Lebih lanjut tentang vapply()

Perbedaan antara vapply() dan sapply() ditunjukkan pada contoh terakhir untuk memperlihatkan bagaimana vapply() gagal secara tepat, tetapi bagaimana jika tidak gagal? Ketika tidak ada kesalahan, vapply() mengembalikan hasil yang disederhanakan sesuai dengan argumen FUN.VALUE.

Himpunan data stock_return yang berisi imbal hasil harian untuk Apple, IBM, dan Microsoft telah disediakan. Fungsi sharpe() juga tersedia.

Latihan ini adalah bagian dari kursus

R Tingkat Menengah untuk Keuangan

Petunjuk latihan

Hitung rasio Sharpe untuk setiap saham menggunakan vapply().
Gunakan summary() pada kolom apple untuk mendapatkan ringkasan 6 angka.
Gunakan vapply() dengan fungsi summary() pada stock_return untuk meringkas setiap kolom.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Sharpe ratio for all stocks
vapply(___, ___, FUN.VALUE = numeric(___))

# Summarize Apple
___

# Summarize all stocks
vapply(___, ___, FUN.VALUE = numeric(___))

Edit dan Jalankan Kode

Latihan ini adalah 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: Relational operators Exercise 2: Relational practice Exercise 3: Vectorized operations Exercise 4: Logical operators Exercise 5: And / Or Exercise 6: Not!Exercise 7: Logicals and subset()Exercise 8: All together now!Exercise 9: If statements Exercise 10: If this Exercise 11: If this, Else that Exercise 12: If this, Else If that, Else that other thing Exercise 13: Can you If inside an 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: Mengapa menggunakan apply?Exercise 2: lapply() pada list Exercise 3: lapply() pada data frame Exercise 4: Argumen FUN Exercise 5: sapply() - sederhanakan!Exercise 6: sapply() vs. lapply()Exercise 7: Gagal menyederhanakan Exercise 8: vapply() - tentukan keluaran Anda!Exercise 9: vapply() vs. sapply()Exercise 10: Lebih lanjut tentang vapply()

Latihan Saat Ini

Exercise 11: Fungsi anonim Exercise 12: Selamat