1. Apprendre
    2. /
  2. Cours
    3. /
  3. Scalable Data Processing in R
  • 1

    Working with increasingly large data sets

    Gratuit

    In this chapter, we cover the reasons you need to apply new techniques when data sets are larger than available RAM. We show that importing and exporting data using the base R functions can be slow and some easy ways to remedy this. Finally, we introduce the bigmemory package.

  • 2

    Processing and Analyzing Data with bigmemory

    Now that you've got some experience using bigmemory, we're going to go through some simple data exploration and analysis techniques. In particular, we'll see how to create tables and implement the split-apply-combine approach.

  • 3

    Working with iotools

    We'll use the iotools package that can process both numeric and string data, and introduce the concept of chunk-wise processing.

  • 4

    Case Study: A Preliminary Analysis of the Housing Data

    In the previous chapters, we've introduced the housing data and shown how to compute with data that is about as big, or bigger than, the amount of RAM on a single machine. In this chapter, we'll go through a preliminary analysis of the data, comparing various trends over time.

Initializing

Exercice

Tidy Big Tables

As a final exercise of using the "tidyverse" packages in combination with the "bigmemory" suite of packages, you will again use the tidyr and ggplot2 packages to plot the Borrower Income ratio over time.

Instructions

100 XP
  • Load the tidyr and ggplot2 packages.
  • Use the pivot_longer() function to gather the counts by year.
  • Create a line plot with Year on the x-axis and Count on the y-axis. Color and group by BIR.