A basic data type in the tidyverse is the tibble. Tibbles store tabular data and are a modern take on the standard R data frame. They have many user-friendly features that are an improvement over standard data frames when doing interactive data analysis. The remainder of this module covers tabular data in spreadsheet formats like Excel, CSV, TSV, and other delimited files.

Data can come in non-tabular formats, especially unstructured data or data that otherwise would not fit into a table. JSON and XML are common formats for storing arbitrarily structured data and this module covers the packages used to read in those data formats. In addition, relational databases are common for storing very large collections of tables where you do not need to read in the entire dataset at once. There are many relational database formats and we will cover the SQLite format, which is a compact and simple to use format.

Reading in data from various Internet sources can be a useful way to build analyses that need to be regularly updated. The rvest and httr packages are useful for connecting to web sites, web APIs and other online sources of data.

Working with others in a data science project often involves reading output or data produced using other statistical analysis packages or other software. This module covers packages for reading in these foreign formats, as well as images and data from Google Drive.

Now we will demonstrate how to import data using our case study examples. When working through the steps of the case studies, you can use either RStudio on your own computer or Coursera lab spaces provided for each case study.

This project will give you the opportunity to read in data from multiple sources and conduct some simple operations on those data.