sapply() vs. lapply()

lapply() sangat berguna, tetapi terkadang Anda menginginkan data keluaran dalam bentuk yang lebih rapi daripada sebuah list. Misalnya, untuk rasio Sharpe, bukankah lebih baik jika nilai rasio Sharpe yang dikembalikan berupa vektor, bukan list? Analisis lanjutan kemungkinan akan menjadi lebih mudah!

Untuk itu, Anda dapat mempertimbangkan sapply(), atau simplify apply. Fungsinya persis seperti lapply(), namun akan berupaya menyederhanakan keluaran jika memungkinkan. Sintaks dasarnya sama, dengan beberapa argumen tambahan:

sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)

Argumen opsional tambahan ini memungkinkan Anda menentukan apakah sapply() harus mencoba menyederhanakan keluaran, dan apakah harus menggunakan nama dari objek pada keluaran.

Dalam latihan ini, Anda akan menghitung ulang rasio Sharpe menggunakan sapply() untuk menyederhanakan keluarannya. stock_return dan fungsi sharpe telah tersedia untuk Anda.

Latihan ini adalah bagian dari kursus

R Tingkat Menengah untuk Keuangan

Petunjuk latihan

Pertama, gunakan lapply() pada stock_return untuk memperoleh kembali rasio Sharpe.
Sekarang, gunakan sapply() pada stock_return untuk melihat keluaran rasio Sharpe yang sudah disederhanakan.
Gunakan sapply() pada stock_return untuk memperoleh rasio Sharpe dengan argumen simplify = FALSE dan USE.NAMES = FALSE. Ini setara dengan lapply()!

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# lapply() on stock_return
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# sapply() on stock_return
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# sapply() on stock_return with optional arguments
___

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

R Tingkat Menengah untuk Keuangan

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

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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!

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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()

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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

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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

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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()

Latihan Saat Ini

Exercise 7: Gagal menyederhanakan Exercise 8: vapply() - tentukan keluaran Anda!Exercise 9: vapply() vs. sapply()Exercise 10: Lebih lanjut tentang vapply()Exercise 11: Fungsi anonim Exercise 12: Selamat