Data Structures and Functions in R and Python

Fundamentals of Data Science

Basic data structures for analysis

Both R and Python have data structures like excel spreadsheets that are the basic way to organize tabular data.

In R, these tools are packaged together in a family of libraries called the tidyverse.

In Python they are packaged in two closely related libraries, numpy (which handles numerical linear algebra) and pandas which handles tabular data.

In Python, these tabular data structures are called dataframes; in R they are called tibbles (there are dataframes in R as well but the tidyverse package mainly uses tibbles.)

Features

The basic operations that both R and Python offer are

• mapping a function to a column of data and creating a new column
• selecting a particular column
• filtering to select rows where column entries meet a condition
• grouping rows by keys
• summarizing data by computing sums, counts, averages, variances, and so on.

Visualization

In addition, both R and Python have plotting libraries that rely on dataframes/tibbles as input and libraries that apply ML algorithms to tabular data stored in dataframes/tibbles.

Walkthroughs

• R/basics of R programming
• R/tidyverse walkthrough
• Python/basics of python programming
• Python/pandas walkthrough

To download the materials including the data so you can work with them locally, follow this link.