follows: ```r gather(data, key = "key", value = "value", ..., .na = NA, convert = FALSE) ``` Here, `data` is the data frame that we want to transform, `key` is the name of the new column that will contain the names of the original columns, `value` is the name of the new column that will contain the values of the original columns, `...` represents the columns that we want to gather, and `.na` and `convert` are optional arguments that can be used to specify the value to use when a value is missing and whether to perform type conversions, respectively. **Example 1: Using gather() to Unpivot Data** Let's consider an example dataset that contains information about the sales of a company. ```r library(tidyr) # Create the dataset sales_data <- data.frame( Month = c("January", "February", "March"), Sales_2018 = c(100, 200, 300), Sales_2019 = c(150, 250, 350) ) # Print the dataset sales_data ``` Output: ``` Month Sales_2018 Sales_2019 1 January 100 150 2 February 200 250 3 March 300 350 ``` This dataset contains three columns: `Month`, `Sales_2018`, and `Sales_2019`. We can use the `gather()` function to unpivot this data and create a new data frame with four columns: `Month`, `Year`, and `Sales`. ```r # Use gather() to unpivot the data sales_data_long <- sales_data %>% gather(Year, Sales, Sales_2018:Sales_2019) # Print the result sales_data_long ``` Output: ``` Month Year Sales 1 January 2018 100 2 February 2018 200 3 March 2018 300 4 January 2019 150 5 February 2019 250 6 March 2019 350 ``` As you can see, the `gather()` function has transformed our data from a wide format to a long format. **The spread() Function** The `spread()` function is used to transform data from a long format to a wide format. It is part of the `tidyr` package and can be used to pivot data. The basic syntax of the `spread()` function is as follows: ```r spread(data, key, value, ..., .na = NA, convert = FALSE) ``` Here, `data` is the data frame that we want to transform, `key` is the name of the column that contains the names of the new columns, `value` is the name of the column that contains the values of the new columns, `...` represents the columns that we want to ignore, and `.na` and `convert` are optional arguments that can be used to specify the value to use when a value is missing and whether to perform type conversions, respectively. **Example 2: Using spread() to Pivot Data** Let's use the `sales_data_long` data frame that we created earlier and pivot it back to the original wide format using the `spread()` function. ```r # Use spread() to pivot the data sales_data_wide <- sales_data_long %>% spread(Year, Sales) # Print the result sales_data_wide ``` Output: ``` Month 2018 2019 1 February 200 250 2 January 100 150 3 March 300 350 ``` As you can see, the `spread()` function has transformed our data back to the original wide format. **Conclusion** In this topic, we have explored the `gather()` and `spread()` functions, which are part of the `tidyr` package. These functions enable us to pivot and unpivot data with ease, transforming it from a wide format to a long format or vice versa. By mastering these functions, you will be able to manipulate and reshape your data to suit your analysis needs. **Additional Resources** For more information on the `tidyr` package and the `gather()` and `spread()` functions, please refer to the official documentation on CRAN: <https://cran.r-project.org/web/packages/tidyr/tidyr.pdf> If you have any questions or need help with this topic, please let us know by leaving a comment below. **What's Next?** In the next topic, we will explore how to combine datasets using joins in `dplyr`.