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.

Memory allocation

Why is this piece of code slow?

Timings - growing a vector

Timings - pre-allocation

Importance of vectorizing your code

Vectorized code: multiplication

Vectorized code: calculating a log-sum

Data frames and matrices

Data frames vs. matrices

Data frames and matrices - column selection

Selecting a row in a data frame

Row timings

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.

Apa itu pemprofilan kode

Profiling sebuah fungsi

Di mana terjadi hambatan?

Profvis dalam praktik

Profvis: Contoh yang lebih besar

Ubah data frame menjadi matriks

Menghitung jumlah per baris

Gunakan && alih-alih &

Tinjauan Monopoly

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.

Profvis: Contoh yang lebih besar

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