Osservare le tendenze sportive

Ora che hai manipolato e suddiviso i tuoi dati sportivi in vari modi, è il momento di fare un passo indietro e valutare alcune tendenze nei dati. Il grafico a destra mostra le medie cumulative vittorie/sconfitte dei Boston Red Sox nella stagione MLB 2015 e dei Boston Bruins nella stagione NHL 2015-2016.

Quale squadra aveva la media vittorie/sconfitte più alta alla fine della rispettiva stagione?

Nel tuo workspace sono disponibili due oggetti xts, redsox_2015 e bruins_2015. Questi oggetti contengono le date di ogni partita giocata in ciascuna stagione, insieme alla media cumulativa vittorie/sconfitte a partire da ogni partita (memorizzata nella colonna win_loss di ciascun oggetto). Puoi esplorare questi oggetti usando comandi come summary(), structure(), periodicity(), head() o tail(), oppure tramite sottoinsiemi.

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Caso di studio: analisi di serie temporali cittadine in R

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Questo esercizio fa parte del corso

Caso di studio: analisi di serie temporali cittadine in R

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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: 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: Funzionalità avanzate di xts Exercise 2: Codifica e grafici dei dati dei Red Sox Exercise 3: Calcola una media di chiusura Exercise 4: Calcolare e tracciare una media stagionale Exercise 5: Calcola e traccia una media mobile Exercise 6: Comandi di indicizzazione in xts Exercise 7: Estrai le partite del weekend Exercise 8: Calcolare una media mobile su tutti gli sport Exercise 9: Osservare le tendenze sportive

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Exercise 10: Congratulazioni