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When working with data we often find a need to transform it in various ways. This might be for reasons of presentation, to make data compatible with a function that requires the input to be in a specific format, or to make it easier to work with. R provides plenty of options for data transformation and the rich package system means that there is...

R is often used interactively and as users grow in confidence they will often begin to find it simpler and more convenient to explore new data with R directly rather than opening the data in another application. A good IDE such as RStudio can make using R in this way easier. Exploring data before proceeding to perform analysis or further process...

A typical workflow using R will likely involve a number of sequential steps from importing the data into the R environment right through to outputting a completed analysis.

In Data Frames Part 1 we looked at creating data.frame objects, accessing their columns, and creating subsets of them using their row and column indices. We also looked at some useful functions for interacting with data.frame objects’s, including ncol(), nrow(), and names(). In the 2nd part we explore these key data structures in more detail.

Data frames are a fundamental part of R and the functionality they provide plays an integral role in many analysis and data science based workflows. Data frames are rectangular, 2 dimensional table structures, resembling rows and columns which makes them flexible and intuitive to work with.

In R, a vector is a series of elements with the same type. There are 4 important types to be aware of, that you will commonly encounter

The R package ecosystem is arguably one of the language’s strongest selling points. A package is typically a collection of functions, and sometimes data, along with documentation. R packages are community developed, typically open source, and serve to provide additional functionality or extend and build upon base R’s functionality.

In terms of famous duos, neither Tom and Jerry, nor Mario and Luigi, have anything on R and RStudio. The two are so commonly used together that many beginners may initially struggle to understand where one starts and the other ends. Despite the association though, RStudio is definitely not R, in fact R doesn’t even come bundled with RStudio.