Individua la serie temporale

Benvenuto/a in questo corso pratico sulla manipolazione di dati di serie temporali con xts & zoo! Questo corso è pensato per ripassare gli strumenti per le serie temporali disponibili in R. Come per molte attività dell’analisi dei dati, gran parte del lavoro riguarda pulizia e manipolazione dei dati. Questo corso si basa direttamente sui concetti chiave trattati nel corso introduttivo di DataCamp su xts & zoo.

Prima di proseguire, rivediamo alcune nozioni fondamentali sulle serie temporali.

Quale dei seguenti insiemi di informazioni è adatto alla manipolazione di dati di serie temporali?

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Caso di studio: analisi di serie temporali cittadine in R

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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: Ripasso dei fondamenti di xts Exercise 2: Individua la serie temporale

Esercizio in corso

Exercise 3: Dati sui voli Exercise 4: Individua l'oggetto xts Exercise 5: Codifica dei dati sui voli Exercise 6: Manipolare e visualizzare i tuoi dati Exercise 7: Esplorare i tuoi dati sui voli Exercise 8: Visualizza i dati sui voli Exercise 9: Calcolare le tendenze delle serie temporali Exercise 10: Salvare ed esportare oggetti xts Exercise 11: Valutare le tendenze dei voli Exercise 12: Salvare tempo - I Exercise 13: Risparmiare tempo - 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