Memperluas data Anda

Sekarang setelah Anda memahami alur kerja deret waktu, Anda siap menilai hipotesis bahwa keterlambatan penerbangan dipengaruhi oleh jarak pandang dan angin.

Dalam latihan ini, Anda akan menambahkan beberapa kolom lagi ke objek xts dengan menggabungkan data rata-rata bulanan jarak pandang (vis) dan kecepatan angin (wind) di wilayah Boston dari 2010 hingga 2015. Data ini berasal dari sumber yang sama dengan data suhu Anda, tetapi telah dimanipulasi dan dikonversi ke xts agar pekerjaan Anda lebih mudah.

Ini mirip dengan yang telah Anda lakukan sebelumnya, tetapi kali ini kode yang disediakan lebih sedikit. Objek xts yang Anda gunakan, flights_temps, juga tersedia di ruang kerja Anda.

Latihan ini merupakan bagian dari kursus

Studi Kasus: Menganalisis Data Deret Waktu Kota di R

Instruksi latihan

Tugas pertama Anda, seperti biasa, adalah memastikan periodisitas dan durasi data vis dan wind dengan menggunakan dua pemanggilan periodicity().
Setelah Anda memastikan bahwa data vis dan wind memiliki periodisitas dan durasi yang sama dengan data yang sudah ada, gunakan merge() untuk menggabungkan ketiga objek menjadi satu objek xts: flights_weather. Untuk menjaga konsistensi, gabungkan data Anda dengan urutan berikut: flights_temps, vis, wind.
Gunakan head() untuk melihat beberapa baris pertama flights_weather dan memastikan penggabungan Anda berhasil.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Confirm the periodicity and duration of the vis and wind data



# Merge vis and wind with your existing flights_temps data
flights_weather <- 

# View the first few rows of your flights_weather data

Edit dan Jalankan Kode

Latihan ini merupakan 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 Exercise 13: Memperluas data Anda

Latihan Saat Ini

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