Langkah berikutnya - II

Data suhu Anda membuka beberapa kemungkinan untuk menelusuri penyebab keterlambatan dan pembatalan penerbangan. Namun, klien Anda bersikeras bahwa pola kedatangan penerbangan di Boston dipengaruhi oleh jarak pandang dan angin, bukan suhu. Sebelum melanjutkan, Anda perlu mengumpulkan lebih banyak data.

Setelah melakukan riset menyeluruh, Anda mengidentifikasi data relevan tentang rata-rata mingguan jarak pandang dan kecepatan angin di wilayah Boston. Langkah mana dari berikut ini yang akan Anda ambil sebelum menggabungkan data ini dengan objek xts bulanan Anda yang sudah ada, flights_temps?

Mengodekan data menjadi objek xts dengan indeks berbasis waktu.
Mengonversi data ke periodisitas bulanan menggunakan to.period() dengan observasi pertama per minggu.
Memastikan setiap objek data hanya memiliki satu kolom informasi.
Mengonversi data ke periodisitas bulanan menggunakan split() dan lapply() untuk menghasilkan rata-rata bulanan.
Memeriksa periodisitas dan durasi objek xts Anda sebelum menggunakan merge().
Menghapus informasi suhu yang ada dari flights_temps sebelum menggunakan merge().

Latihan ini adalah bagian dari kursus

Studi Kasus: Menganalisis Data Deret Waktu Kota di R

Latihan interaktif praktis

Ubah teori menjadi tindakan dengan salah satu latihan interaktif kami.

Mulai berolahraga

Latihan ini adalah bagian dari kursus

Studi Kasus: Menganalisis Data Deret Waktu Kota di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

You've been hired to understand the travel needs of tourists visiting the Boston area. As your first assignment on the job, you'll practice the skills you've learned for time series data manipulation in R by exploring data on flights arriving at Boston's Logan International Airport (BOS) using xts & zoo.

Exercise 1: Review xts fundamentals Exercise 2: Identify the time series Exercise 3: Flight data Exercise 4: Pick out the xts object Exercise 5: Encoding your flight data Exercise 6: Manipulating and visualizing your data Exercise 7: Exploring your flight data Exercise 8: Visualize flight data Exercise 9: Calculate time series trends Exercise 10: Saving and exporting xts objects Exercise 11: Assessing flight trends Exercise 12: Saving time - I Exercise 13: Saving time - II

In this chapter, you'll expand your time series data library to include weather data in the Boston area. Before you can conduct any analysis, you'll need to do some data manipulation, including merging multiple xts objects and isolating certain periods of the data. It's a great opportunity for more practice!

Exercise 1: Menggabungkan data deret waktu berdasarkan baris Exercise 2: Menjelajahi data suhu Exercise 3: Langkah selanjutnya - I Exercise 4: Menggabungkan dengan rbind()Exercise 5: Memvisualisasikan musim dingin di Boston Exercise 6: Menggabungkan data deret waktu berdasarkan kolom Exercise 7: Subset dan penyesuaian periodisitas Exercise 8: Membuat rata-rata bulanan Exercise 9: Menggunakan merge() dan membuat plot dari waktu ke waktu Exercise 10: Apakah penundaan penerbangan terkait dengan suhu?Exercise 11: Alur kerja data deret waktu Exercise 12: Langkah berikutnya - II

Latihan Saat Ini

Exercise 13: Memperluas data Anda Exercise 14: Apakah keterlambatan penerbangan terkait dengan jarak pandang atau angin?

Now it's time to go further afield. In addition to flight delays, your client is interested in how Boston's tourism industry is affected by economic trends. You'll need to manipulate some time series data on economic indicators, including GDP per capita and unemployment in the United States in general and Massachusetts (MA) in particular.

Exercise 1: Handling missingness Exercise 2: Exploring economic data Exercise 3: Replace missing data - I Exercise 4: Replace missing data - II Exercise 5: Estimating missing GDP Exercise 6: Lagging and differencing Exercise 7: Exploring unemployment data Exercise 8: Lagging unemployment Exercise 9: Differencing unemployment Exercise 10: Rolling functions Exercise 11: Add a discrete rolling sum to GDP data Exercise 12: Add a continuous rolling average to unemployment data Exercise 13: Manipulating MA unemployment data

Having exhausted other options, your client now believes Boston's tourism industry must be related to the success of local sports teams. In your final task on this project, your supervisor has asked you to assemble some time series data on Boston's sports teams over the past few years.

Exercise 1: Advanced features of xts Exercise 2: Encoding and plotting Red Sox data Exercise 3: Calculate a closing average Exercise 4: Calculate and plot a seasonal average Exercise 5: Calculate and plot a rolling average Exercise 6: Indexing commands in xts Exercise 7: Extract weekend games Exercise 8: Calculate a rolling average across all sports Exercise 9: Viewing sports trends Exercise 10: Congratulations