xts nesnesini seç

Görünüşe göre flights verilerini bir xts nesnesine dönüştürmeden önce biraz düzenlemen gerekecek. Bildiğin gibi, R'da birçok farklı nesne türü vardır. xts nesneleri özellikle zaman serisi işlevselliği için tasarlanmıştır.

Aşağıdakilerden hangisi bir xts nesnesinin iç yapısını doğru biçimde açıklar?

Bu egzersiz

Vaka Çalışması: R ile Şehir Zaman Serisi Verilerini Analiz Etme

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Bu egzersiz

Vaka Çalışması: R ile Şehir Zaman Serisi Verilerini Analiz Etme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: xts temellerini gözden geçir Exercise 2: Zaman serisini belirle Exercise 3: Uçuş verileri Exercise 4: xts nesnesini seç

Geçerli Egzersiz

Exercise 5: Uçuş verilerini kodlama Exercise 6: Verilerini düzenleme ve görselleştirme Exercise 7: Uçuş verini keşfet Exercise 8: Uçuş verilerini görselleştir Exercise 9: Zaman serisi eğilimlerini hesapla Exercise 10: xts nesnelerini kaydetme ve dışa aktarma Exercise 11: Uçuş eğilimlerini değerlendirme Exercise 12: Zaman kazandıran kayıt - I Exercise 13: Zaman kazandıran kayıt - 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: Merging time series data by row Exercise 2: Exploring temperature data Exercise 3: Next steps - I Exercise 4: Merging using rbind()Exercise 5: Visualizing Boston winters Exercise 6: Merging time series data by column Exercise 7: Subsetting and adjusting periodicity Exercise 8: Generating a monthly average Exercise 9: Using merge() and plotting over time Exercise 10: Are flight delays related to temperature?Exercise 11: Time series data workflow Exercise 12: Next steps - II Exercise 13: Expanding your data Exercise 14: Are flight delays related to visibility or wind?

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