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Manipulasi Data dengan data.table di R

Melakukan chaining pada ekspresi data.table

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Latihan ini adalah bagian dari kursus

Manipulasi Data dengan data.table di R

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4.7+

Mulai Kursus Gratis

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

Exercise 1: Welcome to the course!Exercise 2: data.table pop quiz Exercise 3: Creating a data.table Exercise 4: Introducing bikes data Exercise 5: Filtering rows in a data.table Exercise 6: Filtering rows using positive integers Exercise 7: Filtering rows using negative integers Exercise 8: Filtering rows using logical vectors Exercise 9: Helpers for filtering Exercise 10: I %like% data.tables Exercise 11: Filtering with %in%Exercise 12: Filtering with %between% and %chin%

Just as the <i>i</i> argument lets you filter rows, the <i>j</i> 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.

Exercise 1: Selecting columns from a data.table Exercise 2: Selecting a single column Exercise 3: Selecting columns by name Exercise 4: Deselecting specific columns Exercise 5: Computing on columns the data.table way Exercise 6: Computing in j (I)Exercise 7: Computing in j (II)Exercise 8: Advanced computations in j Exercise 9: Computing in j (III)Exercise 10: Combining i and j

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

Exercise 1: Perhitungan menurut kelompok Exercise 2: Menghitung statistik per grup (I)Exercise 3: Menghitung statistik berdasarkan grup (II)Exercise 4: Menghitung beberapa statistik Exercise 5: Melakukan chaining pada ekspresi data.table

Latihan Saat Ini

Exercise 6: Mengurutkan baris Exercise 7: Apa 5 tujuan teratas?Exercise 8: Apa tujuan paling populer dari setiap stasiun awal?Exercise 9: Menggabungkan i, j, dan by (I)Exercise 10: Perhitungan di j menggunakan .SD Exercise 11: Menggunakan .SD (I)Exercise 12: Menggunakan .SD (II)

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 <i>incredibly fast</i> and is easy to learn.

Exercise 1: Adding and updating columns by reference Exercise 2: Adding a new column Exercise 3: Updating an existing column (I)Exercise 4: Updating an existing column (II)Exercise 5: Grouped aggregations Exercise 6: Adding columns by group Exercise 7: Updating columns by group Exercise 8: Advanced aggregations Exercise 9: Adding multiple columns (I)Exercise 10: Adding multiple columns (II)Exercise 11: Combining i, j, and by (II)

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 <i>quickly</i> and <i>easily</i>!

Exercise 1: Fast data reading with fread()Exercise 2: Fast reading from disk Exercise 3: Importing a CSV file Exercise 4: Importing selected columns Exercise 5: Importing selected rows Exercise 6: Advanced file reading Exercise 7: Reading large integers Exercise 8: Specifying column classes Exercise 9: Dealing with empty and incomplete lines Exercise 10: Dealing with missing values Exercise 11: Fast data writing with fwrite()Exercise 12: Writing files to disk Exercise 13: Writing date and time columns Exercise 14: Fast writing to disk