Operasi tervektorisasi

Anda bisa memperluas konsep operator relasional ke vektor dengan panjang apa pun. Bandingkan dua vektor menggunakan > untuk mendapatkan vektor logis dengan panjang yang sama, berisi TRUE ketika elemen pertama lebih besar dari elemen kedua, dan FALSE jika tidak.

apple <- c(120.00, 120.08, 119.97, 121.88)
datacamp  <- c(118.5, 124.21, 125.20, 120.22)

apple > datacamp
[1]  TRUE FALSE FALSE  TRUE

Membandingkan vektor dan satu angka juga berfungsi. R akan mendaur ulang angka tersebut agar memiliki panjang yang sama dengan vektor:

apple > 120
[1] FALSE  TRUE FALSE  TRUE

Bayangkan bagaimana ini dapat digunakan sebagai sinyal beli/jual dalam analisis saham! Sebuah data frame, stocks, tersedia untuk Anda gunakan.

Latihan ini adalah bagian dari kursus

R Tingkat Menengah untuk Keuangan

Petunjuk latihan

Cetak stocks.
Anda ingin membeli ibm saat harganya menembus di bawah 175. Gunakan $ untuk memilih kolom ibm dan operator logika untuk mengetahui kapan ini terjadi. Tambahkan ke stocks sebagai kolom ibm_buy.
Jika panera menembus di atas 213, jual. Gunakan operator logika untuk mengetahui kapan ini terjadi. Tambahkan ke stocks sebagai kolom panera_sell.
Apakah ibm pernah lebih tinggi daripada panera? Tambahkan hasilnya ke stocks sebagai kolom ibm_vs_panera.
Cetak stocks.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Print stocks
___

# IBM range
___$___ <- 

# Panera range
___$___ <- 

# IBM vs Panera
___$___ <- 

# Print stocks
___

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: Operator relasional Exercise 2: Latihan relasional Exercise 3: Operasi tervektorisasi

Latihan Saat Ini

Exercise 4: Operator logika Exercise 5: And / Or Exercise 6: Not!Exercise 7: Logika dan subset()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