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. 

What is Scalable Data Processing?

Why is your code slow?

How does processing time vary by data size?

Working with "Out-of-Core" Objects using the Bigmemory Project

Reading a big.matrix object

Attaching a big.matrix object

Creating tables with big.matrix objects

Data summary using bigsummary

References vs. Copies

Copying matrices and big matrices

Working with increasingly large data sets

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.

La suite di pacchetti Bigmemory

Tabulazione con bigtable

Razza ed etnia del mutuatario per anno (I)

Split-Apply-Combine

Proporzione di donne che prendono a prestito

Suddividi

Apply

Combina

Visualizza i risultati con il tidyverse

Visualizzare la quota di prestiti alle donne

Il rapporto di reddito del mutuatario

Grandi tabelle in formato tidy

Limiti di bigmemory

Dove dovresti usare bigmemory?

Processing and Analyzing Data with bigmemory

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


Introduction to chunk-wise processing

Can you split-compute-combine it?

Foldable operations (I)

Foldable operations (II)

A first look at iotools: Importing data

Compare read.delim() and read.delim.raw()

Reading raw data and turning it into a data structure

chunk.apply

Reading chunks in as a matrix

Reading chunks in as a data.frame

Parallelizing calls to chunk.apply

Working with iotools

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.

Overview of types of analysis for this chapter

Race and Ethnic Representation in the Mortgage Data

Comparing the Borrower Race/Ethnicity and their Proportions

Are the data missing at random?

Looking for Predictable Missingness

A little more about missingness

Analyzing the Housing Data

Borrower Race and Ethnicity by Year (II)

Visualizing the Adjusted Demographic Trends

Relative change in demographic trend

Borrower Lending Trends: City vs. Rural

Borrower Region by Year

Who is securing federally guaranteed loans?

Congratulations!

Case Study: A Preliminary Analysis of the Housing Data

Mortgage data (sample)

Gli insiemi di dati sono spesso più grandi della RAM disponibile, il che crea problemi a chi programma in R, dato che per impostazione predefinita tutte le variabili vengono memorizzate in memoria. Imparerai strumenti per elaborare, esplorare e analizzare i dati direttamente dal disco. Metterai inoltre in pratica l’approccio split-apply-combine e imparerai a scrivere codice scalabile usando i pacchetti bigmemory e iotools. In questo corso userai i dati della Federal Housing Finance Agency, un insieme di dati pubblico che documenta tutti i mutui detenuti o cartolarizzati sia da Federal National Mortgage Association (Fannie Mae) sia da Federal Home Loan Mortgage Corporation (Freddie Mac) dal 2009 al 2015.

Writing Efficient R Code

Impara a scrivere codice scalabile per big data in R con i pacchetti bigmemory e iotools.

Elaborazione scalabile dei dati in R

Impara a scrivere codice scalabile per lavorare con i big data in R usando i pacchetti bigmemory e iotools.

La suite di pacchetti Bigmemory

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