Ontbrekend bbp schatten

De plot rechts toont je oorspronkelijke bbp-gegevens (bovenaan), gevolgd door elk van de drie xts-objecten die je hebt gemaakt met verschillende technieken om met ontbrekende waarden om te gaan: locf, nocb en approx. Je keuze voor een techniek moet afhangen van de context, waaronder wat de data vertegenwoordigt en welke verbanden je kunt herkennen in de niet-ontbrekende gegevens.

Op basis van je begrip van de data: welke methode past hier het beste?

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: 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: Omgaan met missende waarden Exercise 2: Economische gegevens verkennen Exercise 3: Missende gegevens vervangen - I Exercise 4: Ontbrekende data vervangen - II Exercise 5: Ontbrekend bbp schatten

Huidige oefening

Exercise 6: Vertraging en differentiatie Exercise 7: Werkloosheidsgegevens verkennen Exercise 8: Werkloosheid verschuiven (laggen)Exercise 9: Differenties van werkloosheid Exercise 10: Rol-functies Exercise 11: Voeg een discrete rolling som toe aan BBP-gegevens Exercise 12: Voeg een doorlopende rollende gemiddelde toe aan werkloosheidsdata Exercise 13: Werken met werkloosheidsdata van MA

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