This chapter introduces data.tables as a drop-in replacement for data.frames and shows how to use data.table's i argument to filter rows.

Welcome to the course!

data.table pop quiz

Creating a data.table

Introducing bikes data

Filtering rows in a data.table

Filtering rows using positive integers

Filtering rows using negative integers

Filtering rows using logical vectors

Helpers for filtering

I %like% data.tables

Filtering with %in%

Filtering with %between% and %chin%

Introduction to data.table

Just as the i argument lets you filter rows, the j argument of data.table lets you select columns and also perform computations. The syntax is far more convenient and flexible when compared to data.frames.

Memilih kolom dari sebuah data.table

Memilih satu kolom

Memilih kolom berdasarkan nama

Tidak memilih kolom tertentu

Melakukan komputasi pada kolom dengan cara data.table

Komputasi dalam j (I)

Perhitungan di j (II)

Perhitungan lanjutan di j

Komputasi di j (III)

Menggabungkan i dan j

Selecting and Computing on Columns

This chapter introduces data.table's by argument that lets you perform computations by groups. By the end of this chapter, you will master the concise DT[i, j, by] syntax of data.table.

Computations by groups

Computing stats by groups (I)

Computing stats by groups (II)

Computing multiple stats

Chaining data.table expressions

Ordering rows

What are the top 5 destinations?

What is the most popular destination from each start station?

Combining i, j, and by (I)

Computations in j using .SD

Using .SD (I)

Using .SD (II)

Groupwise Operations

You will learn about a unique feature of data.table in this chapter: modifying existing data.tables in place. Modifying data.tables in place makes your operations incredibly fast and is easy to learn.

Adding and updating columns by reference

Adding a new column

Updating an existing column (I)

Updating an existing column (II)

Grouped aggregations

Adding columns by group

Updating columns by group

Advanced aggregations

Adding multiple columns (I)

Adding multiple columns (II)

Combining i, j, and by (II)

Reference Semantics

Not only does the data.table package help you perform incredibly fast computations, it can also help you read and write data to disk with amazing speeds. This chapter focuses on data.table's fread() and fwrite() functions which let you import and export flat files quickly and easily!

Fast data reading with fread()

Fast reading from disk

Importing a CSV file

Importing selected columns

Importing selected rows

Advanced file reading

Reading large integers

Specifying column classes

Dealing with empty and incomplete lines

Dealing with missing values

Fast data writing with fwrite()

Writing files to disk

Writing date and time columns

Fast writing to disk

Importing and Exporting Data

Paket data.table menyediakan versi berkinerja tinggi dari data.frame milik R dasar dengan peningkatan sintaks dan fitur untuk kemudahan penggunaan, kenyamanan, dan kecepatan pemrograman. Kursus ini menunjukkan cara membuat, menyaring, dan memanipulasi data.table. Anda juga akan mempelajari fitur data.table yang terinspirasi dari basis data, termasuk operasi per kelompok bawaan. Kursus ditutup dengan metode cepat untuk mengimpor dan mengekspor data teks tabular seperti file CSV. Setelah menyelesaikan kursus, Anda akan mampu menggunakan data.table di R untuk proses manipulasi dan analisis yang lebih efisien. Sepanjang kursus Anda akan mengeksplorasi himpunan data perjalanan bike share di Area Teluk San Francisco dari tahun 2014.

Intermediate R

Pelajari konsep inti manipulasi data seperti filter, seleksi, dan hitung statistik grup dengan data.table.

Manipulasi Data dengan data.table di R

Menguasai konsep dasar tentang manipulasi data, seperti penyaringan, pemilihan, dan perhitungan statistik kelompok menggunakan data.table. 

Memilih kolom dari sebuah data.table

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