Logika dan subset()

Berikut masalah yang menarik. Anda sudah tahu cara membuat vektor logika yang memberi tahu saat suatu kondisi bernilai benar, tetapi bisakah Anda membuat subset data frame agar hanya memuat baris ketika kondisi tersebut benar?

Jika Anda mengikuti Introduction to R for Finance, Anda mungkin ingat fungsi subset(). subset() menerima argumen berupa data frame (atau vektor/matriks) dan vektor logika yang menentukan baris mana yang akan dikembalikan:

stocks
        date    ibm panera
1 2017-01-20 170.55 216.65
2 2017-01-23 171.03 216.06
3 2017-01-24 175.90 213.55
4 2017-01-25 178.29 212.22

subset(stocks, ibm < 175)
        date    ibm panera
1 2017-01-20 170.55 216.65
2 2017-01-23 171.03 216.06

Berguna, bukan? Data frame stocks tersedia untuk Anda gunakan.

Latihan ini merupakan bagian dari kursus

R Tingkat Menengah untuk Keuangan

Instruksi latihan

Buat subset stocks untuk menyertakan baris saat panera lebih besar dari 216.
Buat subset stocks untuk mengambil baris saat date sama dengan "2017-01-23". Jangan lupa as.Date()!
Buat subset stocks untuk mengambil baris saat ibm kurang dari 175 dan panera kurang dari 216.50.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Panera range
___

# Specific date
___

# IBM and Panera joint range
___

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

R Tingkat Menengah untuk Keuangan

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

Welcome! Before we go deeper into the world of R, it will be nice to have an understanding of how dates and times are created. This chapter will teach you enough to begin working with dates, but only scratches the surface of what you can do with them.

Exercise 1: An introduction to dates in R Exercise 2: What day is it?Exercise 3: From char to date Exercise 4: Many dates Exercise 5: Date formats and extractor functions Exercise 6: Date formats (1)Exercise 7: Date formats (2)Exercise 8: Subtraction of dates Exercise 9: months() and weekdays() and quarters(), oh my!

Imagine you own stock in a company. If the stock goes above a certain price, you might want to sell. If the stock drops below a certain price, you might want to buy it while it's cheap! This kind of thinking can be implemented using operators and if statements. In this chapter, you will learn all about them, and create a program that tells you to buy or sell a stock.

Exercise 1: Operator relasional Exercise 2: Latihan relasional Exercise 3: Operasi tervektorisasi Exercise 4: Operator logika Exercise 5: And / Or Exercise 6: Not!Exercise 7: Logika dan subset()

Latihan Saat Ini

Exercise 8: Semua digabungkan sekarang!Exercise 9: Pernyataan if Exercise 10: Jika ini Exercise 11: Jika ini, Maka yang lain Exercise 12: Jika ini, Else If itu, Else yang lainnya Exercise 13: Bisakah Anda membuat If di dalam If?Exercise 14: ifelse()

Loops can be useful for doing the same operation to each element of your data structure. In this chapter you will learn all about repeat, while, and for loops!

Exercise 1: Repeat loops Exercise 2: Repeat, repeat, repeat Exercise 3: When to break?Exercise 4: While loops Exercise 5: While with a print Exercise 6: While with a plot Exercise 7: Break it Exercise 8: For loops Exercise 9: Loop over a vector Exercise 10: Loop over data frame rows Exercise 11: Loop over matrix elements Exercise 12: Break and next

If data structures like data frames and vectors are how you hold your data, functions are how you tell R what to do with your data. In this chapter, you will learn about using built-in functions, creating your own unique functions, and you will finish off with a brief introduction to packages.

Exercise 1: What are functions?Exercise 2: Function help and documentation Exercise 3: Optional arguments Exercise 4: Functions in functions Exercise 5: Writing functions Exercise 6: Your first function Exercise 7: Multiple arguments (1)Exercise 8: Multiple arguments (2)Exercise 9: Function scope (1)Exercise 10: Function scope (2)Exercise 11: Packages Exercise 12: tidyquant package

A popular alternative to loops in R are the apply functions. These are often more readable than loops, and are incredibly useful for scaling the data science workflow to perform a complicated calculation on any number of observations. Learn about them here!

Exercise 1: Why use apply?Exercise 2: lapply() on a list Exercise 3: lapply() on a data frame Exercise 4: FUN arguments Exercise 5: sapply() - simplify it!Exercise 6: sapply() vs. lapply()Exercise 7: Failing to simplify Exercise 8: vapply() - specify your output!Exercise 9: vapply() vs. sapply()Exercise 10: More vapply()Exercise 11: Anonymous functions Exercise 12: Congratulations