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.


Importing data into R



  1. Tibble Data Format in R: Best and Modern Way to Work with your Data
  • 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.


tibble data format: tbl_df

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

  1. Tidyr: crucial Step Reshaping Data with R for Easier Analyses
  • 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


Tidyr: crucial Step Reshaping Data with R for Easier Analyses

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



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