In order to make your code go faster, you need to know how long it takes to run. This chapter introduces the idea of benchmarking your code.

Welcome!

R version

Benchmarking

Comparing read times of CSV and RDS files

Operational differences: "<-" and "="

Elapsed time

Relative time

How good is your machine?

DataCamp hardware

Benchmark DataCamp's machine

The Art of Benchmarking

R is flexible because you can often solve a single problem in many different ways. Some ways can be several orders of magnitude faster than the others. This chapter teaches you how to write fast base R code.

Alokasi memori

Mengapa potongan kode ini lambat?

Pengukuran waktu - menumbuhkan vektor

Pengukuran waktu - pra-alokasi

Pentingnya melakukan vektorisasi pada kode Anda

Kode tervektorisasi: perkalian

Kode tervektorkan: menghitung jumlah log

Data frame dan matriks

Data frame vs. matriks

Data frame dan matriks - pemilihan kolom

Memilih satu baris dalam data frame

Pengukuran waktu baris

Fine Tuning: Efficient Base R

Profiling helps you locate the bottlenecks in your code. This chapter teaches you how to visualize the bottlenecks using the profvis package.

What is code profiling

Profiling a function

Where's the hold-up?

Profvis in action

Profvis: Larger example

Change the data frame to a matrix

Calculating row sums

Use && instead of &

Monopoly overview

Diagnosing Problems: Code Profiling

Some problems can be solved faster using multiple cores on your machine. This chapter shows you how to write R code that runs in parallel.

CPUs - why do we have more than one

How many cores does this machine have?

What sort of problems benefit from parallel computing?

Can this loop run in parallel (1)?

Can this loop run in parallel (2)?

The parallel package - parApply

Moving to parallel programming

Moving to parApply

The parallel package - parSapply

Using parSapply()

Timings parSapply()

You can write efficient R code!

Turbo Charged Code: Parallel Programming

Information on 45,000 movies

Keunggulan R adalah dirancang untuk melakukan analisis data. Kekurangannya, terkadang R bisa lambat sehingga menghambat analisis. Oleh karena itu, penting untuk memahami teknik-teknik utama untuk mempercepat analisis Anda, agar waktu komputasi berkurang dan wawasan diperoleh secepat mungkin.

Intermediate R

Pelajari cara menulis kode R lebih cepat, temukan benchmarking dan profiling, serta rahasia pemrograman paralel.

Menulis Kode R yang Efisien

Pelajari cara menulis kode R dengan lebih cepat, temukan teknik benchmarking dan profiling, dan ungkap rahasia pemrograman paralel.

Pentingnya melakukan vektorisasi pada kode Anda

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