# Preparing and Reshaping Data in R for Easier Analyses

Previously, we described the essentials of R programming and provided quick start guides for importing data into **R**. The next crucial step is to set your data into a consistent data structure for easier analyses. Here, you’ll learn modern conventions for **preparing** and **reshaping** **data** in order to facilitate analyses in R.

- Installing and loading tibble package: type
**install.packages**(“tibble”) for installing and**library**(“tibble”) for loading. - Create a new tibble:
**data_frame**(x = rnorm(100), y = rnorm(100)). - Convert your data as a tibble:
**as_data_frame**(iris) - Advantages of tibbles compared to data frames:
**nice printing methods**for large data sets, specification of**column types**.

Read more: Tibble Data Format in R: Best and Modern Way to Work with your Data

- What is a tidy data set?: a data structure convention where each column is a variable and each row an observation
- Reshaping data using tidyr package
- Installing and loading tidyr: type
**install.packages**(“tidyr”) for installing and**library**(“tidyr”) for loading. - Example data sets: USArrests
**gather**(): collapse columns into rows**spread**(): spread two columns into multiple columns**unite**(): Unite multiple columns into one**separate**(): separate one column into multiple**%>%**: Chaining multiple operations

- Installing and loading tidyr: type

Read more: Tidyr: crucial Step Reshaping Data with R for Easier Analyses

## Recommended Books

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**Articles contained by this category :**

Tibble Data Format in R: Best and Modern Way to Work with Your Data

Tidyr: Crucial Step Reshaping Data with R for Easier Analyses