Kies het xts-object

Het lijkt erop dat je flights-gegevens wat aanpassingen nodig hebben voordat je er een xts-object van kunt maken. Zoals je weet zijn er veel verschillende typen objecten in R. xts-objecten zijn speciaal ontworpen voor tijdreeksfunctionaliteit.

Welke van de volgende uitspraken beschrijft de interne structuur van een xts-object het best?

Deze oefening maakt deel uit van de cursus

Casestudy: Tijdreeksgegevens van een stad analyseren in R

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Deze oefening maakt deel uit van de cursus

Casestudy: Tijdreeksgegevens van een stad analyseren in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus 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: Herhaling van de basis van xts Exercise 2: Herken de tijdreeks Exercise 3: Vluchtgegevens Exercise 4: Kies het xts-object

Huidige oefening

Exercise 5: Je vluchtgegevens encoderen Exercise 6: Je gegevens manipuleren en visualiseren Exercise 7: Je vluchtgegevens verkennen Exercise 8: Visualiseer vluchtgegevens Exercise 9: Tijdreekstrends berekenen Exercise 10: xts-objecten opslaan en exporteren Exercise 11: Vluchttendensen beoordelen Exercise 12: Tijd besparen - I Exercise 13: Tijd besparen - 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