Banyak tanggal

Membuat satu tanggal saja memang berguna, tetapi dengan data keuangan Anda sering kali harus menangani banyak tanggal. Dalam kasus seperti ini, Anda perlu mengonversi banyak tanggal dari karakter ke format tanggal. Anda dapat melakukannya sekaligus menggunakan vektor. Bahkan, jika Anda ingat bahwa sebuah karakter tunggal sebenarnya adalah vektor dengan panjang 1, maka Anda akan sadar bahwa selama ini Anda sudah melakukannya!

# Membuat vektor tanggal harian bertipe karakter
dates <- c("2017-01-01", "2017-01-02",
           "2017-01-03", "2017-01-04") 

as.Date(dates)
[1] "2017-01-01" "2017-01-02" "2017-01-03" "2017-01-04"

Seperti sebelumnya, ini mungkin terlihat seolah mengembalikan vektor karakter lain, tetapi secara internal semuanya disimpan sebagai numerik, dengan beberapa properti khusus yang hanya dimiliki tanggal.

Latihan ini adalah bagian dari kursus

R Tingkat Menengah untuk Keuangan

Petunjuk latihan

Buat vektor tanggal lain yang berisi 4 hari dari "2017-02-05" hingga "2017-02-08" (inklusif). Beri nama dates.
Tetapkan nama hari dalam seminggu "Sunday", "Monday", "Tuesday", "Wednesday", dalam urutan tersebut, sebagai names() dari vektor dates.
Lakukan subset pada dates menggunakan [ ] untuk mengambil hanya tanggal untuk "Monday".

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create dates from "2017-02-05" to "2017-02-08" inclusive
dates <- ___

# Add names to dates
___

# Subset dates to only return the date for Monday
___

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: Pengenalan tanggal di R Exercise 2: Hari apa sekarang?Exercise 3: Dari char ke date Exercise 4: Banyak tanggal

Latihan Saat Ini

Exercise 5: Format tanggal dan fungsi ekstraktor Exercise 6: Format tanggal (1)Exercise 7: Format tanggal (2)Exercise 8: Pengurangan tanggal Exercise 9: months() dan weekdays() serta quarters(), wah banyak juga!

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: 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